Public Access ICT across Cultures
Diversifying Participation in the Network Society
© 2015 International Development Research Centre and Contributors
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Foreword by Bruce Girard
Francisco J. Proenza
I IMPACT ON PERSONAL ACHIEVEMENT AND WELL-BEING
2 User Perceptions of Impact of Internet Cafés in Amman, Jordan
Ghaleb Rabab’ah, Ali Farhan AbuSeileek, Francisco J. Proenza, Omar Fraihat, and Saif Addeen Alrababah
3 Impact of Public Access to ICT Skills on Job Prospects in Rwanda
Jean Damascene Mazimpaka, Théodomir Mugiraneza, and Ramata Molo Thioune
4 Personal Objectives and the Impact of Internet Cafés in China
Francisco J. Proenza, Wei Shang, Guoxin Li, Jianbin Hao, Oluwasefunmi ‘Tale Arogundade, and Martin S. Hagger
5 Problematic Internet Use among Internet Café Users in China
Wei Shang, Xuemei Jiang, Jianbin Hao, and Xiaoguang Yang
Sylvie Siyam Siwe, Laurent Aristide Eyinga Eyinga, Avis Momeni, Olga Balbine Tsafack Nguekeng, Abiodun Jagun, Ramata Molo Thioune, and Francisco J. Proenza
II FACILITATING INCLUSION AND ENABLING THE BUILDUP OF SOCIAL CAPITAL
7 The Appropriation of Computer and Internet Access by Low-Income Urban Youth in Argentina
Sebastián Benítez Larghi, Carolina Aguerre, Marina Laura Calamari, Ariel Fontecoba, Marina Moguillansky, Jimena Orchuela, and Jimena Ponce de León
8 Impact of Public Access to Computers and the Internet on the Connectedness of Rural Malaysians
Nor Aziah Alias, Marhaini Mohd Noor, Francisco J. Proenza, Haziah Jamaludin, Izaham Shah Ismail, and Sulaiman Hashim
Jorge Bossio, Juan Fernando Bossio, and Laura León with the collaboration of María Alejandra Campos and Gabriela Perona
10 Women and Cybercafés in Uttar Pradesh
Nidhi Mehta and Balwant Singh Mehta
11 The Impact of Public Access to Telecenters: Social Appropriation of ICT by Chilean Women
Alejandra Phillippi and Patricia Peña
12 Cybercafés and Community ICT Training Centers: Empowering Women Migrant Workers in Thailand
Nikos Dacanay, Mary Luz Feranil, Ryan V. Silverio, and Mai M. Taqueban
IV A PLACE TO LEARN, A PLACE TO PLAY, A PLACE TO DREAM, A PLACE TO FALL FROM GRACE
13 Public Access Impact and Policy Implications
Francisco J. Proenza
with the collaboration of Erwin A. Alampay, Roxana Barrantes, Hernán Galperín, Abiodun Jagun, George Sciadas, Ramata Molo Thioune, and Kentaro Toyama
Amy Kathleen Mahan’s career was marked by a commitment to research and its effective dissemination to improve the communication opportunities of the disadvantaged, as a foundation for human and social development. The focus of much of her work was on telecommunication reform and information and communication technology (ICT) policies, particularly in developing countries.
Although she was a productive researcher, Amy chose to devote most of her activity to helping others through her exceptional skills in research support, editing, and report preparation. She had a rare talent for integrating technical production and substantive content editing to make research results more reader-friendly. She strongly believed the weakest link in the research process was dissemination, and she demonstrated innovation and imagination to improve its effectiveness wherever she worked. She was a team player who preferred to work collaboratively.
Amy Mahan coordinated the Learning Initiatives on Reforms for Network Economies (LIRNE.NET) from Montevideo, Uruguay, and was a member of the Research Working Group of the Global Impact Study of Public Access to Information and Communication Technologies. She was also an active member of the Regional Dialogue on the Information Society (DIRSI) and a founder of Fundación Comunica.
Amy left us at age 47 on March 5, 2009. She left suddenly, giving no warning. She did not want us to be distracted by her illness. She wanted to be known for what she thought, what she wrote, whom she helped.
There are two predominant motivations for investing in and building physical spaces with computers and connections to the Internet: 1) because there is a scarcity of ICT resources that the endeavor seeks to fill (and perhaps benefit from); and 2) because there is a need to build up community resources....
The literature assumes the importance of sustainability for community access points. But perhaps what is needed is a more holistic picture which widens the frame to view and accept some public access points as necessarily ephemeral and fleeting. In many instances what is required for adoption is the impetus (and attraction) of introduction to ICT services and applications, rather than a sustained relationship to provide this access. The establishment of a community capacity building access point considers the broader skill base of the community in its evaluations. Likewise, a commercial (or non-profit) enterprise simply seeking to fulfill an access gap should also be posited in context of the community it is serving and in context of shifts in local ICT adoption indicators during its lifespan.
As is often the case for social exclusion (gender being a key example), lack of data is still a key constraint for measuring positive effects and progress generally. If the design for the telecenter or cybercafé does not particularly target women and girls, or poor people, the handicapped, elderly, people who don’t speak the official language, or others with special circumstances or needs, it is not likely to collect indicators on the access or use by these subgroups of the premises and ICT services. And, on the other hand, the challenges of simultaneously confronting ICT and a public place may prove insurmountable for some socially marginalized sectors. (From internal memoranda prepared by Amy Mahan, 2008)
Amy Mahan’s foremost interest was in ICT as a tool for social inclusion and a means to help women and traditionally marginalized peoples improve their lives and communities. Amy was committed to academic rigor and to learning from field observations and analysis to inform our investments and policy prescriptions, and she understood that rigor required thoroughness and an understanding of context.
At the time of her passing, Amy had made substantial contributions to the research and design of two ICTs for development programs.
It is with deep gratitude for her scholarship, personal courage, and humanity that we honor our friend and colleague by dedicating this book to her memory.
Amy Mahan was no stranger to shared Internet use. While living in Quito, Ecuador, in the mid-1990s, she collaborated internationally on various academic and research projects, including editing a book with colleagues in Denmark and Canada via what she called her sneakernet—a network configuration that involved putting her shoes on and jogging to the local Internet café to download email, conduct research, and share her findings. She worked from a home office, and shared access was the only option available: Quito’s telecom infrastructure was notoriously poor, and commercial Internet access was a monopoly service provided by a bank determined to apply its usurious lending practices to its Internet business model.
At the time, affordable access was often (and naïvely) seen as the only obstacle to the Internet for all initiatives in developing countries. The solutions proposed were equally simplistic: privatize telecoms, invest massively in infrastructure development, and enable telecenters, Internet cafés, and other shared-use solutions for the poor.
Fast forward to 2014, and the world has changed. The mobile phone is everywhere, more than 2.5 billion people are online, and as mobile phones are increasingly Internet-enabled, it seems that the ubiquitous Internet might even be within reach. Why should we worry about shared public access when “everyone” has a smart phone?
The most obvious answer to that question is that in fact not everyone does have a smart phone. The International Telecommunication Union (ITU) estimates that there are 83.7 mobile broadband subscriptions per 100 inhabitants in the developed world but only 19.0 per 100 inhabitants in Africa and 21.1 across the developing world.1 One of the reasons that shared public access continues to be important is that for many people, it is still the only access available. However, that is far from the only reason. Results from the Global Impact Study of Public Access2 and from the research documented in this book provide some surprising findings. Even people with home access to computers and the Internet use public access. In some cases this is because the access is faster or the computers better, but other reasons include the technical support provided by staff and other users or simply because Internet cafés offer people a chance to mingle with friends or other people.
The reports in this book take a fresh look at shared public access to computers and the Internet and provide evidence that the benefits of shared public access go far beyond simply providing affordable access to the infrastructure. Public access venues are also places for learning, sharing, working, finding opportunities, empowering, and solidarity. Facebook may be The Social Network with more than a billion users globally, but Internet cafés and telecenters are hundreds of thousands of social networks, providing tens of millions of users locally with opportunities to improve their livelihoods.
Amy Mahan’s foremost interest was in ICT and media as tools for social inclusion and a means to help women, the poor, and traditionally marginalized peoples improve their lives and communities. Her professional involvement in this area included being a key member of the research team for the Global Impact Study of Public Access to ICTs, but she also developed several handbooks and multimedia kits as training tools for the use of ICT in developing countries, co-authored and edited books on subjects as diverse as global media governance and telecommunications regulation reform, and served as production editor of the respected academic journal Telecommunication Policy.
Amy was also committed to giving young researchers the opportunity to develop their skills and expertise. The Amy Mahan Research Fellowship Program recognizes this commitment to the next generation of researchers, and Amy would have been pleased and honored to have this research dedicated to her. She would, however, have been much happier to have been able to work alongside the young scholars.
Bruce Girard
March 2014
1. Key ICT indicators for developed and developing countries and the world, International Telecommunications Union. http://www.itu.int/en/ITU-D/Statistics/Documents/statistics/2014/ITU_Key_2005-2014_ICT_data.xls
2. Araba Sey, Chris Coward, François Bar, George Sciadas, Chris Rothschild, and Lucas Koepke. 2013. Connecting People for Development: Why Public Access ICTs Matter. Global Impact Study Research Report. http://tascha.uw.edu/publications/connecting-people-for-development/.
This book was made possible by the Amy Mahan Research Fellowship Program, a competitive research grant initiative funded by Canada’s International Development Research Centre and implemented by Universitat Pompeu Fabra, Barcelona, in collaboration with scholars from the University of San Andrés, Buenos Aires, the University of the Philippines, Manila, and South Africa’s LINK Centre at the University of the Witwatersrand, Johannesburg, together with partner institutions from ten participating countries.
Many people helped make the publication of this book possible. The authors gratefully acknowledge the following contributions.
SIRCA colleagues at Nanyang Technological University: Ang Peng Hwa, Arul Chib, Joanna Tan Keng Ling, Naowarat Narula, and Sri Ranjini Mei Hua, as well as IDRC’s program officer, Chaitali Sinha, made detailed comments on the draft submission guidelines. All their suggestions were adopted. SIRCA staff shared invaluable resources and experiences, which served as important guidelines in our own program development. Overall, SIRCA staff remained a frequent and reliable source of information and counsel.
The Global Impact Study (GIS) staff—namely, Araba Sey and Christopher Coward (University of Washington) and François Bar (University of Southern California)— helped us, particularly during the early research planning stages. Another GIS team member, George Sciadas, also part of our research team and co-editor of this book, provided a particularly helpful link with Global Impact Study survey research work.
Professor Steven Pace, grounded theory and ICT specialist from Central Queensland University, gave support to the qualitative analysis work of our teams in Chile and Argentina.
Raul Pertierra, anthropologist and ICT specialist from Ateneo de Manila University, assisted our research team working in Thailand.
Ricardo Gomez, Assistant Professor and Chair of the Information & Society Center at the University of Washington’s Information School, kindly made available advance copies of his research findings on public access. This material provided an overview of the relative significance of various kinds of public access venues worldwide.
The implementation of the Amy Mahan Research Fellowship Program relied on the assistance of many Universitat Pompeu Fabra (UPF) staff. Special recognition is due to Rector Josep Joan Moreso, Research Vice Rector; Clara Riba, Social Sciences Director; and our colleagues David Sancho, Jacint Jordana, Miquel Oliver, Josep Jofre, Robert Fishman, Willem Saris, and Lorena Camats.
At IDRC, Frank Tulus was instrumental in getting the program underway and accompanied the research by providing guidance and support whenever needed. Laurent Elder championed the initial idea for a complementary capacity-building program to the Global Impact Study and remained supportive of the program throughout its implementation. Raymond Hyma provided assistance with the program’s website, especially during the translation process. IDRC’s Nola Haddadian and Matthew Smith, and MIT’s Marguerite B. Avery and Katherine A. Almeida provided invaluable assistance getting this book published. University of Florida Professor Mario Ariet helped check the proofs. Finally, IDRC’s Heloise Emdon, Ben Petrazzini, Michael Clarke, and Florencio Ceballos offered continuous support to the program.
Special thanks are extended to Amy Mahan’s family: Bruce Girard, her husband; Danielle Girard, their daughter; and Marilyn Mahan, Amy’s mother, for allowing us the honor of naming the program in Amy’s memory. Bruce, a renowned specialist in ICT for development, also kindly wrote the foreword to this book.
Francisco J. Proenza
This book presents the findings of ten research teams that worked between 2009 and 2012 across three continents under the auspices of the Amy Mahan Research Fellowship Program to Assess the Impact of Public Access to ICT. It seeks to fill critical gaps in the research literature regarding the impact of public shared access to computers and the Internet. In this introductory chapter, we present the background to the preparation of the book and summarize findings as we overview how the book is organized.
Worldwide, cybercafés are by far the most prevalent type of public access venue. Cybercafés thrive in urban areas, but their survival is challenged in rural settings by low digital literacy and high maintenance and connectivity costs. Two other common venue types are libraries, most often funded and operated by government, and telecenters, mostly funded by government but at times also by private foundations, nongovernmental organizations (NGOs), or international donors, and run by a broad range of institutions, including public agencies, NGOs, religious organizations, and local groups.
Governments have made large investments in public access, some by equipping libraries as venues, but most by subsidizing telecenters of various types, including commercially oriented centers. These interventions have been driven by the desire to include low-income groups in the digital age by expanding access to the Internet at low cost by sharing resources. There have been urban initiatives, but the more significant efforts have sought to bring the presumed benefits of public access to rural communities. Public interventions are usually based on the presumption that the better-substantiated impacts of Internet use can be obtained through public access, while some of the negative effects of public access are observed and speculated on but largely ignored.
All over the world, cybercafé clients receive services they deem beneficial enough to justify paying for their cost. There is also considerable anecdotal evidence suggesting that public access users—of all types of venues—derive significant benefits. What has been missing is a comprehensive body of scientifically validated knowledge regarding what works and what doesn’t and under what circumstances.
This book presents a systematic assessment of the impact of public access across cultures (ten countries in three continents) for a variety of venues operating in different settings for the purpose of informing public policy.
Participating research teams shared three objectives: (1) assess impacts with scientific rigor, (2) acknowledge the reach and limitations of findings, and (3) formulate practical recommendations. Within this broad framework, cybercafés located in urban areas and mid-size towns are examined in the China, India, and Jordan chapters; rural telecenters in the Cameroon and Malaysia chapters; and comparisons across venue types in the Argentina, Chile, Peru, Rwanda, and Thailand chapters. Mixed approaches to data gathering were used in most studies, but qualitative approaches were dominant in Argentina, Chile, Thailand, and Peru, and quantitative approaches were dominant in China, India, Jordan, Malaysia, and Rwanda. Research teams were multidisciplinary: in Thailand the team had expertise in anthropology, sociology, gender studies, and human rights; in China, in economics, systems engineering, marketing and psychology; in Chile, in communications, culture, and gender analysis; in Argentina, in sociology, anthropology, and social communications; and in Malaysia, in education and instructional technology.
The study of different contexts enables the appreciation of differences in policy concerns and under what conditions and to what extent lessons from one setting are applicable elsewhere. Multidisciplinary approaches bring new perspectives and insights. Quantitative methods let us assess the extent of a phenomenon, while qualitative approaches enable a deeper, more nuanced understanding.
In the context of development impact studies, variety in settings and in data and conceptual approaches is an ideal that is seldom achieved in practice because of the high investment and coordination costs involved. The research reported in this book was made possible by the Amy Mahan Research Fellowship Program, a competitive research grant initiative funded by Canada’s International Development Research Centre (IDRC) and implemented by Universitat Pompeu Fabra, Barcelona, in collaboration with scholars from Universidad de San Andrés, Buenos Aires; the University of the Philippines, Manila; South Africa’s LINK Centre at the University of the Witwatersrand, Johannesburg; and partner institutions from participating countries.
The book presents the results of field research in three parts. Part I covers public access impacts on users as individuals, part II on society and networks, and part III on women. Part IV contains a single final overview chapter.
Part I begins with an assessment of cybercafé users in an Arab country: Jordan. It presents the findings of a survey covering 336 users of twenty-four randomly selected cybercafés in Amman. The study finds overwhelming positive perceptions of impact in users’ lives in two areas: communications and social networking, and improving education and learning; and less widespread but positive impacts in a third area, income and employment.
The second chapter in this part examines the impacts of ICT training in public access venues on job skills and employment in Rwanda. A purposive survey was taken of 418 white-collar and office workers who occupy positions likely to involve the use of basic computer skills (e.g., secretaries, receptionists, customer service agents, administrative assistants, finance officers, human resource managers, and public access venue employees). Eighty-seven percent in this group report “getting a new or better job” as an objective for wanting to improve their ICT skills. Sixty-seven percent consider that knowledge of the Internet plays a very important or an important role in the job application process, and 41 percent took an ICT skills test during recruitment for their present job. The skills acquired from public access venues differ and affect job prospects differently depending on venue type, location, competence of instructor, duration of the training, and sex of trainees. The training model used by government-sponsored telecenters appears to be most effective, and its expansion should be considered.
Chapters 4 and 5 cover China, the country with the world’s largest cybercafé user population. Surveys of 975 café users and 964 nonusers were conducted.
The objective of the first China chapter is to understand user motivations and assess whether personal objectives are fulfilled and the extent to which achievement is affected by Internet café use. A first noteworthy finding of the analysis in this chapter, applicable in China and everywhere else, is that the goal content of Internet and Internet café use is predominantly intrinsic. For the most part, people use the Internet and Internet Cafés not because of external pressures or rewards, but as part of their overall search to satisfy basic psychological needs.
Internet café user life goals are not very different from those of nonusers. The goal most highly cherished by both users and nonusers is to “learn more knowledge.” Young (under age 35) urban male users and urban male and female student users report statistically significant higher achievement than their nonuser cohorts for this top priority goal. Young (under age 35) urban male users and female urban student users also report higher achievement than nonusers for the goal “have fun, entertain myself.” Urban female users report higher achievement for the goals “keep frequent contact with those who don’t live nearby” and “relax, relieve tension.” As Internet café users gain experience using the technology, the sense of accomplishment appears to wane for the goals “keep frequent contact with those who don’t live nearby” by urban females and “have fun, entertain myself” by urban males and urban female students. These are significant findings suggesting that nonusers are missing out in the achievement of goals they cherish and, in the case of learning and communicating, are instrumental and valued by society.
In the second China chapter (chapter 5), we find that Internet addiction is not as widespread as is often reported in the media, and we identify some of the features of users and use practices that seem to increase the risk of overuse.
Part I ends with a review of a quasi-experiment in five rural communities of Cameroon, where télécentres communautaires polyvalents (TCPs) are the only places from which students can connect to the Internet. The self-reported academic performance of 1,015 secondary school students interviewed in the five TCPs is used as a yardstick to compare the performance of students who know how to use the Internet and those who do not. Key to academic success are long hours of after-school study and a motivation to learn, but those students who study hard and are motivated to learn get Internet skills in the TCPs in larger proportion than underachievers. The evidence further suggests that, beyond study effort, having access to the Internet gives mid- and upper secondary students a performance edge. There is however some evidence suggesting that spending too much time at the TCP may thwart academic achievement.
The first chapter of part II examines the ways in which Argentina’s low-income urban youth use new technologies in their daily lives. Three venues located in the county of La Matanza are considered: a cybercafé, an access and training center run by a local grassroots organization, and a community technology center run by an organization with government support. The last two centers do not provide access to the public at large, and therefore cannot be considered public access venues. Instead, they focus on providing ICT training services that are valued by our target group of low-income youths. We refer to these two centers as community ICT training centers (CITCs).
The study’s main finding is that the cybercafé and the two CITCs contribute to the social inclusion of youth in poor urban environments. They also satisfy training needs that are not met by market-oriented institutes or formal schooling.
Cybercafés are also valuable as social spaces where young people can put into practice what they learn in the community centers and where the main activities revolve around communication and entertainment over the Internet. Women are less frequent visitors to the cybercafés and therefore derive fewer benefits from their use than men. We recommend the establishment of ICT training centers in marginalized communities, where high rates of alienation among young people are observed. We also encourage the strengthening of links between these spaces and the school environment and the promotion of greater participation of women, especially in cybercafés and job training programs.
In Malaysia we consider social connectedness among users of the country’s forty-two rural Internet centers (RICs). Social connectedness, defined as the “feeling of belongingness, being linked to and related to a network, community or group that one trusts and interacts with,” is a building block of social capital. When a socially connected group establishes trusting relationships, it often finds ways to cooperate in joint activities that are beyond the possibilities of individual members. We examine 300 responses to an online survey on connectedness and find that most RIC users feel a moderate degree of connection, and 27 percent report a relatively high degree of connection with their social network. Nearly 20 percent of respondents feel significantly connected with community leaders.
Part II concludes with an analysis of the impact of public access on the organizational capacity of nine grassroots organizations located in a rural district of Peru’s Andean region. Public access venues such as telecenters and cabinas públicas (Peru’s cybercafés) help make communication processes more effective and facilitate meetings and coordination. These impacts are greatest when the venues have links to the objectives and goals of the organization and when those actors who facilitate information flows with external agents use the Internet to search for funding opportunities. Some organizational skills are more likely to be impacted by information technology (e.g., those related to networking, leadership, infrastructure, and external communications) than others (e.g., supervision, monitoring, and evaluation of plans). The promotion of public access venues as part of universal Internet access initiatives should consider as part of its goal not just the provision of access at the individual level but the inclusion of rural organizations; and initiatives seeking to foster a more productive use of technology by grassroots organizations should focus on developing those capacities that are most impacted.
Part III opens with a discussion of why cybercafés are off-limits to most women in two mid-sized towns of Uttar Pradesh. Limitations on access by women surfaced in several chapters where cybercafés were the subject of study. In China, women account for only 27 percent of survey respondents; in Jordan, for only 24 percent. The original focus of our India study was cybercafé user objectives, but when we found only 12 females in our 300-user sample (4 percent), it became evident that the important societal impact was the exclusion of women from this type of venue. Even acknowledging the limited representativeness of our samples, these figures are alarming because cybercafés are by far the most prevalent type of public access venue worldwide. Accordingly, we conducted a supplementary survey of 200 women (100 users and 100 nonusers) to determine why so few women in these towns were using cybercafés.
Most women in Uttar Pradesh are poor and illiterate, and they have minimal participation in the formal economy. The caste system is firmly entrenched and the society is conservative and generally restricts the movement of women outside the family or immediate community. Females in mid-sized towns generally have little decision-making autonomy, power, or financial control within the household. It is mainly working women and female students who come out of their homes. Others rarely come out of their homes, do not talk to strangers, and are always guarded by male family members; when interacting with outsiders, male family members reply on the women’s behalf. The environment at the cybercafés is generally considered hostile to women because these venues tend to be crowded with young men. Hence, women and their families do not feel comfortable with the notion of women visiting cybercafés. Generally, illiterate females engaged primarily in household activities felt that cybercafés were not useful to them. Some were not even interested in taking part in the survey. Those who do use cybercafés tend to be better off, educated women from higher castes, and find them useful.
There is an urgent need to increase literacy and enhance awareness of the benefits of the technology among women and their male family members, and to implement programs offering cybercafé operators incentives to make their venues more welcoming and accommodating to women.
The second chapter in this part examines the impact on women of public access through Chile’s urban Quiero mi Barrio telecenter network. The study is based on interviews with men and women in two centers located in relatively new neighborhoods created as part of government-sponsored housing. Overall, the interviewees feel these centers have had a positive impact on their communities. These facilities are perceived to be particularly valuable for children and young people, as places where they can learn and do their homework conveniently close to home and at no cost. Adult women also appreciate the digital literacy training imparted in these venues. Impact appears to be highest for women because their options to access the Internet from other venues (e.g., cybercafés) are more limited than for men. The analysis suggests that the State should strengthen urban neighborhood telecenters to better serve women’s needs, encourage greater participation of women, and help women develop digital skills, realize their aspirations, and meet their everyday needs.
Part III concludes with an assessment of the impact of public access on women migrants from Burma in the border town of Mae Sot, Thailand. The migrant population outnumbers Thais in border towns such as Mae Sot but is excluded from Thailand’s ICT development plans. Migrant women in Mae Sot have nevertheless benefited indirectly from two types of venues that facilitate public access: cybercafés and two NGO-operated centers that provide ICT skills training to members of migrant organizations and access to computers and the Internet to their students (i.e. CITCs). These facilities enable dislocated ethnic peoples with families, relatives, friends, and work partners living outside Mae Sot (e.g., Chiang Mai, Bangkok, inside Burma, and in resettled countries) to access the Internet, which for them represents a doorway to a wider space for maintaining and expanding social relationships beyond the geographical boundaries of Mae Sot. Physical distance is partly overcome by the proximity of virtual relationships. Through email and video chat using Skype, Yahoo Messenger, and Gchat, the women in Mae Sot are able to repair kinship ties and extend their familial obligations as daughters, sisters, cousins, and nieces who are physically distant.
Women migrants also use the Internet as a virtual cultural headquarters, providing a space for cultural expression and entertainment. The women express themselves online in their ethnic languages when using email or chat, either in Burmese fonts (which they download online) or in English alphabet. The websites of the community-based organizations advocating for migrants are in Burmese and ethnic languages. The women are also active participants in cultural entertainment—downloading, uploading, and watching and listening to Burmese ethnic music videos and celebrations/festivals.
Use of Internet cafés by migrant women in Mae Sot is in practice limited by direct discrimination of some Thai operators and by the women’s own fears of being detected as illegal migrants by Thai police, which could lead to their being detained, harassed, or even deported. Access to computers and the Internet is feasible only for migrant women who have their own computers and home connections, or who connect from their place of work (mainly community-based organizations) or from a few Thai-owned cybercafés that are friendly and accommodating to the needs of Burmese migrants. Programmatic ICT education needs to be developed and implemented through the cooperation of NGOs, the private sector, and the Thai state. There should also be a concerted effort to influence the Thai government to change policies toward migrants and implement ICT policies for marginalized non-Thais living in Thailand. Without these changes, the welfare of migrants and ICT penetration among migrants cannot progress significantly.
The book’s final chapter highlights findings and draws on prior studies in search for patterns of use and impact across countries to inform critical issues of public access policy.
Ghaleb Rabab’ah, Ali Farhan AbuSeileek, Francisco J. Proenza, Omar Fraihat, and Saif Addeen Alrababah
This study analyzes users’ perception of the impact of Internet cafés on three aspects of their lives: social networking, education and learning, and income and employment. The study is based on a sample of 336 Internet café users and twenty-four operators in Amman, Jordan. Internet café users strongly perceive that these venues have improved their lives by expanding their social networks and improving their education and learning. Perceived impact on income or employment was lower than for the other two impact areas but was not insignificant, especially among male users.
The Jordanian government’s policy of increasing the number of licenses for private café operators is to be commended. There is, however, an area of major concern. Although women benefit from cybercafés as much as men, comparatively few women use these venues, and those who use them do so infrequently. The disparity is most acute in low-income neighborhoods. Understanding whether the observed gender disparities are culturally determined or the result of the environment prevalent in cybercafés and designing and implementing suitable policies to increase women’s use of cybercafés should be high priorities for both researchers and government officials.
Jordan’s privately owned Internet cafés generally provide basic services for a fee (e.g., Internet access, email, chat, games, and printing) and often also serve hot and cold drinks. Some cafés divide their space into booths or cubicles, allowing privacy and a quiet environment. According to Ministry of Trade statistics (June 6, 2010), there are 546 Internet cafés in Jordan distributed among twelve governorates, about 193 (35 percent) in Amman.
Based on 250 interviews with Internet café users in Jordan and Egypt (200 interviews in Amman and Zarka, Jordan, and 50 in Cairo), Wheeler (2004) found that even people with a high school education or less, not fluent in English, and sometimes unemployed are drawn to Internet cafés, where they surf sometimes as many as forty hours a week. The Internet helped users meet new people and stay in touch with family and friends, and it enabled them to learn new things, including practical skills such as typing and English language use. It was also an important tool for job hunting, checking agricultural prices, or corresponding with potential partners to set up new business opportunities. Wheeler’s (2007) study of twenty-five women in five Internet cafés in Cairo gives compelling examples and identifies three kinds of benefits for female users: “1. Increases information access and professional development; 2. Expands or maintains social networks and social capital; and 3. Transforms social and political awareness.”
This chapter presents the findings of a survey conducted in twenty-four Internet cafés in Amman in 2010, where 336 users were asked to provide basic personal information and information about their activities while visiting Internet cafés, and to assess the impact Internet cafés were having on their lives. We do not single out or test for the existence of specific negative effects, but instead we focus on the effect of “instrumental” activities. The study does, however, recognize that cybercafé users may experience both positive and negative experiences. We examine user activities, but we also assess user perceptions of impact in three areas commonly regarded as potentially important: education and learning, social networking, and income and employment.
Of 193 Internet cafés in Amman, a representative sample of twenty-four was selected from three different geographical areas.
When our survey was conducted in mid-2010, half of these establishments had been in business for at least five years, another six had been set up two to five years earlier, and the other six were newer. Fifty-eight percent had between six and fifteen computers, and 37 percent had between sixteen and thirty. About 50 percent charge 0.5 Jordanian dinars (JD) (about US$0.70) per hour, 38 percent 1 JD, and the rest a bit more. Most of these venues offer Internet, printing, typing, CD burning, and scanning services. About 50 percent also offered computer repair services. A few venues offered a variety of other services, including fax, snacks, and public phone, and three offered formal training. Twenty-three of the twenty-four Internet café operators interviewed were men, and one was a woman. Most were between twenty and thirty-four years old and held a BA.
Users visiting these twenty-four cafés were interviewed about their perceptions regarding the impact of these venues on three major areas of their lives: social networking, education and learning, and income and employment. These Internet cafés were visited at two different times of day: morning and evening.
The following procedure was employed. The interviews were conducted in Arabic. The data were collected over eight days (March 10–17, 2010). A team leader arranged for each interviewer to visit three Internet cafés at two time intervals (between 10 A.M. and 2 P.M. and between 3 and 6 P.M.). Permission was obtained from the café operators and visitors. Eight people (seven men and one woman) collected the data.
Participants were told in advance that no names would be revealed, that the study was strictly for research purposes, and that the information collected would be treated confidentially. To encourage participation in the survey, café visitors who agreed to be interviewed were given a prepaid telephone card as compensation for the time spent with the interviewer.
About 30 percent of the women and 10 percent of the men approached in the cafés refused to answer. The reason given was that they were too busy and did not have time to answer the questionnaire. Interviewers were encouraged to stay in the café to try to ensure that a good number of female café users were interviewed. Had there been more female interviewers, some of the women users who refused might have been persuaded to participate.
Except for the observed refusal by some café visitors to be interviewed, there were no major departures from the data-collection plan.
A total of 336 Internet café users (255 men and 81 women) were interviewed. The observed gender imbalance is probably due to Jordanian women’s general reticence to visit an Internet café, but it may also be attributed in part to the use of male interviewers and collection of data during the winter season, which witnessed heavy rain. Most women and some men are reluctant to go out in the rain, especially those who do not own a car. Collecting some data in the evening also might have influenced the type of user respondents, with a bias toward a greater number of students ages 16 to 18: most schoolchildren finish their schooling by 1 or 2 P.M., and more young students will be visiting these Internet cafés at these times.
The operators of these twenty-four cafés were also interviewed to understand their perspective regarding the impact on users of these venues. There were three owners operating their Internet cafés and twenty-one operators. In all, complete data were collected from 336 users and twenty-four owner-operators.
Table 2.1
User Survey Respondents by Gender and Age
|
Male |
|
Female |
|
All Users |
|
|
# |
% |
# |
% |
# |
% |
Age |
|
|
|
|
|
|
16–19 |
58 |
22.7 |
12 |
14.8 |
70 |
20.8 |
20–24 |
106 |
41.6 |
40 |
49.4 |
146 |
43.5 |
25–34 |
61 |
23.9 |
25 |
30.9 |
86 |
25.6 |
35–49 |
24 |
9.4 |
3 |
3.7 |
27 |
8.0 |
50–65 |
6 |
2.4 |
1 |
1.2 |
7 |
2.1 |
Total |
255 |
100.0 |
81 |
100.0 |
336 |
100.0 |
There is considerable gender imbalance in the user sample: 255 respondents were male and only 81 were female. Nearly half the sample (48.8 percent) is made up of young men between the ages of 16 and 24 (table 2.1).
The most frequent age group for both gender cohorts is 20 to 24 years (table 2.1). Proportionately, there are fewer young females ages 16 to 19 (15 percent) than males (23 percent), and more women ages 25 to 49 (35 percent) than men (33 percent).
There were proportionately more women students in the sample than men (58 percent vs. 45 percent; table 2.2). One-third of male respondents and one-fifth of female respondents were employees. Sixteen percent of users were self-employed. Self-employment was more common among men (19 percent) than women (6 percent). About 13 percent of the women interviewed were unemployed, compared with 4 percent of the men.
Nearly all users had completed at least secondary education; only 4 percent of male users had completed only primary (table 2.3). The educational profile of women and men users is similar, with the majority (52 percent for males and 58 percent for females) having undergraduate or even postgraduate degrees (table 2.3).
In a sample composed primarily of young people, most users were single (77 percent), but some were married (20 percent), and a few (about 1 percent) were divorced.
The majority of users (54 percent) are dependent on their families for financial support, more so in the case of women (73 percent) than of men (48 percent). Because family income figures given by a large proportion of survey respondents are based on their perception of what other family members earn, family income figures thus obtained are subject to a wide margin of error. With that caveat in mind, female respondents appear to come from better-off families: about 42 percent of females but only 26 percent of males reported monthly family income higher than 1,500 JD (table 2.4).
Table 2.2
Users by Gender, Age, and Occupation
|
Student |
|
Employee |
|
Self-employed |
|
Unemployed |
|
Total |
|
|
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
Male |
|
|
|
|
|
|
|
|
|
|
16–19 |
47 |
42.7 |
5 |
6.2 |
6 |
12.8 |
– |
– |
58 |
23.5 |
20–24 |
58 |
52.7 |
32 |
39.5 |
10 |
21.3 |
2 |
22.2 |
102 |
41.3 |
25–34 |
5 |
4.5 |
34 |
42.0 |
14 |
29.8 |
4 |
44.4 |
57 |
23.1 |
35–49 |
– |
– |
8 |
9.9 |
15 |
31.9 |
1 |
11.1 |
24 |
9.7 |
50–65 |
– |
– |
2 |
2.5 |
2 |
4.3 |
2 |
22.2 |
6 |
2.4 |
Subtotal |
110 |
100.0 |
81 |
100 |
47 |
100 |
9 |
100 |
247 |
100 |
% |
44.5 |
|
32.8 |
|
19.0 |
|
3.6 |
|
100.0 |
|
Female |
|
|
|
|
|
|
|
|
|
|
16–19 |
12 |
26.1 |
– |
– |
– |
– |
– |
– |
12 |
15.2 |
20–24 |
30 |
65.2 |
5 |
27.8 |
1 |
20.0 |
2 |
20.0 |
38 |
48.1 |
25–34 |
4 |
8.7 |
10 |
55.6 |
4 |
80.0 |
7 |
70.0 |
25 |
31.6 |
35–49 |
– |
– |
2 |
11.1 |
– |
– |
1 |
10.0 |
3 |
3.8 |
50–65 |
– |
– |
1 |
5.6 |
– |
– |
– |
– |
1 |
1.3 |
Subtotal |
46 |
100.0 |
18 |
100 |
5 |
100 |
10 |
100 |
79 |
100 |
% |
58.2 |
|
22.8 |
|
6.3 |
|
12.7 |
|
100 |
|
All |
156 |
|
99 |
|
52 |
|
19 |
|
326 |
|
% |
47.9 |
|
30.4 |
|
16.0 |
|
5.8 |
|
100 |
|
Many cybercafé users have an Internet connection at home (43 percent), but the majority of users (57 percent) do not, and the option to connect from home is least common among adults ages 25 and older (35 percent). Home access also appears more prevalent among men (45 percent) than women (40 percent) (table 2.5).
About 73 percent of users travel less than 2 kilometers (1.25 miles) to the cafés they visit (table 2.6), and about 88 percent visit cafés once a week or more often (table 2.7). The most common reason for choosing to use a particular cybercafé (table 2.8) is that it is close to home (42 percent), but other reasons were also given by respondents: friends also visit (21 percent) or proximity to their place of study (21 percent) or work (16 percent).
Table 2.3
Users by Gender, Age, and Educational Achievement
|
Primary |
|
Secondary |
|
Post-secondary |
|
Undergrad |
|
Postgrad |
|
All Users |
|
|
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
Male |
|
|
|
|
|
|
|
|
|
|
|
|
16–19 |
5 |
45.5 |
51 |
58.0 |
– |
– |
– |
– |
– |
– |
56 |
22.2 |
20–24 |
2 |
18.2 |
21 |
23.9 |
14 |
60.9 |
68 |
59.1 |
1 |
6.7 |
106 |
42.1 |
25–34 |
2 |
18.2 |
13 |
14.8 |
6 |
26.1 |
31 |
27.0 |
8 |
53.3 |
60 |
23.8 |
35–49 |
2 |
18.2 |
3 |
3.4 |
2 |
8.7 |
13 |
11.3 |
4 |
26.7 |
24 |
9.5 |
50–65 |
– |
– |
– |
– |
1 |
4.3 |
3 |
2.6 |
2 |
13.3 |
6 |
2.4 |
Subtotal |
11 |
100 |
88 |
100 |
23 |
100 |
115 |
100 |
15 |
100 |
252 |
100 |
% |
4.4 |
|
34.9 |
|
9.1 |
|
45.6 |
|
6.0 |
|
100 |
|
Female |
|
|
|
|
|
|
|
|
|
|
|
|
16–19 |
– |
– |
12 |
42.9 |
– |
– |
– |
– |
– |
– |
12 |
14.8 |
20–24 |
– |
– |
15 |
53.6 |
– |
– |
24 |
63.2 |
1 |
11.1 |
40 |
49.4 |
25–34 |
– |
– |
1 |
3.6 |
6 |
100 |
13 |
34.2 |
5 |
55.6 |
25 |
30.9 |
35–49 |
– |
– |
– |
– |
– |
– |
– |
– |
3 |
33.3 |
3 |
3.7 |
50–65 |
– |
– |
– |
– |
– |
– |
1 |
2.6 |
– |
– |
1 |
1.2 |
Subtotal |
0 |
|
28 |
100 |
6 |
100 |
38 |
100 |
9 |
100 |
81 |
100 |
% |
0.0 |
|
34.6 |
|
7.4 |
|
46.9 |
|
11.1 |
|
100 |
|
All |
11 |
|
116 |
|
29 |
|
153 |
|
24 |
|
333 |
|
% |
3.3 |
|
34.8 |
|
8.7 |
|
45.9 |
|
7.2 |
|
100 |
|
Table 2.4
Users by Gender, Age, and Monthly Family Income
|
200–700 JD |
|
700–1,500 JD |
|
>1,500 JD |
|
All Users |
|
|
# |
% |
# |
% |
# |
% |
# |
% |
Male |
|
|
|
|
|
|
|
|
16–19 |
22 |
29.7 |
27 |
25.7 |
7 |
10.9 |
56 |
23.0 |
20–24 |
26 |
35.1 |
41 |
39.0 |
33 |
51.6 |
100 |
41.2 |
25–34 |
16 |
21.6 |
25 |
23.8 |
18 |
28.1 |
59 |
24.3 |
35–49 |
8 |
10.8 |
9 |
8.6 |
6 |
9.4 |
23 |
9.5 |
50–65 |
2 |
2.7 |
3 |
2.9 |
– |
– |
5 |
2.1 |
Subtotal |
74 |
100.0 |
105 |
100.0 |
64 |
100.0 |
243 |
100.0 |
% |
30.5 |
|
43.2 |
|
26.3 |
|
100.0 |
|
Female |
|
|
|
|
|
|
|
|
16–19 |
2 |
11.8 |
3 |
10.7 |
6 |
18.8 |
11 |
14.3 |
20–24 |
11 |
64.7 |
13 |
46.4 |
14 |
43.8 |
38 |
49.4 |
25–34 |
4 |
23.5 |
10 |
35.7 |
10 |
31.3 |
24 |
31.2 |
35–49 |
– |
– |
2 |
7.1 |
1 |
3.1 |
3 |
3.9 |
50–65 |
– |
– |
– |
– |
1 |
3.1 |
1 |
1.3 |
Subtotal |
17 |
100.0 |
28 |
100.0 |
32 |
100.0 |
77 |
100.0 |
% |
22.1 |
|
36.4 |
|
41.6 |
|
100.0 |
|
All |
91 |
|
133 |
|
96 |
|
320 |
|
% |
28.4 |
|
41.6 |
|
30.0 |
|
100.0 |
|
JD = Jordanian dinars.
Frequent users of cybercafés (i.e., users who visited at least three times a week) account for 78 percent of the men and 44 percent of the women in the sample (table 2.7). Most café visitors spend from one to two hours (44 percent) or two to three hours (32 percent) per visit (table 2.9). Some users were registered members of the cafés they visited (32 percent) or subscribed to monthly plans (3 percent), but the majority (60 percent) paid on a per-visit basis.
Survey participants were asked to choose from a list of seventeen activities which may be classified into four broadly defined groups: communication, education and learning, income and employment, and entertainment (tables 2.10a–2.10d). By far the most popular activity among users is communication—primarily by email, an activity in which 64 percent of all users engaged, but also chatting (63 percent) and making calls over the Internet (50 percent; table 2.10a). Among activities involving some form of learning (table 2.10b), searching for news was the most popular (33 percent), followed by application for college admission (31 percent) and to a lesser extent typing or printing homework, especially by women (24 percent). Some users engaged in searching (18 percent) and applying for a job (15 percent), but the most popular income and employment activity (table 2.10c) was buying products online (25 percent), especially among female users (42 percent). Entertainment activities were also popular (table 2.10d). Playing computer games was selected by 38 percent of sample users and was particularly popular among young people ages 16 to 19, both men (59 percent) and women (67 percent).
Table 2.5
Users with Internet Connection at Home, by Gender and Age
|
# |
# With Data |
% |
Male |
|
|
|
16–19 |
34 |
57 |
59.6 |
20–24 |
47 |
105 |
44.8 |
25–34 |
22 |
61 |
36.1 |
35–49 |
9 |
24 |
37.5 |
50–65 |
1 |
6 |
16.7 |
Subtotal |
113 |
253 |
44.7 |
Female |
|
|
|
16–19 |
6 |
12 |
50.0 |
20–24 |
16 |
40 |
40.0 |
25–34 |
10 |
25 |
40.0 |
35–49 |
– |
3 |
– |
50–65 |
– |
1 |
– |
Subtotal |
32 |
81 |
39.5 |
All |
145 |
334 |
43.4 |
# with data |
334 |
|
|
Table 2.6
Users by Gender, Age, and Distance Traveled to Internet Café
|
<1 km |
|
1–2 km |
|
2–5 km |
|
>5 km |
|
Total |
|
|
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
Male |
|
|
|
|
|
|
|
|
|
|
16–19 |
28 |
25.5 |
18 |
23.4 |
11 |
22.4 |
1 |
6.7 |
58 |
23.1 |
20–24 |
53 |
48.2 |
26 |
33.8 |
21 |
42.9 |
3 |
20.0 |
103 |
41.0 |
25–34 |
22 |
20.0 |
23 |
29.9 |
10 |
20.4 |
6 |
40.0 |
61 |
24.3 |
35–49 |
6 |
5.5 |
8 |
10.4 |
6 |
12.2 |
4 |
26.7 |
24 |
9.6 |
50–65 |
1 |
0.9 |
2 |
2.6 |
1 |
2.0 |
1 |
6.7 |
5 |
2.0 |
Subtotal |
110 |
100.0 |
77 |
100.0 |
49 |
100.0 |
15 |
100.0 |
251 |
100.0 |
% |
43.8 |
|
30.7 |
|
19.5 |
|
6.0 |
|
100.0 |
|
Female |
|
|
|
|
|
|
|
|
|
|
16–19 |
5 |
20.0 |
2 |
6.5 |
3 |
17.6 |
2 |
28.6 |
12 |
15.0 |
20–24 |
12 |
48.0 |
16 |
51.6 |
9 |
52.9 |
2 |
28.6 |
39 |
48.8 |
25–34 |
8 |
32.0 |
11 |
35.5 |
3 |
17.6 |
3 |
42.9 |
25 |
31.3 |
35–49 |
– |
– |
2 |
6.5 |
1 |
5.9 |
– |
– |
3 |
3.8 |
50–65 |
– |
– |
– |
– |
1 |
5.9 |
– |
– |
1 |
1.3 |
Subtotal |
25 |
100.0 |
31 |
100.0 |
17 |
100.0 |
7 |
100.0 |
80 |
100.0 |
% |
31.3 |
|
38.8 |
|
21.3 |
|
8.8 |
|
100.0 |
|
All |
135 |
|
108 |
|
66 |
|
22 |
|
331 |
|
% |
40.8 |
|
32.6 |
|
19.9 |
|
6.6 |
|
100.0 |
|
Table 2.7
Users by Gender, Age, and Frequency of Visits to Internet Cafés
|
Daily or Almost Daily |
|
Three Times a Week |
|
At Least Once a Week |
|
At Least Once a Month |
|
A Few Times a Year |
|
Total |
|
|
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
Male |
|
|
|
|
|
|
|
|
|
|
|
|
16–19 |
36 |
62.1 |
13 |
22.4 |
4 |
6.9 |
5 |
8.6 |
– |
– |
58 |
100.0 |
20–24 |
38 |
36.2 |
44 |
41.9 |
6 |
5.7 |
10 |
9.5 |
7 |
6.7 |
105 |
100.0 |
25–34 |
29 |
47.5 |
18 |
29.5 |
10 |
16.4 |
2 |
3.3 |
2 |
3.3 |
61 |
100.0 |
35–49 |
7 |
29.2 |
10 |
41.7 |
4 |
16.7 |
2 |
8.3 |
1 |
4.2 |
24 |
100.0 |
50–65 |
2 |
33.3 |
1 |
16.7 |
2 |
33.3 |
1 |
16.7 |
– |
– |
6 |
100.0 |
Subtotal |
112 |
44.1 |
86 |
33.9 |
26 |
10.2 |
20 |
7.9 |
10 |
3.9 |
254 |
100.0 |
Female |
|
|
|
|
|
|
|
|
|
|
|
|
16–19 |
1 |
8.3 |
3 |
25.0 |
6 |
50.0 |
2 |
16.7 |
– |
– |
12 |
100.0 |
20–24 |
6 |
15.0 |
13 |
32.5 |
17 |
42.5 |
1 |
2.5 |
3 |
7.5 |
40 |
100.0 |
25–34 |
3 |
12.0 |
8 |
32.0 |
10 |
40.0 |
2 |
8.0 |
2 |
8.0 |
25 |
100.0 |
35–49 |
– |
– |
1 |
33.3 |
1 |
33.3 |
1 |
33.3 |
– |
– |
3 |
100.0 |
50–65 |
1 |
100.0 |
– |
– |
– |
– |
– |
– |
– |
– |
1 |
100.0 |
Subtotal |
11 |
13.6 |
25 |
30.9 |
34 |
42.0 |
6 |
7.4 |
5 |
6.2 |
81 |
100.0 |
All |
123 |
36.7 |
111 |
33.1 |
60 |
17.9 |
26 |
7.8 |
15 |
4.5 |
335 |
100.0 |
Most websites viewed were in Arabic (64 percent) or English (62 percent). Only rarely were websites in other languages visited.
Using a separate questionnaire, we asked the twenty-four café owners and operators which websites were most frequently used by their customers, and we gave them four options plus a write-in possibility. All twenty-four indicated that networking (email, Facebook, etc.) was most frequently used. News websites were marked by fifteen operators (62 percent), pornographic sites by five (22 percent), and sport sites by one.
Table 2.8
Reason for Using This Venue, by Age and Gender
|
Close to Work |
|
Close to Home |
|
Friends Go There |
|
Close to Place of Study |
|
Total |
|
|
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
Male |
|
|
|
|
|
|
|
|
|
|
16–19 |
5 |
6.0 |
40 |
48.2 |
29 |
34.9 |
9 |
10.8 |
83 |
100.0 |
20–24 |
25 |
16.6 |
64 |
42.4 |
33 |
21.9 |
29 |
19.2 |
151 |
100.0 |
25–34 |
16 |
21.3 |
34 |
45.3 |
15 |
20.0 |
10 |
13.3 |
75 |
100.0 |
35–49 |
11 |
39.3 |
10 |
35.7 |
4 |
14.3 |
3 |
10.7 |
28 |
100.0 |
50–65 |
1 |
14.3 |
4 |
57.1 |
1 |
14.3 |
1 |
14.3 |
7 |
100.0 |
Subtotal |
58 |
16.9 |
152 |
44.2 |
82 |
23.8 |
52 |
15.1 |
344 |
100.0 |
Female |
|
|
|
|
|
|
|
|
|
|
16–19 |
2 |
11.8 |
4 |
23.5 |
5 |
29.4 |
6 |
35.3 |
17 |
100.0 |
20–24 |
3 |
6.1 |
16 |
32.7 |
4 |
8.2 |
26 |
53.1 |
49 |
100.0 |
25–34 |
6 |
23.1 |
10 |
38.5 |
3 |
11.5 |
7 |
26.9 |
26 |
100.0 |
35–49 |
– |
– |
3 |
75.0 |
– |
– |
1 |
25.0 |
4 |
100.0 |
50–65 |
– |
– |
1 |
100.0 |
– |
– |
– |
– |
1 |
100.0 |
Subtotal |
11 |
11.3 |
34 |
35.1 |
12 |
12.4 |
40 |
41.2 |
97 |
100.0 |
All users |
69 |
15.6 |
186 |
42.2 |
94 |
21.3 |
92 |
20.9 |
441 |
100.0 |
Note: Respondents could select more than one reason. This is why total responses exceed sample size.
Ten indicators were considered, grouped into three categories depending on whether the perceived impact was on social networking, education and learning, or income and employment (tables 2.11a–2.11c). For each indicator, users were given a choice of five options depending on their perception of impact as highly or slightly positive, highly or slightly negative, or no impact.
Social Networking The indicator most frequently ranked highly positive by both men (65 percent) and women (48 percent) was “maintaining communication with family and friends by using social networking” (table 2.11a). The two other social networking indicators—“Meeting new people online” and “Knowing about the culture of other people around the world”—were both ranked highly positive by 38 percent of users and slightly positive by 33 and 38 percent, respectively.
Table 2.9
Users by Gender, Age, and Hours Spent During Each Visit to Internet Cafés
|
1–2 Hours |
|
2–3 Hours |
|
>3 Hours |
|
Total |
|
|
# |
% |
# |
% |
# |
% |
# |
% |
Male |
|
|
|
|
|
|
|
|
16–19 |
19 |
35.8 |
22 |
41.5 |
12 |
22.6 |
53 |
100.0 |
20–24 |
29 |
30.2 |
35 |
36.5 |
32 |
33.3 |
96 |
100.0 |
25–34 |
29 |
49.2 |
15 |
25.4 |
15 |
25.4 |
59 |
100.0 |
35–49 |
12 |
52.2 |
5 |
21.7 |
6 |
26.1 |
23 |
100.0 |
50–65 |
4 |
66.7 |
– |
– |
2 |
33.3 |
6 |
100.0 |
Subtotal |
93 |
39.2 |
77 |
32.5 |
67 |
28.3 |
237 |
100.0 |
Female |
|
|
|
|
|
|
|
|
16–19 |
8 |
66.7 |
4 |
33.3 |
|
|
12 |
100.0 |
20–24 |
28 |
70.0 |
7 |
17.5 |
5 |
12.5 |
40 |
100.0 |
25–34 |
11 |
45.8 |
10 |
41.7 |
3 |
12.5 |
24 |
100.0 |
35–49 |
– |
– |
3 |
100.0 |
– |
– |
3 |
100.0 |
50–65 |
1 |
100.0 |
– |
– |
– |
– |
1 |
100.0 |
Subtotal |
48 |
60.0 |
24 |
30.0 |
8 |
10.0 |
80 |
100.0 |
All (with data) |
141 |
44.5 |
101 |
31.9 |
75 |
23.7 |
317 |
100.0 |
Table 2.10a
Communication Activities Done When Visiting Internet Cafés, by Gender and Age
|
|
VoIP |
|
Chat |
|
|
|
# |
% |
# |
% |
# |
% |
Male |
|
|
|
|
|
|
16–19 |
36 |
62.1 |
37 |
63.8 |
38 |
65.5 |
20–24 |
58 |
54.7 |
60 |
56.6 |
78 |
73.6 |
25–34 |
41 |
67.2 |
31 |
50.8 |
36 |
59.0 |
35–49 |
17 |
70.8 |
5 |
20.8 |
11 |
45.8 |
50–65 |
4 |
66.7 |
1 |
16.7 |
1 |
16.7 |
Subtotal |
156 |
61.2 |
134 |
52.5 |
164 |
64.3 |
Female |
|
|
|
|
|
|
16–19 |
9 |
75.0 |
6 |
50.0 |
7 |
58.3 |
20–24 |
30 |
75.0 |
16 |
40.0 |
26 |
65.0 |
25–34 |
17 |
68.0 |
10 |
40.0 |
15 |
60.0 |
35–49 |
3 |
100.0 |
– |
– |
1 |
33.3 |
50–65 |
– |
– |
1 |
100.0 |
– |
– |
Subtotal |
59 |
72.8 |
33 |
40.7 |
49 |
60.5 |
All users |
215 |
64.0 |
167 |
49.7 |
213 |
63.4 |
Note: Percentage figures are in relation to total number of users in the sample.
Table 2.10b
Education and Learning Activities Done When Visiting Internet Cafés, by Gender and Age
|
Type or Print Homework |
|
Take an Online Course |
|
Conduct Research |
|
Create Web Page |
|
Search for Local and International News |
|
Apply for College Admission |
|
|
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
Male |
|
|
|
|
|
|
|
|
|
|
|
|
16–19 |
6 |
10.3 |
5 |
8.6 |
0 |
|
5 |
8.6 |
17 |
29.3 |
32 |
55.2 |
20–24 |
37 |
34.9 |
5 |
4.7 |
1 |
0.9 |
14 |
13.2 |
37 |
34.9 |
45 |
42.5 |
25–34 |
9 |
14.8 |
5 |
8.2 |
3 |
4.9 |
5 |
8.2 |
22 |
36.1 |
14 |
23.0 |
35–49 |
1 |
4.2 |
– |
– |
5 |
20.8 |
– |
– |
12 |
50.0 |
2 |
8.3 |
50–65 |
1 |
16.7 |
– |
– |
– |
– |
1 |
16.7 |
3 |
50.0 |
– |
– |
Subtotal |
54 |
21.2 |
15 |
5.9 |
9 |
3.5 |
25 |
9.8 |
91 |
35.7 |
93 |
36.5 |
Female |
|
|
|
|
|
|
|
|
|
|
|
|
16–19 |
4 |
33.3 |
– |
– |
– |
– |
– |
– |
2 |
16.7 |
4 |
33.3 |
20–24 |
19 |
47.5 |
4 |
10.0 |
– |
– |
– |
– |
11 |
27.5 |
3 |
7.5 |
25–34 |
2 |
8.0 |
4 |
16.0 |
– |
– |
4 |
16.0 |
5 |
20.0 |
6 |
24.0 |
35–49 |
1 |
33.3 |
1 |
33.3 |
– |
– |
– |
– |
1 |
33.3 |
– |
– |
50–65 |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
Subtotal |
26 |
32.1 |
9 |
11.1 |
0 |
|
4 |
4.9 |
19 |
23.5 |
13 |
16.0 |
All users |
80 |
23.8 |
24 |
7.1 |
9 |
2.7 |
29 |
8.6 |
110 |
32.7 |
106 |
31.5 |
Notes: Percentage figures are in relation to total number of users in the sample. Creating web pages or searching for local and international news are only indirectly linked to education and learning.
Education and Learning Using the Internet for educational purposes—for example, doing research online, writing homework, or sending emails to teachers—was perceived as slightly or highly positive by 61 percent of respondents (table 2.11b). Ninety-three percent had positive perceptions of improving computer skills and 84 percent of improving English language skills. Attending online classes and workshops was apparently least useful: 63 percent ranked this activity as having no impact.
There are interesting differences in the responses regarding activities performed and perceptions of impact. The number of users who say they use the venue to conduct research (3 percent; table 2.10b) or type or print out their homework (24 percent; table 2.10b) is relatively small, but when users are asked instead, “Which of the following has had an impact on you from using the Internet at the Internet cafés?”, a larger number of users (38 percent; table 2.11b) report a highly positive impact from “Education (e.g., doing research, writing homework, sending e-mail to teachers),” and an additional 23 percent report a slightly positive impact.
Table 2.10c
Income and Employment Activities Done When Visiting Internet Cafés, by Gender and Age
|
Search for a Job |
|
Apply for a Job |
|
Interview for a Job |
|
Make Money From Online Business |
|
Buy Products Online |
|
|
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
Male |
|
|
|
|
|
|
|
|
|
|
16–19 |
4 |
6.9 |
2 |
3.4 |
– |
– |
6 |
10.3 |
6 |
10.3 |
20–24 |
19 |
17.9 |
15 |
14.2 |
6 |
5.7 |
4 |
3.8 |
26 |
24.5 |
25–34 |
20 |
32.8 |
20 |
32.8 |
3 |
4.9 |
1 |
1.6 |
14 |
23.0 |
35–49 |
5 |
20.8 |
6 |
25.0 |
– |
– |
– |
– |
3 |
12.5 |
50–65 |
2 |
33.3 |
1 |
16.7 |
1 |
16.7 |
– |
– |
2 |
33.3 |
Subtotal |
50 |
19.6 |
44 |
17.3 |
10 |
3.9 |
11 |
4.3 |
51 |
20.0 |
Female |
|
|
|
|
|
|
|
|
|
|
16–19 |
– |
– |
– |
– |
– |
– |
– |
– |
4 |
33.3 |
20–24 |
1 |
2.5 |
2 |
5.0 |
– |
– |
– |
– |
24 |
60.0 |
25–34 |
10 |
40.0 |
6 |
24.0 |
1 |
4.0 |
1 |
4.0 |
5 |
20.0 |
35–49 |
1 |
33.3 |
– |
– |
– |
– |
– |
– |
1 |
33.3 |
50–65 |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
Subtotal |
12 |
14.8 |
8 |
9.9 |
1 |
1.2 |
1 |
1.2 |
34 |
42.0 |
All users |
62 |
18.5 |
52 |
15.5 |
11 |
3.3 |
12 |
3.6 |
85 |
25.3 |
Notes: Percentage figures are in relation to total number of users in the sample. Strictly speaking, buying products online is not an income-generating activity, except in the sense that users may feel it saves them money.
Income and Employment Impact on income or employment was rated much lower than for the other two impact categories (table 2.11c). Nevertheless, 36 percent of men perceived a (highly or slightly) positive impact on finding a job, 24 percent on getting a promotion, and 27 percent on increasing their income. Women’s perceptions of impact in this area were lower, perhaps on account of the relatively lower rate of labor force participation of the women surveyed (table 2.2).
Table 2.10d
Entertainment Activities Internet Café Users Engage In, by Gender and Age
|
Watch Movies Online |
|
Listen to and Download Music Files |
|
Play Computer Games Online |
|
|
# |
% |
# |
% |
# |
% |
Male |
|
|
|
|
|
|
16–19 |
1 |
1.7 |
23 |
39.7 |
34 |
58.6 |
20–24 |
6 |
5.7 |
22 |
20.8 |
52 |
49.1 |
25–34 |
1 |
1.6 |
10 |
16.4 |
16 |
26.2 |
35–49 |
1 |
4.2 |
3 |
12.5 |
4 |
16.7 |
50–65 |
– |
– |
– |
– |
– |
– |
Subtotal |
9 |
3.5 |
58 |
22.7 |
106 |
41.6 |
Female |
|
|
|
|
|
|
16–19 |
1 |
8.3 |
6 |
50.0 |
8 |
66.7 |
20–24 |
– |
– |
8 |
20.0 |
11 |
27.5 |
25–34 |
– |
– |
6 |
24.0 |
4 |
16.0 |
35–49 |
– |
– |
– |
– |
– |
– |
50–65 |
– |
– |
– |
– |
– |
– |
Subtotal |
1 |
1.2 |
20 |
24.7 |
23 |
28.4 |
All users |
10 |
3.0 |
78 |
23.2 |
129 |
38.4 |
Nearly 15 percent of respondents had apparently been negatively impacted with respect to “finding a job.” This is the single indicator for which the most negative impact was perceived. Interestingly, however, within the Economic and Employment category, this is also the indicator with the largest proportion of positive impact marks (33 percent; table 2.11c).
Does Having a Home Connection Make a Difference? Because more than 40 percent of café users also had a home connection (table 2.5), it is reasonable to ask to what extent the perceived impact is due to the public access provided—primarily if not exclusively—by the Internet café, as opposed to the impact perceived by café users who had the added convenience of being able to connect to the Internet from their homes.
The one observable marked difference is the negative impact with respect to meeting people online reported by ten female café users, for which there was no counterpart among female users with home connections (table 2.12a). The sample size is small, and we cannot determine whether this difference is statistically significant. Furthermore, because the questions asked refer to online meetings regardless of access place, there is no reason for the place of access to give rise to a difference.
Table 2.11a
User Perceptions of Impact on Social Networking, by Gender
|
|
Positive |
|
|
Negative |
|
|
|
|
Perception of Impact on |
|
Highly |
Slightly |
Highly or Slightly |
Slightly |
Highly |
Slightly or Highly |
No Impact* |
All Users |
Meeting new people online |
Male |
103 |
83 |
186 |
22 |
7 |
29 |
40 |
255 |
|
% |
40.4 |
32.5 |
72.9 |
8.6 |
2.7 |
11.4 |
15.7 |
100.0 |
|
Female |
24 |
27 |
51 |
4 |
6 |
10 |
20 |
81 |
|
% |
29.6 |
33.3 |
63.0 |
4.9 |
7.4 |
12.3 |
24.7 |
100.0 |
|
Total |
127 |
110 |
237 |
26 |
13 |
39 |
60 |
336 |
|
% |
37.8 |
32.7 |
70.5 |
7.7 |
3.9 |
11.6 |
17.9 |
100.0 |
Maintaining communication with family and friends using social networking |
Male |
166 |
47 |
213 |
13 |
3 |
16 |
26 |
255 |
|
% |
65.1 |
18.4 |
83.5 |
5.1 |
1.2 |
6.3 |
10.2 |
100.0 |
|
Female |
39 |
27 |
66 |
2 |
3 |
5 |
10 |
81 |
|
% |
48.1 |
33.3 |
81.5 |
2.5 |
3.7 |
6.2 |
12.3 |
100.0 |
|
Total |
205 |
74 |
279 |
15 |
|
21 |
36 |
336 |
|
% |
61.0 |
22.0 |
83.0 |
4.5 |
0.0 |
6.3 |
10.7 |
100.0 |
Knowing about the culture of other people around the world |
Male |
105 |
91 |
196 |
13 |
5 |
18 |
41 |
255 |
|
% |
41.2 |
35.7 |
76.9 |
5.1 |
2.0 |
7.1 |
16.1 |
100.0 |
|
Female |
22 |
36 |
58 |
3 |
3 |
6 |
17 |
81 |
|
% |
27.2 |
44.4 |
71.6 |
3.7 |
3.7 |
7.4 |
21.0 |
100.0 |
|
Total |
127 |
127 |
254 |
16 |
8 |
24 |
58 |
336 |
|
% |
37.8 |
37.8 |
75.6 |
4.8 |
2.4 |
7.1 |
17.3 |
100.0 |
*Includes respondents who left this question unanswered.
Table 2.11b
User Perceptions of Impact on Education and Learning, by Gender
|
|
Positive |
|
|
Negative |
|
|
|
|
Perception of Impact on |
|
Highly |
Slightly |
Highly or Slightly |
Slightly |
Highly |
Slightly or Highly |
No Impact* |
All Users |
Education (e.g., doing research, writing homework, sending emails to teachers) |
Male |
91 |
58 |
149 |
8 |
9 |
17 |
89 |
255 |
|
% |
35.7 |
22.7 |
58.4 |
3.1 |
3.5 |
6.7 |
34.9 |
100.0 |
|
Female |
36 |
19 |
55 |
2 |
8 |
10 |
16 |
81 |
|
% |
44.4 |
23.5 |
67.9 |
2.5 |
9.9 |
12.3 |
19.8 |
100.0 |
|
Total |
127 |
77 |
204 |
10 |
17 |
27 |
105 |
336 |
|
% |
37.8 |
22.9 |
60.7 |
3.0 |
5.1 |
8.0 |
31.3 |
100.0 |
Improving computer skills |
Male |
134 |
104 |
238 |
9 |
2 |
11 |
6 |
255 |
|
% |
52.5 |
40.8 |
93.3 |
3.5 |
0.8 |
4.3 |
2.4 |
100.0 |
|
Female |
28 |
48 |
76 |
3 |
– |
3 |
2 |
81 |
|
% |
34.6 |
59.3 |
93.8 |
3.7 |
– |
3.7 |
2.5 |
100.0 |
|
Total |
162 |
152 |
314 |
12 |
2 |
14 |
8 |
336 |
|
% |
48.2 |
45.2 |
93.5 |
3.6 |
0.6 |
4.2 |
2.4 |
100.0 |
Improving English language skills |
Male |
105 |
110 |
215 |
11 |
4 |
15 |
25 |
255 |
|
% |
41.2 |
43.1 |
84.3 |
4.3 |
1.6 |
5.9 |
9.8 |
100.0 |
|
Female |
19 |
47 |
66 |
2 |
1 |
3 |
12 |
81 |
|
% |
23.5 |
58.0 |
81.5 |
2.5 |
1.2 |
3.7 |
14.8 |
100.0 |
|
Total |
124 |
157 |
281 |
13 |
5 |
18 |
37 |
336 |
|
% |
36.9 |
46.7 |
83.6 |
3.9 |
1.5 |
5.4 |
11.0 |
100.0 |
Attending online classes and workshops |
Male |
30 |
37 |
67 |
18 |
9 |
27 |
161 |
255 |
|
% |
11.8 |
14.5 |
26.3 |
7.1 |
3.5 |
10.6 |
63.1 |
100.0 |
|
Female |
8 |
13 |
21 |
2 |
7 |
9 |
51 |
81 |
|
% |
9.9 |
16.0 |
25.9 |
2.5 |
8.6 |
11.1 |
63.0 |
100.0 |
|
Total |
38 |
50 |
88 |
20 |
16 |
36 |
212 |
336 |
|
% |
11.3 |
14.9 |
26.2 |
6.0 |
4.8 |
10.7 |
63.1 |
100.0 |
*Includes respondents who left this question unanswered.
Table 2.11c
User Perceptions of Impact on Income and Employment, by Gender
|
|
Positive |
|
|
Negative |
|
|
|
|
Perception of Impact on |
|
Highly |
Slightly |
Highly or Slightly |
Slightly |
Highly |
Highly Slightly or |
No Impact* |
All Users |
Getting a promotion at work |
Male |
24 |
37 |
61 |
13 |
9 |
22 |
172 |
255 |
|
% |
9.4 |
14.5 |
23.9 |
5.1 |
3.5 |
8.6 |
67.5 |
100.0 |
|
Female |
5 |
7 |
12 |
2 |
1 |
3 |
66 |
81 |
|
% |
6.2 |
8.6 |
14.8 |
2.5 |
1.2 |
3.7 |
81.5 |
100.0 |
|
Total |
29 |
44 |
73 |
15 |
10 |
25 |
238 |
336 |
|
% |
8.6 |
13.1 |
21.7 |
4.5 |
3.0 |
7.4 |
70.8 |
100.0 |
Increasing your income |
Male |
26 |
42 |
68 |
27 |
6 |
33 |
154 |
255 |
|
% |
10.2 |
16.5 |
26.7 |
10.6 |
2.4 |
12.9 |
60.4 |
100.0 |
|
Female |
2 |
6 |
8 |
6 |
2 |
8 |
65 |
81 |
|
% |
2.5 |
7.4 |
9.9 |
7.4 |
2.5 |
9.9 |
80.2 |
100.0 |
|
Total |
28 |
48 |
76 |
33 |
8 |
41 |
219 |
336 |
|
% |
8.3 |
14.3 |
22.6 |
9.8 |
2.4 |
12.2 |
65.2 |
100.0 |
Finding a job |
Male |
30 |
63 |
93 |
28 |
13 |
41 |
121 |
255 |
|
% |
11.8 |
24.7 |
36.5 |
11.0 |
5.1 |
16.1 |
47.5 |
100.0 |
|
Female |
4 |
15 |
19 |
3 |
5 |
8 |
54 |
81 |
|
% |
4.9 |
18.5 |
23.5 |
3.7 |
6.2 |
9.9 |
66.7 |
100.0 |
|
Total |
34 |
78 |
112 |
31 |
18 |
49 |
175 |
336 |
|
% |
10.1 |
23.2 |
33.3 |
9.2 |
5.4 |
14.6 |
52.1 |
100.0 |
*Includes respondents who left this question unanswered.
Overall, there appears to be no major systematic difference in the perceived impact among Internet café users between those who had Internet at home and those who did not (tables 2.12a–12c). We must conclude that the perceived impacts with respect to the ten indicators result from user access to the Internet, irrespective of the place of access. Internet cafés provide valuable, affordable access to both kinds of users: those who have and those who do not have an Internet connection at home.
The rate of economic participation among Internet café users—employed plus unemployed as a percentage of the total—is higher among men (55 percent) than among women (42 percent). This lower rate among women users is much higher than for Jordan as a whole, where women’s rate of participation in the labor force is about 15 percent (Tabbaa 2010).
Within the limited subsample of non-dependent respondents ages 20 or older, women seem to have a personal income advantage over males, with only 22 percent of men (twenty-seven observations) reporting monthly income of 700 JD or higher compared with 52 percent (eleven observations) of women (table 2.13).
Women users visit Internet cafés less frequently and spend fewer hours there than men. About 56 percent of the women interviewed visited cybercafés once a week or less frequently, compared with 22 percent of men (table 2.7), whereas 61 percent of men spent two or more hours per visit and only 40 percent of women did (table 2.9). The lower frequency and duration of women’s visits to cybercafés probably apply to Amman’s café user population as a whole and help explain in part our sample’s gender imbalance.
There are no major gender-related differences in user perceptions of impact (tables 2.11a–2.11c). The proportion of women (68 percent) positively impacted, either highly or slightly, in terms of education is higher than that of men (58 percent; table 2.11b), but this may be due to the larger proportion of students among women (58 percent, table 2.2) than among men (44 percent).
Table 2.14 disaggregates interviewees by gender in the twenty-four Internet cafés studied. Women visitors are relatively more important in venues situated near the University of Jordan, a female-dominated institution with a student body that is more than 70 percent female. This is the case of the University Center Café and Evolution Café (numbered 10 and 11 in table 2.14). Also, cafés serving high- and upper middle-class areas of Amman, such as Sweifeyeh and Khalda, exhibit a higher proportion of female users. This includes Waves Café, Rehaf Net, Zorona Café, City View Café, and Hanin Net. Altogether, these seven cafés where women users are in the majority account for 59 percent of the females in our sample, but they do not cater exclusively to women: about 42 percent of their customers are men. In contrast, the dominance of male users in the remaining cafés surveyed is overwhelming: 87 percent male versus 13 percent female.
Table 2.12a
User Perceptions of Impact on Social Networking, by Gender and Whether User had Internet Connection at Home
|
|
|
Positive |
|
|
Negative |
|
|
No Impact |
|
All |
|
Perception of Impact on |
M/F |
Net at Home |
Highly |
Slightly |
% |
Slightly |
Highly |
% |
# |
% |
# |
% |
Meeting new people online |
M |
Yes |
50 |
37 |
79.8 |
9 |
2 |
10.1 |
11 |
10.1 |
98 |
100.0 |
|
|
No |
53 |
46 |
72.3 |
12 |
5 |
12.4 |
21 |
15.3 |
116 |
100.0 |
|
F |
Yes |
13 |
12 |
80.6 |
– |
– |
– |
6 |
19.4 |
25 |
100.0 |
|
|
No |
11 |
15 |
54.2 |
4 |
6 |
20.8 |
12 |
25.0 |
36 |
100.0 |
Maintaining communication with family and friends using social networking |
M |
Yes |
85 |
15 |
90.9 |
3 |
1 |
3.6 |
6 |
5.5 |
104 |
100.0 |
|
|
No |
81 |
32 |
81.3 |
9 |
2 |
7.9 |
15 |
10.8 |
124 |
100.0 |
|
F |
Yes |
19 |
7 |
83.9 |
1 |
|
3.2 |
4 |
12.9 |
27 |
100.0 |
|
|
No |
20 |
20 |
83.3 |
1 |
3 |
8.3 |
4 |
8.3 |
44 |
100.0 |
Knowing about the culture of other people around the world |
M |
Yes |
44 |
44 |
80.0 |
6 |
2 |
7.3 |
14 |
12.7 |
96 |
100.0 |
|
|
No |
61 |
47 |
78.8 |
6 |
3 |
6.6 |
20 |
14.6 |
117 |
100.0 |
|
F |
Yes |
10 |
12 |
71.0 |
1 |
1 |
6.5 |
7 |
22.6 |
24 |
100.0 |
|
|
No |
12 |
24 |
75.0 |
2 |
2 |
8.3 |
8 |
16.7 |
40 |
100.0 |
Table 2.12b
User Perceptions of Impact on Education and Learning, by Gender and Whether User Had Internet Connection at Home
|
|
|
Positive |
|
|
Negative |
|
|
No impact |
|
All |
|
Perception of Impact on |
M/F |
Net at Home |
Highly |
Slightly |
% |
Slightly |
Highly |
% |
# |
% |
# |
% |
Education (e.g., doing research, writing homework, sending emails to teachers) |
M |
Yes |
46 |
31 |
80.2 |
5 |
3 |
8.3 |
11 |
11.5 |
85 |
100.0 |
|
|
No |
45 |
27 |
71.3 |
4 |
4 |
7.9 |
21 |
20.8 |
80 |
100.0 |
|
F |
Yes |
13 |
8 |
63.6 |
4 |
2 |
18.2 |
6 |
18.2 |
27 |
100.0 |
|
|
No |
23 |
11 |
68.0 |
4 |
|
8.0 |
12 |
24.0 |
38 |
100.0 |
Improving computer skills |
M |
Yes |
66 |
39 |
89.7 |
1 |
5 |
5.1 |
6 |
5.1 |
111 |
100.0 |
|
|
No |
68 |
64 |
86.8 |
1 |
4 |
3.3 |
15 |
9.9 |
137 |
100.0 |
|
F |
Yes |
12 |
17 |
82.9 |
– |
2 |
5.7 |
4 |
11.4 |
31 |
100.0 |
|
|
No |
16 |
31 |
90.4 |
– |
1 |
1.9 |
4 |
7.7 |
48 |
100.0 |
Improving English language skills |
M |
Yes |
51 |
50 |
83.5 |
1 |
5 |
5.0 |
14 |
11.6 |
107 |
100.0 |
|
|
No |
54 |
60 |
80.3 |
3 |
5 |
5.6 |
20 |
14.1 |
122 |
100.0 |
|
F |
Yes |
8 |
14 |
73.3 |
– |
1 |
3.3 |
7 |
23.3 |
23 |
100.0 |
|
|
No |
11 |
33 |
81.5 |
1 |
1 |
3.7 |
8 |
14.8 |
46 |
100.0 |
Attending online classes and workshops |
M |
Yes |
13 |
21 |
55.7 |
4 |
9 |
21.3 |
14 |
23.0 |
47 |
100.0 |
|
|
No |
17 |
16 |
50.0 |
5 |
8 |
19.7 |
20 |
30.3 |
46 |
100.0 |
|
F |
Yes |
4 |
2 |
42.9 |
1 |
– |
7.1 |
7 |
50.0 |
7 |
100.0 |
|
|
No |
4 |
11 |
48.4 |
6 |
2 |
25.8 |
8 |
25.8 |
23 |
100.0 |
Table 2.12c
User Perceptions of Impact on Income and Employment, by Gender and Whether User Had Internet Connection at Home
|
|
|
Positive |
|
|
Negative |
|
|
No Impact |
|
All |
|
Perception of Impact on |
M/F |
Net at Home |
Highly |
Slightly |
% |
Slightly |
Highly |
% |
# |
% |
# |
% |
Getting a promotion at work |
M |
Yes |
46 |
20 |
48.2 |
5 |
2 |
5.1 |
64 |
46.7 |
73 |
100.0 |
|
|
No |
45 |
17 |
39.0 |
8 |
7 |
9.4 |
82 |
51.6 |
77 |
100.0 |
|
F |
Yes |
13 |
2 |
35.7 |
– |
– |
|
27 |
64.3 |
15 |
100.0 |
|
|
No |
23 |
5 |
43.8 |
2 |
1 |
4.7 |
33 |
51.6 |
31 |
100.0 |
Increasing your income |
M |
Yes |
10 |
18 |
26.9 |
7 |
3 |
9.6 |
66 |
63.5 |
38 |
100.0 |
|
|
No |
14 |
24 |
27.7 |
19 |
3 |
16.1 |
77 |
56.2 |
60 |
100.0 |
|
F |
Yes |
2 |
– |
6.3 |
1 |
1 |
6.3 |
28 |
87.5 |
4 |
100.0 |
|
|
No |
3 |
6 |
18.0 |
5 |
1 |
12.0 |
35 |
70.0 |
15 |
100.0 |
Finding a job |
M |
Yes |
12 |
31 |
43.4 |
6 |
3 |
9.1 |
47 |
47.5 |
52 |
100.0 |
|
|
No |
14 |
32 |
34.8 |
22 |
10 |
24.2 |
54 |
40.9 |
78 |
100.0 |
|
F |
Yes |
1 |
4 |
16.1 |
– |
2 |
6.5 |
24 |
77.4 |
7 |
100.0 |
|
|
No |
1 |
11 |
26.7 |
3 |
3 |
13.3 |
27 |
60.0 |
18 |
100.0 |
Table 2.13
Monthly Personal Income of Nondependent Adults Ages 20 or Older, by Gender
|
200–700 JD |
700–1,500 JD |
>, JD |
All Non-dependent ≥20 Years |
Nondependent users |
108 |
32 |
6 |
146 |
|
74% |
22% |
4% |
100% |
Nondependent men |
98 |
25 |
2 |
125 |
|
78% |
20% |
2% |
100% |
Nondependent women |
10 |
7 |
4 |
21 |
|
48% |
33% |
19% |
100% |
JD = Jordanian dinars.
Table 2.14
Distribution of Interviewees by Gender in the 24 Participating Cafés
# |
Café Name |
# Computers |
Females |
Males |
Total |
1 |
Another World Café |
13 |
1 |
12 |
13 |
2 |
Al-Sultan Café |
5 |
2 |
3 |
5 |
3 |
Chit Chat Café |
13 |
2 |
11 |
13 |
4 |
Waves Café |
12 |
5 |
5 |
10 |
5 |
Rehaf Net |
15 |
6 |
6 |
12 |
6 |
Zorona Café |
9 |
5 |
4 |
9 |
7 |
Hanin Net |
10 |
6 |
3 |
9 |
8 |
City View Café |
16 |
7 |
5 |
12 |
9 |
Square Café |
23 |
5 |
15 |
20 |
10 |
University Internet Center |
37 |
12 |
7 |
17 |
11 |
Evolution Café |
12 |
7 |
5 |
12 |
12 |
Aldawqa Café |
12 |
3 |
10 |
13 |
13 |
Al-Shmeisani Café |
10 |
2 |
9 |
11 |
14 |
Al-Tahawor Café |
9 |
1 |
10 |
11 |
15 |
California Café |
20 |
2 |
18 |
20 |
16 |
Al-Ekhtessase Café |
10 |
2 |
9 |
11 |
17 |
Facebook Café |
30 |
3 |
25 |
28 |
18 |
Ghost Café |
24 |
1 |
18 |
19 |
19 |
Lojain Café |
17 |
2 |
15 |
17 |
20 |
Al-Serat Café |
18 |
2 |
18 |
20 |
21 |
Al-Bader Café |
10 |
1 |
8 |
9 |
22 |
Pluto Café |
10 |
2 |
10 |
12 |
23 |
Amman Online |
12 |
1 |
13 |
14 |
24 |
Kaza Café |
20 |
1 |
18 |
19 |
|
Total |
367 |
81 |
257 |
336 |
Our findings contradict Wheeler’s (2004) assertion that in Jordan “most cafés have an equal number of male and female users.” Perhaps Wheeler based her observation on a small sample. Gender disparity would not have been detected if only a few cafés were sampled and these happened to be gender balanced.
More than 70 percent of users interviewed report having benefited from using Internet cafés by expanding their social networks, maintaining communications with family and friends, and learning about other cultures. Positive assessments of impact on education and learning indicators were also reported by 60 percent of users. Income or employment impacts were less common but are not insignificant, especially among men. The Jordanian government’s policy of increasing the number of licenses for private café operators is commendable and should be maintained.
Our study’s findings put the spotlight on a major area of concern. Although women and men benefit equally from cybercafés, comparatively few women use these venues, and those who use them do so infrequently. If the benefits for women so vividly described by Wheeler (2004, 2006, 2007) and confirmed by this study are to be widely achieved and obtained, then the observed imbalance in access must be addressed.
Are these differences culturally determined, perhaps a choice dictated by the norms of a society trying to modernize but not yet comfortable with a more active engagement by women? Or are there features of the cybercafé environment that make women feel at risk of being harassed or disturbed?
The appropriate policy response would differ depending on which of these sets of factors underlies the observed gender imbalance. If it is a matter of culture, campaigns to sensitize families and potential female users to the potential benefits of cybercafés might be in order. However, if the environment of these venues prevents greater use by women, regulatory measures (non-discrimination and a suitable open environment as a requirement for licensing), combined perhaps with incentives to motivate greater use by women (e.g., IT training scholarships to encourage females to use cybercafés), would increase the demand for cybercafé services and at the same time make it in operators’ best interest to maintain a female-friendly environment in their cybercafés.
Identifying the obstacles that prevent women, especially low-income women, from using cybercafés and designing suitable policies to overcome them should be a priority subject of research as well as an urgent concern of policymakers. We urge Jordanian researchers and government officials to take up the challenge.
Tabbaa, Yasmeen. 2010. Female Labour Force Participation in Jordan. Policy paper prepared for the Jordan Economic and Social Council, Amman, Jordan. http://esc.jo/NewsViewer.aspx?NewsId=38#.UvNVzfbRuwo
Wheeler, Deborah L. 2004. The Internet in the Arab World: Digital Divides and Cultural Connections. Lecture presented on June 16, 2004, in Amman, Jordan, at the Royal Institute for Inter-Faith Studies. http://208.112.119.94/guest/lecture_text/internet_n_arabworld_all_txt.htm
Wheeler, Deborah L. 2006. Empowering Publics: Information Technology and Democratization in the Arab World—Lessons from Internet Cafés and Beyond. Oxford Internet Institute, Research Report 11. http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1308527
Wheeler, Deborah L. 2007. Empowerment Zones? Women, Internet Cafés, and Life Transformations in Egypt. Information Technologies and International Development 4 (2): 89–104.
Jean Damascène Mazimpaka, Théodomir Mugiraneza, and Ramata Molo Thioune
The Social Economic Development Strategy of Rwanda specifically highlights the creation of an ICT, knowledge-based society as key to the country’s development. Public access venues present a way through which ICT skills can be acquired by a large segment of the population. This chapter presents research on users of such venues and assesses the contribution ICT skills make to their job prospects. The research adopted a case study methodology: a questionnaire was administered in both urban and rural areas, and interviews were conducted with key stakeholders. The research finds that ICT skills acquired from such venues help users to get recruited, although the level of impact is modest because of limited job opportunities in the country and users’ gap in satisfying other skill requirements of existing jobs. We recommend that government support of telecenters be continued and that the feasibility of supporting training in private urban venues be assessed.
Rwanda is a landlocked country in central East Africa with an area of 26,338 km2 and a population of 10,718,379 inhabitants (2012), 85 percent of whom live in rural areas (National Institute of Statistics of Rwanda 2012). Rwanda has limited resources and identifies its people as its principal asset. Rwanda’s development strategy, Vision 2020 (Republic of Rwanda 2000), acknowledges the shortage of technically qualified people at all levels and lists among its targets to have adequate, highly skilled technicians to satisfy the needs of the national economy.
Access to skills training through schools and universities is common but limited by cost, age, learning timetables, and entry requirements. Formal institutions charge high fees that low-income people cannot afford (Freistadt, Pal, and Alves da Silva 2009). Workers are excluded by inflexible learning timetables. Mature workers in particular seek suitable places where they can learn ICT skills that were not part of the curriculum at the time of their formal education. When low-income people and workers want to acquire ICT skills, their main options are public access venues. In addition, many people who learned basic ICT skills through formal education often further that learning at public access venues.
Rwanda has sought to address this need for low-cost skills training, in part, through a community telecenter project sponsored by the Rwanda Information Technology Authority. By 2012, ninety-four telecenters had been established throughout the country’s thirty districts (Republic of Rwanda 2013).
The present study looks at ICT skills acquisition in various types of public venues in Rwanda that provide access to computers and the Internet. A mixed method approach combining qualitative and quantitative methods is used to assess the extent to which the skills acquired in these venues help people get jobs or progress in their career.
Becker et al. (2010) studied the uses of computers and Internet access in U.S. public libraries and found they help users acquire ICT skills that allow them to maintain or obtain employment. Garrido, Rothschild, and Oumar (2009) found that basic ICT skills training combined with assistance in both job search and application process make a significant contribution to job acquisition. Mariscal, Gutierrez, and Botelho (2009) assessed the effect of ICT training provided by NGOs and found that this training offered unique opportunities for integrating marginalized youth into the labor force in Brazil, Colombia, and Mexico.
We find that in Rwanda, ICT skills acquired from public access venues have a positive impact on job prospects, but that the degree of impact depends on the level of education of venue users and on venue type and location, competence of the instructor, duration of the training, and sex of trainees.
Three types of public access venues are common in Rwanda (table 3.1).
Telecenters are government-financed venues that provide public access to computers and the Internet, as well as ICT training and ICT services such as printing. They are generally located in rural areas that are not served by commercial venues. The Rwanda government has also funded mobile telecenters: buses equipped with computers, Internet access, and a power generator. ICT buses take telecenter services to remote areas lacking electricity.
Table 3.1
Basic Features of Rwanda’s Public Access Venues
|
Telecenters |
Cybercafés |
Public Secretariats |
Total Rwanda |
20 |
128 |
130–200 |
Sponsor |
Government |
Private firms |
Private firms |
Rural/urban |
Rural |
Urban |
Urban |
Primary service: |
Rural access to computers & Internet, including training. |
Public access to computers & Internet for a fee. |
Office support such as typing, printing, scanning, photocopying. Some also offer Internet access. |
Equipment |
|
|
|
Computers |
12 small centers have 15 computers; 18 new ones have 40. All have MS Office suite. |
Varies widely, from about 4 to 20 computers per center. Usually equipped with MS Office suite. |
Varies widely, from 3 in small workroom to 10 in big room shared by individuals, each with one computer, or by several people employed by owner. |
Internet |
All computers are connected, but quality of connection is low and breakdowns are frequent. |
All computers are connected, but quality of connection is low and breakdowns are frequent. |
Although some provide public access, only 1 of the 5 visited had Internet connection. |
Training services |
All telecenters have well-structured ICT training program in basic computer skills. Internet training is mainly on how to use email system. |
Café users rely mainly on their friends to teach them and on self-training. |
Training is of secondary importance. Staff engage in training only when there is demand and some staff are free to train. |
Instructor qualifications |
Diploma holders in IT/computer science/electronics, possibly with additional IT training (e.g., Cisco certificate). |
Mainly secondary school certificate holders with additional training in basic IT skills. |
Mainly secondary school certificate holders with additional training in basic IT skills. |
Cybercafés are commercial enterprises that provide public access to computers and the Internet for a fee. In general, cybercafé staff members do not train users: rather, users learn ICT skills on their own by trial and error or with the help of some other person.
Public secretariats are commercial enterprises primarily oriented to ICT services such as typing, printing, scanning, and photocopying. Some also provide access to computers and the Internet to the public at large. ICT training covering word processing, spreadsheet software, and sometimes also Internet use occurs but tends to be a secondary activity.
Cybercafés and public secretariats are found mainly in urban areas, whereas telecenters, in line with government policy, are located mainly in rural areas. In practice, few telecenters (perhaps three) are located in remote small villages; most are located in areas that, although not considered urban, have many characteristics of urban areas: they are reached by the national power line and the national water supply system, and they have secondary schools, health centers, markets, and district and sector offices. These localities are generally unable to sustain commercial venues such as cybercafés, but they can nevertheless accommodate a lot of people and serve a significant user base.
When our survey was conducted in 2010, there were about twenty telecenters, around 130 cybercafés, and a somewhat larger number of public secretariats, possibly 140 or more, in operation in Rwanda.1
The impact of public access venues on “job prospects” may be perceived in one of three ways: getting a job as a result of skills acquired from a public access venue (PAV), self-employment in own ICT-based business after acquiring ICT skills from a PAV, or career advancement as a result of acquired ICT skills. The following research questions are addressed:
1. Do acquired ICT skills differ by venue type, venue location, instructor competence, or gender of trainees?
2. How do ICT skills acquired from public access venues affect user job prospects?
Figure 3.1 shows our view of the process of acquiring ICT skills in public access venues and how these skills impact user job prospects.2 Inputs include the ICT infrastructure, the instructor, and users’ motivation. These inputs enable activities such as learning basic computer skills, Internet-based communication, and online information search to take place. The resulting outputs include basic computer skills needed for some jobs, skills for information search over the Internet, and Internet-based communication that can help trainees exchange job-related information. Outputs may lead to outcomes such as searching and applying for a job, meeting ICT competences required for a job, doing ICT tests required in recruitment, and planning one’s own business as a result of ICT skills acquired. Finally, the impact is the extent to which the acquisition of ICT skills enhances job prospects, with people either getting their first job or a promotion or setting up their own ICT-based business.
Figure 3.1
Logic Model of ICT skills acquisition in PAVs and impact on job prospects.
The data used were collected through a formal purposive sample survey (Kumar 2005) complemented by interviews of key stakeholders and observations in public access venues.
Purposive Sample Survey A questionnaire was administered to 418 white-collar and office workers who occupy positions that are likely to involve the use of basic computer skills, including secretaries, receptionists, customer care officers, administrative assistants, finance officers, and human resource managers. Respondents were selected from both public and private institutions where workers occupying these positions are usually found. The sample was drawn from 100 secondary schools,3 48 bank branches, 5 provincial government offices, 21 district government offices, 6 ministries, 6 other government institutions, 54 cybercafés, and 48 public secretariats.
Every person surveyed had ICT skills. We classify respondents in three categories:
1. Primary user-trainees are those who use PAVs to acquire ICT skills.
2. Supplementary user-trainees acquired basic ICT skills elsewhere, but the time they spend at PAVs is more than 50 percent of the time they spend using either the computer or the Internet.
3. Occasional users acquired their basic ICT skills from a place other than a PAV, and they use PAVs less than 50 percent of the time they spend using the Internet and the computer.
We refer to the first two categories as public access user-trainees.
Table 3.2 shows the distribution of respondents according to rural–urban status, gender, and age and trainee type. Occasional users represent 60 percent of the sample and public access user-trainees (primary and supplementary) account for the remaining 40 percent. About 52 percent of respondents were male and 48 percent female. Respondents between the ages of 20 and 29 make up 49 percent of the sample. Frequent PAV users have more limited formal training than occasional users (table 3.3). Many occasional users are formally educated and either have a university degree or finished secondary school, where ICT training was part of the curriculum. About 60 percent of occasional users have a post-secondary education compared to only about 35 percent of user-trainees.
About 78 percent of user-trainees acquired their ICT skills in urban areas. The 22 percent trained in rural areas include two primary users trained at an ICT bus (table 3.4).
Qualitative Data When we started collecting data, there were twelve fully operational telecenters, each having fifteen PCs. For our qualitative analysis, we visited ten of the twelve existing small telecenters, plus two recently established large telecenters. We also visited five cybercafés and five public secretariats. The smaller sample of commercial venues is due to difficulties experienced when trying to get operation-related information from these centers.
In the sampled venues, we conducted interviews with ICT instructors, made observations, and read the registry of trainees where available. From the registries of trainees, we identified former trainees whom we then met based on their availability. These former trainees answered the questionnaire and had an unstructured interview with us.
Table 3.2
Distribution of Survey Respondents by Age, Gender, and Trainee Type
|
Primary |
|
Supplementary |
|
Occasional |
|
All types |
|
|
# |
% |
# |
% |
# |
% |
# |
% |
Rural—Male |
|
|
|
|
|
|
|
|
17–19 |
– |
– |
– |
– |
– |
– |
– |
– |
20–29 |
8 |
38.1 |
7 |
70.0 |
24 |
39.3 |
39 |
42.4 |
30–35 |
6 |
28.6 |
2 |
20.0 |
17 |
27.9 |
25 |
27.2 |
≥ 36 |
7 |
33.3 |
1 |
10.0 |
20 |
32.8 |
28 |
30.4 |
Subtotal |
21 |
100.0 |
10 |
100.0 |
61 |
100.0 |
92 |
100.0 |
Rural—Female |
|
|
|
|
|
|
|
|
17–19 |
– |
– |
– |
– |
– |
– |
– |
– |
20–29 |
9 |
50.0 |
3 |
50.0 |
16 |
41.0 |
28 |
44.4 |
30–35 |
4 |
22.2 |
2 |
33.3 |
11 |
28.2 |
17 |
27.0 |
≥ 36 |
5 |
27.8 |
1 |
16.7 |
12 |
30.8 |
18 |
28.6 |
Subtotal |
18 |
100.0 |
6 |
100.0 |
30 |
100.0 |
63 |
100.0 |
Subtotal Rural |
39 |
|
16 |
|
100 |
|
153 |
|
% |
25.2 |
|
10.3 |
|
64.5 |
|
100.0 |
|
Urban—Male |
|
|
|
|
|
|
|
|
17–19 |
– |
– |
– |
– |
– |
– |
– |
– |
20–29 |
18 |
69.2 |
22 |
62.9 |
24 |
39.3 |
64 |
52.5 |
30–35 |
4 |
15.4 |
5 |
14.3 |
21 |
34.4 |
30 |
24.6 |
≥ 36 |
4 |
15.4 |
8 |
22.9 |
16 |
26.2 |
28 |
23.0 |
Subtotal |
26 |
100.0 |
35 |
100.0 |
61 |
100.0 |
122 |
100.0 |
Urban—Female |
|
|
|
|
|
|
|
|
17–19 |
– |
4.5 |
1 |
3.3 |
– |
– |
1 |
0.7 |
20–29 |
9 |
40.9 |
22 |
73.3 |
40 |
44.9 |
71 |
51.8 |
30–35 |
4 |
22.7 |
3 |
10.0 |
19 |
21.3 |
26 |
19.0 |
≥ 36 |
5 |
31.8 |
4 |
13.3 |
30 |
33.7 |
39 |
28.5 |
Subtotal |
18 |
100.0 |
30 |
100.0 |
89 |
100.0 |
137 |
100.0 |
Subtotal Urban |
44 |
|
65 |
|
150 |
|
259 |
|
% |
17.0 |
|
25.1 |
|
57.9 |
|
100.0 |
|
All respondents |
83 |
|
81 |
|
250 |
|
414 |
|
% |
20.0 |
|
19.6 |
|
60.4 |
|
100.0 |
|
Note: The number given as “All respondents” includes the number of respondents with complete information on rural/urban status, age, and gender.
Table 3.3
Level of Education of Respondents by User Category
|
Primary User-Trainee |
|
Supplementary User-Trainee |
|
Occasional User |
|
Total |
|
|
# |
% |
# |
% |
# |
% |
# |
% |
Primary school |
3 |
3.4 |
– |
– |
– |
– |
3 |
0.7 |
Interrupted secondary |
10 |
11.5 |
5 |
6.4 |
– |
– |
15 |
3.6 |
Finished secondary |
44 |
50.6 |
45 |
57.7 |
101 |
39.9 |
190 |
45.5 |
Post-secondary |
30 |
34.5 |
28 |
35.9 |
152 |
60.1 |
210 |
50.2 |
Total |
87 |
100.0 |
78 |
100.0 |
253 |
100.0 |
418 |
100.0 |
Table 3.4
Distribution of Survey Respondents by User Type, Location (Rural/Urban), Where ICT Skills were Acquired, and Gender
|
Gender |
|
|
|
|
|
|
Male |
|
Female |
|
All |
|
Rural/urban status |
# |
% |
# |
% |
# |
% |
User-trainees |
|
|
|
|
|
|
Rural |
22 |
26.8 |
12 |
17.4 |
34 |
22.5 |
% |
64.7 |
|
35.3 |
|
100.0 |
|
Urban |
60 |
73.2 |
57 |
82.6 |
117 |
77.5 |
% |
52.1 |
|
47.9 |
|
100.0 |
|
Total user-trainees |
82 |
100.0 |
69 |
100.0 |
151 |
100.0 |
% |
54.3 |
|
45.7 |
|
100.0 |
|
Occasional users |
|
|
|
|
|
|
Rural |
31 |
29.0 |
34 |
27.9 |
65 |
28.3 |
% |
46.3 |
|
53.7 |
|
100.0 |
|
Urban |
76 |
71.0 |
88 |
72.1 |
164 |
71.7 |
% |
46.7 |
|
53.3 |
|
100.0 |
|
Total occasional users |
107 |
100.0 |
122 |
100.0 |
229 |
100.0 |
% |
46.7 |
|
53.3 |
|
100.0 |
|
ICT Infrastructure The ICT infrastructure varies from one venue to another. Telecenters are most uniform. Telecenters use their PCs for training and Internet access service, and they usually also have two printers, a scanner, and a server to control the networked computers. In addition, each telecenter has a dish antenna through which it connects to the Internet, but the quality and speed of connection are generally poor. All computers are equipped with Microsoft’s (MS) Office suite (Word, Excel, and PowerPoint). In some telecenters (e.g., the Nyabihu telecenter), additional programs such as video editing software are available as a result of the instructor’s commitment and users’ requests.
Cybercafés and public secretariats differ one from another in terms of ICT infrastructure. All cybercafés have an Internet connection, but apparently few public secretariats do. Given that cybercafés are commercially oriented and their primary interest is in selling Internet-based services, MS Office is not largely used even if it is installed on the computers. Clients normally come in to check their email or search for information on the web. The number of computers in cybercafés ranges from about four to twenty.
While a network of computers including a server is common in cybercafés and telecenters, the standalone workstation is the predominant setup in public secretariats. In one instance, each computer in the secretariat was connected to a printer and a scanner, and several operators could work in the same room. However, most public secretariats have many standalone computers, some of them connected to printers. In these cases, the venue provides training in basic computer skills in addition to typing services. Of the five public secretariats visited, four had no Internet connection and hence do not teach how to use the Internet. Even in the one with an Internet connection, we did not observe people learning how to use the Internet.
Our observations in twenty-two training venues (twelve telecenters, five cybercafés, and five public secretariats) and our interviews with ICT instructors in training venues and with two policymakers in the domain of public access to ICT venues tend to confirm that PAVs have an ICT infrastructure that is adequate for ICT skills training.
Instructor Telecenters have a standardized model. The instructors in three telecenters who agreed to reveal their qualifications are diploma holders with additional relevant certificates such as a Cisco training certificate. This is in line with the following statement from a policymaker interviewed: “Each telecenter has two employees: one with a bachelor’s degree in a business administration field who manages the telecenter, and one with a diploma (A1) in an IT field who is an IT technician and instructor.”
From direct observation and field visits, we find that the qualification of ICT instructors in cybercafés and public secretariats is lower and not standardized. A cybercafé normally has one or two employees who are in charge of billing and solving computer problems that may occur; they also sometimes teach users depending on how much free time they have and on their personal interest and initiative. The number of employees in public secretariats varies depending on the number of computers they have. Each employee is busy on one computer, handling typing services, but he or she may also be training a person at the same time depending on whether there is a trainee present and how much time the employee has allocated to providing this service.
Primary user-trainees are generally satisfied with the level of support they get from instructors, which they rate as either high (13 percent), moderately high (62 percent), or medium (18 percent) (table 3.5). The level of satisfaction with instructors’ support is higher in telecenters than in cybercafés and, to a lesser degree, than in public secretariats. Most people who acquired ICT skills from public secretariats are fairly satisfied with the support they received from instructors. Over time, however, the training role of public secretariats—many of which do not have Internet access—appears to have diminished.
Table 3.5
Level of Support Received from Instructors by Primary User-trainees, by Venue Used
|
Level of Support |
|
|
|
|
|
Venue type |
High |
Moderately High |
Medium |
Moderately Low |
Low |
All Primary User-trainees |
Cybercafé |
1 |
5 |
4 |
3 |
– |
13 |
% |
7.7 |
38.5 |
30.8 |
23.1 |
– |
100.0 |
Telecenter |
7 |
23 |
3 |
1 |
– |
34 |
% |
20.6 |
67.6 |
8.8 |
2.9 |
– |
100.0 |
Public secretariat |
2 |
15 |
6 |
1 |
– |
24 |
% |
8.3 |
62.5 |
25.0 |
4.2 |
– |
100.0 |
Telecenter and |
– |
2 |
– |
– |
– |
2 |
public secretariat % |
– |
100.0 |
– |
– |
– |
100.0 |
Telecenter, café, and public secretariat |
– |
1 |
– |
– |
– |
1 |
% |
– |
100.0 |
– |
– |
– |
100.0 |
Total |
10 |
46 |
13 |
5 |
– |
74 |
% |
13.5 |
62.2 |
17.6 |
6.8 |
– |
100.0 |
Telecenters were introduced in 2008. A few years previously, when there were only a few cybercafés and no telecenters, public secretariats were the only PAVs people could use to acquire ICT skills. The considerable number of people who reported a high to medium level of satisfaction with the support received from public secretariat instructors are probably those who acquired their training when they had no choice other than public secretariats. Nowadays, the affordability of telecenters, the structured training they provide, and their improving ICT infrastructure due to government support have made them the most-used PAV type for ICT skills acquisition. This is true despite their location in what are predominantly rural (albeit not remote) areas.
Motivation Although the most frequently used media for job advertisement in Rwanda are still newspapers and radio, some job vacancies are posted on institutions’ websites. Some employers require that job seekers submit their applications by: (1) email, (2) completing and submitting an electronic form via the employer’s website, or (3) filling out an application form downloaded from the website and submitting it in hard copy format. This form of job advertisement and submission is normally used by international NGOs, UN agencies, and some government agencies. These institutions also use the Internet to give feedback to job applicants, although phone calls are more common.
According to survey respondents knowing how to use the Internet is highly important (38 percent) or important (29 percent) in the job application process (table 3.6). This high level of appreciation of the importance of Internet use does not vary by either age or gender.
It is not just Internet use that is attractive to PAV users: they are also interested in other basic computer skills. For instance, many secondary school students interviewed said that attending ICT training in public access venues could help them master computer skills that had recently been introduced to their curriculum but were not well taught. They considered these skills to be among the most sought after in the job market.
PAV users pursue various objectives that lead them to acquire ICT skills. An estimated 82 percent of user-trainees had the objective of improving their skills so they could get a new or better job (table 3.7). The percentage of urban users with this objective is higher than among rural users, and this applies among both user-trainees and occasional users.
Table 3.6
Assessment by All Respondents of the Role of Internet Use in Job Application Process
|
Level of Appreciation |
|
|
|
|
Gender and Age Range |
Highly Important |
Important |
Less Important |
Not Important |
Total |
Male |
|
|
|
|
|
17–19 |
– |
– |
– |
– |
– |
% |
– |
– |
– |
– |
– |
20–29 |
49 |
27 |
6 |
18 |
100 |
% |
49.0 |
27.0 |
6.0 |
18.0 |
100.0 |
30–35 |
15 |
16 |
7 |
17 |
55 |
% |
27.3 |
29.1 |
12.7 |
30.9 |
100.0 |
≥ 36 |
17 |
15 |
6 |
10 |
48 |
% |
35.4 |
31.3 |
12.5 |
20.8 |
100.0 |
Subtotal |
81 |
58 |
19 |
45 |
203 |
% |
39.9 |
28.6 |
9.4 |
22.2 |
100.0 |
Female |
|
|
|
|
|
17–19 |
– |
– |
– |
2 |
2 |
% |
– |
– |
– |
100.0 |
100.0 |
20–29 |
33 |
31 |
11 |
19 |
94 |
% |
35.1 |
33.0 |
11.7 |
20.2 |
100.0 |
30–35 |
18 |
9 |
8 |
9 |
44 |
% |
40.9 |
20.5 |
18.2 |
20.5 |
100.0 |
≥ 36 |
19 |
16 |
6 |
13 |
54 |
% |
35.2 |
29.6 |
11.1 |
24.1 |
100.0 |
Subtotal |
70 |
56 |
25 |
43 |
194 |
% |
36.1 |
28.9 |
12.9 |
22.2 |
100.0 |
Total |
151 |
114 |
44 |
88 |
397 |
% |
38.0 |
28.7 |
11.1 |
22.2 |
100.0 |
Computer Skills Training A public access venue can teach basic or advanced ICT skills or both. Basic computer skills include an introduction to computers, basic file management operations such as renaming and deleting files, and training in MS Office programs, mainly Word, Excel, and PowerPoint. Advanced skills include software programming, web page creation, troubleshooting hardware, updating antivirus software, and installing basic applications such as Office suites.
Telecenters have a well-structured ICT training program in basic computer skills. Cybercafés, in contrast, have no structured training. Customers are given occasional help, but cybercafé personnel are primarily engaged in billing and troubleshooting computer problems; they can teach only when they have the time and inclination. In general, if users want to learn basic computer skills at a cybercafé, they bring in a friend who can teach them. Similarly, public secretariats concentrate mainly on ICT services such as typing, photocopying, and printing. They provide basic computer training to a few people occasionally when their staff members have a light workload.
Table 3.7
Respondents Who Had “Improving ICT Skills to Get a New or Better Job” as an Objective
|
Male |
|
Female |
|
Total |
|
User Category |
# |
% |
# |
% |
# |
% |
User-trainees |
|
|
|
|
|
|
Rural |
20 |
13.4 |
16 |
10.7 |
36 |
24.2 |
Urban |
44 |
29.5 |
42 |
28.2 |
86 |
57.7 |
Subtotal |
64 |
43.0 |
58 |
38.9 |
122 |
81.9 |
Occasional users |
|
|
|
|
|
|
Rural |
44 |
19.8 |
29 |
13.1 |
73 |
32.9 |
Urban |
50 |
22.5 |
78 |
35.1 |
128 |
57.7 |
Subtotal |
94 |
42.3 |
107 |
48.2 |
201 |
90.5 |
Grand total |
158 |
42.6 |
165 |
44.5 |
323 |
87.1 |
Note: Percentages are calculated with respect to the number of respondents who answered about the objective of improving ICT skills to get a new or better job (149 user-trainees, 222 occasional users, and 371 total respondents).
Forty-five percent of the respondents reported that the training they received lasted between one and three months. According to instructors in telecenters, some training can last between three and six months—for example, the IT Essentials certificate (a Cisco-certified training program that includes advanced ICT skills and video editing) offered in four of the twelve visited telecenters. Users who followed this training acquired advanced ICT skills given the content of the material used.
Internet Skills Training All telecenters teach Internet-based communication, mainly how to use email. Other Internet-based communications (such as chatting, web-based social networking, and voice calls over Internet) are not included in the training program. In cybercafés, individuals generally bring friends who can teach them different forms of Internet-based communication, or the cybercafé employees teach them how to use email, but only when the employees are free.
Similar to Internet-based communication, information search is taught at all telecenters. As explained by instructors at two telecenters, training in information search is customized based on each trainee’s interest and background, but this is an initiative of the instructor. Cybercafé employees teach information search only when they have the time and inclination, and so individuals tend to bring in friends to teach them. In general, public secretariats do not teach information search because they do not have an Internet connection, and their staff concentrate on other ICT services.
Differences by Type of Venue Telecenters appear to be better equipped than cybercafés and public secretariats to deliver the kind of ICT skills that are useful to trainees wanting to set up their own ICT-based businesses, prepare for recruitment tests, or perform newly acquired jobs.
In cybercafés and public secretariats, priority is given to selling Internet and typing and printing services, respectively, and training is considered an additional revenue-generating service. Telecenters offer solid training in basic and advanced ICT skills and specific types of training that can greatly contribute to job creation; these services are not offered in cybercafés or public secretariats. As well, ICT training certificates issued by telecenters are trusted locally and internationally, which is not the case for those issued by cybercafés and public secretariats. Typical examples are the Cisco-certified IT Essentials certificate and the International Computer Driving License (ICDL).4 These certificates appear to give trainees who use telecenters an advantage in being recruited and setting up ICT-based businesses compared with trainees who use cybercafés and public secretariats.
Basic Skills During our site visits, we observed the training process (including lecture sessions and practical sessions) in four of the twelve telecenters visited. In one telecenter, we saw the results of the test given at the end of ICT training, proving that trainees acquire some ICT skills and are evaluated. No training was taking place in the five cybercafés or the five public secretariats we visited.
The fact that trainees feel confident or feel the need to improve their ICT skills suggests that they have at least basic ICT skills (table 3.8). These basic skills include file management, mastered with confidence by about 90 percent of the respondents, word processing (about 89 percent), and use of spreadsheets (about 77 percent). Basic computer skills taught at PAVs also include identifying different hardware and software components, connecting various computer peripherals, and scanning for viruses, thus allowing trainees to get acquainted with a computer.
Table 3.8
Level of User Confidence in Selected Basic Computer Skills, by User Type
|
Not Acquired |
|
Confident |
|
Need to Improve |
|
All |
|
|
# |
% |
# |
% |
# |
% |
# |
% |
User-trainees |
|
|
|
|
|
|
|
|
File management |
2 |
1.2 |
145 |
90.1 |
14 |
8.7 |
161 |
100 |
Word processing |
3 |
1.9 |
142 |
88.8 |
15 |
9.4 |
160 |
100 |
Spreadsheet |
3 |
1.9 |
122 |
76.7 |
34 |
21.4 |
159 |
100 |
Connecting computer peripherals |
9 |
5.5 |
149 |
90.3 |
7 |
4.2 |
165 |
100 |
Occasional users |
|
|
|
|
|
|
|
|
File management |
3 |
1.2 |
231 |
92.8 |
15 |
6.0 |
249 |
100 |
Word processing |
1 |
0.4 |
218 |
87.2 |
31 |
12.4 |
250 |
100 |
Spreadsheet |
2 |
0.8 |
180 |
72.3 |
67 |
26.9 |
249 |
100 |
Connecting computer peripherals |
25 |
10 |
206 |
82.1 |
20 |
8.0 |
251 |
100 |
Advanced IT Skills Some user-trainees have advanced skills (table 3.9). These people attended the Cisco-certified IT Essentials training program included in the telecenter training curriculum. Others (e.g., those employed in cybercafés) make their own arrangements to learn advanced skills. (Although the IT Essentials program is included in all telecenters’ training plans, not all telecenters actually teach it: only five of the twelve visited telecenters delivered IT Essentials training.) User-trainees as well as occasional users have limited advanced skills (table 3.9).
Internet-Based Communications Most user-trainees (88 percent) are able to use email with confidence. Additionally, 70 percent can use the Internet for chatting, and at least 58 percent can communicate using web-based social networking platforms such as Facebook. Nevertheless, fewer than 50 percent can make voice calls using computer programs (table 3.10). In our opinion, the unstable Internet connection and its low speed make it difficult to learn and use Internet-based voice calls.
Information Search In the PAVs we visited trainees practiced information search during their break time. In one telecenter, one trainee was searching prices of agricultural products using the “e-Soko”5 service available on the Ministry of Agriculture website. Similarly, various users in cybercafés were searching information on the web with the help of others. About 76 percent of user-trainees are confident in their capacity to search for information, whereas 13 percent feel the need to improve this skill (table 3.9). According to the instructors interviewed, trainees’ level of confidence in their information search skills depends on the instructors’ commitment because Internet use training generally focuses mainly on using email.
Table 3.9
Level of User Confidence in Select Advanced Computer Skills and Information Search, by User Type
|
Not Acquired |
|
Confident |
|
Need to Improve |
|
All |
|
|
# |
% |
# |
% |
# |
% |
# |
% |
User-trainees - Primary & Supplementary |
|
|
|
|
|
|
|
|
Disk formatting |
45 |
27.3 |
102 |
61.8 |
18 |
10.9 |
165 |
100 |
Disk partitioning |
68 |
42.2 |
74 |
46.0 |
19 |
11.8 |
161 |
100 |
Configuration of computer peripherals |
33 |
20.0 |
118 |
71.5 |
14 |
8.5 |
165 |
100 |
Network configuration |
73 |
45.1 |
59 |
36.4 |
30 |
18.5 |
162 |
100 |
Network troubleshooting |
68 |
42.5 |
65 |
40.6 |
27 |
16.9 |
160 |
100 |
Website design |
126 |
77.8 |
13 |
8 |
23 |
14.2 |
162 |
100 |
Program installation |
54 |
32.7 |
85 |
51.5 |
26 |
15.8 |
165 |
100 |
Information search |
18 |
11.3 |
122 |
76.3 |
20 |
12.5 |
160 |
100 |
Occasional users |
|
|
|
|
|
|
|
|
Disk formatting |
112 |
44.6 |
112 |
44.6 |
27 |
10.8 |
251 |
100 |
Disk partitioning |
142 |
57.0 |
84 |
33.7 |
23 |
9.3 |
249 |
100 |
Configuration of computer peripherals |
72 |
28.7 |
144 |
57.4 |
35 |
13.9 |
251 |
100 |
Network configuration |
156 |
63.2 |
67 |
27.1 |
24 |
9.7 |
247 |
100 |
Network troubleshooting |
146 |
60.1 |
65 |
26.7 |
32 |
13.2 |
243 |
100 |
Website design |
209 |
83.9 |
15 |
6.0 |
25 |
10.1 |
249 |
100 |
Program installation |
123 |
49.6 |
94 |
37.9 |
31 |
12.5 |
248 |
100 |
Information search |
36 |
14.4 |
168 |
66.9 |
47 |
18.7 |
251 |
100 |
Table 3.10
Level of Confidence of User-trainees in Internet-based Communication Skills
|
Confident |
|
Need to Improve |
|
Estimate with Skills |
|
Total |
Skill Type |
# |
% |
# |
% |
# |
% |
# |
Using email system |
141 |
87.6 |
12 |
7.5 |
153 |
95.0 |
161 |
Chatting |
114 |
70.4 |
20 |
12.3 |
134 |
82.7 |
162 |
Web-based social networking |
94 |
58.4 |
16 |
9.9 |
110 |
68.3 |
161 |
Internet-based voice call |
52 |
32.7 |
26 |
16.4 |
78 |
49.1 |
159 |
Average level of confidence |
|
62.3 |
|
11.5 |
|
73.8 |
|
Note: The total in each skill type is the total number of respondents who answered about their level in that skill type.
Gender Differences We found no major differences between male and female interviewees in their level of confidence regarding basic computer skills, but there are significant gaps regarding advanced computer skills (table 3.11). Men were more confident than women regarding all advanced skills areas considered, and this is largely due to the significant difference in skills acquisition. The percentage of women that did not acquire skills such as network configuration, file management, network troubleshooting, and program installation, exceeded 50 percent of women users interviewed, but was below 40 percent in the case of men (table 3.11). Apparently, in Rwanda, as happens in many other places, these technical skills are considered the purview of men but not as much of women.
The immediate outcomes that trainees can get from using the PAVs are ICT skills that they can use to search and apply for jobs, meet ICT job requirements, sit and/or pass ICT tests required for particular jobs, and set up their own ICT-based businesses.
Search and Apply for Jobs Many Rwandan institutions have their own websites and these often include job vacancy announcements. Fifty-five percent of survey respondents appreciated the importance of the Internet as a job advertisement and job application channel. In practice, however, out of ten trainees from one of the telecenters visited, only one person had obtained information on job opportunities from the Internet and applied for this job through the Internet.
Meet ICT Requirements of Jobs In most job announcements in Rwanda, the ICT skill most often listed as desirable, if not required, is familiarity with the MS Office suite of programs, particularly Word, Excel, and PowerPoint. This knowledge is mandatory in office or white-collar jobs such as typist/secretary in a private or government institution. In most recruitment tests, the ICT component is part of the evaluation. These ICT skills are obtained from formal schooling and/or PAVs, either exclusively or as a supplement to previously acquired skills. Skills most frequently used are word processing and spreadsheet tasks, and training customers in telecenters and cybercafés in the basics of Internet browsing and using email. For trainees with more advanced skills (e.g., IT Essentials) part- or full-time job opportunities in hardware maintenance, software installation, and network troubleshooting are available.
Table 3.11
Level of User Confidence in Basic and Advanced Skills, by Gender
|
Not acquired |
|
Confident |
|
Need to Improve |
|
All |
|
|
# |
% |
# |
% |
# |
% |
# |
% |
Male |
|
|
|
|
|
|
|
|
Basic skills |
|
|
|
|
|
|
|
|
File Management |
5 |
2.4 |
190 |
90.9 |
14 |
6.7 |
209 |
100.0 |
Word Processing |
4 |
1.9 |
185 |
88.5 |
20 |
9.6 |
209 |
100.0 |
Spreadsheet |
4 |
1.9 |
165 |
78.6 |
41 |
19.5 |
210 |
100.0 |
Connecting Computer Peripherals |
18 |
8.5 |
183 |
85.9 |
12 |
5.6 |
213 |
100.0 |
Advanced skills Network configuration |
81 |
39.7 |
87 |
42.6 |
36 |
17.6 |
204 |
100.0 |
Website design |
137 |
70.3 |
22 |
11.3 |
36 |
18.5 |
195 |
100.0 |
Network Troubleshooting |
69 |
35.6 |
90 |
46.4 |
35 |
18.0 |
194 |
100.0 |
Program Installation |
63 |
30.1 |
120 |
57.4 |
26 |
12.4 |
209 |
100.0 |
Information search |
16 |
7.8 |
156 |
75.7 |
34 |
16.5 |
206 |
100.0 |
Female |
|
|
|
|
|
|
|
|
Basic Skills |
|
|
|
|
|
|
|
|
File Management |
0 |
0.0 |
186 |
92.5 |
15 |
7.5 |
201 |
100.0 |
Word Processing |
0 |
0.0 |
175 |
87.1 |
26 |
12.9 |
201 |
100.0 |
Spreadsheet |
1 |
0.5 |
137 |
69.2 |
60 |
30.3 |
198 |
100.0 |
Connecting Computer Peripherals |
16 |
7.9 |
172 |
84.7 |
15 |
7.4 |
203 |
100.0 |
Advanced Skills Network configuration |
130 |
69.5 |
39 |
20.9 |
18 |
9.6 |
187 |
100.0 |
Website design |
160 |
90.9 |
16 |
9.1 |
0 |
0.0 |
176 |
100.0 |
Network Troubleshooting |
126 |
66.3 |
40 |
21.1 |
24 |
12.6 |
190 |
100.0 |
Program Installation |
104 |
53.6 |
59 |
30.4 |
31 |
16.0 |
194 |
100.0 |
Information search |
30 |
15.4 |
132 |
67.7 |
33 |
16.9 |
195 |
100.0 |
In discussions with ten trainees from public access venues, all expressed their confidence in meeting the ICT requirement of jobs. This is especially true of those trained in telecenters because these venues provide them with widely recognized certificates. Those who had had training in other kinds of PAV also testified that the skills acquired allowed them to get jobs without having to take recruitment tests. They stressed that potential employers who knew they had taken ICT training gave them temporary jobs such as computer maintenance and providing support to clients visiting cybercafés or public secretariats. For instance, a former trainee said he had taken temporary ICT jobs shortly after completing his ICT training at the Kibungo telecenter, and these jobs enabled him to raise the funds he needed to enroll at the Kigali Institute of Science and Technology (KIST). Similarly, a woman trained at the Nyabihu telecenter was subsequently recruited as a computer maintenance technician by a local tea factory. She stressed that the IT Essentials training she received had helped her get this job, which in turn enabled her to finance her studies at the Institute of Higher Education (INES Ruhengeri). Other former trainees attributed their career advancement to their ICT training. For instance, a trainee who was working in a cybercafé got a better job in a company based in South Africa after he completed his Cisco training at the Gicumbi telecenter. As noted by an ICT instructor in one telecenter in the Northern Province, “ICT skills are an added advantage on one’s CV and increase [one’s] chances of getting a job.”
Take ICT Test Required of Job Applicants ICT skills acquired through training in PAVs enable former trainees to take ICT tests required for jobs. These tests typically cover word processing, spreadsheet handling, and Internet use. Former trainees mentioned, for example, tests taken during the recruitment of staff for the recently created microfinance institutions locally known as “Umurenge SACCO.”
Plan and Set Up Own ICT-based Business ICT skills acquired from PAVs have also allowed some trainees to plan and set up their own ICT-based business. An example is a former trainee at the Gicumbi telecenter, who set up a cybercafé in Kigali city after successfully completing his training in IT Essentials. Two other trainees trained in IT Essentials and using video editing software at the Nyabihu telecenter (located in a rural area in northwestern Rwanda) were also able to set up their business as a result of their training. One uses the acquired software skill to clean and duplicate photographs in his photo studio. Without the skills, he could not produce many refined photographs in a limited amount of time. Another trainee uses the same software to produce music CDs in his music studio.
We assess the impact on users’ job prospects of ICT skills acquired from public access venues. The indicators measured (table 3.12) are the number of people who: (1) learned about job opportunities via the Internet, (2) submitted their job applications online, (3) took an ICT skills test as part of their recruitment, (4) were recruited mainly because they had ICT skills, or (5) created their own ICT-based business using the skills acquired.
In Rwanda, the use of the Internet to either search for a job or submit an application appears to be minimal. Fewer than 7 percent of the persons surveyed used the Internet to find job opportunities, and only 3 percent submitted their job application online. However, having ICT skills is an important requirement for getting a job. In many job advertisements, ICT literacy is one of the requirements for being shortlisted, qualifying to take a recruitment test, and getting employed. The proportion of interviewees who took an ICT test during recruitment is significant (41 percent).
Table 3.12
Job-related Impacts of Using the Internet, by User Type
|
Primary User-Trainees |
|
Supp. User-Trainees |
|
Occasional Users |
|
Total |
|
|
# |
% |
# |
% |
# |
% |
# |
% |
Used Internet to get info about job opportunities |
4 |
4.6 |
9 |
11.5 |
15 |
5.9 |
28 |
6.7 |
Submitted a job application online |
4 |
4.6 |
5 |
6.4 |
4 |
1.6 |
13 |
3.1 |
Took ICT skills test during recruitment |
27 |
31.0 |
32 |
41.0 |
114 |
45.1 |
173 |
41.4 |
Was recruited mainly because had ICT skills |
42 |
48.3 |
46 |
59.0 |
145 |
57.3 |
233 |
55.7 |
Created an ICT-based business |
18 |
20.7 |
17 |
21.8 |
20 |
7.9 |
55 |
13.2 |
Sample size |
87 |
|
78 |
|
253 |
|
418 |
|
Nearly 56 percent of the users interviewed were recruited because they had ICT skills. Primary user-trainees whose ICT skills were acquired at public access venues did not fare as well as supplementary or occasional users. The proportion of user-trainees who took ICT skills test during recruitment was 31 percent, compared with 41 percent among supplementary and 45 percent among occasional users. Similarly, the proportion of primary user-trainees recruited mainly because of their ICT skills was 48 percent, compared with 59 percent among supplementary and 57 percent among occasional users (table 3.12). We do not have enough information to sort out these differences, but perhaps having, on average, a higher formal education degree (table 3.3) gives supplementary and occasional users an edge that allows them to aspire to and get higher level positions than can primary user-trainees.
Nearly 13 percent of all survey respondents were self-employed in their own ICT-based business (table 3.13). Most of these businesses (fifty-three of fifty-five cases) are public secretariats or cybercafés, mainly because a significant number of respondents were drawn purposively from 54 cybercafes and 48 public secretariats. The two non-PAV businesses were established by rural male respondents. Most PAV businesses (76 percent) were set up by urban respondents. Self-employment using ICT skills acquired was proportionately lowest among occasional users (8 percent) perhaps because of their less frequent contact with PAVs or because these users have higher levels of education (table 3.3) and therefore have access to a broader job market.
Table 3.13
Self-employment in ICT Skills-based Businesses Observed in Sample
|
Primary User-trainees |
Supp. User-trainees |
Occasional Users |
Total |
Self-employment in ICT-based business |
|
|
|
|
Rural |
|
|
|
|
Male |
4 |
2 |
1 |
7 |
Female |
2 |
– |
4 |
6 |
Subtotal rural |
6 |
2 |
5 |
13 |
Urban |
|
|
|
|
Male |
10 |
10 |
4 |
24 |
Female |
2 |
5 |
11 |
18 |
Subtotal urban |
12 |
15 |
15 |
42 |
Total |
18 |
17 |
20 |
55 |
Self-employment in ICT non-PAV business |
|
|
|
|
Rural—Male |
2 |
– |
– |
2 |
Self-employment in PAV business |
|
|
|
|
Rural |
|
|
|
|
Male |
2 |
– |
– |
2 |
Female |
2 |
– |
4 |
6 |
Subtotal rural |
4 |
– |
4 |
8 |
Urban |
|
|
|
|
Male |
10 |
10 |
4 |
24 |
Female |
2 |
5 |
11 |
18 |
Subtotal urban |
12 |
15 |
15 |
42 |
Total |
16 |
15 |
19 |
50 |
PAV as % of total self-employment |
88.9 |
100.0 |
100.0 |
96.4 |
Rural as % of total self-employment |
33.3 |
11.8 |
25.0 |
23.6 |
Female as % of total self-employment |
22.2 |
– |
75.0 |
43.6 |
Female rural as % of total female self-employment |
50.0 |
100.0 |
73.3 |
75.0 |
Female urban as % of total female self-employment |
50.0 |
– |
26.7 |
25.0 |
Male rural as % of total male self-employment |
28.6 |
16.7 |
20.0 |
22.6 |
Male urban as % of total male self-employment |
71.4 |
83.3 |
80.0 |
77.4 |
% of males in sample self-employed in ICT |
30.4 |
29.3 |
4.1 |
14.8 |
% of females in sample self-employed in ICT |
10.0 |
14.3 |
12.0 |
12.0 |
The main gender disparity in impact is observed in ICT-based self-employment: nearly 30 percent of male user-trainees set up their own ICT-based businesses (essentially PAVs) compared with only 11 percent of female user-trainees. Oddly, the pattern is reversed among occasional users, with 4 percent of males and 12 percent of females setting up their own business—again, by and large cybercafés and public secretariats. Other than ICT-based self-employment, the data show recruitment of an almost equal number of male and female PAV user-trainees.
The level of ICT skills acquired from PAVs varies from venue to venue, from one venue type to another, and from one trainee to another depending on factors such as instructors’ skills, instructors’ willingness to take initiative, and the training environment. Venues that include training among their main services deliver a higher level of skills, as do venues that have instructors with good qualifications and a willingness to take initiative, and venues with a good ICT infrastructure, wide training rooms, and well-maintained, up-to-date equipment. In the case of advanced skills, males have more confidence in skills taught than do females.
The ICT skills acquired by users of PAVs appear to have had a positive albeit modest impact on their job prospects (table 3.12). We suspect that impact is dampened by the limited job opportunities in the country, a lack of additional skills such as entrepreneurship that would be useful for self-employment, and, in the case of primary user-trainees, a lack of the level of formal schooling required for some jobs.
The Universal Access Fund implemented by the Rwanda Utilities Regulatory Agency has subsidized the provision of connectivity and equipment to public institutions such as telecenters, schools, and public agencies (Republic of Rwanda 2013). By enabling the deployment of ICT services to rural areas, these subsidies have benefited user-trainees who acquired or strengthened their ICT skills in telecenters and improved their job prospects. On the basis of these findings, we endorse the continuation of policies supporting telecenter development in relatively large rural communities.
Currently, telecenters are the venues best equipped to provide ICT skills training, but this has not always been the case. Prior to the appearance of telecenters, public secretariats served an important training function. One of the strengths of telecenters is that they have developed a solid training program in both basic and advanced ICT skills; they also certify proficiency, awarding trainees with internationally accredited certificates such as the International Computer Driving License (ICDL) and Cisco’s IT Essentials. In principle, there is no reason that similar training services could not also be provided by private venues such as public secretariats and cybercafés. If these services are not being provided, it is probably because they are not financially attractive to private entities: urban PAVs may not have enough customers willing to pay for training services.
In the PAVs we visited, especially telecenters, which charge lower (subsidized) fees for services, we observed a strong demand for training. Perhaps there are not many low-income urban users in a position to pay for acquiring marketable ICT skills with their own resources, given their limited knowledge of the returns such an investment would yield. If this is the case, government support may be warranted.
Government subsidies in the form of training scholarships combined with the training of trainers could enable some urban-based public secretariats and cybercafés to develop their own training programs, using the telecenters’ experience as a guide. We recommend that the economic feasibility of such a program be examined. This would make it easier to reach a broader audience, likely at a lower cost than through telecenters, which, to avoid unfair competition with private venues, are by design meant to serve rural communities.
1. According to the Director of Rural Community Access of the Rwanda Development Board (verbal communication), in 2011, there were twelve fully operational telecenters in Rwanda, and some eighteen were being planned or under construction. The estimate of cybercafés is from Ndayisaba (2011). The number of public secretariats changes often but is generally considered to be higher than the number of cybercafés, perhaps more than 130.
2. Figure 3.1 was constructed using materials presented in Innovation Network Inc. (2006).
3. Rwanda is divided into four provinces (Eastern, Western, Northern, and Southern) plus Kigali City, the country’s capital and its largest city. The four provinces and Kigali City are subdivided into thirty districts. Our sample was drawn from 100 schools selected out of 1,399 secondary schools, following a proportionate quota sampling with respect to provinces and districts. The forty-eight bank branches were also selected following proportionate quota sampling from groups of bank branches by district. The twenty-one district offices were selected from thirty, again following a proportionate quota sampling with respect to provinces. Six ministries and another six institutions were also sampled randomly. All five provincial offices are represented in the sample.
Considering that the staff of cybercafés and public secretariats have ICT skills, likely learned at PAVs, we also sampled from a pool of fifty-four cybercafés and forty-eight public secretariats following a proportionate quota sampling with respect to cities. At each place, one to three people were surveyed depending on the availability of the desired positions. The institutions were carefully sampled to account for both rural and urban areas.
4. The ICDL is a widely used standard for computer skills. See http://www.icdl.org.za/.
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Freistadt, Jay Oliver, Joyojeet Pal, and Regina Helena Alves da Silva. 2009. ICT Centers and the Access Gap to Formal Higher Education for the Poor in Brazil. Paper presented at the Community Informatics Conference 2009: Empowering Communities: Learning from Community Informatics Practice.Prato,Italy,October.http://tascha.uw.edu/publications/ict-centers-and-the-access-gap-to-formal-higher-education-for-the-poor-in-brazil/
Garrido, María, Chris Rothschild, and Thierno Oumar. 2009. Technology for Employability in Washington State: The Role of ICT Training on the Employment, Compensation and Aspirations of Low-skilled, Older, and Unemployed Workers. Research report. Seattle: Technology & Social Change Group (TASCHA), University of Washington Information School. https://digital.lib.washington.edu/researchworks/bitstream/handle/1773/16298/TASCHA_Washington-State_2009.pdf.
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National Institute of Statistics of Rwanda (NISR). 2012. Statistical Yearbook 2012. Kigali, Rwanda: NISR. http://www.statistics.gov.rw/system/files/user_uploads/files/books/YEAR%20BOOK_2012.pdf.
Ndayisaba, Jean. 2011. ICT, a Growing Market in Rwanda. Regulator 1 (March): 6–8.
Republic of Rwanda. 2000. Rwanda Vision 2020. Kigali, Rwanda. http://www.minecofin.gov.rw/fileadmin/General/Vision_2020/Vision-2020.pdf.
Republic of Rwanda. 2013. Draft (1st Physical Meeting)—WSIS+10: Overall Review of the Implementation of the WSIS Outcomes. http://www.itu.int/wsis/review/inc/docs/rcreports/WSIS10_Country_Reporting-RWA.pdf.
Francisco J. Proenza, Wei Shang, Guoxin Li, Jianbin Hao, Oluwasefunmi ‘Tale Arogundade, and Martin S. Hagger
China has the largest population of Internet cafés in the world. Chinese users of cafés are predominantly young males, but there are also mature users, females, and migrant workers. There are few exclusive Internet café users, as most users connect to the Internet from a variety of places, including cafés, home, school, office, and mobile devices. Users engage in a variety of activities, the most common being chatting, gaming, and Internet surfing.
The Chinese government has an aggressive Internet café policy that aims to protect minors, ensure a safe user environment, and curb Internet addiction and undesirable social behaviors, but it seems to be largely driven by misconceptions about the impact of Internet cafés on users’ lives.
The objective of this study is to understand the perceived value of Internet café use to users as individuals and to China as a society. We examine the objectives users pursue when they visit such venues and the extent to which they feel they have achieved their objectives. An understanding of user motives and perceived achievements is key to understanding the phenomenal growth in China’s Internet cafés and why China’s restrictive policies have been difficult to enforce.
We find that users’ objectives for using Internet cafés are reasonable and common among young people. According to self-determination theory, they are the types of goals people pursue to satisfy psychological needs for autonomy, competence, and relatedness.
In the coming years, Internet cafés are bound to remain critical access venues, especially for rural communities and migrant workers. China is rapidly modernizing, but some of its current policies to limit if not prevent use of Internet cafés are controlling and undermining of autonomous motivation and are bound to fail. They also threaten adaptive activities and motives (such as gaining new knowledge), the psychological needs of users, and, by implication, their psychological well-being. Given the difficulties experienced to date with controlling regulatory policies, we recommend that government consider alternative strategies that help advance the country’s digital agenda and facilitate self-determination and psychological well-being.
Figure 4.1
China: Millions of Internet users by access mode and in total.
Internet use in China has experienced phenomenal growth, the equivalent of 24 percent a year between 2006 and 2013 (Figure 4.1).1 Access to the Internet from net bars (as Internet cafés are known locally) reached a peak of 163 million users in December 2010, but has since subsided to 116 million in December 2013 (19 percent of Internet users). However, the number of people connecting from telecenters, negligible in 2006, reached 90 million people or nearly 15 percent of Internet users in 2013. Although hard to ascertain with confidence, the number of users accessing from PAVs (i.e. from either Internet cafés or telecenters combined) may have grown during this period.2
With somewhere between 144,000 (Kan 2011) and 136,000 (Jou 2013) Internet cafés and about 116 million users (China Internet Network Information Center 2014), China has the largest population of Internet café users in the world.3
Internet cafés are especially important in small cities and rural communities. In 2007, about 28 percent of Internet users in metropolitan areas connected to the Internet from Internet cafés, fewer than in provincial capitals such as Xi’an (51 percent) and Shenyang (36 percent; Liang 2007). Separate data for 2007 show that about 54 percent of rural Internet users connected to the net through Internet cafés, compared with a national figure of only 33 percent of Internet users (China Internet Network Information Center 2007a). Computer ownership is significantly lower in rural (3 percent) than in urban (47 percent) households (China Internet Network Information Center 2007a).
Migrant workers—rural residents who leave their home to work in urban areas— constitute another important group of Internet café users. In 2012, there were about 262.6 million rural migrant workers in China (National Bureau of Statistics of China 2013). About 40 percent of migrants are less than 30 years old (China Labour Bulletin 2013). A large proportion of migrants, especially among the young, regularly surf the Internet as a major leisure activity and to keep in touch with family members. Many migrants live in dormitories provided by their employers and, of necessity, must rely on nearby net bars to access the Internet.
Media accounts of Internet cafés in China usually begin with the story of how in 2002 two boys ages 13 and 14 were refused service in a net bar located in the university district in Beijing (Linchuan Qiu 2009). The disgruntled youths retaliated by setting fire to the Internet café, killing 25 people and injuring many others (BBC News 2002; Xinhua 2004). A tightening of regulations and crackdown on unlicensed centers ensued (BBC News 2002). Within six months, 90,000 unlicensed net bars were closed, leaving only 110,000 Internet cafés operating (Xinhua 2004). In Beijing, only 30 legal Internet cafés remained open out of more than 2,200 previously in operation (Liang 2002). The crackdown continues to this day, with 7,000 net bars suspended in 2010 (Xinhua 2011).
Government regulations aim to ensure a safe user environment and curb Internet addiction, pornography, and undesirable social behaviors, with the protection of minors a foremost concern (Information Office of the State Council 2010). Regulations are extensive at both national and local levels. In 2003, the Chinese Central Government began to promote Internet café chains, and licenses were issued to only ten chain operators. No increase in the number of licenses issued is foreseen, and existing Internet cafés are expected to join chains or shut down.
Many stakeholders—central and local government officials, Internet café operators, telecom operators, and users—vie for influence over policy regarding licensing and control of Internet café use (Cartier, Castells, and Linchuan Qiu 2005; Linchuan Qiu 2009; Linchuan Qiu and Liuning 2005). Officials in Beijing setting central government policy live in large cities, use the Internet at home and at work, and seldom visit Internet cafés. Their fears about the welfare of minors mirror similar parental concerns in other countries (Livingston and Haddon 2008; Synovate 2009; Turow 1999) and are stoked by sensationalist media accounts.
At the local level where policy is implemented, there tends to be leniency, particularly in small towns. This is partly in recognition of the difficulties of enforcing the policy in the face of a huge demand for Internet café services, but also because of the revenues that licensing generates for local governments. Rent-seeking by public officials also occurs. When asked how he managed to survive, one illegal Internet café operator in Gedong replied, “Well, that’s kind of hard to explain.” Pressed for an answer, he said the secret was in the “‘relationships’ between the owner and the police” (Cody 2007).
Restrictive policies appear to have been costly to Internet café patrons but not very effective.4 As of 2010, only five chains had started operations (Junlong 2010), and these ran at most 40 percent of the country’s Internet cafés (Earp 2013; Kan 2011). Many illegal net bars continue to operate (Cody 2007; Hong 2007; Hong and Huang 2005). These are usually standalone, individually run operations, much smaller than chains. All that is needed is a small venue, perhaps 100 m2, a few computers (as opposed to 300–400 in legal Internet cafés), and broadband connectivity. These centers are set up close to schools and universities, have no clear distinguishing signs, regularly cater to minors, and often run twenty-four hours a day (Hong and Huang 2005). Even if it is illegal, spending the night at a net bar is commonplace.
Negative views of the impact of Internet cafés are not shared by everyone in China (Tian 2010) but are widespread (box 4.1) and are an important force driving policy (Xueqin 2009). They contrast with the vision—articulated for example by Hoffman (2012) and Brynjolfsson and Saunders (2010), and largely shared by the Information Office of the State Council (2010)—of the Internet as a transformational technology that enables people to communicate and learn, firms to innovate and compete effectively, and consumers to benefit from greater access to products and services.
The objective of this study is to understand the perceived value of Internet café use to users as individuals as well as China as a society. We examine the objectives users pursue when they visit Internet cafés, as well as the extent to which users feel they have achieved their situational objectives and life goals, and we compare their life goals and achievements with those of nonusers. An understanding of user motives and perceived achievements is key to understanding the phenomenal growth in China’s Internet cafés and why China’s restrictive policies have been difficult to enforce. Our aim is to contribute to Chinese officials in their deliberations, as they consider alternative policies that are effective and sustainable in dissuading harmful overuse.
Box 4.1
Sample Negative Perceptions of Internet Café Impact in Chinese Media
We started with a simple logic model of how a cybercafé could bring about change. The model suggested that user behavior was a key input (i.e., that significant outcomes came about as a result of the uses customers make of the center’s services and facilities). Accordingly, it was essential to examine the objectives users pursue when they visit cafés and the achievements they realize. Moreover, finding a way to compare these objectives and achievements with those of nonusers might enable us to detect differences that could be attributed to Internet café use.
The central idea is that nonusers of the Internet will have personal motivations similar to users’ but would not define them in terms of online activities. We made the distinction between objectives for using Internet cafés and broader life objectives to which both users and nonusers would aspire but that, in the case of users, might be affected by the experience of Internet café use. A comparison in the perception of users and nonusers regarding achievement of life objectives might help us identify the presence or absence of change.
From the outset, we called the goals used in our surveys self-determined objectives. Shortly after data collection started, we stumbled on self-determination theory (SDT) and were drawn to the parallels between our approach and SDT’s comprehensive framework, validated in a broad range of spheres (including ICT)5 and its emphasis on “self-determination” of goals as a motivational force behind everyday activities. We adapted our terminology to make it compatible (e.g., using Vallerand’s distinction between situational goals and life goals) and proceeded to use SDT as our guiding framework.
Goals are central to human behavior. Goals are shaped by personal views of the future and what people expect and feel they can accomplish, taking into account culture, social values, and institutions. When a person visits a cybercafé, he or she is pursuing specific goals (e.g., entertainment, relaxation, communication) that may contribute to or detract from the pursuit of “worthier” objectives (e.g., studying for a test). Long-term goals are a point of reference that drive everyday short-term goals and behavior and in turn help shape people’s short- and long-term plans. Goals affect how people view and feel about themselves and their social milieu and help determine their mental health and well-being (Bargh, Gollwitzer, and Oettingen 2010; Deci and Ryan 2000; Freund and Riediger 2006; Greene and DeBacker 2004; Nurmi 1991).
One of the leading approaches to understanding motivation is SDT. According to SDT, people are intrinsically motivated when they derive a sense of enjoyment, interest, and personal satisfaction from engaging in an activity. When experiencing activities as intrinsically motivated, no external contingency (e.g., reward, deadline, etc.) is necessary because people will view the cause of their actions as emanating from the self (i.e., they will choose to act of their own free will). In contrast, people are extrinsically motivated when they pursue an activity or action to obtain an instrumental reward that is separable from the activity. An extrinsically motivated person acts in recognition of external benefits to which the activity is a stepping stone or in response to parental or peer pressure, government laws and regulations, societal mores, codes of conduct, or school or work requirements such as deadlines. SDT principles have been studied, refined, and validated through numerous experiments in a broad range of human endeavors (Deci and Ryan 2000 2008; Ryan and Deci 2000a 2000b; Vallerand 2007; Vansteenkiste, Lens, and Deci 2006).
Within SDT, intrinsic motivation is proposed as an adaptive motivating force that enhances personal well-being (Ryan and Deci 2000a; Ryan, Kuhl, and Deci 1997). Intrinsic motivation helps people satisfy three innate psychological needs: autonomy, competence, and relatedness. People need to feel autonomous: that they are acting of their own volition to organize personal experiences and that they are doing so on their own initiative. People need to feel they are competent: that their actions have an effect on their environment that yields valued positive outcomes (Deci and Moller 2005; Elliot, McGregor, and Thrash 2002). People also need to feel connected to their significant others: to care for others and feel cared for—what Baumeister and Leary (1995) refer to as “a desire for interpersonal attachment” (see also Moller, Deci, and Elliot 2010; Reis et al. 2000).
Well-being in the form of mental health and vitality—”a positive feeling of aliveness and energy,” according to Ryan and Frederick (1997)—is enhanced when people satisfy these three basic needs. In contrast, well-being is undermined when psychological needs are thwarted or unfulfilled (Vallerand 2007). Other psychological needs may influence people’s behavior, but research has consistently shown that these three are fundamental (Ryan and Deci 2000b). The need for autonomy has been studied the most, but all three needs make an independent contribution to motivation (Sheldon and Filak 2008), and the satisfaction of all three is necessary for optimal functioning (Deci and Ryan 2000; Sheldon and Niemiec 2006).
Extrinsic motivation is less adaptive because actions are performed and goals pursued for reasons external to the person. People who perform activities for extrinsic motives will continue to be engaged in the activity only so long as the external contingency (i.e., reward or deadline) is present; if not they will desist. Pursuing an extrinsic goal such as fame, wealth, or beauty may undermine well-being, but these goals are still pursued for a variety of reasons, including external parental, social, cultural, or institutional pressures (Sheldon and Kasser 2008), as well as mistaken expectations regarding the benefits they will derive from achieving these goals (Sheldon et al. 2010).
People can, however, internalize extrinsic motives (e.g., “I study because doing so will help me pursue the career of my choosing”), and there is a continuum in the degree of autonomy or self-determination of extrinsic forms of motivation. To the extent that individuals view the cause of their behavior as satisfying their psychological needs and emanating from the self, their locus of causality will be perceived to be internal. To the extent that an extrinsic contingency such as a reward is perceived by the individual as contributing to the satisfaction of a basic psychological need, it will be internalized by the individual and become a more effective source of motivation. When external rewards, evaluations, pressures, or punishment are made contingent on a behavior, in situations that the person perceives are beyond his or her control, they tend to undermine intrinsic motivation (Deci, Koestner, and Ryan 1999).
Goals are idiosyncratic, change throughout a person’s life cycle, and vary depending on context, culture, and personal experience (Massey, Gebhardt, and Garnefski 2008; Nurmi 1991). Adolescents tend to focus on educational and family-related goals. In middle adulthood, goals are more closely linked to property and children’s lives, and as people get older, health, world affairs, and death become important (Nurmi 1992). Goal priorities change even within adolescence, with the importance of leisure peaking early, around the ages of 10 to 14 (Massey, Gebhardt, and Garnefski 2008). Older adults in the United States and Singapore exhibit greater internalization of their social and civic duties than their children (Sheldon et al. 2005). As people mature, personal goals become more intrinsically motivated and autonomous (Sheldon, Houser-Marko, and Kasser 2006).
People in different cultures navigate their environment differently as they choose personal goals to satisfy their psychological needs (Deci and Ryan 2000).6 Cultural and societal values and institutions may play supportive or undermining roles in satisfying psychological needs.7 Regardless of the opportunities or constraints that culture and institutions afford, the three basic needs for autonomy, competence, and relatedness are universal. They are operative in every culture,8 and “in general, some goals are expected to be more closely linked to basic or intrinsic need satisfaction than are others” (Deci and Ryan 2000). Goals validated cross-culturally, including in China, as intrinsically oriented and satisfying basic internal human needs include: community feeling, affiliation (i.e., having satisfying personal relationships), self-acceptance (i.e., feeling autonomous and competent), and physical health, whereas goals validated as extrinsically oriented (contingent on external rewards) include financial success, image, and popularity (Grouzet et al. 2005).
The extent to which goal choice satisfies psychological needs affects mental health and well-being. Kasser and Ryan (1996) found higher levels of well-being among late adolescents who prioritized intrinsic aspirations (community feeling, self-acceptance, and affiliation) over extrinsic goals (financial success). Deci and Vansteenkiste (2004) cite studies showing a positive correlation between people’s sense of well-being and their perceived achievement of intrinsic goals but not of extrinsic goals. Furthermore, attainment of intrinsically oriented goals leads to reports of greater psychological well-being, which is not the case for extrinsically oriented goals (Sheldon and Kasser 1998).
Following Vallerand (2007), situational goals (SGs) are set for specific activities associated with Internet café or Internet use. Life goals (LGs) are construed more broadly as personal aspirations that anyone can pursue.9 Nonusers will have life goals similar to users’, but their situational goals will not be defined in relation to Internet or Internet café activities. The user survey was more extensive than the nonuser survey because users were asked to identify their LGs as well as their SGs for visiting Internet cafés or for using the Internet from home or other venues, whereas nonusers were asked only about their LGs.
We examine situational goals to understand users’ motives and perceptions of achievement using Internet cafés. We assess impact by comparing differences between users’ and nonusers’ life goal choices and achievements.
Situational Goals The starting point for defining users’ SGs was the 2009 list of Internet use activities in the Pew Internet Project “Usage Over Time” database (see Pew Internet Project 2012). From this list of activities, we identified likely underlying goals of Internet café users. The complete list of possible SGs constructed this way appears in the left-hand column of table 4.1. The relationship between activities and the objectives thus constructed is indirect. For example, email may be used to keep in touch with family and friends (SG number 13, table 4.1); meet new friends, a mate, or a companion (SG12); get information about physical (SG18) or mental (SG19) health.
Practically all SGs are associated with the use of computers and Internet services accessible through Internet cafés. The one exception is SG14, “Socialize and make friends with people in Internet cafés,” which is unrelated to access to the venue’s equipment or services.
Table 4.1
Situational Goals (SGs) and Life Goals (LGs) Offered as Options in Surveys, Classification of Goals Applying SDT Criteria, and Correspondence Mapping between SGs and LGs
aSG classification: A: Autonomy; C: Competence; R: Relatedness; EXT: Extrinsically oriented; U: Unclassified
bLG classification: INT: Intrinsically oriented; EXT: Extrinsically oriented; U: Unclassified
Life Goals The LGs on the right-hand side of table 4.1 were selected because they could be linked with the 29 SGs associated with Internet café use (left-hand side of table 4.1). To illustrate, a person may visit an Internet café to improve school performance (SG1), acquire skills to become a better worker or a better entrepreneur (SG2), get a new or better job (SG3), or learn how to use computers and the Internet (SG4). Once these four Internet-specific SGs were defined, the LG “Learn more knowledge” (LG1, table 4.1) was included in both user and nonuser surveys because it encompasses these four situational goals. The same reasoning was used to define all LGs in user and nonuser surveys. Table 4.1 maps the correspondence between SGs and LGs.
Goal Classification Based on Self-Determination Theory Survey goals were not worded beforehand to fit SDT’s taxonomy. However, using SDT as a guiding framework would suggest that these motives are likely to emerge from the data, particularly given recent research showing that people can and do make the distinction between intrinsic and extrinsic motives, even at the implicit, nonconscious level (McLachlan and Hagger 2010, 2011). In the present study, we sought to establish whether the motives identified from common Internet use practices could also be classified following SDT precepts. Once the economist, systems analyst, marketing specialist, and statistician in the team became aware of SDT, they used SDT criteria to classify SGs and LGs and subsequently called on a professional psychologist to join the research effort and confirm the classification.10
In table 4.1, SGs are classified as satisfying one of the three psychological needs— autonomy, competence, or relatedness—or as extrinsically oriented. Where SGs might satisfy more than one need, only one dominant need is identified. In the case of SG15, all three needs are probably satisfied,11 but the goal’s wording, “Entertainment (play games, listen to music, watch movies, online video, etc.),” suggests autonomy as the dominant need this goal would fulfill.
LGs are more broadly defined than SGs and are classified as either intrinsically or extrinsically oriented (right-hand side of table 4.1). The LG “Learn more knowledge” used in this study (LG1) is similarly worded (in Chinese and in English) to “Developing yourself and learning new things,” a goal that has been validated as intrinsic by Vansteenkiste, Lens, and Deci (2006), and “To grow and learn new things,” an intrinsic goal in Kasser’s Aspirations Index.12 LGs 9 and 10, “Improve the physical or mental health of myself or my family,” are similar to the aspiration “To be physically healthy” used by Niemiec, Ryan, and Deci (2009). Goals such as “Art creation (fiction, poetry, art, music, etc.)” (LG4) and “Leisure, entertainment” (LG15) imply personal enjoyment and are therefore clearly intrinsically oriented goals.
Goals that imply a contingency, such as the SG “Make money (e.g., online store, doing web pages, etc.)” (SG8) or the LG “Obtain better products and services at lower cost” (LG7), may be readily identified as extrinsically oriented.
Information on the motives behind goal choice was not collected. Both goal content and the underlying motivation determine behavior, and this can make it difficult to classify a goal on the basis of its apparent content. An intrinsic goal may be chosen for controlled reasons, and an extrinsic goal may be chosen for autonomous reasons (Kasser and Ryan 1996; Sheldon et al. 2004). The presence of tangible rewards, threats, deadlines, or coercion may shift the locus of causality, and an ordinarily intrinsic goal may in practice be chosen for extrinsically motivated reasons (Ryan and Deci 2000c). Typically, to “find an additional/new job” (SG6) is a tedious, unattractive, extrinsically oriented goal, especially when it involves offline job seeking, but “looking for a job using the Internet” might be perceived by some to be an engaging and even enjoyable activity. We therefore leave SG6 unclassified. Other goals defy classification because they are too broadly defined or because we know little about the underlying motivation: accessing information (including news, weather forecasts, stock information, sports, gossip, etc.). (SG11), planning trips (SG26 and LG8), helping to improve government (SG29), and participating in social or civic activities (LG11). Given a well-established correlation between content and the motives behind goal choice, goal content is a useful indicator on its own (Sheldon et al. 2004).
The Intrinsic Nature of Internet Café Use Different people will have different life aspirations and articulate them differently. One of the most widely used lists in goal content research is Kasser’s Aspirations Index (Massey, Gebhardt, and Garnefski 2008). In 1993, this index included twenty-one goals (Kasser and Ryan 1993). It presently has thirty-five goals, twenty of them intrinsic and fifteen extrinsic.
Of the thirty SGs included in our user surveys, six (including SG30, “Other”) cannot be classified, four are classified as extrinsic, and the remaining twenty are intrinsic goals associated with one of the psychological needs: five with autonomy, eight with relatedness, and seven with competence (see left-hand side of table 4.1). Of the eighteen LGs included in user and nonuser surveys, twelve were classified as intrinsic, four were left unclassified, and only two could be clearly identified as extrinsic (right-hand side of table 4.1).
Considering that the survey goals were constructed before SDT criteria were applied, the results of this exercise suggest that the goal content of Internet and Internet café use is predominantly intrinsic.
User and Nonuser Survey Options Users and nonusers of Internet cafés were presented in their surveys with predefined lists of goals (SGs and LGs in the case of users, LGs only in the case of nonusers). They were asked to select their own goals from the lists and indicate the extent of their dedication to achieving these goals (café time spent pursuing each goal in the case of SGs or relative “importance” in the case of LGs) and their perceptions regarding goal achievement in the past twelve months (for both SGs and LGs).
Achieving similar frames of minds of users and nonusers was challenging because for practical reasons the user surveys included cybercafé usage questions before life goal choices were presented. Respondents to nonuser surveys were identified in the vicinity of the Internet cafés and interviewed on Internet café premises because the heat at the time of the survey did not make street interviewing practicable.
Basic Features of Sample Populations The profile of the user sample differs markedly from that of China’s general population (table 4.2). More than half the users (56 percent) are males under 25 years old, and most users (81 percent) are urban residents. Men account for 73 percent of users and females for 27 percent. Rural residents comprise 19 percent of sample users. This relatively high number in an essentially urban sample was obtained because we surveyed two cafés located near factories employing migrant workers. (Sampling procedures are described in appendix 4.A.)
The age structures of our samples are compatible with approximate profiles of China’s Internet users and nonusers constructed using China Internet Network Information Center (CNNIC) and population data (table 4.3). Urban nonusers under 30 years of age represent 33 percent of our urban subsample (table 4.2) and 29 percent of China’s urban nonusers. Fifty-eight percent of China’s urban Internet user population is less than 30 years old. Urban café users are even younger; those less than 30 years old account for 90 percent of our urban user subsample (table 4.2).
The nonuser sample is more gender balanced, more in line with China’s urban population. Urban females comprise 40 percent of nonuser survey respondents, compared with 45 percent in the case of males. The age structure of the urban nonuser sample is also closer to that of China’s population, with urban nonusers 30 years or older accounting for 57 percent of the subsample. There were no users over 49 years old, but respondents in this age bracket make up nearly a third of the nonuser sample. As in the user survey, only a few (14 percent) rural residents were captured by the nonuser survey. Nearly 90 percent of elderly nonusers (49 years or older) are urban residents. The reasons given by nonusers for not using the Internet are presented in table 4.4.
Table 4.2
Gender, Rural–Urban Status, and Age Distribution of Respondents of User and Nonuser Surveys
|
Users |
|
Nonusers |
|
|
# |
% |
# |
% |
Urban male, by age |
|
|
|
|
< 19 |
86 |
8.8 |
42 |
4.4 |
19 to < 25 |
341 |
34.9 |
58 |
6.0 |
25 to < 30 |
78 |
8.0 |
32 |
3.3 |
30 to < 49 |
70 |
7.2 |
140 |
14.5 |
≥ 49 |
– |
– |
164 |
17.0 |
Subtotal |
575 |
58.9 |
436 |
45.2 |
Urban female, by age |
|
|
|
|
< 19 |
33 |
3.4 |
57 |
5.9 |
19 to < 25 |
125 |
12.8 |
46 |
4.8 |
25 to < 30 |
41 |
4.2 |
35 |
3.6 |
30 to < 49 |
12 |
1.2 |
140 |
14.5 |
≥ 49 |
– |
– |
110 |
11.4 |
Subtotal |
211 |
21.6 |
388 |
40.2 |
Subtotal urban |
786 |
80.5 |
824 |
85.5 |
Rural male, by age |
|
|
|
|
< 19 |
33 |
3.4 |
5 |
0.5 |
19 to < 25 |
87 |
8.9 |
14 |
1.5 |
25 to < 30 |
11 |
1.1 |
8 |
0.8 |
30 to < 49 |
5 |
0.5 |
31 |
3.2 |
≥ 49 |
– |
– |
19 |
2.0 |
Subtotal |
136 |
13.9 |
77 |
8.0 |
Rural female, by age |
|
|
|
|
< 19 |
17 |
1.7 |
4 |
0.4 |
19 to < 25 |
27 |
2.8 |
8 |
0.8 |
25 to < 30 |
3 |
0.3 |
5 |
0.5 |
30 to < 49 |
7 |
0.7 |
30 |
3.1 |
≥ 49 |
– |
– |
16 |
1.7 |
Subtotal |
54 |
5.5 |
63 |
6.5 |
Subtotal rural |
190 |
19.5 |
140 |
14.5 |
All users |
976 |
100.0 |
964 |
100.0 |
Table 4.3
Approximate Profiles of China’s Internet Users and Non-users
Summary profile of user population |
% |
Summary profile of non-user population |
% |
Urban users < 30 as % of China’s population |
15.4 |
Urban non–users < 30 as % of China’s population |
7.2 |
Urban users ≥ 30 as % of China’s population |
11.1 |
Urban non–users ≥ 30 as % of China’s population |
17.4 |
Rural users < 30 as % of China’s population |
5.7 |
Rural non–users < 30 as % of China’s population |
16.0 |
Rural users ≥ 30 as % of China’s population |
4.1 |
Rural non–users ≥ 30 as % of China’s population |
23.2 |
Urban users < 30 as % of all urban users |
58.1 |
Urban non–users < 30 as % of all urban non–users |
29.3 |
Urban users ≥ 30 as % of all urban users |
41.9 |
Urban non–users ≥ 30 as % of all urban non–users |
70.7 |
Rural users < 30 as % of all rural users |
58.1 |
Rural non–users < 30 as % of all rural non–users |
40.7 |
Rural users ≥ 30 as % of all rural users |
41.9 |
Rural non–users ≥ 30 as % of all rural non–users |
59.3 |
Urban users < 30 as % of urban population < 30 |
68.1 |
Urban non-users < 30 as % of urban population < 30 |
31.9 |
Urban users ≥ 30 as % of urban population ≥ 30 |
38.9 |
Urban non–users ≥ 30 as % of urban population ≥ 30 |
61.1 |
Rural users < 30 as % of rural population < 30 |
26.2 |
Rural non–users < 30 as % of rural population < 30 |
73.8 |
Rural users ≥ 30 as % of rural population ≥ 30 |
15.0 |
Rural non–users ≥ 30 as % of rural population ≥ 30 |
85.0 |
All urban users as % of China’s population |
26.4 |
All urban non–users as % of China’s population |
24.6 |
All rural users as % of China’s population |
9.8 |
All rural non–users as % of China’s population |
39.2 |
All user penetration rate (= CNNIC rate) |
36.2 |
Rate of non–use in China’s population (= CNNIC rate) |
63.8 |
Estimates are based on:
1. World Bank estimate (at time of writing) of China’s urban population (51%) http://data.worldbank.org/topic/urban-development
2. Wolfram/Alpha: Age distribution (at time of writing) of China’s population: 27.7% < 19; 16.5% 20–29; 55.8% ≥ 30 http://www.wolframalpha.com/input/?i=China+population+distribution
3. CNNIC 28th Statistical Report, July 2011a: Age distribution of Internet users: 27.3% < 19; 30.8% 20–29; 42% ≥ 30; rural users as % of total: 27%; and overall penetration rate: 36.2%.
Table 4.4
Nonusers: Main Reason for Not Using the Internet (# of responses)
|
No skills |
No time |
No need or Interest |
No access |
Expensive |
Other |
All |
Urban–Male |
|
|
|
|
|
|
|
<19 |
6 |
14 |
9 |
5 |
4 |
4 |
42 |
19 to <30 |
12 |
35 |
23 |
10 |
6 |
4 |
90 |
30 to <49 |
40 |
35 |
47 |
12 |
4 |
2 |
140 |
≥49 |
103 |
14 |
39 |
6 |
1 |
1 |
164 |
Subtotal |
161 |
98 |
118 |
33 |
15 |
11 |
436 |
Urban – Female |
|
|
|
|
|
|
|
<19 |
7 |
24 |
14 |
8 |
3 |
1 |
57 |
19 to <30 |
4 |
34 |
18 |
12 |
10 |
3 |
81 |
30 to <49 |
46 |
40 |
36 |
13 |
4 |
1 |
140 |
≥49 |
69 |
9 |
21 |
6 |
3 |
2 |
110 |
Subtotal |
126 |
107 |
89 |
39 |
20 |
7 |
388 |
Urban – Male & Female |
|
|
|
|
|
|
|
<19 |
13 |
38 |
23 |
13 |
7 |
5 |
99 |
19 to <30 |
16 |
69 |
41 |
22 |
16 |
7 |
171 |
30 to <49 |
86 |
75 |
83 |
25 |
8 |
3 |
280 |
≥49 |
172 |
23 |
60 |
12 |
4 |
3 |
274 |
All Urban |
287 |
205 |
207 |
72 |
35 |
18 |
824 |
Rural – Male & Female |
|
|
|
|
|
|
|
<19 |
1 |
1 |
3 |
3 |
0 |
1 |
9 |
19 to <30 |
6 |
15 |
6 |
5 |
3 |
0 |
35 |
30 to <49 |
20 |
16 |
18 |
4 |
2 |
1 |
61 |
≥49 |
16 |
3 |
15 |
1 |
0 |
0 |
35 |
All Rural |
43 |
35 |
42 |
13 |
5 |
2 |
140 |
All Nonusers |
330 |
240 |
249 |
85 |
40 |
20 |
964 |
The 2010 Network World League Cybercafé Survey sampled 8,759 users and found that women represented only about 11 percent of users (TXWM 2011). During fieldwork, a greater willingness by women to participate in the survey was detected, and our estimate of 27 percent may overestimate female representation as users of Internet cafés.
Table 4.5
Places of Access to the Internet of Survey Population
|
Use of Venue by Frequency of Use |
|
|
|
|
|
|||||
Venue |
Every Time |
Most of the Time |
Sometimes |
Seldom |
Never |
All Respondents |
|||||
Cybercafé |
141 |
341 |
221 |
218 |
55 |
976 |
|||||
Home |
60 |
201 |
166 |
127 |
422 |
976 |
|||||
Mobile phone |
51 |
190 |
229 |
152 |
354 |
976 |
|||||
School |
30 |
141 |
125 |
145 |
535 |
976 |
|||||
Office |
13 |
49 |
76 |
118 |
720 |
976 |
|||||
Friend’s house |
5 |
14 |
93 |
326 |
538 |
976 |
|||||
Library |
3 |
22 |
33 |
82 |
836 |
976 |
|||||
Other |
2 |
6 |
9 |
49 |
910 |
976 |
Place of Access, Distance from Home, and Patterns of Internet Café Use Sample users exhibit complex patterns of access to the Internet (tables 4.5–4.7). Out of 976 valid responses, only 90 were from users who used Internet cafés exclusively. Most Internet café users also use other venues; about 50 percent used three or more other types of venues. The other most common place of access (table 4.7) is the home, followed by school.
Predominant Internet café users (i.e., those who report using Internet cafés “all of the time” or “most of the time”) represent nearly half (49 percent) of user respondents (table 4.5). Another 31 percent are predominant users of other places of access and do not make as frequent use of Internet cafés (table 4.7). The remaining 20 percent vary in the extent of their commitment to either Internet cafés or other venue types.
There are no Internet cafés in remote rural areas of China, and none of the towns where the surveys were implemented may be considered rural. The surveys’ classification of users and nonusers into rural and urban is based on self-reports of interviewees, which would tend to follow China’s HuKuo system of registration that identifies a person’s place of permanent residence. The user survey was taken in twenty-two Internet cafés and the nonuser survey in the vicinity of those twenty-two Internet cafés (see appendix 4.A). The proportion of users who identified themselves as urban exceeded 50 percent in all but two of these venues.
Most users (90 percent) travel no more than two kilometers to reach an Internet café (table 4.8). About 57 percent visit a café daily or at least once a week (table 4.9), but there are relatively more male daily visitors (19 percent) than female (7 percent). About 70 percent of users visit during the day or at dusk, but some (23 percent) prefer to visit in the evenings, and about 6 percent stay late into the night or even overnight. About two-thirds of all users stay less than three hours when they visit a café. More males (26 percent) than females (14 percent) tend to stay more than three hours.
Table 4.6
Use of Cybercafés by Survey Population
|
|
|
Cybercafé Users Who Also Use Other Venue Types |
|
|
|
|||
|
Cybercafé Users (All) |
Exclusive Cybercafé Users (No Other Venue Used) |
1 More |
2 More |
3 More |
Cyber +4 or More |
|||
Uses cybercafés all the time |
141 |
60 |
31 |
15 |
16 |
19 |
|||
Uses cybercafés most of the time |
341 |
25 |
74 |
75 |
74 |
93 |
|||
Sometimes uses cybercafés |
221 |
3 |
30 |
42 |
57 |
89 |
|||
Seldom uses cybercafés |
218 |
1 |
32 |
47 |
65 |
73 |
|||
Never uses cybercafés |
55 |
1 |
28 |
11 |
4 |
11 |
|||
Total |
976 |
90 |
195 |
190 |
216 |
285 |
Table 4.7
Predominant Place of Access of Internet Café Users
Predominant Place of Access* |
# |
% |
No predominant place |
82 |
8 |
Only one predominant place |
|
|
Cybercafé |
289 |
30 |
School |
99 |
10 |
Home |
130 |
13 |
Office |
25 |
3 |
Friend’s house |
8 |
1 |
Library |
3 |
– |
Mobile phone |
34 |
3 |
Predominant mobile users who are also predominant users of: |
|
|
Cybercafés |
107 |
11 |
Home |
48 |
5 |
School |
15 |
2 |
Office |
2 |
0.3 |
Users with two predominant places not including mobiles |
87 |
9 |
Users with three or more predominant places |
46 |
5 |
All user respondents |
975 |
100 |
*A place is considered predominant if the respondent used it to access the Internet. “Every time” or “Most of the time.”
Table 4.8
Distance of Usual Internet Café from Home
|
All |
|
|
Urban Males |
|
|
Urban Females |
|
|
All Urban Users |
|
|
Rural Users in Sample |
|
|
|
# |
% |
Cum% |
# |
% |
Cum% |
# |
% |
Cum% |
# |
% |
Cum% |
# |
% |
Cum% |
< 300 m |
291 |
29.8 |
29.8 |
176 |
30.6 |
30.6 |
68 |
32.2 |
32.2 |
244 |
31.0 |
31.0 |
47 |
24.7 |
24.7 |
300–500 m |
359 |
36.8 |
66.6 |
215 |
37.4 |
68.0 |
84 |
39.8 |
72.0 |
299 |
38.0 |
69.1 |
60 |
31.6 |
56.3 |
500 m–1 km |
161 |
16.5 |
83.1 |
96 |
16.7 |
84.7 |
28 |
13.3 |
85.3 |
124 |
15.8 |
84.9 |
37 |
19.5 |
75.8 |
1–2 km |
75 |
7.7 |
90.8 |
40 |
7.0 |
91.7 |
15 |
7.1 |
92.4 |
55 |
7.0 |
91.9 |
20 |
10.5 |
86.3 |
> 2 km |
90 |
9.2 |
100.0 |
48 |
8.3 |
100.0 |
16 |
7.6 |
100.0 |
64 |
8.1 |
100.0 |
26 |
13.7 |
100.0 |
Total |
976 |
|
|
575 |
|
|
211 |
|
|
786 |
|
|
190 |
|
|
Table 4.9
Frequency of Visits, Usual Time of Visit, and Amount of Time Spent During Each Visit to Internet Cafés
|
All Users |
|
Males |
|
Females |
|
Young Urban Males |
|
Overusers |
|
|
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
Frequency of visits |
|
|
|
|
|
|
|
|
|
|
Daily or almost daily |
151 |
15.5 |
133 |
18.7 |
18 |
6.8 |
15 |
17.4 |
36 |
34.6 |
At least once a week |
408 |
41.8 |
296 |
41.6 |
112 |
42.3 |
36 |
41.9 |
43 |
41.3 |
At least once a month |
222 |
22.7 |
152 |
21.4 |
70 |
26.4 |
26 |
30.2 |
13 |
12.5 |
A few times a year |
195 |
20.0 |
130 |
18.3 |
65 |
24.5 |
9 |
10.5 |
12 |
11.5 |
Subtotal |
976 |
|
711 |
|
265 |
|
86 |
|
104 |
|
Usual time of visit |
|
|
|
|
|
|
|
|
|
|
Morning |
89 |
9.1 |
66 |
9.3 |
23 |
8.7 |
9 |
10.5 |
9 |
8.7 |
Noon |
64 |
6.6 |
53 |
7.5 |
11 |
4.2 |
13 |
15.1 |
12 |
11.5 |
Afternoon |
343 |
35.1 |
234 |
32.9 |
109 |
41.1 |
33 |
38.4 |
35 |
33.7 |
At dusk |
194 |
19.9 |
134 |
18.8 |
60 |
22.6 |
10 |
11.6 |
17 |
16.3 |
Evening |
223 |
22.8 |
176 |
24.8 |
47 |
17.7 |
18 |
20.9 |
19 |
18.3 |
Late night/early morning |
15 |
1.5 |
9 |
1.3 |
6 |
2.3 |
1 |
1.2 |
3 |
2.9 |
Overnight |
48 |
4.9 |
39 |
5.5 |
9 |
3.4 |
2 |
2.3 |
9 |
8.7 |
Subtotal |
976 |
|
711 |
|
265 |
|
86 |
|
104 |
|
Usual amount of time |
|
|
|
|
|
|
|
|
|
|
Less than an hour |
37 |
3.8 |
23 |
3.2 |
14 |
5.3 |
4 |
12.1 |
2 |
1.9 |
Around 1hour |
145 |
14.9 |
87 |
12.2 |
58 |
21.9 |
9 |
27.3 |
10 |
9.6 |
Around 2–3 hours |
484 |
49.6 |
345 |
48.5 |
139 |
52.5 |
14 |
42.4 |
46 |
44.2 |
Around 4–5 hours |
145 |
14.9 |
116 |
16.3 |
29 |
10.9 |
4 |
12.1 |
18 |
17.3 |
More than 5 hours |
78 |
8.0 |
71 |
10.0 |
7 |
2.6 |
– |
– |
12 |
11.5 |
Not sure |
87 |
8.9 |
69 |
9.7 |
18 |
6.8 |
2 |
6.1 |
16 |
15.4 |
Subtotal |
976 |
|
711 |
|
265 |
|
33 |
|
104 |
|
1. Young here indicates respondents who are less than 19 years old.
2. Overusers are sample respondents who answered “sometimes,” “every time,” or “most of the time” to five or more of the eight questions proposed by Young (1996) to assess Internet addiction. This modified application of Young’s criteria will classify a larger number of users as overusers than Young would classify as “addicted.”
Young (under age 19) male urban users visit cafés about as frequently as all sample males (i.e., 59 percent daily or almost daily). Contrary to what might be anticipated from the concerns expressed in the media (box 4.1), only 12 percent of these youngsters spend more than three hours per visit to a café, and only 3 percent stay late into the night or overnight (table 4.9).
Overusers13 visit Internet cafés more frequently (35 percent daily) than users overall (15 percent). Twenty-nine percent of this group spend four hours or longer during each visit, and 12 percent are night owls who stay late into the night or overnight (table 4.9).
Occupation and Income The occupations and incomes of users are quite different from those of nonusers. Students are dominant among users, and income-earning employees are dominant among nonusers (table 4.10). Occupational and income patterns also vary between urban and rural residents. Students represent about 40 percent of urban users and 50 percent of rural users but only 15 and 10 percent of urban and rural nonusers, respectively. There are no retirees among users, but retirees account for 19 percent of urban and 2 percent of rural nonusers. Nonusers earn more than users, and rural users and nonusers have lower incomes than their urban counterparts (table 4.11).
Table 4.10
Distribution of User and Nonuser Sample Respondents by Occupation and Urban/Rural Status
|
All |
|
Urban |
|
Rural |
|
|
Users |
Nonusers |
Users |
Nonusers |
Users |
Nonusers |
Student |
419 |
142 |
322 |
128 |
97 |
14 |
Government employee |
20 |
37 |
18 |
33 |
2 |
4 |
Migrant/domestic worker |
47 |
75 |
30 |
41 |
17 |
34 |
Business administration |
34 |
22 |
32 |
22 |
2 |
– |
Employee |
142 |
81 |
123 |
77 |
19 |
4 |
Technical worker |
97 |
61 |
81 |
56 |
16 |
5 |
Factory or service worker |
56 |
72 |
45 |
67 |
11 |
5 |
Self-employed |
76 |
160 |
63 |
139 |
13 |
21 |
Farmer |
3 |
43 |
3 |
11 |
– |
32 |
Soldier/military |
10 |
3 |
9 |
2 |
1 |
1 |
Unemployed |
18 |
79 |
16 |
71 |
2 |
8 |
Retired |
– |
164 |
– |
161 |
– |
3 |
Other |
54 |
25 |
44 |
16 |
10 |
9 |
Student users earn less than nonstudent users, as the latter are mostly workers (table 4.12). Student users prefer to access the Internet from cafés and schools. Among nonstudent café users, those who connect mainly from home have higher incomes: about 73 percent of nonstudents connecting predominantly from home earned more than 1,500 yuan per month (approx. US$240), compared with only 57 percent of predominant café users.
We consider two divergent views of the impact of Internet cafés: one based on media accounts suggesting that impact on users is negative, and a positive view based on Internet impact studies. Self-determination theory guides the analysis.
Are the Motives of Internet Café Users Socially Valuable? (H1a) Media accounts of the impact of Internet cafés (box 4.1) are largely based on the observation that gaming and entertainment are commonplace and a dominant venue activity. We posit that café visitors, even if engaged extensively in entertainment, also engage in many other activities and have multiple objectives when they visit Internet cafés, including some commonly regarded as “instrumental” and socially desirable.
H1a. The situational goals that Internet café users pursue include common human objectives, some of which have value recognized by society.
Do Users Learn Computer and Internet Skills in Internet Cafés? (H1b & H1c) In China, it is sometimes claimed, “The Internet cafés are not places where the youth learn Internet skills!” (Yu 2006). This perception runs counter to what is observed in other countries (see e.g., the chapters on Jordan and Cameroon in this book). User experience may also affect the achievement of other situational goals. We therefore propose to test the following hypotheses:
H1b. The proportion of inexperienced Internet users having computer and Internet training as a situational goal (SG4 in table 4.1) is greater than among experienced users.
H1c. Experience in the use of the Internet increases the sense of achievement of Internet-related situational goals reported by users.
Table 4.11
Distribution of User and Nonuser Sample Respondents by Income and Urban/Rural Status
|
Whole Sample |
|
|
|
Urban |
|
|
|
Rural |
|
|
|
Monthly Income (in Yuan) |
Users |
|
Nonusers |
|
Users |
|
Nonusers |
|
Users |
|
Nonusers |
|
|
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
< 500 |
240 |
24.6 |
194 |
20.1 |
170 |
21.6 |
159 |
19.3 |
70 |
36.8 |
35 |
25.0 |
501–1,000 |
220 |
22.5 |
133 |
13.8 |
173 |
22.0 |
98 |
11.9 |
47 |
24.7 |
35 |
25.0 |
1,001–1,500 |
164 |
16.8 |
180 |
18.7 |
139 |
17.7 |
149 |
18.1 |
25 |
13.2 |
31 |
22.1 |
1,501–2,000 |
168 |
17.2 |
201 |
20.9 |
142 |
18.1 |
180 |
21.8 |
26 |
13.7 |
21 |
15.0 |
2,001–3,000 |
127 |
13.0 |
163 |
16.9 |
113 |
14.4 |
152 |
18.4 |
14 |
7.4 |
11 |
7.9 |
3,001–5,000 |
40 |
4.1 |
71 |
7.4 |
35 |
4.5 |
65 |
7.9 |
5 |
2.6 |
6 |
4.3 |
5,001–8,000 |
9 |
0.9 |
12 |
1.2 |
9 |
1.1 |
12 |
1.5 |
– |
– |
– |
– |
> 8,000 |
8 |
0.8 |
10 |
1.0 |
5 |
0.6 |
9 |
1.1 |
3 |
1.6 |
1 |
0.7 |
Total |
976 |
100.0 |
964 |
100.0 |
786 |
100.0 |
824 |
100 |
190 |
100 |
140 |
100 |
Table 4.12
Income by Predominant Place of Accessa and Student Status
|
Students |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Monthly Income (in Yuan)b |
Cybercafé |
|
School |
|
Home |
|
Office |
|
Friend’s House |
|
Library |
|
Mobile Phone |
|
All Students |
|
|
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
< 500 |
82 |
51.6 |
61 |
46.6 |
46 |
47.4 |
3 |
60.0 |
5 |
83.3 |
– |
– |
– |
– |
219 |
52.3 |
501–1,000 |
59 |
37.1 |
63 |
48.1 |
41 |
42.3 |
2 |
40.0 |
– |
– |
– |
– |
– |
– |
167 |
39.9 |
1,001–1,500 |
11 |
6.9 |
7 |
5.3 |
7 |
7.2 |
– |
– |
– |
– |
– |
– |
– |
– |
21 |
5.0 |
1,501–2,000 |
3 |
1.9 |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
4 |
1.0 |
2,001–3,000 |
4 |
2.5 |
– |
– |
2 |
2.1 |
– |
– |
– |
– |
– |
– |
– |
– |
6 |
1.4 |
3,001–5,000 |
– |
– |
– |
– |
1 |
1.0 |
– |
– |
1 |
16.7 |
– |
– |
– |
– |
2 |
0.5 |
5,001–8,000 |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
> 8,000 |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
– |
Subtotal |
159 |
100 |
131 |
100 |
97 |
100 |
5 |
100 |
6 |
100 |
– |
– |
– |
– |
419 |
100 |
|
38% |
|
31% |
|
23% |
|
1% |
|
1% |
|
– |
|
– |
|
|
|
|
Nonstudentsc |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Cybercafé |
|
School |
|
Home |
|
Office |
|
Friend’s House |
|
Library |
|
Mobile Phone |
|
All Nonstudents |
|
|
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
> 500 |
9 |
2.8 |
3 |
7.5 |
6 |
3.7 |
1 |
1.8 |
– |
– |
– |
– |
2 |
1.5 |
21 |
3.8 |
501–1,000 |
32 |
9.9 |
5 |
12.5 |
13 |
7.9 |
3 |
5.3 |
1 |
7.7 |
1 |
16.7 |
6 |
4.5 |
53 |
9.5 |
1,001–1,500 |
98 |
30.3 |
10 |
25.0 |
25 |
15.2 |
13 |
22.8 |
7 |
53.8 |
1 |
16.7 |
31 |
23.1 |
143 |
25.7 |
1,501–2,000 |
98 |
30.3 |
13 |
32.5 |
40 |
24.4 |
19 |
33.3 |
4 |
30.8 |
2 |
33.3 |
47 |
35.1 |
164 |
29.4 |
2,001–3,000 |
62 |
19.2 |
6 |
15.0 |
55 |
33.5 |
13 |
22.8 |
– |
– |
2 |
33.3 |
38 |
28.4 |
121 |
21.7 |
3,001–5,000 |
15 |
4.6 |
3 |
7.5 |
15 |
9.1 |
6 |
10.5 |
1 |
7.7 |
– |
– |
10 |
7.5 |
38 |
6.8 |
5,001–8,000 |
2 |
0.6 |
– |
– |
7 |
4.3 |
2 |
3.5 |
– |
– |
– |
– |
– |
– |
9 |
1.6 |
> 8,000 |
7 |
2.2 |
– |
– |
3 |
1.8 |
– |
– |
– |
– |
– |
– |
– |
– |
8 |
1.4 |
Subtotal |
323 |
100 |
40 |
100 |
164 |
100 |
57 |
100 |
13 |
100 |
6 |
100 |
134 |
100 |
557 |
100 |
|
44% |
|
5% |
|
22% |
|
8% |
|
2% |
|
1% |
|
18 |
|
|
|
aA user may have more than one place of access, and some users will appear in more than one place of access category.
bStudents were asked to report their cost of living expenses as income.
cThe classification nonstudent refers to occupation. There are probably some part-time students among nonstudents.
Are There Differences in Internet User Goals by Place of Access? (H1d, H1e, & H1f) Using the Internet is not the same as using an Internet café, but Internet use is central to the experience of using an Internet café. Yet Internet café venues in China are singled out for closure or regulation. From a user perspective, do the two activities differ?
We identify common places of access in our sample and examine whether users’ SGs vary according to place of access and whether differences in goal achievement are linked to place of access. Our tests take into account that goal choice is affected by demographic differences (Massey, Gebhardt, and Garnefski 2008; Nurmi 1991).
H1d. There are no major differences in situational goal content between Internet café users and users who access the Internet primarily from other venues.
H1e. There are no major differences in situational goal achievement between Internet café users and users who primarily use other venues.
The data collected should also help assess whether users perceive benefits—over and above the advantages associated with access to computers and the Internet—in socializing with other Internet café patrons.
H1f. Benefits associated with socializing at cafés are not significant.
Are User Objectives Different from Those of Nonusers? (H2a & H2b) Given the large differences in the makeup of sample users and nonusers (tables 4.2, 4.10, and 4.11), and that demographic differences are frequently associated with differences in goal content (Massey, Gebhardt, and Garnefski 2008), we expect life goals to vary according to demographics (H2a) but not otherwise (H2b).
H2a. There are significant differences in the life goals of users and nonusers, and these can be largely attributed to demographic differences, especially age, gender, urban-rural (Hokuo) status, and possibly also occupation (student status).
H2b. Holding demographic features constant, there are no major differences in the life goals of users and nonusers.
Is Life Goal Achievement Affected by Internet Café Use? (H3) To test the two contrasting views of Internet cafés, as places where youth are either corrupted and harmed or changed for the better, we propose H3.
H3. For demographically similar cohorts, users and nonusers of Internet cafés will report similar life goal achievement.
The 2010 Network World League (TXWM) Cybercafé Survey focuses on user activities. In response to the question, “What is your one main purpose while in a cybercafé?”, 55 percent of the users surveyed indicated games, 10 percent downloading resources, 9 percent information search, 8 percent watching movies or listening to music, 7 percent chatting and making friends, 0.7 percent shopping, 0.7 percent email, and 5 percent other activities.
The activities sample users engage in are consistent with the findings of the Network World League Cybercafé Survey, but notice the difference that question wording makes. When users are asked to name their one single purpose for visiting cybercafés, gaming takes top billing, whereas chatting, making friends, and email appear insignificant; but when users are asked to identify all their activities, communication—mainly through chatting but also email—is far more prominent. Gamers tend to be very committed: nearly 49 percent of sample users play games “every time” or “most times.” But chatting is even more popular, with 59 percent engaging in this activity just as frequently (table 4.13).
When people visit cybercafés, they engage in not just one activity (of the ten choices offered) but in about 2.8 activities per person either every time or most times, and 4.4 activities if you include activities that users engage in sometimes or more frequently (table 4.14). Understanding variety in cybercafé use is important. It is even more important to understand user motives.
User Objectives (H1a) Users pursue multiple objectives with varying degrees of interest when they visit Internet cafés. On average, each user selected 8.4 SGs, and about 57 percent identified with five or more SGs for using Internet cafés. Table 4.B.1 in appendix 4.B lists the thirty SGs ranked by popularity among 935 users with complete data. For each SG, the table gives average self-reported achievement, percentage of cybercafé time dedicated to pursuing each goal, and the goal’s classification according to SDT.
There is no reason to expect popular situational goals to be associated with higher achievement, but this correlation is clear from the data. The most popular SGs are also the ones that users spend the most time pursuing while visiting an Internet café, and this probably accounts for the high correlation between goal achievement and goal popularity. On a scale from 3 (full achievement) to 0 (no achievement), the average rating for the top twelve goals ranged from 1.5 to 2.1, compared with a range of 1.0 to 1.5 for the least popular twelve.
Table 4.13
Frequency with Which Users Engage in One of Ten Common Internet Activities When They Visit Internet Cafés
|
Every Time |
Most Times |
Sometimes |
Seldom |
Seldom or More Seldom Frequently |
|
|
(a) |
(b) |
(c) |
(d) |
(a + b + c + d) |
Never |
Activity |
% |
% |
% |
% |
% |
% |
Send and receive email |
6.4 |
7.0 |
13.9 |
27.7 |
55.0 |
45.1 |
Chat |
26.1 |
32.8 |
19.7 |
10.9 |
89.4 |
10.7 |
Browse the web, surf the Internet |
15.3 |
24.4 |
23.2 |
13.7 |
76.6 |
23.5 |
Write blog |
2.6 |
4.6 |
10.6 |
20.1 |
37.8 |
62.3 |
Social networking (e.g., Happy Network) |
4.4 |
8.5 |
12.1 |
23.2 |
48.2 |
51.9 |
Watch movies or TV |
10.2 |
19.4 |
29.1 |
18.5 |
77.1 |
23.0 |
Play online games |
23.5 |
25.1 |
16.1 |
13.8 |
78.6 |
21.5 |
Download and listen to music |
9.0 |
16.5 |
29.0 |
21.9 |
76.5 |
23.6 |
Watch current events |
8.5 |
11.3 |
19.1 |
29.4 |
68.3 |
31.8 |
Shop on the web |
1.4 |
1.8 |
7.4 |
20.1 |
30.8 |
69.3 |
Note: One respondent did not engage in any of the ten activities. Accordingly, 975 observations were used to calculate percentages.
Table 4.14
Distribution of Users According to the Number of Activities They Engage in When They Visit Internet Cafés, by Frequency of Engagement
|
Engagement Frequency |
|
|
|
|
|
|
Number of Activities Engaged in |
Every Time |
Most of the Time |
Sometimes |
Occasionally |
At Least Sometimes |
At Least Occasionally |
Either Every Time or Most of the Time |
1 |
260 |
272 |
225 |
205 |
76 |
37 |
232 |
2 |
111 |
208 |
198 |
210 |
110 |
56 |
243 |
3 |
62 |
126 |
153 |
169 |
161 |
76 |
192 |
4 |
38 |
61 |
84 |
94 |
161 |
78 |
133 |
5 |
19 |
15 |
32 |
41 |
178 |
87 |
57 |
6 |
5 |
8 |
14 |
19 |
120 |
99 |
29 |
7 |
2 |
4 |
5 |
6 |
93 |
159 |
14 |
8 |
5 |
2 |
3 |
6 |
33 |
153 |
8 |
9 |
3 |
– |
2 |
3 |
18 |
131 |
4 |
10 |
2 |
– |
2 |
– |
17 |
99 |
4 |
11 |
– |
– |
– |
– |
– |
– |
– |
≥12 |
– |
– |
– |
– |
– |
– |
– |
Number of users |
507 |
696 |
718 |
753 |
967 |
975 |
916 |
As % of respondents |
52.0 |
71.4 |
73.6 |
77.2 |
99.2 |
100.0 |
93.9 |
Av. no. of activities per engagement level |
2.1 |
2.1 |
2.4 |
5.9 |
4.4 |
10.3 |
2.8 |
The four extrinsic SGs are among the twelve least popular SGs. All of the top twelve SGs are intrinsic, except for one left unclassified (SG11), “Access information (news, weather forecasts, stock info, sports, gossip, etc.).” This is consistent with Kasser’s (2002) proposition that, “on average, people are more oriented toward intrinsic values than toward extrinsic values” (p. 31).
As expected by H1a, some of the twelve most popular goals are instrumental goals valued by society (table 4.B.1)—for example, keeping in touch with family and friends (SG13, a goal of 64 percent of users), accessing information (SG11, 51 percent), meeting new friends or finding a mate or companion (SG12, 36 percent), learning to use computers and the Internet (SG4, 36 percent), improving job skills (SG2,30 percent), and improving school performance (SG1, 29 percent).
Next, consider the top twelve situational goals of young urban students less than 19 years old, a cohort of special interest to China’s policymakers (table 4.15). The main difference between this group and the entire sample of users is the higher popularity among youngsters of entertaining themselves and having fun, making new friends, socializing in the venue, and improving computer and Internet skills and school performance, and the somewhat lower popularity of enhancing occupational skills. Notice in particular that SG1, “Improve my performance in school,” is an objective for using Internet cafés of 29 percent of all users but of 41 to 43 percent of young urban student users.
The top twelve SGs of the 255 women users interviewed (calculations not shown) coincide with those of the whole sample, but women’s most popular SG is keeping in touch with family and friends (SG13, 72 percent, compared with 64 percent for the whole user sample), and a greater proportion of women (34 percent, compared with 30 percent for all users) have blogging as an objective (SG23). Among urban student users, blogging is a situational objective of 57 percent of females and 33 percent of males. Rural users’ top twelve include eleven of the overall top twelve, but “Increase self-confidence” (SG9) was selected by 31 percent (compared with 23 percent for all users), replacing SG5 on their “top twelve” list.
ICT Training and User Experience (H1b) and Achievement (H1c) When it came to learning how to use computers, Internet cafés and schools were almost equally important (38 and 37 percent, respectively), but Internet cafés were more important when it came to learning how to use the Internet (52 percent for cafés vs. 24 percent for schools) (table 4.16). These proportions vary for females: more women in our user sample learned to use computers in schools (43 percent) than in cafés (27 percent), but female users more frequently learned how to use the Internet in cafés (45 percent) than in schools (28 percent). (Calculations are not shown.) It should be noted that because our survey sampled café users, our figures will necessarily overestimate the overall significance of these venues as places for learning computer and Internet use among the ICT user population. But because nearly one-fifth of China’s Internet users connect at Internet cafés, these venues are an important place where people are first introduced to ICT. Many users may first learn to use ICT at Internet cafés and afterwards buy their own computer to connect from home.
Table 4.15
Proportion of Users Selecting Overall Top-12 Situational Goals (SGs)–All Users and Young Urban Student Male and Female Subsamples
|
|
% in Young (< 19) Urban Student Subsamples |
|
12 Select SGs |
% in All Users sample |
Male |
Femaleb |
Entertainment (SG15, A) a |
74 |
81 |
82 |
Keep in touch with family and friends (SG13, R) |
64 |
71 |
75 |
Access information (news, … gossip, etc.) (SG11, U) |
51 |
49 |
50 |
Relax, relieve tension (SG17, A) |
43 |
40 |
36 |
Meet new friends or a mate or companion (SG12, R) |
36 |
46 |
54 |
Learn to use computers and the Internet (SG4, C) |
36 |
41 |
50 |
Socialize and make friends with people in Internet cafés (SG14, R) |
33 |
38 |
46 |
Spend time on a hobby or pastime (SG16, A) |
33 |
41 |
46 |
Contribute to other people’s blogs (SG23, R) |
30 |
33 |
57 |
Improve my job skills to work better (SG2, C) |
30 |
25 |
25 |
Improve my performance in school (SG1, C) |
29 |
41 |
43 |
Complete work (SG5, C) |
28 |
16 |
29 |
# of observations in sample/subsample |
963 |
63 |
28 |
Average number of significant goals |
8.4 |
8.1 |
11.4 |
aSDT classification: A: Autonomy, C: Competence, R: Relatedness, U: Unclassified.
bThe larger number of goals of young female students in part accounts for the higher percentage observed for their SGs.
Table 4.16
Response to “Where Did You Learn How to Use …”
|
Computers? |
|
The Internet? |
|
|
# |
% |
# |
% |
Internet café |
371 |
38 |
503 |
52 |
School |
358 |
37 |
238 |
24 |
Home |
175 |
18 |
177 |
18 |
Work |
39 |
4 |
27 |
3 |
Friend’s house |
31 |
3 |
27 |
3 |
Library |
2 |
0.2 |
3 |
0.3 |
Other venues |
– |
– |
1 |
0.1 |
Total |
976 |
100 |
976 |
100 |
Given the rapid growth of Internet café use in China, we expected to survey many novice users. In practice, we sampled well-established user populations, and, after discarding respondents with incomplete data, only eighteen users had less than one year of experience, and another thirty-six had between one and two years. To test H1b, we defined “inexperienced users” as those with less than two years of experience using the Internet. According to this definition, our sample had 52 inexperienced and 911 experienced users.
H1b was tested for the entire user sample and for two subsamples: young users 19 years or less and mature users older than 19 (table 4.17). As anticipated by H1b, the percentage of inexperienced users choosing computer and Internet training as a situational goal is statistically significantly higher than for experienced users. A large number of experienced users have such a goal (35 percent), even if it is not as popular as it is among users with two years of experience or less (52 percent). Within the two sub-samples, this goal is more popular among inexperienced than experienced users, but sample sizes are too small to assert statistical significance.
There is little evidence in support of H1c (i.e., the notion that experience [years using the Internet] improves users’ SG achievement). Further, none of the main demographic variables (gender, age, urban/rural classification, education, income, student/nonstudent status) are correlated with self-reported achievement of the twelve most popular SGs (table 4.18). Only effort, defined as the proportion of time users reportedly spent pursuing each goal when visiting an Internet café (i.e., less than 25 percent, 25–50 percent, 50–75 percent, or more than 75 percent), is significantly correlated with achievement in the case of ten of the top twelve SGs. The higher the effort, the higher the achievement.
Table 4.17
Test of H1b for Whole Sample and for Subsamples of Young Users and Mature Users
|
All Users |
|
Young Users (<19 yrs.) |
|
Mature Users (≥19 yrs.) |
|
|
Inexp. |
Exp. |
Inexp. |
Exp. |
Inexp. |
Exp. |
# of observations |
52 |
911 |
26 |
143 |
26 |
768 |
% selecting SG4 |
51.9 |
35.0 |
61.5 |
53.1 |
42.3 |
31.6 |
Z statistic |
–2.4714 |
|
–.7903 |
|
–1.1468 |
|
Probability that proportions are equal |
.0068 |
|
.2148 |
|
.1271 |
|
1. H1b. The proportion of inexperienced Internet users having computer and Internet training as a situational goal (SG4 in table 4.1) is larger than for experienced users.
2. Calculations based on Mendenhall (1975).
Many institutions conduct formal computer training programs, but in Internet cafés, self-training is the rule. Self-training takes place at the user’s own initiative and pace, with occasional help from café operators but mainly from peers, and would thus help satisfy user needs for autonomy and relatedness. To the extent that training is effective, which largely depends on personal effort, it would also satisfy the user’s need for competence.
Place of Access and SG Achievement (H1d & H1e) Other venues used predominantly by some café users are the home, the school, and, among those who chose the goal “Shop online …” (SG24), mobile phones (table 4.19).
As hypothesized by H1d, there are no major differences in the content of situational goals of predominant café users and predominant users of other venues. The two groups share the same four top SGs, all of which were chosen by at least 44 percent of both groups (table 4.20). The next six most popular SGs of predominant Internet café users were chosen by at least 30 percent in this group, as well as by at least 30 percent of café users who are predominant users of other venues (table 4.20).
As foreseen by H1e, there are no big differences in SG achievement between these two groups (table 4.20). There are, however, four SGs for which statistically significant differences in achievement are observed. For SG15, “Entertainment (play games, listen to music, watch movies, online video, etc.),” predominant users of Internet cafés report higher achievement. In contrast, for SGs 5, 17, and 24, predominant users of other venues report statistically significant higher achievement.
In hindsight, these differences are reasonable. An Internet user primarily interested in entertainment does well to visit Internet cafés where he or she will find fast machines and high-resolution graphics. A user interested in relaxing, completing work, or getting better products online would best achieve these goals from home or school (if the option is available) or, in the case of getting better products, using a mobile phone.
Table 4.18
Correlation Coefficient for Achievement Level for Twelve Most Popular Situational Goals (SGs) and Demographic Variables and Time Dedicated to SGs
|
Male |
Age |
Urban |
Education |
Income |
Student |
Experience |
Café Time |
# |
Entertainment (play games, listen to music, etc.)—SG15 |
0.105 |
–0.076 |
0.019 |
–0.006 |
–0.034 |
0.020 |
–0.024 |
0.352** |
685 |
Keep in touch with family and friends (email, QQ etc.)—SG13 |
0.024 |
–0.065 |
0.018 |
0.069 |
–0.117 |
0.100 |
–0.108 |
0.287** |
585 |
Access information (news, sports, gossip, etc.)—SG11 |
0.028 |
0.081 |
0.091 |
0.182* |
0.039 |
–0.004 |
0.063 |
0.187* |
466 |
Relax, relieve tension—SG17 |
0.023 |
–0.004 |
0.090 |
0.133 |
–0.031 |
0.069 |
–0.004 |
0.119 |
393 |
Meet new friends or a mate or companion—SG12 |
–0.018 |
0.021 |
0.062 |
0.040 |
–0.037 |
0.029 |
–0.051 |
0.249** |
323 |
Learn to use computers and the Internet—SG4 |
–0.015 |
–0.018 |
0.086 |
–0.009 |
0.057 |
–0.066 |
–0.059 |
0.202** |
305 |
Socialize and make friends with people in Internet cafés—SG14 |
0.022 |
0.026 |
0.104 |
–0.007 |
–0.028 |
–0.016 |
0.023 |
0.197** |
293 |
Spend time on a hobby or pastime—SG16 |
0.122 |
–0.013 |
0.064 |
0.035 |
–0.080 |
0.060 |
–0.041 |
0.336** |
290 |
Improve my job skills to work better—SG2 |
0.051 |
0.032 |
–0.0005 |
0.076 |
0.109 |
–0.125 |
0.035 |
0.189* |
266 |
Contribute to other people’s web pages or blogs—SG23 |
0.087 |
0.012 |
0.073 |
0.128 |
–0.001 |
0.010 |
0.010 |
0.186* |
269 |
Improve my performance in school—SG1 |
0.077 |
0.083 |
0.009 |
0.172* |
0.044 |
0.059 |
0.021 |
0.159 |
263 |
Complete work—SG6 |
–0.144 |
0.105 |
0.130 |
0.218** |
0.135 |
–0.077 |
0.105 |
0.205** |
252 |
Notes: Higher achievement values are indicative of more complete achievement.
Dummy variables are used for “Male,” “Urban,” and “Student.”
Two-tailed statistical significance: *10%, **5%.
Table 4.19
Predominant Users of Other Venues: Distribution of Predominant Venue by Situational Goal Chosen
*Number in parentheses shows the order in which SG appears in questionnaire.
Predominant users of other venues are those who did not use Internet cafés “every time” or “most of the time” but instead reportedly used the other venues indicated “every time” or “most of the time.”
There were no predominant users of the other two venue categories offered in the questionnaire, namely, “office” and “friend’s house.”
Do Internet Café Users Benefit from Socializing in the Venue? (H1f) We examine this hypothesis through two questions in the user survey. First, users were asked to state directly their main reason for using Internet cafés. Their responses, tabulated in table 4.21, show that 30 percent of all users frequent cafés to work or be with friends and other people. This is a significant figure, although not as high as the 42 percent of respondents who visit cafés because they have no other option for using computers or the Internet. Better quality equipment was the reason chosen by 19 percent of respondents; only 3 percent of all users indicated getting help from venue staff as their primary reason for visiting cafés.
Different cohorts answered this first question differently. To work or be with friends was popular among urban women, 35 percent of whom chose this response. It was also chosen by 45 percent of café users who predominantly connected to the Internet from home. Thirty percent of this group also chose the availability of better equipment as a reason to use cafés. The group that appears to be most reliant on Internet cafés for access to computers and the Internet (56 percent) is rural residents.
Table 4.20
Predominant Users of Cybercafés or Other Venues: Percentage Choosing Various SGs and Average Achievement of Goals Chosen
Table 4.21
Main Reason for Visiting Internet Cafés
|
|
|
|
|
|
|
|
|
Predominant Users of: |
|
|
|
|
|
|
All |
|
Urban Male |
|
Urban Female |
|
Rural |
|
Internet Cafés |
|
Home |
|
Mobile |
|
Reason Given |
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
# |
% |
1: No other option for computer access |
251 |
25.7 |
132 |
23.0 |
41 |
19.4 |
78 |
41.1 |
143 |
36.1 |
12 |
6.7 |
10 |
29.4 |
2: No other option for Internet access |
157 |
16.1 |
90 |
15.7 |
39 |
18.5 |
28 |
14.7 |
71 |
17.9 |
16 |
9.0 |
7 |
20.6 |
3: To work or be with friends, other people |
295 |
30.2 |
169 |
29.4 |
75 |
35.5 |
51 |
26.8 |
80 |
20.2 |
81 |
45.5 |
8 |
23.5 |
4: To get help from venue staff |
28 |
2.9 |
17 |
3.0 |
4 |
1.9 |
7 |
3.7 |
7 |
1.8 |
6 |
3.4 |
1 |
2.9 |
5: Better equipment than home or work |
188 |
19.3 |
131 |
22.8 |
39 |
18.5 |
18 |
9.5 |
69 |
17.4 |
54 |
30.3 |
8 |
23.5 |
6: Other, please specify |
57 |
5.8 |
36 |
6.3 |
13 |
6.2 |
8 |
4.2 |
26 |
6.6 |
9 |
5.1 |
– |
– |
Total |
976 |
|
575 |
|
211 |
|
190 |
|
396 |
|
178 |
|
34 |
|
Note: A user is defined as a “predominant Internet café user” or “predominant home user” if he or she reports using one of these venues “most of the time” or “some of the time” and uses other venues less frequently (except for mobiles, which may be used as frequently). A “predominant mobile user” is one who connects by mobile “every time” or “most of the time” and more frequently than from elsewhere.
Second, we also included as a possible SG choice SG14, “Socialize and make friends with people in Internet cafés.” This is the seventh most popular SG, chosen by 33 percent of all users (appendix 4.B, table 4.B.1). It is also the ninth most popular goal among young male students (38 percent) and the eighth most popular among female students (46 percent) (appendix 4.B, table 4.B.2). It was selected as a goal for using cafés by 34 percent of predominant users of Internet cafés and in the same proportion by café users who predominantly use other venues (table 4.20).
Content (H2a & H2b) Notwithstanding major demographic differences and contrary to expectations (H2a), there is remarkable agreement in the LGs endorsed by users and nonusers (table 4.22).
Five LGs stand out as most popular (i.e., were chosen as either “most important” or “very important” by at least 30 percent of both users and nonusers).
|
% of Users |
% of Nonusers |
Learn more knowledge (LG1, I) |
53 |
44 |
Leisure, entertainment (LG15, I) |
46 |
32 |
Keep in touch with friends and family who don’t live nearby (LG14, I) |
40 |
45 |
Keep up to date (LG5, U) |
31 |
37 |
Get stable, high-paying job, better business opportunities (LG2, U) |
31 |
32 |
There is also agreement on three LGs not prioritized by at least 25 percent of respondents.
|
% of Users |
% of Nonusers |
Plan a trip (LG8, U) |
15 |
23 |
Art creation (fiction, poetry, art, music, etc.) (LG4, I) |
8 |
7 |
Participate in community or village activities (LG11, I) |
4 |
15 |
Three differences in LG priorities stand out. Improving the physical health of self or family was a high priority for 57 percent of nonusers but only 25 percent of users. Mental health (LG10) was a high priority for 36 percent of nonusers but only 17 percent of users. Being relaxed and relieving tension was a priority for 35 percent of users but only 19 percent of nonusers.
Table 4.22
Life Goals (LGs): Rank of LGs Chosen as Either “Most Important” or “Very Important,” Average Achievement, and Achievement Differences between Users and Nonusers
|
Users |
|
|
|
Nonusers |
|
|
|
|
|
|
|
|
# Obs. |
846 |
|
|
# Obs. |
927 |
|
|
|
|
|
|
|
# LGs |
4.4 |
Average Achieve.b |
|
# LGs |
4.9 |
Average Achieve. |
|
Difference in Achievement, t-test, and Stat. Significance |
|
|
|
LGs Chosen as Either “Most Important” or “Very Important”a |
Rank |
% |
Av. |
# |
Rank |
% |
Av. |
# |
Diff. |
P(t) |
Sig. |
|
Learn more knowledge (1, I) |
1 |
53 |
1.7 |
450 |
3 |
44 |
1.5 |
405 |
0.2 |
0.0001 |
** |
|
Leisure, entertainment (15, I) |
2 |
46 |
2.2 |
388 |
6 |
32 |
2.1 |
294 |
0.1 |
0.0514 |
* |
|
Keep in touch with friends and family who don’t live nearby (14, I) |
3 |
40 |
2.1 |
341 |
2 |
45 |
2.1 |
417 |
– |
0.4033 |
|
|
Relax, relieve tension (17, I) |
4 |
35 |
1.9 |
295 |
14 |
19 |
1.7 |
179 |
0.2 |
0.0029 |
** |
|
Get stable, high-paying job, better business opportunities (2, U) |
5 |
31 |
1.1 |
266 |
7 |
32 |
1.3 |
293 |
–0.1 |
0.1258 |
|
|
Keep up to date (5, U) |
6 |
31 |
1.9 |
261 |
4 |
37 |
2.1 |
341 |
–0.2 |
0.0017 |
** |
|
Spend time on a hobby or pastime (16, I) |
7 |
30 |
2.0 |
250 |
9 |
26 |
1.9 |
244 |
– |
0.4670 |
|
|
Self-realization, enhance self-confidence (3, I) |
8 |
28 |
1.7 |
239 |
11 |
25 |
1.5 |
228 |
0.2 |
0.0208 |
** |
|
Get together with friends (face to face) (13, I) |
9 |
26 |
1.9 |
219 |
12 |
23 |
2.0 |
214 |
–0.1 |
0.3612 |
|
|
Improve the physical health of myself or my family (9, I) |
10 |
25 |
1.8 |
212 |
1 |
57 |
2.0 |
531 |
–0.2 |
0.0001 |
** |
|
Look for and meet new friends or a mate or companion (12, I) |
11 |
24 |
1.8 |
202 |
15 |
17 |
1.7 |
162 |
0.1 |
0.2365 |
|
|
Get information on government policies, regulations, and services (6, E) |
12 |
17 |
1.8 |
147 |
10 |
26 |
1.7 |
238 |
0.1 |
0.5061 |
|
|
Improve the mental health of myself or my family (10, I) |
13 |
17 |
1.8 |
143 |
5 |
36 |
1.9 |
333 |
–0.2 |
0.0294 |
** |
|
Plan a trip (8, U) |
14 |
15 |
1.4 |
126 |
13 |
23 |
1.8 |
210 |
–0.4 |
0.0005 |
** |
|
Obtain better products and services at lower cost (7, E) |
15 |
14 |
1.8 |
118 |
8 |
27 |
1.9 |
251 |
–0.1 |
0.3876 |
|
|
Art creation (fiction, poetry, music, etc.) (4, I) |
16 |
8 |
1.4 |
67 |
17 |
7 |
1.5 |
68 |
–0.1 |
0.7084 |
|
|
Participate in community or village activities (11, I) |
17 |
4 |
1.7 |
36 |
16 |
15 |
2.0 |
139 |
–0.3 |
0.0655 |
* |
|
Other (18, U) |
18 |
1 |
2.1 |
9 |
18 |
1 |
1.5 |
6 |
0.6 |
0.3111 |
|
aThe first number in parenthesis indicates LG identifying number; the second number stands for goal classification according to SDT: I = intrinsic goal, E = extrinsic goal, U = unclassified.
bFull achievement = 3, No achievement = 0.
Shading: Unshaded LGs are those that are popular with 30% or more of the user or nonuser samples. Dark shaded LGs are those popular with fewer than 25% of user or nonuser samples. Light shaded LGs are those popular with 30 to 25% of user or nonuser samples.
As anticipated by H2b, when the comparison is between similar cohorts, salient differences in goal priorities dissipate—for example, in our priority policy group, urban male students less than 19 years old, a subsample comprised of 39 users and 30 nonusers. The two LGs rated “most important” by this group were “Learn more knowledge” (LG1), chosen by 64 percent of users and 60 percent of nonusers, and “Leisure, entertainment” (LG15), selected by 33 percent of users and 40 percent of nonusers (table 4.23).14
Achievement (H3) To test H3, we focus on the top five LGs of users and the top five of nonusers (table 4.24). Because two goals are among the top five of both groups, a total of eight LGs are considered.15
Scholars have argued that the Internet has radically improved our ability to learn on our own (Hiemstra 2006), find information to improve our health (Boase et al. 2006), communicate with family and friends (Wang and Wellman 2010), entertain ourselves (Duffy, Liying, and Ong 2010), and relax (Russoniello, O’Brien, and Parks 2009). Choi et al. (2004) found “stay informed about what is going on” and “get up-to-date information” to be important components of a general motivation to use the Internet for “Surveillance Information Seeking,” a motivation that in practice might confer an advantage to Internet users over nonusers.
Do the purported positive impacts of the Internet carry over to Internet café use? Do these positive impacts make a difference in user perceptions of life goal achievement? If they do, the performance of café users in their achievement of LGs 1, 5, 9, 10, 14, 15, and 17 would be enhanced, in contradiction of the negative view of cafés common in Chinese media (box 4.1). We have no expectations regarding LG 2, but because it is also a top LG, we also subject it to scrutiny.
H3 was refined and tested as follows:
H3. For similar demographic cohorts, users will report the same levels of achievement of LGs 1, 5, 9, 10, 14, 15, and 17 as nonusers.
Table 4.22 reports on tests—over the whole sample—of differences in mean reported achievement of users versus nonusers. Those tests suggest that Internet café use is associated with higher achievement of LG1, “Learn more knowledge,” LG15, “Leisure, entertainment,” and LG17, “Relax, relieve tension.” In contrast, lower user achievement appears associated with LG5, “Keep up to date,” and LG9 and LG10, “Improve the physical and mental health of myself or my family.” There are, however, confounding factors influencing these tests. For example, the higher popularity and achievement of LG9 and LG10 among nonusers might be linked to greater concern for health because nonusers are on average older than users.
Table 4.23
Young (< 19) Urban Male Student Users and Nonusers: Percentage Choosing Various Life Goals (LGs) as Most Important
|
Users |
|
Nonusers |
|
|
# Obs. |
39 |
# Obs. |
30 |
|
Av # LGs |
2.9 |
Av # LGs |
2.5 |
LG Chosen as Most Important |
Rank |
% |
Rank |
% |
Learn more knowledge (1) |
1 |
64.1 |
1 |
60.0 |
Spend time on a hobby or pastime (16) |
2 |
33.3 |
2 |
40.0 |
Leisure, entertainment (15) |
3 |
30.8 |
10 |
13.3 |
Relax, relieve tension (17) |
4 |
28.2 |
12 |
10.0 |
Keep in touch with friends and family who don’t live nearby (14) |
5 |
23.1 |
9 |
13.3 |
Self-realization, enhance self-confidence (3) |
6 |
20.5 |
3 |
23.3 |
Look for and meet new friends or a mate or companion (12) |
7 |
17.9 |
11 |
10.0 |
Keep up to date (5) |
8 |
15.4 |
7 |
13.3 |
Improve the physical health of myself or my family (9) |
8 |
15.4 |
5 |
16.7 |
Get together with friends (face to face) (13) |
8 |
15.4 |
13 |
6.7 |
Obtain better products and services at lower cost (7) |
11 |
10.3 |
8 |
13.3 |
Improve the mental health of myself or my family (10) |
11 |
10.3 |
6 |
16.7 |
Get information on government policies, regulations, and services (6) |
13 |
5.1 |
– |
– |
Get stable, high-paying job, better business opportunities (2) |
14 |
2.6 |
– |
– |
Art creation (fiction, poetry, music, etc.) (4) |
14 |
2.6 |
– |
– |
Plan a (leisure) trip (8) |
– |
– |
4 |
16.7 |
Participate in community or village activities (11) |
– |
– |
– |
– |
Other (18) |
– |
– |
– |
– |
Table 4.24
Five Most Popular Life Goals (LGs) of Users and Nonusers
|
Users |
|
Nonusers |
|
Popular LGs |
Rank |
# |
Rank |
# |
Learn more knowledge (1, I) |
1 |
450 |
3 |
405 |
Leisure, entertainment (15, I) |
2 |
388 |
6 |
294 |
Keep in touch with friends and family who don’t live nearby (14, I) |
3 |
341 |
2 |
417 |
Relax, relieve tension (17, I) |
4 |
295 |
14 |
179 |
Get stable, high paying job, better business opport. (2, U) |
5 |
266 |
7 |
293 |
Keep up to date (5, U) |
6 |
261 |
4 |
341 |
Improve the physical health of myself or my family (9, I) |
10 |
212 |
1 |
531 |
Improve the mental health of myself or my family (10, I) |
13 |
143 |
5 |
333 |
In parentheses: number showing position of LG in the questionnaire and its SDT classification (see table 4.1). See also table 4.22.
To distinguish the effect of Internet café use from that of other factors, we ran regressions on perceived achievement for each of the eight top goals, using as independent variables user/nonuser status, HuKuo residency status (urban/rural), student/nonstudent status, income, education level, age (in logarithmic form), gender, years of experience using the Internet, relative importance of the LG, and Internet overuse. We also included two control variables: the total number of life goals chosen and the relative importance of each goal. Notice that the user, over-user, and experience variables need to be considered jointly because a nonuser cannot have experience with or overuse the Internet.
Regressions were run for the whole sample and for key subsamples (listed in appendix 4.C, table 4.C.1). Results are reported in appendix 4.C, tables 4.C.2 through 4.C.9, and summarily depicted in table 4.25.
Regression analysis confirms the positive relationships between achievement and Internet café use for LG1, LG15, and LG17, previously identified through tests of differences in mean achievement (table 4.22), but makes evident the need to take demographic differences into consideration.16 Regression analysis lends support to H3 in the case of four of the seven life goals for which we anticipated a positive effect: LG1, LG14, LG15, and LG17. Nevertheless, the effect of café use on achievement varies depending on LG, gender, age, student occupational status, and HuKuo status.17
• Urban male Internet café users less than 35 years old and urban male and female students report statistically significant higher achievement than nonusers for LG1, “Learning more knowledge.”
• Urban male users less than 35 years old and female urban student users also report higher achievement than nonusers for LG15, “Leisure, entertainment.”
• Urban female users report higher achievement for LG14, “Keep in touch with friends and family who don’t live nearby,” and LG17, “Relax, relieve tension.”
• As users gain experience using the technology, part of the “enthusiasm” in their sense of accomplishment appears to wane. Achievement reports become lower with Internet use experience, with statistical significance in the case of LG14 (“Keep in touch with friends and family who don’t live nearby”) by urban females and of LG15 (“Leisure, entertainment”) by urban males and urban female students.
• Among urban male nonstudents, a group made up largely of workers, there is a negative relationship between Internet café use and perceived achievement of LG2, “Get stable, high-paying job, better business opportunities.” This troubling result deserves greater scrutiny in future studies. Male urban nonstudent users may be reporting lower achievement than the nonuser cohort because they are wasting time using the Internet, or it could be that those workers who visit Internet cafés are dissatisfied with their work and want to relax or change their situation.
• Rural residents who are overusers of the Internet report higher achievement of LG2, “Get stable, high-paying job, better business opportunities,” than rural users and nonusers.
• Among rural residents, Internet café use is associated not with higher but with lower achievement of LG10, “Improve the mental health of myself or my family.” Rural Internet overusers’ achievement perceptions of LG10 are higher than those of users but still lower than those of nonusers.
• Urban male Internet overusers less than 35 years old and urban male overusers who are not students report lower achievement of LG5, “Keep up to date,” than users and nonusers.
An extensive discussion of the effect of demographic variables on goal achievement is beyond the scope of this study. These variables were included in the regressions to avoid biasing tests of hypotheses. The LGs in the surveys are limited to those that could be linked with Internet café use, whereas in practice, people may have other aspirations and express them differently. For completeness, we briefly highlight the most salient links between demographic variables and goal achievement.
• Rural residents report lower achievement than urban residents regarding LG1, “Learn more knowledge,” and LG5, “Keep up to date” (tables 4.C.2 and 4.C.4).18
• Notable gender differences are a higher sense of achievement by males regarding LG5, “Keep up to date,” and LG15, “Leisure, entertainment” (tables 4.C.4 and 4.C.8).
• Students in general (users and nonusers) perceive higher achievement than nonstudents for LG1, “Learn more knowledge,” and LG2, “Get stable, high-paying job, better business opportunities” (tables 4.C.2 and 4.C.3) but lower achievement for LG15, “Leisure, entertainment” and LG5, “Keep up to date” (tables 4.C.8 and 4.C.4).
• As urban women age, their sense of achievement is higher for LG2, “Get stable, high-paying job, better business opportunities” (table 4.C.3), but lower for LG14, “Keep in touch with friends and family who don’t live nearby” (table 4.C.7), and LG15, “Leisure, entertainment.” Reported achievement of urban males decreases with age for LG17, “Relax, relieve tension” (table 4.C.9). Among urban males less than 35 years old, reported achievement increases with age for LG1, “Learn more knowledge” (table 4.C.2). Among rural residents, perceived achievement decreases with age for LG10, “Improve the mental health of myself or my family” (table 4.C.6).
• Respondents with higher levels of education report lower achievement for LG9, “Improve the physical health of myself and my family” (table 4.C.5), and, in the case of urban males less than 35 years old, for LG1, “Learn more knowledge” (table 4.C.2).
• A respondent’s income affects reported achievement: positively for LG1 and LG2 (tables 4.C.2 and 4.C.5), but negatively in the case of male urban students for LG15 (table 4.C.8).
There is no reason to expect a priori that users will outperform nonusers with respect to LG achievement. People pursuing intrinsic goals are generally more effective at achieving their objectives (Bargh, Gollwitzer, and Oettingen 2010), but our achievement comparisons are across goals shared by users and nonusers. Using the Internet or an Internet café is not indispensable for satisfying basic psychological needs. The goal of acquiring more knowledge could perhaps be satisfied by reading books or attending lectures or cultural activities. The Internet is an attractive entertainment medium as well as a way to relax, but a young man might be able to satisfy his psychological needs in other ways (e.g., board games, dancing, or sports).
If nonusers can in principle accomplish these LGs through other activities, why do they report lower achievement than café users do?
We cannot rule out that the environment where interviews took place (i.e., an Internet café) affected perceptions of achievement. We cannot claim, for example, that young males and student female users outperform nonusers in school. To do so would require a comparison of actual school performance not self-reports of achievement.
Table 4.25
Statistically Significant Relationships Observed between Internet Café Use on Reported Life Goal (LG) Achievement, by Life Goal and Subsample
|
LG1 |
LG2 |
LG5 |
LG9 |
LG10 |
LG14 |
LG15 |
LG17 |
All |
o+ |
|
o- |
|
|
+e- |
+e- |
+ |
Urban |
+ |
|
o- |
|
|
|
+e- |
+ |
Urban males |
|
|
|
|
|
|
|
|
Urban males < 35 yrs. old |
+ |
|
o- |
|
|
|
+e- |
|
Urban male nonstudents |
|
- |
e-o- |
|
|
|
|
|
Urban male students |
+o+ |
|
|
|
|
|
|
|
Urban females |
|
|
|
|
|
+e- |
|
+ |
Urban female students |
+ |
|
|
|
|
|
+e- |
|
Rural |
|
o+ |
|
|
-o+ |
+e- |
|
|
+ |
Positive relationship between Internet café use and achievement |
|||||||
– |
Negative relationship between Internet café use and achievement |
|||||||
e+ |
Positive relationship between Internet use experience and achievement |
|||||||
e- |
Negative relationship between Internet use experience and achievement |
|||||||
o+ |
Positive relationship between Internet overuse and achievement |
|||||||
o- |
Negative relationship between Internet overuse and achievement |
|||||||
LG1 |
Learn more knowledge (Intrinsic) |
|||||||
LG2 |
Get a stable, high-paying job, better business opportunities (Unclassified) |
|||||||
LG5 |
Keep up to date (Unclassified) |
|||||||
LG9 |
Improve the physical health of myself or my family (Intrinsic) |
|||||||
LG10 |
Improve the mental health of myself or my family (Intrinsic) |
|||||||
LG14 |
Keep in touch with friends and family who don’t live nearby (Intrinsic) |
|||||||
LG15 |
Leisure, entertainment (Intrinsic) |
|||||||
LG17 |
Relax, relieve tension (Intrinsic) |
We suspect that goal contagion (Aarts and Hassin 2005; Loersch et al. 2008) is playing a part, the goal in this case being to visit Internet cafés. Users perceive cafés as places that help them achieve some of their intrinsic SGs and LGs. Regular café users may be playing an informational role among peers, priming synchronistic behavior by first-time users, and this priming is likely to be particularly effective given the intrinsic nature of most Internet use SGs (Friedman et al. 2010). Once novice users become familiar with the technology, the enhanced perception of achievement of self-endorsed intrinsic LGs is bound to become a powerful autonomous motivational force. The more a person satisfies his or her psychological needs and feels self-determined by going to an Internet café, the more inclined he or she will be to return.
Goal contagion may explain the popularity of Internet cafés, but the question remains: why the difference in achievement? If Internet cafés help users achieve their LGs, why is it that nonusers do not join in? In fact, most of them do. Only about 32 percent of China’s young (under age 30) urban population does not use the Internet (table 4.3). In China, the Internet is very much part of the youth culture, and evidence suggests that young people view Internet cafés as places where they can have fun and accomplish goals they cherish.
Not everyone has to use the Internet to achieve self-determination and thrive. Some young (under age 30) urban nonusers gave autonomous reasons for not using the Internet—for example, 23 percent had no need for the Internet, and 39 percent had no time (table 4.4). However, it is important to bear in mind that these responses come from a group that represents only about one-third of China’s urban youth.
Users engage in many activities and pursue multiple objectives when they visit China’s Internet cafés. They entertain themselves (SG15, chosen by 73 percent of users), keep in touch with friends and family (SG13, 63 percent), become better informed (SG11, 51 percent), relax and relieve tension (SG17, 42 percent), and meet new friends or a mate or companion (SG12, 35 percent). Considering that urban youths (especially males) are the dominant user group, this list of goals is hardly surprising.
Internet cafés are often people’s first place of contact with ICT. More café users learned how to use computers and the Internet in cafés than in any other type of venue. We expected the desire to learn computer skills (SG4) to emerge as important among new users, and this is the case for about half of inexperienced users, but one-third of more experienced users also pursue this objective. Contrary to expectations, we found no evidence linking SG achievement with user experience. SG achievement is linked to the amount of time users spend pursing these goals when they visit cafés, but we found no evidence of a link between years of use and achievement.
Users’ goals for visiting cybercafés vary according to their gender, place of residence, working/student status, and stage in life. About 41 percent of adolescent students who visit cafés do so in part to improve their school performance (SG1), compared with only 28 percent for the user population as a whole.
We found no major differences in the goals pursued by predominant users of Internet cafés and café users who are predominant users of other venues, but we detected sensible differences in goal achievement between these two groups. Predominant users of cafés do better than predominant users of other venues with regard to SG15, “Leisure, entertainment,” whereas predominant users of other venues (mainly home and school and, in the case of SG24, mobiles) outperform café users when it comes to SG5, “Keep up to date,” SG17, “Relax, relieve tension,” and SG24, “Shop online or get product information online.”
Most user motives for visiting cafés are reasonable, socially valuable, and common among young people (Choi et al. 2004; Gabrielsen, Ulleberg, and Watten 2012). The classification of user SGs according to SDT criteria suggests that these goals are pursued because they help users fulfill important psychological needs for autonomy, competence, and relatedness.
There is considerable overlap in the LG choices of users and nonusers, particularly for cohorts with similar demographics. For the whole sample, LG1, “Learn more knowledge,” is the most popular goal among both users (53 percent) and nonusers (44 percent). Among young urban male students, LG1 is also the most popular LG of both groups, with a high proportion of both users (64 percent) and nonusers (60 percent) selecting this LG.
Internet café users report higher achievement than nonusers in their pursuit of some LGs. Young (under age 35) male users and male and female student users reportedly “learn more knowledge” (LG1) than nonusers; young male users and female student users report higher achievement than nonusers for LG15, “Leisure, entertainment”; and urban female users report higher achievement than nonusers for LG14, “Keep in touch with friends and family who don’t live nearby,” and LG17, “Relax, relieve tension.” Some of these achievement advantages wane with age and Internet use experience, but findings that some users outperform nonusers in achievement perceptions of four intrinsic LGs (LG1, LG14, LG15, and LG17) suggest that nonusers who do not visit Internet cafés are missing out by not engaging in activities that facilitate the satisfaction of human needs.
We also detected some negative effects associated with Internet café use. Among the urban working (nonstudent) subsample, nonusers reported higher achievement than users for LG5, “Get stable, high-paying job, better business opportunities.” We cannot tell, however, if users are reporting lower achievement than nonusers because they are wasting time using the Internet or because the workers who visit Internet cafés are those who are dissatisfied with their present work situation. Among rural users, lower perceptions of achievement were reported for LG10, “Improve the mental health of myself or my family.” Our rural sample was too small to venture an interpretation, but this finding deserves further scrutiny, perhaps through a study exclusively focused on rural residents using urban cafés. In the few instances of overuse observed in our sample, overuse also adversely affected achievement of LG5, “Keep up to date,” mainly among young urban males under age 35, particularly nonstudents.
User perceptions of the impact of Internet café use and media accounts stigmatizing these facilities are difficult to reconcile. The two views are separated not just by different perceptions but by age, limited experience with Internet use on the part of mature adults, and differing concerns, each appropriate to people at different life stages. To help bridge these divides, government may want to consider fostering greater diversity in the customer base of Internet cafés, which would encourage the pursuit of a broader range of LGs. We also hypothesize that diversity would be conducive to informed understanding on the part of parents, authorities, and the media of the value of Internet cafés; improved rapport among authorities, users, and Internet café operators; and café environments perceived by parents as more wholesome and by users as more supportive of their self-determination.
Many ways to promote diversity in café use can be envisaged. We outline one possible approach:
• Diversity in café use could be promoted as part of government’s Internet development strategy. China’s Internet development frontier is largely rural, but even in urban areas, there is room for expansion, particularly among older (over age 30) urban nonusers, who represent 27 percent of China’s nonusers overall and 71 percent of its urban nonusers (table 4.3). A few respondents in this group indicated they had no need (26 percent) or no time (18 percent) to use the Internet, but the most common reason given (47 percent) was lack of skills (table 4.4).
• Fear of technology is common among mature adult (over age 30) nonusers,19 and overcoming that fear often requires the implementation of digital literacy programs. That a significant proportion of the urban nonuser population does not use the technology because of a lack of skills represents an opening that government and café operators could seize to expand Internet use in a supportive environment—for example, through an adult literacy education program imparted in urban cybercafés and implemented in partnership with operators. Such a program could begin with a small pilot focused on the mature adult urban population, especially women, and local authorities. It would also help foster diversity in Internet café use.
Notwithstanding the importance that government assigns to Internet café policy, there does not seem to be much open scientific discussion of the underlying issues. China is rapidly becoming a modern society but appears to be relying primarily on control techniques that go counter to what we scientifically know about human nature and well-being. The majority of respondents’ top-ranked goals are autonomousoriented, suggesting that ICT and cybercafé use is an autonomous-oriented activity for most users (and potentially also nonusers) in China. However, the policies to stymie and limit use of cybercafés in China are controlling and undermining of autonomous motivation and, therefore, threaten the psychological needs of users (and, by implication, their psychological well-being). Given the difficulties experienced with controlling regulatory policies, the time may be ripe to consider alternative strategies that support user self-control, help advance the country’s digital agenda, and facilitate self-determination and psychological well-being.
1. Figure 4.1 was constructed using CNNIC data on the total number of Internet users and the proportion of those users who access the Internet from home, Net bars, office, school, public places, or through their mobile phones. These data are available in China Internet Network Information Center (2007a, 2008, 2009, 2010, 2011a, 2012, 2013, 2014) reports published in January and based on surveys conducted in December of the previous year.
2. We cannot estimate the total number of PAV users by simply adding the number of users of Internet Cafés to the number of telecenter users because there is no way of determining how many Internet users use access the Internet using both modes.
3. The estimate of 136,000 Internet cafés in 2012 reported by Jou (2013) was produced by Tencent and covers only licensed cafés. Unlicensed cafés are not mentioned.
4. Xueqin (2009) gives an insider view of the difficulties experienced by the Chinese government in its attempts to regulate Internet cafés.
5. Self-determination theory has informed understanding of the motivation of users of social networks (Miller and Prior 2010), communities of practice (Palmisano 2009), virtual worlds (Verhagen et al. 2009), online learning (Hartnett, St. George, and Dron 2011), and video games (Ryan, Rigby, and Przybylski 2006; Wang et al. 2008; Rigby and Przybylski 2009; Przybylski et al. 2009; Przybylski, Ryan, and Rigby 2009, 2010); and of the effects of the Internet on adolescent video game overuse (Wan and Chiou 2007), personal relationships (Séguin-Lévesque et al. 2003), the pursuit of e-learning in schools (Sørebø et al. 2009) and the workplace (Roca and Gagné 2008) and, in combination with the theory of planned behavior, of the effectiveness of ICT skills training in rural public access centers in Thailand (Techatassanasoontorn and Tanvisuth 2008).
6. In low-wealth rural societies, where children represent an important resource for family survival, early economic independence is discouraged, whereas in high-income urban settings, independence and economic self-reliance are encouraged. Adolescents in both societies can satisfy their need for autonomy and relatedness: in an urban culture by internalizing the value of independence and living on their own early in life, and in an agrarian setting by deciding, of their own volition, to remain close to help and work alongside their family (Kagitcibasi 2005).
7. Kasser and Ryan (1993, 1996), Kasser et al. (2007), and Kasser (2011) show how capitalism undervalues basic needs satisfaction, especially as practiced in the United States and the UK, where financial rewards and prestige are often contingent on performance. For example, remuneration for long hours of work undermines a worker’s ability to spend time with family or on vacation with friends (adversely affecting relatedness). The limited participation of employees in decisions regarding work schedules and processes undermines worker autonomy. Similarly, militaristic dictatorships or very rigid religious traditions are prone to undermine citizen’s abilities to satisfy their need for autonomy.
Institutional pressures may also affect behavior and well-being in more confined spheres. The inordinate pressures that legal schools in the United States generate and the detrimental effects on students are well documented. Self-determination theory has shown how these pressures undermine basic needs satisfaction, intrinsic motivation, and well-being and shift the value orientation of students from goals such as helping the community toward extrinsic goals such as image and fame. Moreover, the specific approach that a school follows also affects behavior. Students at a school that is supportive of autonomy, gives students choices, and is focused on satisfying student concerns will perform better than students in an environment that is insensitive to their preferences (Sheldon and Krieger 2004, 2007).
8. According to Zhou, Ma, and Deci (2009):
People’s need for relatedness leads them to want, to some degree, to be dependent on trusted others rather than fully independent of them, but their need for autonomy also leads them to want to experience a sense of volition and choice about their dependence and their behavior. In eastern countries such as China which stress conformity, SDT maintains that it is the degree of subjective endorsement and ownership of the norms that determines whether the conformity constitutes authenticity and self-determination versus alienation and coercion. As a consequence, in the process of acting in accord with societal norms and expectations, one does not necessarily feel controlled in one’s actions (and hence might not experience low levels of self-determination).
9. Vallerand (2007) also speaks of an intermediate level of contextual goals that are defined over broad domains such as education, personal relations, and sport. Contextual goals were not considered in this study.
10. Goal classification is grounded on the work of Kasser and Ryan (1996), Sheldon et al. (2004), Grouzet et al. (2005), Vansteenkiste, Lens, and Deci (2006), Sebire, Standage, and Vansteenkiste (2008), and McLachlan and Hagger (2011).
The intrinsic nature of the health goal was confirmed through personal correspondence with Tim Kasser. The psychologist on our team introduced the link between psychological needs and SGs. Because LGs are more broadly defined and in some instances may be linked to more than one SG (see table 4.1), each associated with a different need, for LGs we kept the more broad classification between intrinsic and extrinsic that is widely used by SDT researchers—for example, by Kasser and Ryan (1996), Sheldon et al. (2004), and Grouzet et al. (2005).
11. According to Przybylski, Ryan, and Rigby (2010), “the industry started with games tailored to meet competence needs through games focused on challenges and goals to be mastered. Over time, video game developers have broadened game designs and environments to better meet the autonomy need by providing flexibility in goals, choice over strategies, and opportunities for action in novel environments. Along with expanding autonomy, games have also increasingly been apt at satisfying relatedness needs by providing opportunities for engaging in online interactions and communities.”
12. This goal appears in the revised version of the Aspirations Index first developed by Kasser and Ryan (1996). The scale and its description are available on the following website: http://faculty.knox.edu/tkasser/aspirations.html.
13. Young (1996) proposed to classify an Internet user as addicted if he or she answered yes to five or more of the following eight questions:
1. Do you feel preoccupied with the Internet (think about previous online activity or anticipate next online session)?
2. Do you feel the need to use the Internet with increasing amounts of time in order to achieve satisfaction?
3. Have you repeatedly made unsuccessful efforts to control, cut back, or stop Internet use?
4. Do you feel restless, moody, depressed, or irritable when attempting to cut down or stop Internet use?
5. Do you stay online longer than originally intended?
6. Have you jeopardized or risked the loss of a significant relationship, job, educational, or career opportunity because of the Internet?
7. Have you lied to family members, therapist, or others to conceal the extent of involvement with the Internet?
8. Do you use the Internet as a way of escaping from problems or relieving a dysphoric mood (e.g., feelings of helplessness, guilt, anxiety, depression)?
Our user survey includes the eight-item questionnaire proposed by Young (1996) (validated in Chinese by Cao et al. 2007) but allowed a graduated response to each question: 1. Usually; 2. Most times; 3. Sometimes; 4. Seldom; 5. Never. Our “overuser” variable assigns a value of 1 to respondents who answered 1, 2, or 3 (“Usually,” “Most times,” or “Sometimes”) to five or more of Young’s eight questions. This is a lax criterion that will tend to classify more respondents as “overusers” than Young would classify as “addicted.” A total of 104 overusers so defined were found in our sample.
Internet addiction in China is the subject of the next chapter in this book.
14. User and nonuser surveys gave interviewees the option to assign one of five levels of importance to the various LGs presented: “Most important,” “Very important,” “Important,” “Less important,” and “Not so important.” Percentages in table 4.22 and the regressions in appendix 4.C consider LGs that respondents identified as either “Most important” or “Very important.” Observations that identified LGs but gave no information on achievement were discarded. The average number of LGs chosen by the two groups and the number of observations for each choice group are given below.
|
Most Important |
Most or Very Important |
Total LGs Chosen |
Users |
1.2 |
3.9 |
7.4 |
Nonusers |
1.6 |
4.7 |
6.8 |
Number of Observations with At Least One LG Chosen as:
|
Most Important |
Most or Very Important |
Users: |
494 |
846 |
Nonusers: |
1.6 |
4.7 |
Many interviewees did not mark any LG as “most important,” whereas others chose more than one goal as “most important,” which suggests that the distinction between choosing a goal as “most important” or “very important” is highly subjective. Furthermore, in the regressions reported in appendix 4.C, we include a control independent variable identifying the choice as most important when this is the case.
When making comparisons among a small subsample, such as the young urban male student subsample in table 4.23, for the sake of precision, we focus on the choice of “most important” LG.
15. The focus on popular goals allows us to cover important goals but is also necessary given the large differences in user and nonuser demographics known to affect goal priorities. For example, there are no users older than 49 and very few nonusers younger than 19 years old, and the LGs and achievements of these two groups are bound to differ, making it difficult to disentangle the effects attributable to demographic differences from those that could be associated with Internet café use. By focusing on goals popular for both groups, we increase the number of overlapping user/nonuser observations and enhance our ability to make meaningful comparisons.
16. Apparently other factors lie behind the seemingly positive relationship (suggested by the results presented in table 4.22) between Internet use and achievement for LG5, “Keep up to date,” and LG9, “Improve the physical health of myself or my family,” while masking the positive relationship among urban females for LG14, “Keep in contact with friends and family who are not nearby,” and the negative relationship among urban working males for LG2, “Get stable, high-paying job, better business opportunities.”
17. We only point out differences found to be statistically significant, with at least a 10 percent probability that a regression coefficient is not equal to zero.
18. The top five LG priorities of rural users and the top five of rural nonusers are all in the list of eight top goals under consideration. Nevertheless, with few observations (table 4.2) on a heterogeneous group (tables 4.10 and 4.11), the rural subsample contains limited information to assess LG achievement differences within the group.
Notwithstanding these limitations, some differences in the effect of achievement variables stand out. High-income rural residents have a higher sense of achievement of LG1. Rural residents with higher incomes have a greater sense of achievement of LG2 as do Internet overusers. Being a user has a detrimental effect on the sense of achievement of mental health objectives (LG10), yet overusers apparently fare better than users. Internet users report higher achievement than nonusers with respect to LG14, “Keep in contact with friends and family who don’t live nearby.” This effect wears off with time: rural experienced Internet users report lower LG14 achievement than novice users. Better-educated rural users apparently have a higher sense of achievement of LG17, “Be relaxed, relieve tension.”
19. “Respondents told us that they were embarrassed about their lack of computer skills, and some were intimidated or worried that they would need to already know certain skills, like typing, before they could learn to use the Internet. A novice user we interviewed told the story of going to get his hair cut and being told by the woman behind the counter at the hair salon to sign in on a computer terminal in the waiting area. He was deeply embarrassed when he could barely figure out how to use the keyboard to punch in his name, and resolved then to come to the Community Technology Center in his apartment complex to learn to type, use computers and the Internet.” (Lenhart et al. 2003, p. 12)
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A multistaged cluster sampling method was used in the study (Babbie 2008). The target research population consisted of users of Internet cafés in China. The sampling frame of Internet cafés was provided by Hintsoft & Pubwin Media Corporation, currently the biggest Internet café administration system provider, with around 60 percent of Internet café market share in China.
In the first sampling stage, we randomly stratified select large urban communities and less densely populated areas as clusters according to the GDP levels of different cities and counties. In the second stage, we conducted simple random sampling from the list of all operating Internet cafés using the Hintsoft & Pubwin system for every selected area. Because there are no Internet cafés in China’s truly rural areas, we considered that rural residents could be reached in the county nearest to where they live. The distinction between rural and urban residents was determined by the subjects’ self-reports. Finally, twenty Internet cafés were randomly selected from ten cities and nearby counties. Users going to these Internet cafés on a specific survey day (or two days) were asked to answer the questionnaire. In each Internet café, about fifty users’ answers were collected.
The survey was administered by volunteers from a university: most were undergraduate students majoring in marketing science, while others were undergraduate students in other management-related majors. Two meetings were arranged before the survey was implemented. At the first meeting, held in July and attended by all the volunteer investigators, the project and the survey were explained, and the questionnaire and data input software were introduced and discussed. The second meeting was an online meeting held just before survey implementation. The final version of the questionnaire was distributed, and information and experiences from the pilot survey were discussed. After the investigators went into the field, they kept in touch with one of the research team members, who provided help and suggestions as and when they met difficulty in the field.
The questionnaire was piloted at a small Internet café near Zhongguancun Area, Beijing. Ten users answered the questionnaire; two of them self-reported as rural residents. Some questions and options were rephrased to make them easier to understand, and the wording of some options was revised. The final version of the questionnaire was administered during the summer vacation. Answers to questionnaires were input into SPSS data management software and cleaned. Questionnaire results with incomplete answers were deleted, leaving 976 valid questionnaires. Among the collected questionnaires, 20 percent of the subjects were rural users.
Table 4.A.1
City Population, Sample Size, and Number of Rural Residents in Internet Cafés Surveyed
|
|
|
Rural Res. |
|
Cafe Code |
City Population |
Sample Size |
# |
% |
No. 1 |
4,316,600 |
50 |
5 |
10.0 |
No. 2 |
1,368,500 |
43 |
– |
– |
No. 3 |
1,368,500 |
6 |
1 |
16.7 |
No. 4 |
7,677,089 |
46 |
9 |
19.6 |
No. 5 |
10,635,971 |
50 |
1 |
2.0 |
No. 6 |
834,437 |
50 |
21 |
42.0 |
No. 7 |
4,414,681 |
40 |
9 |
22.5 |
No. 8 |
10,357,938 |
46 |
– |
– |
No. 9 |
10,357,938 |
46 |
– |
– |
No. 10 |
8,004,680 |
50 |
11 |
22.0 |
No. 11 |
723,958 |
50 |
6 |
12.0 |
No. 12 |
3,262,548 |
50 |
17 |
34.0 |
No. 13 |
231,853 |
50 |
4 |
8.0 |
No. 14 |
2,187,009 |
50 |
8 |
16.0 |
No. 15 |
3,318,057 |
50 |
8 |
16.0 |
No. 16 |
2,552,097 |
50 |
– |
– |
No. 17 |
2,117,000 |
52 |
13 |
25.0 |
No. 18 |
4,613,873 |
50 |
29 |
58.0 |
No. 19 |
4,613,873 |
50 |
12 |
24.0 |
No. 20 |
14,047,625 |
51 |
12 |
23.5 |
No. 21 |
4,472,001 |
45 |
24 |
53.3 |
No. 22 |
– |
1 |
– |
– |
There were huge differences in our randomly selected venues, ranging from small, shabby Internet cafés to luxurious Internet cafés in downtown areas. This is consistent with the diversity of cafés in China. Among the twenty Internet cafés sampled, the number of seats per center ranged from 56 to 561, but most cafés had approximately 100 seats. Half of the Internet cafés surveyed had private rooms (or cubicles)—on average, thirteen per venue. Overall occupancy of Internet cafés was reportedly 53 percent. The list provided by Hintsoft & Pubwin and used to draw our sample listed four of the twenty Internet cafés sampled as located in rural areas; however, the division between rural and urban is sometimes vague. We used the café users’ self-reported answers to determine whether they were rural or urban residents.
Table 4.A.1 shows the population of the city where each cybercafé is located, the sample size, and the number of rural residents in the cafés from which the sample was drawn.
Table 4.B.1
Situational Goals (SGs): Popularity Ranking, Achievement, Percentage of Café Time Spent Pursuing Goal, and SDT Classification
aNumber in parentheses shows the order in which SG appears in questionnaire.
bGoal fully achieved: 3, No achievement: 0.
cSDT classification: A: Autonomy, C: Competence, R: Relatedness, U: Unclassified, EXT: Extrinsic.
Table 4.B.2
Top 12 Situational Goals (SGs) of Young (<19) Urban Student Users of Internet Cafés, by Gender
SGsa |
Rank |
% |
# Obs |
SDT Classificationb |
Male (63 observations) |
|
|
|
|
Entertainment (play games, listen to music, watch movies, online video, etc.) (15) |
1 |
81 |
51 |
A |
Keep in touch with family and friends (email, QQ, etc.) (13) |
2 |
71 |
45 |
R |
Learn to use computers and the Internet (4) |
3 |
56 |
35 |
C |
Access information (news, weather forecasts, stock info, sports, gossip, etc.) (11) |
4 |
49 |
31 |
U |
Meet new friends or a mate or companion (12) |
5 |
46 |
29 |
R |
Improve my performance in school (1) |
6 |
41 |
26 |
C |
Spend time on a hobby or pastime (16) |
6 |
41 |
26 |
A |
Relax, relieve tension (17) |
8 |
40 |
25 |
A |
Socialize and make friends with people in Internet cafés (14) |
9 |
38 |
24 |
R |
Search for spiritual comfort (10) |
10 |
33 |
21 |
A |
Create or update own personal website (home page, blog, microblog) (22) |
10 |
33 |
21 |
C |
Contribute to other people’s web pages or blogs (23) |
10 |
33 |
21 |
R |
|
|
|
|
|
Entertainment (play games, listen to music, watch movies, online video, etc.) (15) |
1 |
82 |
23 |
A |
Keep in touch with family and friends (email, QQ, etc.) (13) |
2 |
75 |
21 |
R |
Contribute to other people’s web pages or blogs (23) |
3 |
57 |
16 |
R |
Meet new friends or a mate or companion (12) |
4 |
54 |
15 |
R |
Learn to use computers and the Internet (4) |
5 |
50 |
14 |
C |
Access information (news, weather forecasts, stock info, sports, gossip, etc.). (11) |
5 |
50 |
14 |
U |
Create or update own personal website (home page, blog, microblog) (22) |
5 |
50 |
14 |
C |
Socialize and make friends with people in Internet cafés (14) |
8 |
46 |
13 |
R |
Spend time on a hobby or pastime (16) |
8 |
46 |
13 |
A |
Improve my performance in school (1) |
10 |
43 |
12 |
C |
Support social groups I like (participate in forums, blogs, microblogs) (28) |
11 |
39 |
11 |
|
Increase self-confidence (9) |
12 |
36 |
10 |
C |
Search for spiritual comfort (10) |
12 |
36 |
10 |
A |
Relax, relieve tension (17) |
12 |
36 |
10 |
A |
Shop online or get product information online (24) |
12 |
36 |
10 |
EXT |
aNumber in parentheses shows the order in which SG appears in questionnaire.
bSDT classification: A: Autonomy, C: Competence, R: Relatedness, U: Unclassified, EXT: Extrinsic.
Notes to the Tables
1. Figures given are regression estimates of the coefficient for each independent variable, the corresponding t-statistic, and the probabilities that the coefficients depart from zero (i.e., that the independent variables have an effect, positive or negative, on reported achievement).
Significant probabilities are marked as follows:
< 10%: •
< 5%: *
< 1%: **
< 0.1%: ***
2. The dependent variable is the perceived achievement of each LG, with achievement measured as 3 = All to 0 = None. Only those respondents choosing a goal as either “most important” or “very important” are considered.
3. The following independent variables are considered:
User = 1 for users, 0 for nonusers.
Exp (“experience”) is the number of years using the Internet.
Import1,..., Import17 are dummy variables to acknowledge that goal choices for “most important” goal (= 1) may be more valued by the respondent (and imply greater effort by the respondent) than “very important” LGs.
NumLGs is the number of goals that a respondent selected as one of his or her life goals. Respondents were able to choose up to 18 LGs and assign them varying levels of importance.
Urban and Male are dummy variables (Urban = 1; Rural = 0; Male = 1; Female = 0).
UMS stands for “urban male students,” UFS for “urban female students.”
LNAGE is the logarithm of the respondent’s age.
Student = 1 if the respondent is a student, 0 if a nonstudent.
Y is a categorical variable for the user’s income, with eight possible values.
EDCN (“education”) takes one of six education values, ranging from 1 for primary to 6 for a master’s degree or above.
Overuser = 1 if the respondent answered “sometimes,” “every time,” or “most of the time” to 5 or more of Young’s proposed questions to assess Internet addiction. This is a soft version of Young’s criteria for classifying user addiction.
4. The variables User, Overuser, and Exp are complementary and must be considered jointly. There is no “nonuser overuser” or “experienced nonuser.”
Table 4.C.1
Regression Sample and Subsamples and Number of Observations
|
Users |
|
Nonusers |
|
|
# |
% |
# |
% |
All |
976 |
100.0 |
964 |
100.0 |
Urban |
786 |
80.5 |
824 |
85.5 |
Urban males |
575 |
58.9 |
436 |
45.2 |
Urban males < 35 yrs. old |
546 |
55.9 |
158 |
16.4 |
Urban male nonstudents |
356 |
36.5 |
377 |
39.1 |
Urban male students |
219 |
22.4 |
59 |
6.1 |
Urban females |
211 |
21.6 |
388 |
40.2 |
Urban female students |
103 |
10.6 |
69 |
7.2 |
Rural |
190 |
19.5 |
140 |
14.5 |
Table 4.C.2
Coefficient, T-statistic, and Probability of No Effect on Life Goal (LG) Achievement: LG1, Learn More Knowledge
Table 4.C.3
Coefficient, T-statistic, and Probability of No Effect on Life Goal (LG) Achievement: LG2, Get Stable, High-Paying Job, Better Business Opportunities
Table 4.C.4
Coefficient, T-statistic, and Probability of No Effect on Life Goal (LG) Achievement: LG5, Keep Up to Date
Table 4.C.5
Coefficient, T-statistic, and Probability of No Effect on Life Goal (LG) Achievement: LG9, Improve the Physical Health of Myself or My Family
Table 4.C.6
Coefficient, T-statistic, and Probability of No Effect on Life Goal (LG) Achievement: LG10, Improve the Mental Health of Myself or My Family
Table 4.C.7
Coefficient, T-statistic, and Probability of No Effect on Life Goal (LG) Achievement: LG14, Keep in Touch with Friends and Family Who Don’t Live Nearby
Table 4.C.8
Coefficient, T-statistic, and Probability of No Effect on Life Goal (LG) Achievement: LG15, Leisure, Entertainment
Table 4.C.9
Coefficient, T-statistic, and Probability of No Effect on Life Goal (LG) Achievement: LG17, Relax, Relieve Tension
Wei Shang, Xuemei Jiang, Jianbin Hao, and Xiaoguang Yang
Problematic Internet use (PIU), defined as “excessive use or addictive tendencies toward the Internet” (Czincz and Hechanova 2009), is an issue of global importance, particularly among adolescents. The Chinese media often portray Internet cafés as places where young people only play online games and waste time. This chapter reviews the incidence of PIU among Internet café users in China. A distinction is made between Internet overuse and users with addiction tendencies. It finds that of the 976 users surveyed, 2.2 percent could be defined as Internet “addicts,” and another 16 percent display some signs of overuse. Males between 18 and 25 years old, and self-identifying as coming from a rural area, are most likely to display signs of PIU. There are significant differences between Internet addicts, overusers, and common users. Overusers start to use the Internet or Internet cafés at a younger age; they also visit more cafés, spend more time there, prefer to use Internet cafés located near them, play more games, and believe that the option to use the Internet café overnight is an important feature. Willingness to pay for Internet café services does not vary much between problematic Internet users and ordinary users. Nevertheless, “addicted” users show greater willingness to visit Internet cafés more often should the price of services come down. This suggests that subsidies lowering the price to encourage more use among low-income people would also induce problematic users to spend even more time in Internet cafés. However, limiting overnight Internet café use while increasing government subsidies for Internet cafés in rural areas or those servicing low-income users could increase information and communication technology penetration without encouraging problematic Internet use.
In China, as Internet penetration has increased, public concern about adolescents’ obsession with Internet browsing and online games has also grown. Parents see Internet cafés as incubators of Internet addiction by adolescents and a major factor contributing to a perceived loss of control over their children.1
In response to public outcry, a member of the Eleventh National People’s Congress (2010) proposed that all Internet cafés be shut down to prevent Internet addiction among adolescents. Café operators retorted in their own defense that Internet cafés are only places for young people to get together and socialize after school or work, and therefore provide a function similar to that of karaoke bars.
Young (1996) was one of the first to propose that Internet addiction be considered a mental health disorder, and in her 1998 paper, she proposed an eight-item questionnaire to diagnose Internet addiction. According to Young, time spent on the Internet is one of the most direct indicators of Internet addiction, and spending between 40 and 80 hours per week on the Internet is considered to be overuse (Young 2004). Internet addiction is considered harmful because it leads to or is highly correlated with little or no social life, poor school performance, and online affairs (Kubey, Lavin, and Barrows 2001; Young 2004).
Problematic Internet use (PIU) is defined as “an inability to control one’s use of the Internet” (Billieux and Van der Linden 2012, p. 24). It is associated with low satisfaction with life (Wang et al. 2008) and psychological factors such as shyness, defined as the fear of meeting people and discomfort in the presence of others (Chak and Leung 2004), and loneliness (Morahan-Martin and Schumacher 2000). It may or may not be linked to other psychiatric disorders (Beard and Wolf 2001; Shapira et al. 2003).
This chapter reviews existing research related to PIU and analyzes the results of a survey of Internet café users in China to determine the presence and levels of Internet “addiction” and overuse. Policy implications for Internet café regulation are proposed in the last part of the chapter.
Excessive Internet use is not necessarily a sign of addiction. Griffiths, for example, found only two addicted subjects out of five cases of excessive Internet use (Griffiths 2000). Overuse, problematic Internet use, and excessive Internet use are more frequently identified among users than is addiction. The Wang et al. (2011) research on 12,446 high school Internet users found that 12.2 percent were problematic Internet users. Whang, Lee, and Chang (2003) conducted research on 13,588 Internet users and determined that 3.5 percent could be considered Internet addicts (IAs) and 18.4 percent possible Internet addicts. Identifying different degrees of PIU is therefore an important first step to diagnosing and treating the condition.
The characteristics and causes of Internet addiction are also important research issues. According to Brenner (1997), Internet addiction or problematic Internet use is often found among young male users. Besides social demographic characteristics of IAs, Armstrong, Phillips, and Saling (2000) found low self-esteem to be a predictor of Internet addiction. Kim and Kim (2010) provide more information on predictors of Internet game addiction. Using a decision tree model, they found several causative factors, including gender, type of school, number of siblings, economic status, religion, time spent alone, gaming place, payment to Internet café, frequency and duration of visits, parents’ ability to use Internet, occupation (mother), trust (father), expectations regarding adolescent’s study (mother), supervising (both parents), and rearing attitude (both parents). Odaci’s (2011) research on college students found a negative relationship between a student’s self-perception of academic efficacy and PIU. Although most existing research targeted college or high school students in various countries, the findings may serve as the basis for research into Internet addiction among Internet café users in China.
Several diagnostic measures of Internet addiction exist, and a growing body of research reveals the causes and consequences of problematic Internet use. However, research on the incidence of overuse among China’s Internet café users is limited. The present research aims to help fill this gap. Three research questions are addressed: (1) What is the extent of problematic Internet use in Internet cafés in China? (2) What are the demographic characteristics of problematic Internet users? and (3) What Internet café regulation measures may alleviate problematic Internet use, especially among adolescents?
Young’s (1996) criteria for screening Internet addiction are based on those established in the Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM–IV), to identify persons affected by pathological gambling. She proposed that persons who answered “yes” to five or more of the following questions be classified as addicted:
1. Do you feel preoccupied with the Internet (think about previous online activity or anticipate next online session)?
2. Do you feel the need to use the Internet with increasing amounts of time in order to achieve satisfaction?
3. Have you repeatedly made unsuccessful efforts to control, cut back, or stop Internet use?
4. Do you feel restless, moody, depressed, or irritable when attempting to cut down or stop Internet use?
5. Do you stay online longer than originally intended?
6. Have you jeopardized or risked the loss of a significant relationship, job, educational, or career opportunity because of the Internet?
7. Have you lied to family members, therapist, or others to conceal the extent of your involvement with the Internet?
8. Do you use the Internet as a way of escaping from problems or relieving a dysphoric mood (e.g., feelings of hopelessness, guilt, anxiety, or depression)?
Beard and Wolf (2001) noted that the first five questions are qualitatively different from the last three. A person might answer “yes” to these first five questions without impairing his or her ability to function. Beard and Wolf illustrate this point using as an example a young mother overly preoccupied with her new baby (question 1), who feels a need to be constantly near her newborn (question 2), has repeatedly but unsuccessfully tried not to obsess over caring for her child (question 3), feels restless or moody on account of her excessive concern (question 4), and spends more time near the baby than she feels is necessary (question 5).
Answering “yes” to the last three questions, however, would suggest that a user’s ability to cope with the demands of daily life was being impaired (feelings of depression, helplessness, anxiety, escapism) or that his or her interactions with others were being affected (lying, underperformance at school or work, damage to a significant relationship). Beard and Wolf (2001) in general object to the use of the term addiction and prefer a less theoretically contentious term such as problematic Internet use. They propose that a better diagnosis of the underlying pathology would be achieved by modifying the original criteria to acknowledge the observed qualitative differences in the questions used so that a person be considered affected by the disorder only if he or she answered “yes” to all of the first five questions and to at least one of the last three.
To assess the extent to which Internet café users are affected by addiction or overuse, our questionnaire in China included Young’s eight-question screening tool.2 We used the translation of these eight questions introduced by Cao et al. (2007). Two levels of problematic Internet use are considered, according to the user’s degree of Internet dependency:
• A respondent is considered an Internet overuser if he or she answers “usually,” “most times,” “sometimes,” or “rarely” to all of the first five questions and at least one of the last three. This is a broad definition of overuse that would classify as overusers persons whose risk of having PIU affect their lives is small.3
• A respondent is considered an Internet “addict” if he or she answers “usually” or “most times” to all of the first five questions and at least one of the last three. This criterion is similar to the one proposed by Beard and Wolf (2001), except that these authors had “yes” or “no” as possible answers to each question, whereas we allow a five-scale answer and consider “usually” and “most times” as a “yes.”
In addition to Young’s eight Internet addiction diagnostic questions, our questionnaire contains four other sections designed to investigate: (1) Internet use, (2) Internet café use, (3) user objectives, and (4) user demographic features. The third category, user objectives, is analyzed in detail in the previous chapter on China (chapter 4).
The focus in this chapter is on questions appearing in the first, second, and fourth parts of the questionnaire. The first section contains four questions addressing when and where a user first used the Internet and computer and his or her most frequent (preferred and/or most visited) Internet access venues (i.e., school, home, Internet cafés, etc.). The second section comprises fifteen questions about what kind of Internet café users usually visit, how much they are willing to pay for the service, their usage patterns, the activities they usually perform in Internet cafés, and their motivation for using an Internet café. With regard to their willingness to pay, a question about how much they are paying now and two questions about how much more or less they would likely use the café if the price were to go down or up were included to ascertain users’ valuation of Internet café services. The fourth section of the questionnaire includes demographic questions about respondents’ gender, age, education, occupation, and income.
Three groups of users are identified: normal users, overusers, and “addicted” users. The percentages of each group in our user sample are as follows:
|
# |
% |
Cumulative % |
Addicted |
22 |
2.2 |
2.2 |
Overusers |
155 |
15.9 |
18.1 |
Normal |
799 |
81.9 |
100 |
Total |
976 |
100.0 |
|
Overall, the proportion of IAs is low (2.2 percent). Most cybercafé users can be classified as normal users (i.e., they do not display characteristics of overuse or addiction).
Table 5.1
Problematic Internet Use (PIU): Recent Estimates for Korea and China
|
Students (%) |
All (%) |
Basic features of sample |
Measure of PIU used |
Whang, Lee, and Chang (2003) |
|
3.5 |
7,878 respondents to online survey |
Modified Korean version of Young (1998)’s 20-question Internet addiction test |
Cao et al. (2007) |
2.4 |
|
2,620 from 4 high schools in Changsha City |
Young 8-item questionnaire using Beard and Wolf (2001) criteria |
Kim et al. (2010) |
21.8 |
|
853 Korean junior high school students |
20-item addiction questionnaire by Korea Agency for Digital Opportunity and Promotion. % given is “high risk” users |
Wang et al. (2011) |
12.2 |
|
12,446 high school student Internet users in Guandong Province |
Young (1998)’s 20-question Internet addiction test |
Our estimate (2010 survey) |
|
|
|
|
Modified Young criteria |
6.3 |
4.2 |
Students subsample: 189 middle & high school |
8-item Young questionnaire, using |
Modified Beard and Wolf criteriaa |
3.2 |
2.2 |
student PAV users; Average age 17.7 years, range 12–25; 72% male, 28% female. All user sample: 976 PAV users; Average age 23 years; 73% male, 27% female |
either Young (1998) or Beard and Wolf (2001) criteria, modified so that “usually” or “most times” = “yes” |
aModified Beard and Wolf criteria: All of the first five of Young’s questions and at least one of the last three marked as “usually” or “most times.”
Figure 5.1
Scatter chart of PIU risk index by user type.
We also considered an Internet addiction group of special interest from a public policy perspective: high school students. Cao et al. (2007) estimated that 2.4 percent of high school students from four schools in Changsha City could be classified as addicted to the Internet. Using the same questionnaire and criteria, we arrived at an estimate of 3.2 percent “addicts” in a subsample of 189 middle and high school student users of Internet cafés. Other estimates (shown in table 5.1) are higher. These estimates are not fully comparable. There is consensus that overuse is associated with health and behavioral problems, but there is no agreement on how to define or measure “addiction” or “overuse.”
We also calculated a PIU risk index by treating all eight questions as equally important and normalizing its value to lie between 0 and 1. This essentially follows Young’s criteria but disregards Beard and Wolf’s distinction between the first five and the last three questions. The PIU risk index lets us get an appreciation of how much at risk of problematic Internet use a user may be.
This PIU index is highly correlated with PIU types. The Pearson correlation coefficient is 0.760 and is significant at the level 0.01. As shown in the scatter chart of PIU types and PIU risk index (figure 5.1), the normal users and addicted users are quite separate in terms of the value of the PIU risk index. There are some outliers in the separation of normal users and overusers, as shown in the boxplot in figure 5.2.
Figure 5.2
Boxplot of PIU risk index by user type.
PIU types are significantly correlated with gender, age, and rural/urban residency status but not with education, income, or different professions (table 5.2). Male rural residents ages 18 to 25 are the most likely to be at risk of PIU.
Male users are more likely than females to be Internet addicted or overusers. Among all survey respondents, using Beard and Wolf’s (2001) criteria, 2.5 percent of male users may be classified as IAs and 17.4 percent as Internet overusers. Among female users, only 1.5 percent may be classified as addicted and 11.7 percent as overusers. The correlation between gender and PIU type is significant at 0.05 level.
Age and PIU risk index are significantly correlated at 0.05 level. The Pearson correlation coefficient is –0.078, which means younger people have a higher tendency toward PIU. When users are grouped into the following age categories—under 18, between 18 and 25, and over 25—users between the ages of 18 and 25 have a higher ratio of addiction and overuse than the other two age groups.
Table 5.2
Internet Use and Demographic Features
|
|
Level of Internet Use |
|
|
|
|
|
Normal (%) |
Overuse (%) |
Addict (%) |
Count |
Gender |
Male |
80.0 |