Adoption of M-Commerce Amongst Pakistani Consumers: By Using TAM with Effect of Social Influence and Mobile Self-Efficacy

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Farah Naz
Safeer Haider
Shakeel Ahmed

Abstract

M-commerce, or mobile commerce, has gained popularity due to its expanded possibilities over Electronic commerce, which has been growing at a far faster pace because to the Covid-19 outbreak in Pakistan. The utilization of wireless technologies has increased in the M-commerce sector. There are more mobile phone users than personal computer users. In spite of Pakistan's apparent interest in M-commerce as a means of trade, nothing is known about the country's citizens' desire to utilize this new wireless electronic platform. Thus, customers' behavioral intention to use m-commerce has been investigated using an improved technology acceptance model (TAM) that takes into account the consequences of social influence and mobile self-efficacy. The sample size for this study was 220 people from Lahore, Karachi, Islamabad, and Multan, which is a statistically significant number. A variety of statistical techniques, including factor and reliability analysis as well as correlation and regression, were used to examine the data by using SPSS version 19. Customers' attitudes toward adopting M-commerce are positively influenced by their perceptions of its utility, ease of use, social influence, and personal efficacy on their mobile devices, according to this study, which also found a link between these factors and their behavioral intent to use M-commerce.

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How to Cite
[1]
Naz, F., Haider, S. and Ahmed, S. 2023. Adoption of M-Commerce Amongst Pakistani Consumers: By Using TAM with Effect of Social Influence and Mobile Self-Efficacy. Journal of Policy Research. 9, 4 (Dec. 2023), 303–317. DOI:https://doi.org/10.61506/02.00153.

References

  1. AlSondos, I., & Salameh, A. (2020). The effect of system quality and service quality toward using m-commerce service, based on consumer perspective. Management Science Letters, 10(11), 2589-2596. DOI: https://doi.org/10.5267/j.msl.2020.3.035
  2. Anonymous, Nokia Partnering with VISA on Transaction and Payment Using Cell Phone, Dow Johns, 2003 (Sep 23).
  3. Anonymous, South Korea Promotes Cell Phone ElectronicCash Purse, United News, 2003 (Oct 14).
  4. Alrakaba, 2014, Alrakaba article, http://www.alrakoba.net/articles2/showarticle.php?article=26398
  5. Aldas-Manzano, J., Ruiz-Mafe, C., & Sanz-Blas, S. (2009). Exploring individual personality factors as drivers of M-shopping acceptance. Industrial Management DOI: https://doi.org/10.1108/02635570910968018
  6. & Data Systems, 109(6), 739–757.
  7. A. Jeyaraj, J.W. Rottman,M.C. Lacity,A reviewof the predictors, linkages, and biases in IT innovation adoption research, Journal of Information Technology 21 (2006) 1–23.
  8. Armitage, C. J., & Conner, M. (2001). Efficacy of the Theory of Planned Behaviour: A metaanalytic, review. British Journal of Social Psychology, 40, 471-499. DOI: https://doi.org/10.1348/014466601164939
  9. A.L. Lederer, D.J. Maupin, M.P. Sena, Y. Zhuang, The technology acceptance model and the World Wide Web, Decision Support Systems 29 (2000) 269–282. DOI: https://doi.org/10.1016/S0167-9236(00)00076-2
  10. A. Jeyaraj, J.W. Rottman,M.C. Lacity,A reviewof the predictors, linkages, and biases in IT innovation adoption research, Journal of Information Technology 21 (2006) 1–23. DOI: https://doi.org/10.1057/palgrave.jit.2000056
  11. A.Y.L. Chong, N. Darmawan, K.B. Ooi, B. Lin, Adoption of 3G services among Malaysian consumers: an empirical analysis, International Journal of Mobile Communications 8 (2010) 129–149.
  12. Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological Bulletin, 82(2), 261-277. DOI: https://doi.org/10.1037/h0076477
  13. Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckman (Eds.), Action-control: From cognition to behavior (pp. 11- 39). Heidelberg, Germany: Springer. DOI: https://doi.org/10.1007/978-3-642-69746-3_2
  14. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179e211. DOI: https://doi.org/10.1016/0749-5978(91)90020-T
  15. Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.
  16. Ajzen, I. (in press). Perceived Behavioral Control, Self-Efficacy, Locus of Control, and the
  17. Theory of Planned Behavior, Journal of Applied Social Psychology
  18. Ashraf, A. R., Tek, N. T., Anwar, A., Lapa, L., & Venkatesh, V. (2021). Perceived values and motivations influencing m-commerce use: A nine-country comparative study. International Journal of Information Management, 59, 102318. DOI: https://doi.org/10.1016/j.ijinfomgt.2021.102318
  19. Aboelmaged, M. (2021). E-waste recycling behaviour: An integration of recycling habits into the theory of planned behaviour. Journal of Cleaner Production, 278, 124182. DOI: https://doi.org/10.1016/j.jclepro.2020.124182
  20. Balapour, A., Reychav, I., Sabherwal, R., & Azuri, J. (2019). Mobile technology identity and self-efficacy: Implications for the adoption of clinically supported mobile health apps. International Journal of Information Management, 49, 58-68. DOI: https://doi.org/10.1016/j.ijinfomgt.2019.03.005
  21. Chau, N. T., Deng, H., & Tay, R. (2020). Critical determinants for mobile commerce adoption in Vietnamese small and medium-sized enterprises. Journal of Marketing Management, 36(5-6), 456-487. DOI: https://doi.org/10.1080/0267257X.2020.1719187
  22. Chi, T. (2018). Understanding Chinese consumer adoption of apparel mobile commerce: An extended TAM approach. Journal of Retailing and Consumer Services, 44, 274-284. DOI: https://doi.org/10.1016/j.jretconser.2018.07.019
  23. Chong, A. Y. L., Chan, F. T., & Ooi, K. B. (2012). Predicting consumer decisions to adopt mobile commerce: Cross country empirical examination between China and Malaysia. Decision Support Systems, 53(1), 34-43. DOI: https://doi.org/10.1016/j.dss.2011.12.001
  24. Capistrano, E. P., Gomez, M. M. E., & Isleta, A. P. M. (2023). Examining trust, self-efficacy, and technology acceptance in the Philippines’e-commerce sectors. Information Development, 02666669231153837 DOI: https://doi.org/10.1177/02666669231153837
  25. Chad Terhune, Gabriel Kahn, Coke lures Japanese customers with cellphone come-ons, Wall Street Journal (2003 Sept 8).
  26. Chong, A. Y. L., Darmawan, N., Ooi, K. B., & Lin, B. (2010). Adoption of 3G services among Malaysian consumers: An empirical analysis. International Journal of Mobile Communications, 8(2), 129–149. DOI: https://doi.org/10.1504/IJMC.2010.031444
  27. Chong, A. Y. L., Ooi, K. B., Lin, B., & Bao, H. J. (2011). An empirical analysis of the determinants of 3G adoption in China. Computers in Human Behavior, 28(2), 360–369. DOI: https://doi.org/10.1016/j.chb.2011.10.005
  28. C.L. Hsu, H.P. Lu, Why do people play on-line games? An extended TAM with social influences and flow experience, Information Management 41 (2004) 853–868. DOI: https://doi.org/10.1016/j.im.2003.08.014
  29. Chang, S. H., and Tung, F. C. An empirical investigation of students’ behavioral intentions to use the online learning course websites. British Journal of Educational Technology, 39, 1, 2008, 71–83. DOI: https://doi.org/10.1111/j.1467-8535.2007.00742.x
  30. Compeau, R. D., and Higgins, A. C. Computer self-efficacy: development of a measure and initial test. MIS Quarterly, 19, 2, 1995, 189–211. DOI: https://doi.org/10.2307/249688
  31. V. Venkatesh, Understanding usability in mobile commerce, CACM 46 (12) (2003 (December)) 53– 56.
  32. Cockrill, A., Goode, M. H., and Beetles, A. The critical role of perceived risk and trust in determining customer satisfaction with automated banking channels. Services Marketing Quarterly, 30, 2, 2009, 174–193. DOI: https://doi.org/10.1080/15332960802619231
  33. Chen, Y. S., Qiao, J., & Yan, F. Z. (2009). Factors that best predict the intention of producing green vegetables. Evidence from Beijing city. Issues in Agricultural Economy, 634e639.
  34. Conner, M., & Armitage, C. J. (1998). Extending the theory of planned behavior: A reviewand avenues for further research. Journal of Applied Social Psychology, 28(15), 1429- DOI: https://doi.org/10.1111/j.1559-1816.1998.tb01685.x
  35. Chen, Z. S., Li, R., Chen, X., & Xu, H. (2011). A Survey Study on Consumer Perception of Mobile-Commerce Applications. Procedia Environmental Sciences, 11, 118-124. DOI: https://doi.org/10.1016/j.proenv.2011.12.019
  36. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptanceof information technology. MIS Quarterly, 13(3), 319–339.
  37. Y. Lee, I. Benbasat, User interface design for mobile commerce, CACM 46 (12) (2003 (December)) 49–52. DOI: https://doi.org/10.1145/953460.953487
  38. Dumanska, I., Hrytsyna, L., Kharun, O., & Matviiets, O. (2021). E-commerce and M-commerce as Global Trends of International Trade Caused by the Covid-19 Pandemic. DOI: https://doi.org/10.37394/232015.2021.17.38
  39. F.D. Davis, Perceived usefulness, perceived ease of use, and user acceptance of information technologies, MIS Quarterly 13(3), 1989, pp. 319–340. DOI: https://doi.org/10.2307/249008
  40. Featherman, M., and Pavlos, P. Predicting E-services adoption: a perceived risk facets perspective. International Journal of Human–Computer Studies, 59, 4, 2003, 451–474. DOI: https://doi.org/10.1016/S1071-5819(03)00111-3
  41. Featherman, M. S., Miyazaki, A. D., & Sprott, D. E. (2010). Reducing online privacy risk to facilitate e-service adoption: The influence of perceived ease of use and corporate credibility. Journal of Services Marketing, 24(3), 219–229. DOI: https://doi.org/10.1108/08876041011040622
  42. H. Feng, T. Hoegler, W. Stucky, Exploring the critical success factors for mobile commerce, 2007, p. 40. DOI: https://doi.org/10.1109/ICMB.2006.15
  43. Ha, I., Yoon, Y., & Choi, M. (2007). Determinants of adoption of mobile games under
  44. mobile broadband wireless access environment. Information & Management,
  45. (3), 276–286.
  46. Hsu, C. I., Shih, M. L., Huang, B. W., Lin, B. Y., & Lin, C. N. (2009). Predicting tourism loyalty using an integrated Bayesian network mechanism. Expert Systems with Applications, 36(9), 11760–11763. DOI: https://doi.org/10.1016/j.eswa.2009.04.010
  47. Igbaria, M., and Iivari, J. The effects of self-efficacy on computer usage. Journal of Management Science, 23, 6, 1995, 587–605. DOI: https://doi.org/10.1016/0305-0483(95)00035-6
  48. Johnson, R. D. An empirical investigation of sources of application-specific computer-self-efficacy and mediators of the efficacy-performance relationship. International Journal of Human–Computer Studies, 62, 2005, 737– 758. DOI: https://doi.org/10.1016/j.ijhcs.2005.02.008
  49. Jobodwana, Z. N. (2009). E-Commerce and Mobile Commerce in South Africa: Regulatory Challenges. J. Int'l Com. L. & Tech., 4, 287.
  50. J.S. Pascoe, V.S. Sunderam, U. Varshney, R.J. Loader, Middleware enhancements for metropolitan area wireless Internet access, Future Generation Computer Systems 18(5), 2002, pp. 721–735. DOI: https://doi.org/10.1016/S0167-739X(02)00037-7
  51. J.H. Wu, S.C. Wang, What drives mobile commerce?: an empirical evaluation of the revised technology acceptancemodel, InformationManagement 42 (2005) 719–729.
  52. Jung, Y., Perez-Mira, B., & Wiley-Patton, S. (2009). Consumer adoption of mobile TV: Examining psychological flow and media content. Computers in Human DOI: https://doi.org/10.1016/j.chb.2008.07.011
  53. Behavior, 25(1), 123–129.
  54. 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,
  55. (1), 103–110.
  56. Keen, P.G.W., Mackintosh, R., 2001. The Freedom Economy: Gaining the M-Commerce Edge in the Era of the Wireless Internet. Osborne/ McGrew-Hill, Berkeley.
  57. 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. DOI: https://doi.org/10.1016/j.dss.2005.05.009
  58. Kannan, P.K., Chang, A.M., Whinston, A.B., 2001. Wireless Commerce: Marketing Issues and Possibilities. Proceeding of the 34th Annual Hawaii International Conference on System Sciences. Maui, Hawaii.
  59. K. Lee, A. Yan, K. Joshi, Understanding the dynamics of users' belief in softwareapplication adoption, International Journal of Information Management 31 (2) (2011) 160–170. DOI: https://doi.org/10.1016/j.ijinfomgt.2010.07.009
  60. L.D. Chen, M.L. Gillenson, D.L. Sherrell, Enticing online consumers: an extended technology acceptance perspective, Information & Management 39(8), 2002, pp. 705–719. DOI: https://doi.org/10.1016/S0378-7206(01)00127-6
  61. Lai, V. S., and Li, H. Technology acceptance model: an invariance analysis. Journal of Information and Management, 42, 2, 2005, 373–386. DOI: https://doi.org/10.1016/j.im.2004.01.007
  62. MISHRA, S. (2014). Adoption of M-commerce in India: Applying Theory of Planned Behaviour Model. Journal of Internet Banking and Commerce,19(1).
  63. M. Igbaria, N. Zinatelli, P. Cragg, A. Cavaye, Personal computingacceptance factors in small firms: a structural equationmodel, MIS Quarterly 21(3), 1997, pp. 279–302. DOI: https://doi.org/10.2307/249498
  64. M.A. Fishbein, I. Ajzen, Belief, Attitude, Intention and Behavior:An Introduction to Theory and Research, Addison- Wesley, Reading, MA, 1975.
  65. Mcilroy, D., Sadler, C., and Boojawon, N. Computer phobia and computer self-efficacy: their association with undergraduates’ use of university computer facilities. Computers in Human Behavior, 23, 3, 2007, 1285–1299. DOI: https://doi.org/10.1016/j.chb.2004.12.004
  66. Mallat, N., Rossi, M., Tuunainen, V. K., & Oorni, A. (2009). The impact of use context on mobile services acceptance: The case of mobile ticketing. Information & DOI: https://doi.org/10.1016/j.im.2008.11.008
  67. Management, 46(3), 190–195.
  68. Mostafa, A. A., & Eneizan, B. (2018). Factors affecting acceptance of mobile banking in developing countries. International Journal of Academic Research in Business and Social Sciences, 8(1), 340-351. DOI: https://doi.org/10.6007/IJARBSS/v8-i1/3812
  69. Nguyen, G. D., & Ha, M. T. (2021). The role of user adaptation and trust in understanding continuance intention towards mobile shopping: An extended expectation-confirmation model. Cogent Business & Management, 8(1), 1980248. DOI: https://doi.org/10.1080/23311975.2021.1980248
  70. Ngai, E. W. T., and Gunasekaran, A. A review for mobile commerce research and applications. Decision Support Systems, 43, 1, 2007, 3–15. DOI: https://doi.org/10.1016/j.dss.2005.05.003
  71. Omar, S., Mohsen, K., Tsimonis, G., Oozeerally, A., & Hsu, J. H. (2021). M-commerce: The nexus between mobile shopping service quality and loyalty. Journal of Retailing and Consumer Services, 60, 102468. DOI: https://doi.org/10.1016/j.jretconser.2021.102468
  72. Pejic Bach, M., Aleksic, A., & Merkac-Skok, M. (2018). Examining determinants of entrepreneurial intentions in Slovenia: applying the theory of planned behaviour and an innovative cognitive style. Economic research-Ekonomska istraživanja, 31(1), 1453-1471. DOI: https://doi.org/10.1080/1331677X.2018.1478321
  73. Singh, S., Sahni, M. M., & Kovid, R. K. (2020). What drives FinTech adoption? A multi-method evaluation using an adapted technology acceptance model. Management Decision. DOI: https://doi.org/10.1108/MD-09-2019-1318
  74. iometric Analysis Using R Biblioshiny. Sustainability, 15(15), 11835.
  75. Salam, M. A., Saha, T., Rahman, M. H., & Mutsuddi, P. (2021). Challenges to Mobile Banking Adaptation in COVID-19 Pandemic.'. Journal of Business and Management Sciences, 9, 101-113. DOI: https://doi.org/10.12691/jbms-9-3-2
  76. Singh, N., Sinha, N., & Liébana-Cabanillas, F. J. (2020). Determining factors in the adoption and recommendation of mobile wallet services in India: Analysis of the effect of innovativeness, stress to use and social influence. International Journal of Information Management, 50, 191-205 DOI: https://doi.org/10.1016/j.ijinfomgt.2019.05.022
  77. Sabir, H. M., Arshad, A., Sardar, S., & Latif, B. (2014). VAIC and Firm Performance: Banking Sector Of Pakistan. In Information and Knowledge Management (Vol. 4, No. 2, pp. 100-107).
  78. Shin, D. H. (2009). Understanding user acceptance of DMB in South Korea using the DOI: https://doi.org/10.1080/10447310802629785
  79. modified technology acceptance model. International Journal of Human–Computer Interaction, 25(3), 173–198.
  80. Shih, H. P. An empirical study on predicting user acceptance of e-shopping on the web. Information and Management, 41, 3, 2004, 351–368. DOI: https://doi.org/10.1016/S0378-7206(03)00079-X
  81. Tsalgatidou, A., Pitoura, E., 2001. Business models and transaction in mobile electronic commerce: requirements and properties. Computer DOI: https://doi.org/10.1016/S1389-1286(01)00216-X
  82. Networks 37 (2), 221–236.
  83. Thangavel, P., & Chandra, B. (2023). Two Decades of M-Commerce Consumer Research: A Bibliometric Analysis Using R Biblioshiny. Sustainability, 15(15), 11835. DOI: https://doi.org/10.3390/su151511835
  84. Tsalgatidou, A., Veijalainen, J., 2000. Mobile electronic commerce: emerging issues. In: Proceedings of EC-WEB 2000 1st International Conference on E-Commerce and Web Technologies. London, Greenwich, UK, September, pp. 477–486. DOI: https://doi.org/10.1007/3-540-44463-7_42
  85. T.T. Wei, G. Marthandan, A.Y.L. Chong, K.B. Ooi, S. Arumugam, What drives Malaysian m-commerce adoption? An empirical analysis, Industrial Management & Data Systems 109 (2009) 370–388.
  86. Taylor S, Todd P. Assessing IT usage the role of prior experience. MIS Quart 1995;19(4):561–70. DOI: https://doi.org/10.2307/249633
  87. To, P. L., Liao, C., Chiang, J. C., Shih, M. L., & Chang, C. Y. (2008). An empirical investigation of the factors affecting the adoption of instant messaging in organizations. Computer Standards & Interfaces, 30(3), 148–156. DOI: https://doi.org/10.1016/j.csi.2007.08.019
  88. T. Cheng, D.Y.C. Lam, A.C.L. Yeung, Adoption of internet banking: an empirical study in Hong Kong, Decision Support Systems 42 (2006) 1558–1572. DOI: https://doi.org/10.1016/j.dss.2006.01.002
  89. Thongpapanl, N., Ashraf, A. R., Lapa, L., & Venkatesh, V. (2018). Differential effects of customers’ regulatory fit on trust, perceived value, and m-commerce use among developing and developed countries. Journal of International Marketing, 26(3), 22-44. DOI: https://doi.org/10.1509/jim.17.0129
  90. T.P. Liang, C.P.Wei, Introduction to the special issue:mobile commerce applications,
  91. International Journal of Electronic Commerce 8 (3) (2004) 7–17. DOI: https://doi.org/10.1080/10864415.2004.11044303
  92. Tornikoski, E., & Maalaoui, A. (2019). Critical reflections–The Theory of Planned Behaviour: An interview with Icek Ajzen with implications for entrepreneurship research. International Small Business Journal, 37(5), 536-550. DOI: https://doi.org/10.1177/0266242619829681
  93. Lee_, Y. H. (2008). Exploring factors affecting mobile commerce adoption in the US,
  94. Korea and China: The tests of theories, models and cultural differences. Honors
  95. Thesis of University of South California.
  96. Leong, L. Y., Ooi, K. B., Chong, A. Y. L., & Lin, B. (2011). Influence of individual characteristics, perceived usefulness and perceived ease of use on mobile entertainment adoption in Malaysia – an SEM approach. International Journal of Mobile Communications, 9(44), 359–382. DOI: https://doi.org/10.1504/IJMC.2011.041141
  97. Lucas, G. A., Lunardi, G. L., & Dolci, D. B. (2023). From e-commerce to m-commerce: An analysis of the user’s experience with different access platforms. Electronic Commerce Research and Applications, 58, 101240. DOI: https://doi.org/10.1016/j.elerap.2023.101240
  98. Venkatesh, V., Ramesh, V., and Massey, A. P. Understanding usability in mobile commerce. Communications of the ACM, 46, 12, 2003, 53–56. DOI: https://doi.org/10.1145/953460.953488
  99. Verhagen, T., Tan, Y., and Meents, S. An empirical exploration of trust and risk associated with purchasing at electronic marketplaces. In Proceedings of the 17th Bled Ecommerce Conference, June 21–23, Bled, Slovenia, 2004.
  100. V. Venkatesh, M.G. Morris, Why do not men ever stop to ask for directions? Gender, social influence, and their role in technology acceptance and usage behavior MIS Quarterly 24(1), 2000, pp. 115–139. DOI: https://doi.org/10.2307/3250981
  101. Vinerean, S., Budac, C., Baltador, L. A., & Dabija, D. C. (2022). Assessing the Effects of the COVID-19 Pandemic on M-Commerce Adoption: An Adapted UTAUT2 Approach. Electronics, 11(8), 1269. DOI: https://doi.org/10.3390/electronics11081269
  102. Verkijika, S. F. (2018). Factors influencing the adoption of mobile commerce applications in Cameroon. Telematics and Informatics, 35(6), 1665-1674. DOI: https://doi.org/10.1016/j.tele.2018.04.012
  103. Wu, J. H., & Wang, S. C. (2005). What drives mobile commerce?: An empirical evaluation of the revised technology acceptance model. Information & management, 42(5), 719-729. DOI: https://doi.org/10.1016/j.im.2004.07.001
  104. Wei, T. T., Marthandan, G., Chong, A. Y. L., Ooi, K. B., & Arumugam, S. (2009). What drives Malaysian m-commerce adoption? An empirical analysis. Industrial Management & Data Systems, 109(3), 370–388. DOI: https://doi.org/10.1108/02635570910939399
  105. W.T. Rupp, A.D. Smith, Mobile commerce: new revenue machine or black hole? Business Horizons 2002, pp. 26– 29. DOI: https://doi.org/10.1016/S0007-6813(02)00223-9
  106. Wu, C. S., Cheng, F. F., Yen, D. C., & Huang, Y. W. (2011). User acceptance of wireless technology in organizations: A comparison of alternative models. Computer DOI: https://doi.org/10.1016/j.csi.2010.03.002
  107. Standards & Interfaces, 33(1), 50–58.
  108. Wang, Y., Wang, Y., Lin, H., and Tang, T. Determinants of user acceptance of internet banking: an empirical study. International Journal of Service Industry Management, 14, 5, 2003, 501–519. DOI: https://doi.org/10.1108/09564230310500192
  109. www.pta.gov.pk/assets/media/pta_annual_report_2022_10012023.pdf
  110. Yeow, P. H., and Yee, Y. Y. User acceptance of Internet banking service in Malaysia. Lecture Notes in Business Information Processing, Vol. 18). Springer, New York, NY, 2008.
  111. Yanga, H. D., and Yoo, Y. It’s all about attitude: revisiting the technology acceptance model. Decision Support Systems, 38, 2004, 19–31. DOI: https://doi.org/10.1016/S0167-9236(03)00062-9
  112. Y.-F. Kuo, S.-N. Yen, Towards an understanding of the behavioral intentionto use 3G mobile value-added services, Computers in Human Behavior 25 (1) (2009) 103–110. DOI: https://doi.org/10.1016/j.chb.2008.07.007
  113. Zhang, L., Zhu, J., & Liu, Q. (2012). A meta-analysis of mobile commerce adoption and the moderating effect of culture. Computers in Human Behavior, 28(5), 1902-1911. DOI: https://doi.org/10.1016/j.chb.2012.05.008