Title

Examining the Determinants of Smartphone User's User Satisfaction and Intention to Use Social Networking Service with TAM: The Case of Facebook

Translated Titles

Examining the Determinants of Smartphone User's User Satisfaction and Intention to Use Social Networking Service with TAM: The Case of Facebook

DOI

10.6841/NTUT.2014.00408

Authors

金兌禧

Key Words

Technology Acceptance Model (TAM) ; Mobile applications ; Social Networking Service (SNS) ; Facebook ; Technology Acceptance Model (TAM) ; Mobile applications ; Social Networking Service (SNS) ; Facebook

PublicationName

臺北科技大學管理國際學生碩士專班 (IMBA)學位論文

Volume or Term/Year and Month of Publication

2014年

Academic Degree Category

碩士

Advisor

林鳳儀

Content Language

英文

Chinese Abstract

Smartphones have become much more ubiquitous and smartphone users are increasingly relying on them to store and handle personal information and communicate with many people by social networking service (SNS) such as Facebook. Using SNS applications is common on most mobile device by individuals today and they are main key to providing online service or site to construct social networks or social relations among people. This study involves extended Technology Acceptance Model (TAM) to investigate the effect on intention to use Facebook and user satisfaction. The TAM model has been widely used to identify the determinants of technology acceptance in many contexts, especially for predicting people’s acceptance of information technology. This research model employed basic TAM model and eight constructs: Social influence, personal innovativeness, motivation for instrumental use, information quality, system quality, service quality, and user satisfaction. The research result shows that user perception toward using Facebook which is influenced significantly by perceived ease of use, perceived usefulness, and satisfaction of Facebook. Also, based on the finding, this study provides theoretical and practical implication for service providers by suggesting significant factors to use SNS application.

English Abstract

Smartphones have become much more ubiquitous and smartphone users are increasingly relying on them to store and handle personal information and communicate with many people by social networking service (SNS) such as Facebook. Using SNS applications is common on most mobile device by individuals today and they are main key to providing online service or site to construct social networks or social relations among people. This study involves extended Technology Acceptance Model (TAM) to investigate the effect on intention to use Facebook and user satisfaction. The TAM model has been widely used to identify the determinants of technology acceptance in many contexts, especially for predicting people’s acceptance of information technology. This research model employed basic TAM model and eight constructs: Social influence, personal innovativeness, motivation for instrumental use, information quality, system quality, service quality, and user satisfaction. The research result shows that user perception toward using Facebook which is influenced significantly by perceived ease of use, perceived usefulness, and satisfaction of Facebook. Also, based on the finding, this study provides theoretical and practical implication for service providers by suggesting significant factors to use SNS application.

Topic Category 管理學院 > 管理國際學生碩士專班 (IMBA)
社會科學 > 管理學
Reference
  1. Agarwal, R., & Prasad, J. (1998a). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204–215.
    連結:
  2. Agarwal, R., & Prasad, J. (1998b). The antecedents and consequents of user perceptions in information technology adoptions. Decisions Support System, 22, 15–29.
    連結:
  3. Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: cognitive absorption and beliefs about information technology usage 1. MIS quarterly, 24(4), 665-694.
    連結:
  4. Ahn, T., Ryu, S., & Han, I. (2007). The impact of web quality and playfulness on user acceptance of online retailing. Information & Management, 44(3), 263-275.
    連結:
  5. Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change.
    連結:
  6. Psychological Review, 84, 191e215.
    連結:
  7. Bandura, A. (1988). Self-efficacy conception of anxiety. Anxiety Research, 1, 77e98.
    連結:
  8. Barczak, G., Ellen, P., & Pilling, B. K. (1997). Developing typologies of consumer motives for use of technologically based banking services. Journal of Business Research, 38, 131–139.
    連結:
  9. Burton-Jones, A., & Hubona, G. S. (2006). The mediation of external variables in the technology acceptance model. Information & Management, 43(6), 706-717.
    連結:
  10. Celik, V., & Yesilyurt, E. (2013). Attitudes to technology, perceived computer self-efficacy and computer anxiety as predictors of computer supported education. Computers & Education, 60(1), 148-158.
    連結:
  11. Chang, Y. F., & Chen, C. S. (2005). Smart phone—the choice of client platform for mobile commerce. Computer Standards & Interfaces, 27(4), 329-336.
    連結:
  12. Chau, P. Y. (1996). An empirical assessment of a modified technology acceptance model. Journal of management information systems, 13(2), 185-204.
    連結:
  13. Chen, K., Chen, J. V., & Yen, D. C. (2011). Dimensions of self-efficacy in the study of smart phone acceptance. Computer Standards & Interfaces, 33(4), 422-431.
    連結:
  14. Chen, J. V., Yen, D. C., & Chen, K. (2009). The acceptance and diffusion of the innovative smart phone use: A case study of a delivery service company in logistics. Information & Management, 46(4), 241-248.
    連結:
  15. Choi, J., Jung, J., & Lee, S. W. (2013). What causes users to switch from a local to a global social network site? The cultural, social, economic, and motivational factors of Facebook’s globalization. Computers in Human Behavior,29(6), 2665-2673.
    連結:
  16. Chow, M., Herold, D. K., Choo, T. M., & Chan, K. (2012). Extending the technology acceptance model to explore the intention to use Second Life for enhancing healthcare education. Computers & Education, 59(4), 1136-1144.
    連結:
  17. Chuttur, M. (2009). Overview of the technology acceptance model: Origins, developments and future directions.
    連結:
  18. Compeau, D. R., & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS quarterly, 189-211.
    連結:
  19. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
    連結:
  20. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8), 982-1003.
    連結:
  21. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace1. Journal of applied social psychology, 22(14), 1111-1132.
    連結:
  22. Dulcic, Z., Pavlic, D., & Silic, I. (2012). Evaluating the intended use of Decision Support System (DSS) by applying Technology Acceptance Model (TAM) in business organizations in Croatia. Procedia-Social and Behavioral Sciences,58, 1565-1575.
    連結:
  23. Farahat, T. (2012). “Applying the Technology Acceptance Model to Online Learning in the Egyptian Universities.” Procedia-Social and Behavioral Sciences,64, 95-104.
    連結:
  24. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 39-50.
    連結:
  25. Gomez-Iturriaga, A., Bilbao, P., Casquero, F., Cacicedo, J., & Crook, J. (2012). “Smartphones and tablets: Reshaping radiation oncologists’ lives.” Reports of Practical Oncology & Radiotherapy.
    連結:
  26. Gorla, N., & Somers, T. M. (2014). The impact of IT outsourcing on information systems success. Information & Management, 51(3), 320-335.
    連結:
  27. Gu, J. C., Lee, S. C., & Suh, Y. H. (2009). Determinants of behavioral intention to mobile banking. Expert Systems with Applications, 36(9), 11605-11616.
    連結:
  28. Hayduk, L., Cummings, G., Boadu, K., Pazderka-Robinson, H., & Boulianne, S. (2007). Testing! Testing! One, two, three–testing the theory in structural equation models!. Personality and Individual Differences, 42(5), 841-850.
    連結:
  29. Hong, W., Thong, J. Y., Wong, W. M., & Tam, K. Y. (2002). Determinants of user acceptance of digital libraries: an empirical examination of individual differences and system characteristics. J. of Management Information Systems, 18(3), 97-124.
    連結:
  30. Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
    連結:
  31. Igbaria, M., & Iivari, J. (1995). The effects of self-efficacy on computer usage.Omega, 23(6), 587-605.
    連結:
  32. Igbaria, M., Guimaraes, T., & Davis, G. B. (1995). Testing the determinants of microcomputer usage via a structural equation model. Journal of Management Information Systems, 11(4), 87-114.
    連結:
  33. Jin, B. (2013). How lonely people use and perceive Facebook. Computers in Human Behavior, 29(6), 2463-2470.
    連結:
  34. Jin, C. (2013). The perspective of a revised TRAM on social capital building: The case of Facebook usage. Information & Management, 50(4), 162-168.
    連結:
  35. Joo, J., & Sang, Y. (2013). Exploring Koreans’ smartphone usage: An integrated model of the technology acceptance model and uses and gratifications theory. Computers in Human Behavior, 29(6), 2512-2518.
    連結:
  36. Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS quarterly, 183-213.
    連結:
  37. Kim, S. H. (2010). Effects of perceived attributes on the purchase intention of smart- phone. Journal of Korea Contents Association, 10(9), 318-326.
    連結:
  38. Kher, H. V., Downey, J. P., & Monk, E. (2013). A longitudinal examination of computer self-efficacy change trajectories during training. Computers in Human Behavior, 29(4), 1816-1824.
    連結:
  39. Klobas, J. E., & Clyde, L. A. (2001). Social influence and Internet use. Library Management, 22(1/2), 61-68.
    連結:
  40. Kotler, P. (1988). Marketing management: Analysis, planning, implementation, and control (Vol. 8). Englewood Cliffs: Prentice-Hall.
    連結:
  41. Kwak, K. T., Choi, S. K., & Lee, B. G. (2014). SNS flow, SNS self-disclosure and post hoc interpersonal relations change: Focused on Korean Facebook user. Computers in Human Behavior, 31, 294-304.
    連結:
  42. Kuo, Y. F., & Yen, S. N. (2009). Towards an understanding of the behavioral intention to use 3G mobile value-added services. Computers in Human Behavior, 25(1), 103-110.
    連結:
  43. Kuo, Y. F., Wu, C. M., & Deng, W. J. (2009). The relationships among service quality, perceived value, customer satisfaction, and post-purchase intention in mobile value-added services. Computers in Human Behavior, 25(4), 887-896.
    連結:
  44. Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). The technology acceptance model and the World Wide Web. Decision support systems, 29(3), 269-282.
    連結:
  45. Lee, K. C., & Chung, N. (2009). Understanding factors affecting trust in and satisfaction with mobile banking in Korea: A modified DeLone and McLean’s model perspective. Interacting with computers, 21(5), 385-392.
    連結:
  46. Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: an extension of the technology acceptance model. Computers & Education, 61, 193-208.
    連結:
  47. Lee, H., Kim, D., Ryu, J., Lee, S., 2011. Acceptance and rejection of mobile TV among young adults: A case of college students in South Korea. Telematics and Informatics 28, 239–250.
    連結:
  48. Lee, S., Suh, J., & Park, H. D. (2013). Smart Compass-Clinometer: A smartphone application for easy and rapid geological site investigation. Computers & Geosciences, 61, 32-42.
    連結:
  49. Legris, P., Ingham, J., & Collerette, P. (2003). Why do people use information technology? A critical review of the technology acceptance model. Information & management, 40(3), 191-204.
    連結:
  50. Lin, H. H., & Wang, Y. S. (2006). An examination of the determinants of customer loyalty in mobile commerce contexts. Information & management,43(3), 271-282.
    連結:
  51. Lu, J., Yao, J. E., & Yu, C. S. (2005). Personal innovativeness, social influences and adoption of wireless Internet services via mobile technology. The Journal of Strategic Information Systems, 14(3), 245-268.
    連結:
  52. Lu, J., Liu, C., Yu, C. S., & Wang, K. (2008). Determinants of accepting wireless mobile data services in China. Information & Management, 45(1), 52-64.
    連結:
  53. Luarn, P., & Lin, H. H. (2005). Toward an understanding of the behavioral intention to use mobile banking. Computers in Human Behavior, 21(6), 873-891.
    連結:
  54. Madhavan, P., & Phillips, R. R. (2010). Effects of computer self-efficacy and system reliability on user interaction with decision support systems. Computers in Human Behavior, 26(2), 199-204.
    連結:
  55. Mahat, J., Ayub, A. F. M., & Luan, S. (2012). An Assessment of Students’ Mobile Self-Efficacy, Readiness and Personal Innovativeness towards Mobile Learning in Higher Education in Malaysia. Procedia-Social and Behavioral Sciences, 64, 284-290.
    連結:
  56. Malhotra, Y., & Galletta, D. F. (1999). Extending the technology acceptance model to account for social influence: theoretical bases and empirical validation. In System Sciences, 1999. HICSS-32. Proceedings of the 32nd Annual Hawaii International Conference on (pp. 14-pp). IEEE.
    連結:
  57. Mason, W. A., Conrey, F. R., & Smith, E. R. (2007). Situating social influence processes: Dynamic, multidirectional flows of influence within social networks.Personality and social psychology review, 11(3), 279-300.
    連結:
  58. Melas, C. D., Zampetakis, L. A., Dimopoulou, A., & Moustakis, V. (2011). Modeling the acceptance of clinical information systems among hospital medical staff: An extended TAM model. Journal of biomedical informatics,44(4), 553-564.
    連結:
  59. Midgely, D.F., Dowling, G.R., 1978. Innovativeness: the concept and its measurement. Journal of Consumer Research 4, 229–242.
    連結:
  60. Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & Management, 38(4), 217-230.
    連結:
  61. Moore, G. C., & Benbasat, I. (1996). Integrating diffusion of innovations and theory of reasoned action models to predict utilization of information technology by end-users. In Diffusion and adoption of information technology (pp. 132-146). Springer US.
    連結:
  62. Neacşu, V. (2013). The Efficiency of a Cognitive-behavioral Program in Diminishing the Intensity of Reactions to Stressful Events and Increasing Self-esteem and Self-efficiency in the Adult Population. Procedia-Social and Behavioral Sciences, 78, 380-384.
    連結:
  63. Oliver, R. L. (2010). Satisfaction: A behavioral perspective on the consumer. ME Sharpe.
    連結:
  64. Parasuraman, A. (2000). Technology readiness index (TRI): A multiple-item scale to measure readiness to embrace new technologies. Journal of Service Research, 2(4), 307–320.
    連結:
  65. Park, N., Kim, Y. C., Shon, H. Y., & Shim, H. (2013). Factors influencing smartphone use and dependency in South Korea. Computers in Human Behavior, 29(4), 1763-1770.
    連結:
  66. Petter, S., DeLone, W., & McLean, E. (2008). Measuring information systems success: models, dimensions, measures, and interrelationships. European Journal of Information Systems, 17(3), 236-263.
    連結:
  67. Plouffe, C. R., Hulland, J. S., & Vandenbosch, M. (2001). Research report: richness versus parsimony in modeling technology adoption decisions—understanding merchant adoption of a smart card-based payment system.Information systems research, 12(2), 208-222.
    連結:
  68. Porter, C. E., & Donthu, N. (2006). Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics. Journal of Business Research, 59(9), 999-1007.
    連結:
  69. Rogers, E. M. (2010). Diffusion of innovations. Simon and Schuster.
    連結:
  70. Saadé, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: an extension of the technology acceptance model. Information & Management, 42(2), 317-327.
    連結:
  71. Seddon, P. B. (1997). A respecification and extension of the DeLone and McLean model of IS success. Information systems research, 8(3), 240-253.
    連結:
  72. Suki, N. M., & Suki, N. M. (2007). Mobile phone usage for m-learning: comparing heavy and light mobile phone users. Campus-Wide Information Systems, 24(5), 355-365.
    連結:
  73. Shin, D. H., Shin, Y. J., Choo, H., & Beom, K. (2011). Smartphones as smart pedagogical tools: Implications for smartphones as u-learning devices. Computers in Human Behavior, 27(6), 2207-2214.
    連結:
  74. Smith, B., Caputi, P., & Rawstorne, P. (2000). Differentiating computer experience and attitudes toward computers: an empirical investigation.Computers in Human Behavior, 16(1), 59-81.
    連結:
  75. Son, H., Park, Y., Kim, C., & Chou, J. S. (2012). Toward an understanding of construction professionals' acceptance of mobile computing devices in South Korea: An extension of the technology acceptance model. Automation in Construction, 28, 82-90.
    連結:
  76. Swanson, E. B. (1974). Management information systems: appreciation and involvement. Management Science, 21(2), 178-188.
    連結:
  77. Szajna, B. (1996). Empirical evaluation of the revised technology acceptance model. Management science, 42(1), 85-92.
    連結:
  78. Teo, T., & Noyes, J. (2011). An assessment of the influence of perceived enjoyment and attitude on the intention to use technology among pre-service teachers: A structural equation modeling approach. Computers & Education,57(2), 1645-1653.
    連結:
  79. Tornatzky, L. G., & Klein, K. J. (1982). Innovation characteristics and innovation adoption-implementation: A meta-analysis of findings. Engineering Management, IEEE Transactions on, (1), 28-45.
    連結:
  80. Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information systems research, 6(2), 144-176.
    連結:
  81. Teo, T. S., Lim, V. K., & Lai, R. Y. (1999). Intrinsic and extrinsic motivation in Internet usage. Omega, 27(1), 25-37.
    連結:
  82. Tseng, F. M., & Lo, H. Y. (2011). Antecedents of consumers’ intentions to upgrade their mobile phones. Telecommunications Policy, 35(1), 74-86.
    連結:
  83. Urbach, N., & Müller, B. (2012). The updated DeLone and McLean model of information systems success. In Information Systems Theory (pp. 1-18). Springer New York.
    連結:
  84. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
    連結:
  85. Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test*. Decision sciences, 27(3), 451-481.
    連結:
  86. Venkatesh, V., & Speier, C. (2000). Creating an effective training environment for enhancing telework. International Journal of Human-Computer Studies,52(6), 991-1005.
    連結:
  87. Venkatesh, V., Speier, C., & Morris, M. G. (2002). User acceptance enablers in individual decision making about technology: Toward an integrated model.Decision Sciences, 33(2), 297-316.
    連結:
  88. Windahl, S. (1981). Uses and gratifications at the crossroads. Mass communication review yearbook, 2(2), 174-85.
    連結:
  89. Wu, J. H., Wang, S. C., & Lin, L. M. (2007). Mobile computing acceptance factors in the healthcare industry: A structural equation model. International journal of medical informatics, 76(1), 66-77.
    連結:
  90. Yang, S., Lu, Y., Gupta, S., Cao, Y., & Zhang, R. (2012). Mobile payment services adoption across time: An empirical study of the effects of behavioral beliefs, social influences, and personal traits. Computers in Human Behavior,28(1), 129-142.
    連結:
  91. Yen, D. C., Wu, C. S., Cheng, F. F., & Huang, Y. W. (2010). Determinants of users’ intention to adopt wireless technology: An empirical study by integrating TTF with TAM. Computers in Human Behavior, 26(5), 906-915.
    連結:
  92. Yi, M. Y., & Hwang, Y. (2003). Predicting the use of web-based information systems: self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International journal of human-computer studies, 59(4), 431-449.
    連結:
  93. Zheng, H., Li, Y., & Jiang, D. (2012). Empirical Study and Model of User’s Acceptance for Mobile Commerce in China. International Journal of Computer Science Issues (IJCSI), 9(6).
    連結:
  94. OECD.org (2000): Regulatory Reform in Korea, Regulatory Reform in the Telecommunications Industry,
    連結:
  95. Gartner (2013): IT Glossary on Mobile Social Networks,
    連結:
  96. statista (2014): Social Media & User-Generated content on Global social networks ranked by number of users 2014,
    連結:
  97. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W. H. Freeman.
  98. Davis Jr, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems: Theory and results. (Doctoral dissertation, Massachusetts Institute of Technology).
  99. DeLone, W. H., & McLean, E. R. (2002, January). Information systems success revisited. In System Sciences, 2002. HICSS. Proceedings of the 35th Annual Hawaii International Conference on (pp. 2966-2976). IEEE.
  100. Freeze, R. D., Alshare, K. A., Lane, P. L., & Joseph Wen, H. (2010). IS success model in e-learning context based on students' perceptions. Journal of Information Systems Education, 21(2), 173.
  101. Kim, K. K., Ryoo, S., Kim, M., & Kim, H. (2009). Determinants of User Intentions to Use Mobile Web Browsing Service: Self‐Efficacy and Social Influences. Journal of Information Technology Applications & Management,16(1), 149-168.
  102. Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (1998, June). The role of ease of use, usefulness and attitude in the prediction of World Wide Web usage. In Proceedings of the 1998 ACM SIGCPR conference on Computer personnel research (pp. 195-204). ACM.
  103. Lee, Y., Kozar, K. A., & Larsen, K. R. (2003). The technology acceptance model: past, present, and future. Communications of the Association for Information Systems, 12(1), 50.
  104. Leea, W. J., Kimb, T. U., & Chungc, J. Y. (2002). User acceptance of the mobile Internet.
  105. Li, L. (2010). A critical review of technology acceptance literature. In Southwest Decision Sciences Institute Conference (pp. 1-20).
  106. Livaditi, J., Vassilopoulou, K., Lougos, C., & Chorianopoulos, K. (2003, January). Needs and gratifications for interactive TV implications for designers. In System Sciences, 2003. Proceedings of the 36th Annual Hawaii International Conference on (pp. 9-pp). IEEE.
  107. L.S. Banda, What Are The Main Determinants for the Attitude to use Mobile Phone Application in Surinam, Master Thesis, Maastricht School of Management, Maastricht, the Netherlands and the FHR Institute for Social Studies, 2011, December)
  108. Lule, I., Omwansa, T. K., & Waema, T. M. (2012). Application of Technology Acceptance Model (TAM) in M-Banking Adoption in Kenya. International journal of computing and ICT research. Presented during the Africa mobile money research conference.
  109. Oliver, R. L. (1981). Measurement and evaluation of satisfaction processes in retail settings. Journal of retailing.
  110. Parveen, F., & Sulaiman, A. (2008). Technology complexity, personal innovativeness and intention to use wireless internet using mobile devices in Malaysia. International Review of Business Research Papers, 4(5), 1-10.
  111. Pyo, J. W., & Kim, I. J., (2012). The affecting Factors on the Usage of Smart Phone Application – The case of Kakao Talk. The Korea Society Management Information Systems, 183-188.
  112. Sohn, S. H., Choi, Y. J., & Hwang, H. S. (2010). Understanding Acceptance of Smartphone among Early Adopters Using Extended Technology Acceptance Model. Korean Society for Journalism & Communication studies, 55(2), 228-251.
  113. Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS quarterly, 561-570.Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
  114. Verkasalo, H., López-Nicolás, C., Molina-Castillo, F. J., & Bouwman, H. (2010). “Analysis of users and non-users of smartphone applications.” Telematics and Informatics, 27(3), 242-255.
  115. Wareham, J., Levy, A., & Shi, W. (2004). Wireless diffusion and mobile computing: implications for the digital divide. Telecommunications Policy,28(5), 439-457. Wu, J. H., Wang, S. C., & Lin, L. M. (2007). Mobile computing acceptance factors in the healthcare industry: A structural equation model. International journal of medical informatics, 76(1), 66-77.
  116. Xu, Q., Erman, J., Gerber, A., Mao, Z., Pang, J., & Venkataraman, S. (2011, November). Identifying diverse usage behaviors of smartphone apps. In Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference (pp. 329-344). ACM.
  117. Yushau, B. (2006). The effects of blended e-learning on mathematics and computer attitudes in pre-calculus algebra. The Montana Mathematics Enthusiast, 3(2), 176-183.
  118. Web reference
  119. IDC.com (2013): Press Release on Apple Cedes Market Share in Smartphone Operating System Market as Android Surges and Windows Phone Gains, http://www.idc.com/getdoc.jsp?containerId=prUS24257413 ; accessed 25th of August, 2013
  120. PhoneArena.com (2013): News on Android’s Google Play beats App Store with over 1 million apps, now officially largest. http://www.phonearena.com/news/Androids-Google-Play-beats-App-Store-with-over-1-million-apps-now-officially-largest_id45680 ; accessed 24th of Jul, 2013
  121. Cnet.com (2013): News on Apple now hosts 900,000 apps in App Store, http://news.cnet.com/8301-13579_3-57588534-37/apple-now-hosts-900000-apps-in-app-store/ ; accessed 10th of June, 2013
  122. Wpcentral.com (2013): Windows phone news on With 160,000+ apps, Microsoft breaks down the number for the Window Phone Store. http://www.wpcentral.com/160000-apps-microsoft-windows-phone-store-numbers ; accessed 26th of Jul, 2013
  123. RnRMarketResearch.com (2013): Telecommunications Market for South Korea, Indonesia & Iraq in New Research Reports at RnRMarketResearch.com,
  124. http://www.prweb.com/releases/2013-telecommunications/industry-analysis-report/prweb10992443.htm; accessed 27th of November, 2013
  125. s-ge.com (2013): South Korea Information and Communication Industry,
  126. http://www.s-ge.com/schweiz/export/de; accessed 27th of November, 2013
  127. http://www.oecd.org/regreform; accessed 27th of November, 2013
  128. isis.kisa.or.kr (2012): Internet Statistics Reports on 2012 Survey on the Wireless Internet Usage Executive Summary, http://isis.kisa.or.kr/eng/board/index.jsp?pageId=040100&bbsId=10&itemId=322&pageIndex=1; accessed 8th of November, 2013
  129. Korea Creative Content Agency, http://www.kocca.kr/knowledge/research/__icsFiles/afieldfile/2013/04/05/DrTb1NL5DYLn.pdf; accessed 1th of December, 2013
  130. Korea Information Society Development Institute, http://www.kisdi.re.kr/kisdi/fp/kr/board/selectSingleBoard.do?cmd=selectSingleBoard&boardId=GPK_PRESS&curPage=1&seq=28102&reStep=1305099&ctx; accessed 1th of December, 2013
  131. nipa.kr (2013): Policy and Statistics on 2013 ICT monthly Statistics on June,
  132. http://isis.kisa.or.kr/board/index.jsp?pageId=060200&bbsId=3&itemId=799&pageIndex=1; accessed 20th of November, 2013
  133. Gartner (2014): Press Release on Gartner Says Annual Smartphone Sales Surpassed Sales of Feature Phone for the First Time in 2013, http://www.gartner.com/newsroom/id/2665715; accessed 19th of June, 2014
  134. http://www.gartner.com/it-glossary/mobile-social-networks; accessed 19th of June, 2014
  135. eMarketer (2013): Article on Smartphone Users Worldwide Will Total 1.75 Billion in 2014,
  136. http://www.emarketer.com/Article/Smartphone-Users-Worldwide-Will-Total-175-Billion-2014/1010536; accessed 19th of June, 2014
  137. statista (2014): Social Media & User-Generated content on Number of social network users worldwide from 2010 to 2017,
  138. http://www.statista.com/statistics/278414/number-of-worldwide-social-network-users/; accessed 19th of June, 2014
  139. http://www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users/; accessed 19th of June, 2014
  140. statista (2014): Social Media & User-Generated content on Most popular social networking sites in Asian countries in 2013,
  141. http://www.statista.com/statistics/224746/leading-social-network-sites-in-asian-countries/; accessed 19th of June, 2014
  142. statista (2013): Mobile Internet on Google’s Mobile Products Reach 92% of US Smartphone Users,
  143. http://www.statista.com/chart/1526/top-mobile-web-properties/; accessed 19th of June, 2014