Keyword

Convenience, Social influence, Hedonic motivations, perceived usefulness, ease of use

Abstract

This paper aims to study the factors that enhance perception of mobile commerce users to promote such form of shopping in Egypt. Researchers explored the factors affecting users of Mobile communication and electronic shopping by identifying all factors considered in technology users' behaviour theories. Researchers conducted an exploratory research, to examine the most relevant factors for mobile commerce adoption, also, tested the reliable and valid measures extracted, for considering its effect on the user’s perception of its usefulness and ease of use. Three factors were considered of main importance: social influence, convenience and hedonic motivations. Researchers tested those factors and they all affected User’s perception, further They affected each other. Thus, researchers concluded that, in Mobile commerce, Social influence were fully mediated with hedonic motivation and convenience. As of Convenience, it tends to be the most affecting for perceptions of consumers usefulness and ease of use also, it has a strong mediation effect between social influence and mobile commerce user’s perceptions.


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References
  1. Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about information technology usage. MIS quarterly, 665-694.
  2. Ajzen, I. (1988). Attitudes, Personality and Behavior.
  3. Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
  4. Ajzen, I. (2002). Perceived behavioral control, Self‐Efficacy, locus of control, and the theory of planned Behavior1. Journal of applied social psychology, 32(4), 665-683.
  5. Alex bank research team, (2015), Egypt digital economy 2015. Alexandria bank, Egypt, 
  6. https://www.alexbank.com/Cms_Data/Contents/AlexBank_En/Media/Publication/Egypts_Digital_Economy_2015_English-pd.pdf
  7. Bagozzi, R. P., Davis, F. D., & Warshaw, P. R. (1992). Development and test of a theory of technological learning and usage. Human relations, 45(7), 659-686.
  8. Bakos, J. Y. (1991). A strategic analysis of electronic marketplaces. MIS quarterly, 295-310.
  9. Berry, L. L., Seiders, K., & Grewal, D. (2002). Understanding service convenience. Journal of marketing, 66(3), 1-17.
  10. Berry, L. L., Seiders, K., & Grewal, D. (2002). Understanding service convenience. Journal of Marketing Research, 66, 1-17.
  11. Brown, S. A., & Venkatesh, V. (2005). Model of adoption of technology in households: A baseline model test and extension incorporating household life cycle. MIS quarterly, 399-426.
  12. Byrne, B. M. (2013). Structural equation modeling with Mplus: Basic concepts, applications, and programming. Routledge.
  13. Chang, C. C., Yan, C. F., & Tseng, J. S. (2012). Perceived convenience in an extended technology acceptance model: Mobile technology and English learning for college students. Australasian Journal of Educational Technology, 28(5).
  14. Childers, T. L., Carr, C. L., Peck, J., & Carson, S. (2001). Hedonic and utilitarian motivations for online retail shopping behavior. Journal of Retailing, 77(4), 511-535. doi:10.1016/s0022-4359(01)00056-2
  15. Conci, M., Pianesi, F., & Zancanaro, M. (2009). Useful, social and enjoyable: Mobile phone adoption by older people. Human-computer interaction–INTERACT 2009, 63-76.
  16. Courneya, K. S., & Friedenreich, C. M. (1999). Utility of the theory of planned behavior for understanding exercise during breast cancer treatment. Psycho‐oncology, 8(2), 112-122.
  17. Davis, F. D. (1985). A technology acceptance model for empirically testing new end-user information systems: Theory and results (Doctoral dissertation, Massachusetts Institute of Technology).
  18. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319. doi:10.2307/249008 
  19. 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. 
  20. doi:10.1287/mnsc.35.8.982 
  21. Diaz, M. C., & Loraas, T. (2010). Learning new uses of technology while on an audit engagement: Contextualizing general models to advance pragmatic understanding. International Journal of Accounting Information Systems, 11(1), 61-77. 
  22. Dwivedi, Y. K., Wastell, D., Laumer, S., Henriksen, H. Z., Myers, M. D., Bunker, D., ... & Srivastava, S. C. (2015). Research on information systems failures and successes: Status update and future directions. Information Systems Frontiers, 17(1), 143-157.
  23. El-Tawila, S., May Gadalla and Einas Ali (2013). “Income Poverty and Inequality in Egypt’s Poorest Villages”. The World Bank and Social Contract Center, Experts’ Group Meeting, May 27th, Cairo, Egypt.
  24. Faqih, K. M. (2011). Integrating perceived risk and trust with technology acceptance model: An empirical assessment of customers' acceptance of online shopping in Jordan. 2011 International Conference on Research and Innovation in Information Systems. doi:10.1109/icriis.2011.6125686
  25. Farahat, T. (2012). Applying the Technology Acceptance Model to Online Learning in the Egyptian Universities. Procedia - Social and Behavioral Sciences, 64, 95-104. doi: 10.1016/j.sbspro.2012.11.012
  26. Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An introduction to theory and research. Reading, MA: Addison-Wesley Pub. 
  27. Foon, Y. S., & Fah, B. C. Y. (2011). Internet banking adoption in Kuala Lumpur: an application of UTAUT model. International Journal of Business and Management, 6(4), 161-167.
  28. Gefen, D., & Straub, J. W. (2000, October). The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption, Journal of the Association of Information System, 1. Retrieved from http://jais.isworld.org/articles/ default.asp?vol¼1&art¼8 
  29. Gwebu, K. L., & Wang, J. (2011). Adoption of Open Source Software: The role of social identification. Decision Support Systems, 51(1), 220-229.
  30. Hair, J. F., Anderson, R. E., Babin, B. J., & Black, W. C. (2010). Multivariate data analysis: A global perspective (Vol. 7). Upper Saddle River, NJ: Pearson.
  31. Hofacker, C.F., (2001), Internet Marketing (3rd ed.), John Wiley & Sons, Inc, New York.
  32. Hohenberg, H. E., & Rufera, S. (2004). Das Mobiltelefon als Geldbörse der Zukunft—Chancen und Potentiale des Mobile Payment (M-Payment). der markt, 43(1), 33-40.
  33. Holbrook, M. B., & Hirschman, E. C. (1982). The experiential aspects of consumption: Consumer fantasies, feelings, and fun. Journal of consumer research, 9(2), 132-140.
  34. Hsu, C. L., & Lu, H. P. (2007). Consumer behavior in online game communities: A motivational factor perspective. Computers in Human Behavior, 23(3), 1642-1659.
  35. Hsu, H. H., & Chang, Y. Y. (2013). Extended TAM model: Impacts of convenience on acceptance and use of Moodle. Online Submission, 3(4), 211-218.
  36. 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.
  37. Islam, M. A., Ahmad, T. S. B., Khan, M. A., & Ali, M. H. (2010). Adoption of M-commerce services: The case of Bangladesh. World Journal of Management, 2(1), 37-54.
  38. Jaganathan, M., Mustapa, A. N., Hasan, W. A. W., Mat, N. K. N., & Alekam, J. M. E. (2014, December). Does dependency make a difference? The role of convenience, social influence, facilitating condition and self-efficacy on student's purchase behaviour of smartphone. In H. Ibrahim, J. Zulkepli, N. Aziz, N. Ahmad, & S. A. Rahman (Eds.), AIP Conference Proceedings (Vol. 1635, No. 1, pp. 332-339). AIP.
  39. Keeling, M. (1999). Spatial models of interacting populations (pp. 64-99). Blackwell Science, Oxford, UK.
  40. Kim, C., Galliers, R. D., Shin, N., Ryoo, J. H., & Kim, J. (2012). Factors influencing Internet shopping value and customer repurchase intention. Electronic Commerce Research and Applications, 11(4), 374-387.
  41. Kim, H. W., Chan, H. C., & Gupta, S. (2007). Value-based adoption of mobile internet: an empirical investigation. Decision support systems, 43(1), 111-126.
  42. Kim, J., Fiore, A. M., & Lee, H. H. (2007). Influences of online store perception, shopping enjoyment, and shopping involvement on consumer patronage behavior towards an online retailer. Journal of retailing and Consumer Services, 14(2), 95-107.
  43. KIT, A. K. L. (2014). UTAUT2 influencing the behavioural intention to adopt mobile applications (Doctoral dissertation, UNIVERSITI TUNKU ABDUL RAHMAN).
  44. Koenig-Lewis, N., Marquet, M., Palmer, A., & Zhao, A. L. (2015). Enjoyment and social influence: predicting mobile payment adoption. The Service Industries Journal, 35(10), 537-554.
  45. Koenig-Lewis, N., Marquet, M., Palmer, A., & Zhao, A. L. (2015). Enjoyment and social influence: predicting mobile payment adoption. The Service Industries Journal, 35(10), 537-554.
  46. Kripanont, N. (2007). Examining a technology acceptance model of internet usage by academics within Thai business schools (Doctoral dissertation, Victoria University).
  47. Lee, M. C. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic commerce research and applications, 8(3), 130-141.
  48. Limayem, M., Khalifa, M., & Chin, W. W. (2004). Factors motivating software piracy: a longitudinal study. IEEE transactions on engineering management, 51(4), 414-425.
  49. Magni, M., Taylor, M. S., & Venkatesh, V. (2010). ‘To play or not to play’: A cross-temporal investigation using hedonic and instrumental perspectives to explain user intentions to explore a technology. International journal of human-computer studies, 68(9), 572-588.
  50. Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of management review, 20(3), 709-734.
  51. Morosan, C., & DeFranco, A. (2016). It's about time: Revisiting UTAUT2 to examine consumers’ intentions to use NFC mobile payments in hotels. International Journal of Hospitality Management, 53, 17-29.
  52. Müller-Veerse, F. (2001), Umts Report - an Investment Perspective, Durlacher
  53. Park, N., Jung, Y., & Lee, K. M. (2011). Intention to upload video content on the internet: The role of social norms and ego-involvement. Computers in Human Behavior, 27(5), 1996-2004.
  54. Park, N., Kee, K. F., & Valenzuela, S. (2009). Being immersed in social networking environment: Facebook groups, uses and gratifications, and social outcomes. CyberPsychology & Behavior, 12(6), 729-733.
  55. Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Educational technology & society, 12(3), 150-162.
  56. Payfort’s Middle East State of Payment Report, (2014). [Online]. Available from: http://digioh. com/emd/1362679/sjei70h29k. Retrieved on February 13th
  57. Perea y Monsuwé, T., Dellaert, B. G., & De Ruyter, K. (2004). What drives consumers to shop online? A literature reviews. International journal of service industry management, 15(1), 102-121.
  58. Pricewaterhouse Coopers (PWC) “Total retail survey.” 2015. 
  59. https://www.pwc.com/gx/en/industries/retail-consumer/total-retail.html 0(Accessed: March 5, 2015).
  60. Ramayah, T., & Ignatius, J. (2005). Impact of perceived usefulness, perceived ease of use and perceived enjoyment on intention to shop online. ICFAI Journal of Systems Management (IJSM), 3(3), 36-51.
  61. Rhodes, R. E., & Courneya, K. S. (2003). Relationships between personality, an extended theory of planned behaviour model and exercise behaviour. British Journal of Health Psychology, 8(1), 19-36.
  62. Rose, J., & Fogarty, G. (2006). Determinants of perceived usefulness and perceived ease of use in the technology acceptance model: senior consumers' adoption of self-service banking technologies. Academy of World Business, Marketing & Management Development, 2(10), 122-129.
  63. Sara Aggour, “Egyptian e-commerce market to reach $ 2.7 bn by 2020,” Daily News Egypt, May 12, 2015, www.dailynewsegypt.com/2015/05/12/egyptian-e-commerce-market-to-reach-2-7bn-by-202/
  64. Schepers, J., & Wetzels, M. (2007). A meta-analysis of the technology acceptance model: Investigating subjective norm and moderation effects. Information & management, 44(1), 90-103.
  65. Srinivasan, R. (2015). Exploring the Impact of Social Norms and Online Shopping Anxiety in the Adoption of Online Apparel Shopping by Indian Consumers. Journal of Internet Commerce, 14(2), 177-199. doi:10.1080/15332861.2015.1008891
  66. Thong, J. Y., Hong, S. J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799-810.
  67. To, P. L., Liao, C., & Lin, T. H. (2007). Shopping motivations on Internet: A study based on utilitarian and hedonic value. Technovation, 27(12), 774-787
  68. UNCTAD (2002), World Investment Report 2002: Transnational Corporations and Export Competitiveness. Geneva: United Nations
  69. Van der Heijden, H. (2004). User acceptance of hedonic information systems. MIS quarterly, 695-704.
  70. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
  71. 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.
  72. 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.
  73. Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology.
  74. Wang, Y. C. (2016, July). Exploring the Causes of Smartphone Dependency and Purchasing Behavior. In Advanced Applied Informatics (IIAI-AAI), 2016 5th IIAI International Congress on (pp. 745-748). IEEE.
  75. Wolfson, G. K., Magnuson, C. W. and Marsom, G. (2005). Changing the nature of the discourse: Teaching field seminars online. Journal of Social Work Education, 41, 355–361. 
  76. Wu, W., & Ke, C. (2016). An Online Shopping Behavior Model Integrating Personality Traits, Perceived Risk, and Technology Acceptance. Social Behavior and Personality: An International Journal Soc Behav Personal, 44(3), 85-97. doi:10.2224/sbp.2015.43.1.85 
  77. Yoon, C., & Kim, S. (2007). Convenience and TAM in a ubiquitous computing environment: The case of wireless LAN. Electronic Commerce Research and Applications, 6(1), 102-112.
  78. Zhang, Y., Lee, W., & Huang, Y. A. (2003). Intrusion detection techniques for mobile wireless networks. Wireless Networks, 9(5), 545-556.