IJSTR >> Volume 9 - Issue 7, July 2020 Edition

[Full Text]

AUTHOR(S)

Mohamed Ahmed Salem, Khalil Md Nor

KEYWORDS

COVID-19, Coronavirus, e-commerce, Saudi Arabia, technology adoption, Technology Acceptance Model (TAM), Theory of planned behaviour (TPB).

ABSTRACT

Individuals’ adoption has been reckoned as an important indicator of the success of new technology. Hence, it is crucial to identify the reasons why individuals choose to use or not to use a particular technology. Understanding one’s behaviour towards accepting or rejecting technologies has been proven as one of the most challenging issues within the information systems (IS) domain, not to mention during exceptional circumstances, such as during pandemic times. As such, this study empirically assessed the factors that affect consumers’ intention to adopt e-commerce during Coronavirus Disease 2019 (COVID-19) in Saudi Arabia. The 10 factors examined in this study are perceived usefulness (PU), perceived ease of use (PEOU), subjective norms (SN), perceived behavioural control (PBC), perceived lack of alternatives, perceived risk, perceived punishable infractions, risk-taking propensity, perceived external pressure, and government support. Data were collected online among social media users by employing the snowball sampling technique. A total of 190 valid responses were obtained. The data analysis showed that PU, risk taking propensity, PBC, perceived lack of alternatives, and government support significantly influenced consumers’ intention to adopt e-commerce during the COVID-19 outbreak in Saudi Arabia. Meanwhile, PEOU, SN, perceived external pressure, perceived risk, and perceived punishable infractions exerted insignificant effect on consumers’ intention to adopt e-commerce.

REFERENCES

[1] SAMA. THE 17TH MEETING OF THE FSB MENA GROUP DISCUSSES REGIONAL FINANCIAL STABILITY AND THE IMPACT OF COVID-19. 2020 [cited 2020 1 April]; Available from: http://www.sama.gov.sa/en-US/News/Pages/allnews.aspx.
[2] Baker, S.R., et al., How Does Household Spending Respond to an Epidemic? Consumption During the 2020 COVID-19 Pandemic. 2020, National Bureau of Economic Research.
[3] Ganong, P. and P. Noel, Consumer spending during unemployment: Positive and normative implications. American economic review, 2019. 109(7): p. 2383-2424.
[4] Manasseh, C.O., et al., The Effects of Interest and Inflation Rates on Consumption Expenditure: Application of Consumer Spending Model. International Journal of Economics and Financial Issues, 2018. 8(4): p. 32.
[5] Mandel, C. and P. Liebens, The Relationship between GDP and Unemployment Rate in the US. International Journal of Business and Social Science, 2019. 10(4).
[6] Govender, N.M. and M. Pretorius, A critical analysis of information and communications technology adoption: The strategy-as-practice perspective. Acta Commercii, 2015. 15(1): p. 1-13.
[7] Ajzen, I., The theory of planned behavior. Organizational behavior and human decision processes, 1991. 50(2): p. 179-211.
[8] Davis, F.D., Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 1989: p. 319-340.
[9] Awa, H.O., O.U. Ojiabo, and B.C. Emecheta, Integrating TAM, TPB and TOE frameworks and expanding their characteristic constructs for e-commerce adoption by SMEs. Journal of Science & Technology Policy Management, 2015.
[10] Qiu, L. and D. Li, Applying TAM in B2C E-commerce research: An extended model. Tsinghua Science and Technology, 2008. 13(3): p. 265-272.
[11] Gefen, D., E. Karahanna, and D.W. Straub, Trust and TAM in online shopping: An integrated model. MIS quarterly, 2003. 27(1): p. 51-90.
[12] MOH, S.M.o.H. MIDDLE EAST RESPIRATORY SYNDROME CORONAVIRUS;. 2018 [cited 2020 10 April]; Available from: https://www.moh.gov.sa/CCC/healthp/regulations/Documents/MERS-CoV%20Guidelines%20for%20Healthcare%20Professionals%20-%20May%202018%20-%20v5.1%20%281%29.pdf.
[13] weqaya. COVID-19 Virus dashboard. 2020 [cited 2020 12 May]; Available from: https://covid19.cdc.gov.sa/daily-updates/.
[14] cnbc. The SARS epidemic threatened Alibaba’s survival in 2003—here’s how it made it through to become a $470 billion company. 2020 [cited 2020 1 April]; Available from: https://www.cnbc.com/2020/03/26/chinas-2002-2003-sars-outbreak-helped-alibaba-become-e-commerce-giant.html.
[15] Reply. A preliminary report on COVID-19 and its implications. 2020 [cited 2020 6 April]; Available from: https://www.reply.com/en/covid-19-report.
[16] Reuters. British online supermarket Ocado sees orders leap as coronavirus spreads. 2020 [cited 2020 5 April]; Available from: https://www.reuters.com/article/health-coronavirus-ocado-group/british-online-supermarket-ocado-sees-orders-leap-as-coronavirus-spreads-idUSL8N2AV2NL.
[17] Albassam, B.A., Economic diversification in Saudi Arabia: Myth or reality? Resources Policy, 2015. 44: p. 112-117.
[18] Esmail, H., Economic growth of Saudi Arabia between present and future according to 2030 vision. Asian Social Science, 2018. 14(12): p. 192.
[19] Alfuraih, S. E-commerce and e-commerce fraud in Saudi Arabia: A case study. in 2008 International Conference on Information Security and Assurance (isa 2008). 2008. IEEE.
[20] AlGhamdi, R., A.T. Nguyen, and V. Jones, Wheel of B2C e-commerce development in Saudi Arabia, in Robot Intelligence Technology and Applications 2012. 2013, Springer. p. 1047-1055.
[21] SADAD. Vision, Mission & Objectives. 2020 [cited 2020 1 april]; Available from: https://www.sadad.com/en/Pages/VMO.html.
[22] Alqahtani, A.S., R.D. Goodwin, and D.B. de Vries, Cultural factors influencing e-commerce usability in Saudi Arabia. 2018.
[23] AlArfaj, A., E. Solaiman, and L. Marshall, Social Media and E-commerce in the Kingdom of Saudi Arabia–Trends and Future Directions. CHI-ArabHCI, 2019.
[24] Agarwal, R. and J. Prasad, A conceptual and operational definition of personal innovativeness in the domain of information technology. Information systems research, 1998. 9(2): p. 204-215.
[25] Carr Jr, V., Technology adoption and diffusion. The Learning Center for Interactive Technology, 1999.
[26] Venkatesh, V. and X. Zhang, Unified theory of acceptance and use of technology: US vs. China. Journal of global information technology management, 2010. 13(1): p. 5-27.
[27] Tarhini, A., N.A.G. Arachchilage, and M.S. Abbasi, A critical review of theories and models of technology adoption and acceptance in information system research. International Journal of Technology Diffusion (IJTD), 2015. 6(4): p. 58-77.
[28] Abu Tair, H.Y. and E.A. Abu-Shanab, Mobile government services: Challenges and opportunities. International Journal of Technology Diffusion, 2014. 5(1): p. 17-25.
[29] Ajzen, H. and M. Fishbein, Understanding attitudes and predicting social behavior. 1980.
[30] Yu, Y., et al. Understanding the intention to use commercial bike-sharing systems: an integration of TAM and TPB. in Proceedings of the 51st Hawaii International Conference on System Sciences. 2018.
[31] Hossain, R., et al., Effects of Cognitive Ability, Trust and Time-Saving: Predicting Further Amelioration and Successive Usage of E-ticketing with TAM, TPB and Cognitive Frameworks, in Information and Communication Technology for Competitive Strategies. 2019, Springer. p. 41-51.
[32] Yang, H.-H. and C.-H. Su, Learner behaviour in a MOOC practice-oriented course: in empirical study integrating TAM and TPB. International Review of Research in Open and Distributed Learning: IRRODL, 2017. 18(5): p. 35-63.
[33] Lee, M.-C., Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic commerce research and applications, 2009. 8(3): p. 130-141.
[34] Venkatesh, V., V. Ramesh, and A.P. Massey, Understanding usability in mobile commerce. Communications of the ACM, 2003. 46(12): p. 53-56.
[35] Fishbein, M., leek Ajzen (1975), Belief, Attitude, Intention, and Behavior: An Introduction to Theory and Research. Read-ing, MA: Addison-Wesley, 1975.
[36] Hsu, C.-L. and H.-P. Lu, Why do people play on-line games? An extended TAM with social influences and flow experience. Information & management, 2004. 41(7): p. 853-868.
[37] Hsu, H.-h. and Y.-y. Chang, Extended TAM model: Impacts of convenience on acceptance and use of Moodle. Online Submission, 2013. 3(4): p. 211-218.
[38] Liao, C., J.-L. Chen, and D.C. Yen, Theory of planning behavior (TPB) and customer satisfaction in the continued use of e-service: An integrated model. Computers in human behavior, 2007. 23(6): p. 2804-2822.
[39] Kurzban, R. and M.R. Leary, Evolutionary origins of stigmatization: the functions of social exclusion. Psychological bulletin, 2001. 127(2): p. 187.
[40] Schaller, M., J. Park, and J. Faulkner, Prehistoric dangers and contemporary prejudices. European review of social psychology, 2003. 14(1): p. 105-137.
[41] Neuberg, S.L. and C.A. Cottrell, Intergroup emotions: A biocultural approach. From prejudice to intergroup emotions: Differentiated reactions to social groups, 2002: p. 265-283.
[42] Deci, E.L. and R.M. Ryan, Self-determination and intrinsic motivation in human behavior. EL Deci, RM Ryan.–1985, 1985.
[43] Ryan, R.M. and E.L. Deci, Intrinsic and extrinsic motivations: Classic definitions and new directions. Contemporary educational psychology, 2000. 25(1): p. 54-67.
[44] Lee, M.K., C.M. Cheung, and Z. Chen, Acceptance of Internet-based learning medium: the role of extrinsic and intrinsic motivation. Information & management, 2005. 42(8): p. 1095-1104.
[45] Teo, T.S., V.K. Lim, and R.Y. Lai, Intrinsic and extrinsic motivation in Internet usage. Omega, 1999. 27(1): p. 25-37.
[46] Venkatesh, V., Creation of favorable user perceptions: Exploring the role of intrinsic motivation. MIS quarterly, 1999: p. 239-260.
[47] Davis, F.D., R.P. Bagozzi, and P.R. Warshaw, Extrinsic and intrinsic motivation to use computers in the workplace 1. Journal of applied social psychology, 1992. 22(14): p. 1111-1132.
[48] Premkumar, G., K. Ramamurthy, and M. Crum, Determinants of EDI adoption in the transportation industry. European Journal of Information Systems, 1997. 6(2): p. 107-121.
[49] Gabryelczvk, R. An Exploration of BPM Adoption Factors: Initial Steps for Model Development. in 2018 Federated Conference on Computer Science and Information Systems (FedCSIS). 2018. IEEE.
[50] Kumar, D., H.V. Samalia, and P. Verma, Factors influencing cloud computing adoption by small and medium-sized enterprises (SMEs) In India. Pacific Asia Journal of the Association for Information Systems, 2017. 9(3).
[51] Muhammad Auwal, A., et al., External pressure influence on entrepreneurship performance of SMEs: A case study of Malaysian herbal industry. Journal of Small Business & Entrepreneurship, 2018: p. 1-22.
[52] Matta, V., D. Koonce, and A. Jeyaraj, Initiation, experimentation, implementation of innovations: The case for radio frequency identification systems. International Journal of Information Management, 2012. 32(2): p. 164-174.
[53] Chong, A.Y.-L. and F.T. Chan, Structural equation modeling for multi-stage analysis on Radio Frequency Identification (RFID) diffusion in the health care industry. Expert Systems with Applications, 2012. 39(10): p. 8645-8654.
[54] Pan, Y., et al., Adoption model of mobile‐enabled systems in supply chain. Industrial Management & Data Systems, 2013.
[55] Jones, M.A., D.L. Mothersbaugh, and S.E. Beatty, Switching barriers and repurchase intentions in services. Journal of retailing, 2000. 76(2): p. 259-274.
[56] Patterson, P.G. and T. Smith, A cross-cultural study of switching barriers and propensity to stay with service providers. Journal of retailing, 2003. 79(2): p. 107-120.
[57] Bendapudi, N. and L.L. Berry, Customers' motivations for maintaining relationships with service providers. Journal of retailing, 1997. 73(1): p. 15-38.
[58] Sharma, N. and P.G. Patterson, Switching costs, alternative attractiveness and experience as moderators of relationship commitment in professional, consumer services. International journal of service industry management, 2000. 11(5): p. 470-490.
[59] Peter, J.P. and M.J. Ryan, An investigation of perceived risk at the brand level. Journal of marketing research, 1976. 13(2): p. 184-188.
[60] Featherman, M.S. and P.A. Pavlou, Predicting e-services adoption: a perceived risk facets perspective. International journal of human-computer studies, 2003. 59(4): p. 451-474.
[61] Lin, W.-B., Investigation on the model of consumers’ perceived risk—integrated viewpoint. Expert Systems with Applications, 2008. 34(2): p. 977-988.
[62] Reavley, N., Securing online banking. Card Technology Today, 2005. 17(10): p. 12-13.
[63] Kuisma, T., T. Laukkanen, and M. Hiltunen, Mapping the reasons for resistance to Internet banking: A means-end approach. International Journal of Information Management, 2007. 27(2): p. 75-85.
[64] Rundmo, T., Perceived risk, health and consumer behaviour. Journal of Risk Research, 1999. 2(3): p. 187-200.
[65] Lee, D., J. Park, and J.-H. Ahn, On the explanation of factors affecting e-commerce adoption. ICIS 2001 Proceedings, 2001: p. 14.
[66] Gefen, D. and D.W. Straub, The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption. Journal of the association for Information Systems, 2000. 1(1): p. 8.
[67] Pavlou, P.A. and M. Fygenson, Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS quarterly, 2006: p. 115-143.
[68] Opia, O., An Exploratory study of the Moderating effects of Trust on E-commerce Adoption Behaviour of Nigerian small Enterprises. African Journal of Entrepreneurship, 2008. 1(1): p. 43-51.
[69] Venkatesh, V. and F.D. Davis, A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 2000. 46(2): p. 186-204.
[70] Shi, H., S. Wang, and D. Zhao, Exploring urban resident’s vehicular PM2. 5 reduction behavior intention: An application of the extended theory of planned behavior. Journal of Cleaner Production, 2017. 147: p. 603-613.
[71] Ajzen, I., Perceived behavioral control, self‐efficacy, locus of control, and the theory of planned behavior 1. Journal of applied social psychology, 2002. 32(4): p. 665-683.
[72] Taylor, S. and P.A. Todd, Understanding information technology usage: A test of competing models. Information systems research, 1995. 6(2): p. 144-176.
[73] Mathieson, K., Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information systems research, 1991. 2(3): p. 173-191.
[74] Chai, L. and P. Pavlou, Customer relationship management. com: a cross-cultural empirical investigation of electronic commerce. AMCIS 2002 Proceedings, 2002: p. 70.
[75] George, J.F., The theory of planned behavior and Internet purchasing. Internet research, 2004.
[76] Karahanna, E., D.W. Straub, and N.L. Chervany, Information technology adoption across time: a cross-sectional comparison of pre-adoption and post-adoption beliefs. MIS quarterly, 1999: p. 183-213.
[77] Gangwal, N. and V. Bansal. Application of decomposed theory of planned behavior for m-commerce adoption in India. in International Conference on Enterprise Information Systems. 2016. SCITEPRESS.
[78] Karami, M., Factors influencing adoption of online ticketing. 2006.
[79] MacPherson, L., et al., Positive and negative reinforcement underlying risk behavior in early adolescents. Prevention Science, 2010. 11(3): p. 331-342.
[80] Lejuez, C., et al., Reliability and validity of the youth version of the Balloon Analogue Risk Task (BART–Y) in the assessment of risk-taking behavior among inner-city adolescents. Journal of Clinical Child and Adolescent Psychology, 2007. 36(1): p. 106-111.
[81] Cossens, J. and S. Gin, Tourism and AIDS: The perceived risk of HIV infection on destination choice. Journal of Travel & Tourism Marketing, 1995. 3(4): p. 1-20.
[82] Coid, J.W., et al., Risk taking and violence, in Improving risk management for violence in mental health services: a multimethods approach. 2016, NIHR Journals Library.
[83] Chan, W. and G.S. Rigakos, Risk, crime and gender. British Journal of Criminology, 2002. 42(4): p. 743-761.
[84] Crowley, T.J., Learning and unlearning drug abuse in the real world: Clinical treatment and public policy. Learning Factors in Substance Abuse. National Institute on Drug Abuse Research Monograph, 1988(84): p. 100-121.
[85] Molgard, R.K., Team Rules and Regulations Used by Football and Basketball Coaches in Utah High Schools. 1973.
[86] Mushonga, M., Curfew and the ‘Man in the Middle’in Zimbabwe’s War of Liberation with special Reference to the Eastern Areas of Zimbabwe, 1977–1980. From National Liberation to Democratic Renaissance in Southern Africa. Dakar: CODESRIA, 2005: p. 171-190.
[87] Hegarty, W.H. and H.P. Sims, Some determinants of unethical decision behavior: An experiment. Journal of applied Psychology, 1978. 63(4): p. 451.
[88] Axelrod, S., Effects of punishment on human behavior. 2013: Academic Press.
[89] Johnston, J.M., Punishment of human behavior. American psychologist, 1972. 27(11): p. 1033.
[90] Ifinedo, P., Facilitating the intention to expand e-business payment systems use in Nigerian small firms: an empirical analysis. E-business–applications and global acceptance, 2012: p. 19.
[91] Chau, P.Y. and C.C. Jim, Adoption of electronic data interchange in small and medium-sized enterprises. Journal of Global Information Management (JGIM), 2002. 10(4): p. 61-85.
[92] Dimitrova, D.V. and Y.-C. Chen, Profiling the adopters of e-government information and services: The influence of psychological characteristics, civic mindedness, and information channels. Social Science Computer Review, 2006. 24(2): p. 172-188.
[93] Tan, M. and T.S. Teo, Factors influencing the adoption of Internet banking. Journal of the Association for information Systems, 2000. 1(1): p. 5.
[94] Besley, T. and R. Burgess, The political economy of government responsiveness: Theory and evidence from India. The quarterly journal of economics, 2002. 117(4): p. 1415-1451.
[95] Kurnia, S., et al., E-commerce technology adoption: A Malaysian grocery SME retail sector study. Journal of Business Research, 2015. 68(9): p. 1906-1918.
[96] Evanschitzky, H., et al., The relative strength of affective commitment in securing loyalty in service relationships. Journal of business research, 2006. 59(12): p. 1207-1213.
[97] Hansen, J.M., G. Saridakis, and V. Benson, Risk, trust, and the interaction of perceived ease of use and behavioral control in predicting consumers’ use of social media for transactions. Computers in Human Behavior, 2018. 80: p. 197-206.
[98] Liao, Q., et al., Workplace management and employee misuse: does punishment matter? Journal of Computer Information Systems, 2009. 50(2): p. 49-59.
[99] Looi, H.C., E-commerce adoption in Brunei Darussalam: A quantitative analysis of factors influencing its adoption. Communications of the Association for Information Systems, 2005. 15(1): p. 3.
[100] Taherdoost, H., Sampling methods in research methodology; How to choose a sampling technique for research. 2016.
[101] Creswell, J.W. and J.D. Creswell, Research design: Qualitative, quantitative, and mixed methods approaches. 2017: Sage publications.
[102] Hair Jr, J.F., et al., A primer on partial least squares structural equation modeling (PLS-SEM). 2016: Sage publications.
[103] Cheah, J.-H., et al., Convergent validity assessment of formatively measured constructs in PLS-SEM. International Journal of Contemporary Hospitality Management, 2018.
[104] Savickas, M.L., B.J. Taber, and A.R. Spokane, Convergent and discriminant validity of five interest inventories. Journal of Vocational Behavior, 2002. 61(1): p. 139-184.
[105] Mittinty, M.M., et al., Assessment of pain-related fear in individuals with chronic painful conditions. Journal of pain research, 2018. 11: p. 3071.
[106] Xu, Y., et al. The Impact of Curriculum Teaching Reform on Graduate Students’ Innovation and Entrepreneurship. in International Conference on Applied Human Factors and Ergonomics. 2017. Springer.
[107] Al-Emran, M., V. Mezhuyev, and A. Kamaludin. PLS-SEM in information systems research: a comprehensive methodological reference. in International Conference on Advanced Intelligent Systems and Informatics. 2018. Springer.
[108] Bagozzi, R.P. and Y. Yi, On the evaluation of structural equation models. Journal of the academy of marketing science, 1988. 16(1): p. 74-94.
[109] Fornell, C. and D.F. Larcker, Structural equation models with unobservable variables and measurement error: Algebra and statistics. 1981, SAGE Publications Sage CA: Los Angeles, CA.
[110] Cho, D.-Y., H.J. Kwon, and H.-Y. Lee. Analysis of trust in internet and mobile commerce adoption. in 2007 40th Annual Hawaii International Conference on System Sciences (HICSS'07). 2007. IEEE.
[111] Wei, T.T., et al., What drives Malaysian m‐commerce adoption? An empirical analysis. Industrial management & data systems, 2009.
[112] Kurnia, S., S.P. Smith, and H. Lee, Consumers’ perception of mobile internet in Australia. Business Review, 2006. 5(1): p. 19-32.
[113] Morowatisharifabad, M.A., et al., Determinants of self-care behaviors in patients with knee osteoarthritis based on the theory of planned behavior in Iran. 2020.
[114] Bozionelos, G. and P. Bennett, The theory of planned behaviour as predictor of exercise: The moderating influence of beliefs and personality variables. Journal of health psychology, 1999. 4(4): p. 517-529.
[115] Deng, C., L. Zhang, and J. Zhang, Individual adoption of mobile commerce: A meta analysis. Library and Information Service, 2012. 56(18): p. 137-143.
[116] Arias-Oliva, M., J. Pelegrín-Borondo, and G. Matías-Clavero, Variables influencing cryptocurrency use: a technology acceptance model in spain. Frontiers in Psychology, 2019. 10.
[117] Jiang, Y. Study of User's Adoption Behavior for Innovative Mobile Commerce—Take the Internet Chauffeured Car as an Example. in Wuhan International Conference on e-Business. 2017. Association For Information Systems.
[118] Wong, C.C. and P.L. Hiew. Diffusion of mobile entertainment in Malaysia: Drivers and barriers. in WEC (5). 2005. Citeseer.
[119] Yu, C.-S., Factors affecting individuals to adopt mobile banking: Empirical evidence from the UTAUT model. Journal of electronic commerce research, 2012. 13(2): p. 104.
[120] Allison, J.J., et al., The association of physician attitudes about uncertainty and risk taking with resource use in a Medicare HMO. Medical Decision Making, 1998. 18(3): p. 320-329.
[121] Capanna, M., et al., Risk-taking in junior doctors working night shifts in intensive care. Intensive care medicine, 2017. 43(5): p. 709-710.
[122] Schoemaker, P.J., Determinants of risk-taking: Behavioral and economic views. Journal of Risk and Uncertainty, 1993. 6(1): p. 49-73.
[123] AlGhamdi, R., S. Drew, and W. Al‐Ghaith, Factors Influencing e‐commerce Adoption by Retailers in Saudi Arabia: a qualitative analysis. The Electronic Journal of Information Systems in Developing Countries, 2011. 47(1): p. 1-23.
[124] Khoo, V., A. Ahmi, and R.A.J. Saad, E-commerce adoption research: a review of literature. The Journal of Social Sciences Research, 2018(6): p. 90-99.
[125] Safeena, R., et al., Combination of TAM and TPB in internet banking adoption. International Journal of Computer Theory and Engineering, 2013. 5(1): p. 146.