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Identifying consumer resistance of mobile payment during COVID-19: an interpretive structural modeling (ISM) approach

Abstract

Purpose – Due to country-wise lockdown and state-wise curfews in COVID-19, people were not able to make offline payments (i.e. cash payments) during purchases in India. So, people are switching their payment behavior from offline to online mode. But, as per the central bank report, the rate of adoption through mobile payments is still slow. The paper focuses on identifying critical barriers to mobile payment systems (MPSs) adoption in India. Innovation resistance theory (IRT) has been used as a base model for barriers, despite the wide range of choices of barriers available in the MPSs context. Additionally, three external variables which are out of the wider coverage of IRT constructs were incorporated in this paper. The study, on the other hand, adds to innovation resistance theory in the frame of reference of MPSs from a theoretical perspective. Interpretive structural modeling (ISM), together with MICMAC analysis is brought into play to analyse the direct and indirect relationship amongst the barriers.


Research methodology – ISM approach has been used to establish the relationship among the eight (08) identified barriers, through literature and expert opinions. The key barriers to high driving power are then identified with the help of MICMAC analysis.


Findings – The results reveal that value barrier (b2), image barrier (b5) and visibility barrier (b7) are the most significant variables. Interestingly, IRTs’ risk barrier (b3) and privacy barrier (b6) from the literature fall in the lowest level of the ISM model. The majority of the barriers fall under quadrant III of MICMAC analysis, indicating the high driving and dependence power.


Research limitations – The developed ISM model is based on the sentiments of five (05) experts, which could be biased and influence the structural model’s final output. Due to COVID-19, data has been collected through online video conferencing mode, this may vary if data will be collected through an offline or face-to-face interview. The proposed model’s key findings aim to assist in explaining the barriers that exist during MPS adoption.


Originality/Value – This study is the first attempt to use the ISM approach in conjunction with IRT to detect barriers within MPSs. The result of this paper will guide and motivate the researcher to analyse more critical barriers with IRT to contribute to the theoretical development.

Keyword : innovation resistance theory (IRT), interpretive structural modelling (ISM), mobile payment systems (MPSs), MICMAC analysis, transitivity analysis, adoption, barriers, leapfrog

How to Cite
Singh, N. K., & Singh, P. (2022). Identifying consumer resistance of mobile payment during COVID-19: an interpretive structural modeling (ISM) approach. Business, Management and Economics Engineering, 20(2), 258–285. https://doi.org/10.3846/bmee.2022.16905
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References

Agarwal, R. & Prasad, J. (1997). The role of innovation characteristics and perceived voluntariness in the acceptance of information technologies. Decision Sciences, 28(3), 557–582. https://doi.org/10.1111/j.1540-5915.1997.tb01322.x

Al-Muftah, H., Weerakkody, V., Rana, N. P., Sivarajah, U., & Irani, Z. (2018). E-diplomacy implementation: Exploring causal relationships using interpretive structural modelling. Government Information Quarterly, 35(3), 502–514. https://doi.org/10.1016/j.giq.2018.03.002

Andrew, J., & Klein, J. (2003). The boycott puzzle: Consumer motivation for purchase sacrifice. Management Science, 49(9), 1196–1209. https://doi.org/10.1287/mnsc.49.9.1196.16569

Armey, L. E., Lipow, J., & Webb, N. J. (2014). The impact of electronic financial payments on crime. Information Economics and Policy, 29, 46–57. https://doi.org/10.1016/j.infoecopol.2014.10.002

Arvidsson, N. (2014). Consumer attitudes on mobile payment services – results from a proof of concept test. International Journal of Bank Marketing, 32(2), 150–170. https://doi.org/10.1108/IJBM-05-2013-0048

Bailey, A. A., Pentina, I., Mishra, A. S., & Mimoun, M. S. B. (2017). Mobile payments adoption by US consumers: An extended TAM. International Journal of Retail & Distribution Management, 45(6), 626–640. https://doi.org/10.1108/IJRDM-08-2016-0144

Boden, J., Maier, E., & Wilken, R. (2020). The effect of credit card versus mobile payment on convenience and consumers’ willingness to pay. Journal of Retailing and Consumer Services, 52, 101910. https://doi.org/10.1016/j.jretconser.2019.101910

Bryman, A. (2012). Social research methods (4th ed.). Oxford University Press.

Chander, M., Jain, S. K., & Shankar, R. (2013). Modeling of information security management parameters in Indian organizations using ISM and MICMAC approach. Journal of Modelling in Management, 8(2), 171–189. https://doi.org/10.1108/JM2-10-2011-0054

Chang, Y., Wong, S. F., Libaque-Saenz, C. F., & Lee, H. (2018). The role of privacy policy on consumers’ perceived privacy. Government Information Quarterly, 35(3), 445–459. https://doi.org/10.1016/j.giq.2018.04.002

Changchit, C., Lonkani, R., & Sampet, J. (2017). Mobile banking: Exploring determinants of its adoption. Journal of Organizational Computing and Electronic Commerce, 27(3), 239–261. http://dx.doi.org/10.1080/10919392.2017.1332145

Chaouali, W., & Souiden, N. (2019). The role of cognitive age in explaining mobile banking resistance among elderly people. Journal of Retailing and Consumer Services, 50, 342–350. https://doi.org/10.1016/j.jretconser.2018.07.009

Chemingui, H., & Ben lallouna, H. (2013). Resistance, motivations, trust and intention to use mobile financial services. International Journal of Bank Marketing, 31(7), 574–592. https://doi.org/10.1108/IJBM-12-2012-0124

Chen, C. S. (2013). Perceived risk, usage frequency of mobile banking services. Managing Service Quality: International Journal, 23(5), 410–436. https://doi.org/10.1108/MSQ-10-2012-0137

Chen, P. T., & Kuo, S. C. (2017). Innovation resistance and strategic implications of enterprise social media websites in Taiwan through knowledge sharing perspective. Technological Forecasting and Social Change, 118, 55–69. https://doi.org/10.1016/j.techfore.2017.02.002

Chowdhury, N. A., Ali, S. M., Mahtab, Z., Rahman, T., Kabir, G., & Paul, S. K. (2019). A structural model for investigating the driving and dependence power of supply chain risks in the readymade garment industry. Journal of Retailing and Consumer Services, 51, 102–113. https://doi.org/10.1016/j.jretconser.2019.05.024

Clarke, R. (1999). Internet privacy concerns confirm the case for intervention. Communications of the ACM, 42(2), 60–67. https://doi.org/10.1145/293411.293475

Cruz, P., Barretto Filgueiras Neto, L., Muñoz-Gallego, P., & Laukkanen, T. (2010). Mobile banking rollout in emerging markets: Evidence from Brazil. International Journal of Bank Marketing, 28(5), 342–371. https://doi.org/10.1108/02652321011064881

de Kerviler, G., Demoulin, N. T. M., & Zidda, P. (2016). Adoption of in-store mobile payment: Are perceived risk and convenience the only drivers?. Journal of Retailing and Consumer Services, 31, 334–344. https://doi.org/10.1016/j.jretconser.2016.04.011

de Sena Abrahão, R., Moriguchi, S. N., & Andrade, D. F. (2016). Intention of adoption of mobile payment: An analysis in the light of the Unified Theory of Acceptance and Use of Technology (UTAUT). RAI Revista de Administração e Inovação, 13(3), 221–230. http://dx.doi.org/10.1016/j.rai.2016.06.003

Deloitte. (2019). Smartphone: The center of life. A study on Nordic mobile consumer behaviour. Deloitte Global Mobile Consumer Survey 2019: The Nordic cut. Retrieved April 13, 2021, from https://www2.deloitte.com/content/dam/Deloitte/se/Documents/technology-media-telecommunications/Global-Mobile-Consumer-Survey-2019-Nordic-Cut.pdf

Diabat, A., Khreishah, A., Kannan, G., Panikar, V., & Gunasekaran, A. (2013). Benchmarking the interactions among barriers in third-party logistics implementation: An ISM approach. Benchmarking: An International Journal, 20(6), 805–824. https://doi.org/10.1108/BIJ-04-2013-0039

Dunphy, S., & Herbig, P. A. (1995). Acceptance of innovations: The customer is the key!. The Journal of High Technology Management Research, 6(2), 193–209. https://doi.org/10.1016/1047-8310(95)90014-4

Dwivedi, Y. K., Janssen, M., Slade, E., Rana, N. P., Weerakkody, V., Millard, J., Hidders, J., & Snijder, D. (2017). Driving innovation through Big Open Linked Data (BOLD): Exploring antecedents using interpretive structural modelling. Information Systems Frontiers, 19(2), 197–212. https://doi.org/10.1007/s10796-016-9675-5

Eriksson, N., Gökhan, A., & Stenius, M. (2021). A qualitative study of consumer resistance to mobile payments for in-store purchases. Procedia Computer Science, 181, 634–641. https://doi.org/10.1016/j.procs.2021.01.212

Fabris, N. (2019). Cashless society–the future of money or a utopia?. Journal of Central Banking Theory and Practice, 8(1), 53–66. https://doi.org/10.2478/jcbtp-2019-0003

Fayard, A.-L., & Weeks, J. (2014). Affordances for practice. Information and Organization, 24(4), 236–249. https://doi.org/10.1016/j.infoandorg.2014.10.001

Ghasemaghaei, M. (2018). The role of positive and negative valence factors on the impact of bigness of data on big data analytics usage. International Journal of Information Management, 50, 395–404. https://doi.org/10.1016/j.ijinfomgt.2018.12.011

Guan, D., Wang, D., Hallegatte, S., Davis, S., Huo, J., Li, S., Bai, Y., Lei, T., Xue, Q., Coffman, D., Cheng, D., Chen, P., Liang, X., Xu, B., Lu, X., Wang, S., Hubacek, K. & Gong, P. (2020). Global supply-chain effects of COVID-19 control measures. Nature Human Behaviour, 4(6), 577–587. https://doi.org/10.1038/s41562-020-0896-8

Gupta, A., & Arora, N. (2017). Understanding determinants and barriers of mobile shopping adoption using behavioral reasoning theory. Journal of Retailing and Consumer Services, 36, 1–7. https://doi.org/10.1016/j.jretconser.2016.12.012

Gupta, S., & Dhingra, S. (2022). Modeling the key factors influencing the adoption of mobile financial services: An interpretive structural modeling approach. Journal of Financial Services Marketing, 27(2), 96–110. https://doi.org/10.1057/s41264-021-00101-4

Hawthorne, R. W., & Sage, A. P. (1975). On applications of interpretive structural modeling to higher education program planning. Socio-Economic Planning Sciences, 9(1), 31–43. https://doi.org/10.1016/0038-0121(75)90039-7

Hayashi, F. (2012). Mobile Payments: What’s in it for consumers? (pp. 35–66). Economic Review. Federal Reserve Bank of Kansas City.

Heidenreich, S., & Handrich, M. (2015). What about passive innovation resistance? Investigating adoption-related behavior from a resistance perspective. Journal of Product Innovation Management, 32(6), 878–903. https://doi.org/10.1111/jpim.12161

Heidenreich, S., & Spieth, P. (2013). Why innovations fail – the case of passive and active innovation resistance. International Journal of Innovation Management, 17(5), 1350021. https://doi.org/10.1142/S1363919613500217

Herrero, A., San Martín, H., & García-de los Salmones, M. d. M. (2017). Explaining the adoption of social networks sites for sharing user-generated content: A revision of the UTAUT2. Computers in Human Behavior, 71, 209–217. https://doi.org/10.1016/j. chb.2017.02.007

Hew, J. J., Leong, L. Y., Tan, G. W. H., Ooi, K. B., & Lee, V. H. (2019). The age of mobile social commerce: An Artificial Neural Network analysis on its resistances. Technological Forecasting and Social Change, 144, 311–324. https://doi.org/10.1016/j.techfore.2017.10.007

Hew, J. J., Tan, G. W. H., Lin, B., & Ooi, K. B. (2017). Generating travel-related contents through mobile social tourism: Does privacy paradox persist?. Telematics and Informatics, 34(7), 914–935. https://doi.org/10.1016/j.tele.2017.04.001

Hirschheim, R., & Newman, M. (1988). Information systems and user resistance: Theory and practice. The Computer Journal, 31(5), 398–408. https://doi.org/10.1093/comjnl/31.5.398

Hsu, C. L., Lu, H. P., & Hsu, H. H. (2007). Adoption of the mobile Internet: An empirical study of multimedia message service (MMS). Omega, 35(6), 715–726. https://doi.org/10.1016/j.omega.2006.03.005

Hughes, D. L., Dwivedi, Y. K., Rana, N. P., & Simintiras, A. C. (2016). Information systems project failure – analysis of causal links using interpretive structural modelling. Production Planning & Control, 27(16), 1313–1333. https://doi.org/10.1080/09537287.2016.1217571

Janssen, M., Rana, N. P., Slade, E., & Dwivedi, Y. K. (2018). Trustworthiness of digital government services: Deriving a comprehensive theory through interpretive structural modelling. Public Management Review, 20(5), 647–671. https://doi.org/10.1080/14719037.2017.1305689

Jansukpum, K., & Kettem, S. (2015). Applying innovation resistance theory to understand consumer resistance of using online travel in Thailand. In 14th International Symposium on Distributed Computing and Applications for Business Engineering and Science (DCABES) (pp. 139–142). IEEE. https://doi.org/10.1109/DCABES.2015.42

Jharkharia, S., & Shankar, R. (2005). IT-enablement of supply chains: understanding the barriers. Journal of Enterprise Information Management, 18(1), 11–27. https://doi.org/10.1108/17410390510571466

Joachim, V., Spieth, P., & Heidenreich, S. (2018). Active innovation resistance: An empirical study on functional and psychological barriers to innovation adoption in different contexts. Industrial Marketing Management, 71, 95–107. https://doi.org/10.1016/j.indmarman.2017.12.011

Johnson, V. L., Kiser, A., Washington, R., & Torres, R. (2018). Limitations to the rapid adoption of M-payment services: Understanding the impact of privacy risk on M-Payment services. Computers in Human Behavior, 79, 111–122. https://doi.org/10.1016/j.chb.2017.10.035

Kahneman, D., Knetsch, J. L., & Thaler, R. H. (2012). Anomalies: The endowment effect, loss aversion, and status quo bias. Journal of Economic Perspectives, 5(1), 193–206. https://doi.org/10.1257/jep.5.1.193

Kannan, G., & Haq, A. N. (2007). Analysis of interactions of criteria and sub-criteria for the selection of supplier in the built-in-order supply chain environment. International Journal of Production Research, 45(17), 3831–3852. https://doi.org/10.1080/00207540600676676

Kaur, P., Dhir, A., Singh, N., Sahu, G., & Almotairi, M. (2020). An innovation resistance theory perspective on mobile payment solutions. Journal of Retailing and Consumer Services, 55, 102059. https://doi.org/10.1016/j.jretconser.2020.102059

Keramati, A., Taeb, R., Larijani, A. M., & Mojir, N. (2012). A combinative model of behavioural and technical factors affecting ‘Mobile’-payment services adoption: An empirical study. The Service Industries Journal, 32(9), 1489–1504. http://dx.doi.org/10.1080/02642069.2011.552716

Khanra, S., Dhir, A., Islam, A. N., & Mäntymäki, M. (2020). Big data analytics in healthcare: A systematic literature review. Enterprise Information Systems, 14(7), 878–912. https://doi.org/10.1080/17517575.2020.1812005

Kim, J., & Seo, J. (2017). User resistance to digital goods: a case of e-books. In 14th Asia-Pacific Regional Conference of the International Telecommunications Society (ITS): Mapping ICT into Transformation for the Next Information Society (pp. 1–21). Kyoto, Japan. http://hdl.handle.net/10419/168502

Kim, S. (2020). South Korea bets on ‘untact’ for the post-pandemic economy. Bloomberg Businessweek. Retrieved April 26, 2021, from https://www.bloomberg.com/news/articles/2020-06-10/south-korea-untact-plans-for-the-post-pandemic-economy

Kokolakis, S. (2017). Privacy attitudes and privacy behaviour: A review of current research on the privacy paradox phenomenon. Computers & Security, 64, 122–134. https://doi.org/10.1016/j.cose.2015.07.002

Kuo, R. Z. (2020). Why do people switch mobile payment service platforms? An empirical study in Taiwan. Technology in Society, 62, 101312. https://doi.org/10.1016/j.techsoc.2020.101312

Kushwah, S., Dhir, A., & Sagar, M. (2019). Understanding consumer resistance to the consumption of organic food. A study of ethical consumption, purchasing, and choice behaviour. Food Quality and Preference, 77, 1–14. https://doi.org/10.1016/j.foodqual.2019.04.003

Laukkanen, T., & Cruz, P. (2010). What determines mobile banking non-adoption?. In Proceedings of Australian and New Zealand Marketing Academy (ANZMAC) Conference 2010. University of Canterbury, Christchurch, New Zealand. http://www.anzmac2010.org/proceedings/pdf/ANZMAC10Final00387.pdf

Laukkanen, T., & Kiviniemi, V. (2010). The role of information in mobile banking resistance. International Journal of Bank Marketing, 28(5), 372–388. https://doi.org/10.1108/02652321011064890

Laukkanen, T. (2015). How uncertainty avoidance affects innovation resistance in mobile banking: the moderating role of age and gender. In Proceedings of 48th Hawaii International Conference on System Sciences (pp. 3601–3610). Kauai, HI, USA. https://doi.org/10.1109/HICSS.2015.433

Laukkanen, T. (2016). Consumer adoption versus rejection decisions in seemingly similar service innovations: The case of the Internet and mobile banking. Journal of Business Research, 69(7), 2432–2439. https://doi.org/10.1016/j.jbusres.2016.01.013

Laukkanen, T., Sinkkonen, S., Kivijärvi, M., & Laukkanen, P. (2007). Innovation resistance among mature consumers. Journal of Consumer Marketing, 24(7), 419–427. https://doi.org/10.1108/07363760710834834

Laukkanen, T., Sinkkonen, S., Laukkanen, P., & Kivijärvi, M. (2008). Segmenting bank customers by resistance to mobile banking. International Journal of Mobile Communications, 6(3), 309–320. https://doi.org/10.1504/IJMC.2008.017513

Lee, S. M., & Lee, D. (2020). Lessons learned from battling COVID-19: the Korean experience. International Journal of Environmental Research and Public Health, 17(20), 7548. https://doi.org/10.3390/ijerph17207548

Lee, S. M. & Lee, D. (2021). Opportunities and challenges for contactless healthcare services in the post-COVID-19 Era. Technological Forecasting and Social Change, 167, 120712. https://doi.org/10.1016/j.techfore.2021.120712

Lian, J. W., & Yen, D. C. (2013). To buy or not to buy experience goods online: Perspective of innovation adoption barriers. Computers in Human Behavior, 29(3), 665–672. https://doi.org/10.1016/j.chb.2012.10.009

Lian, J. W., & Yen, D. C. (2014). Online shopping drivers and barriers for older adults: Age and gender differences. Computers in Human Behavior, 37, 133–143. https://doi.org/10.1016/j.chb.2014.04.028

Luo, X., Li, H., Zhang, J., & Shim, J. P. (2010). Examining multi-dimensional trust and multi- faceted risk in initial acceptance of emerging technologies: An empirical study of mobile banking services. Decision Support Systems, 49(2), 222–234. https://doi.org/10.1016/j.dss.2010.02.008

Ma, L., & Lee, C. S. (2019). Understanding the barriers to the use of MOOCs in a developing country: An innovation resistance perspective. Journal of Educational Computing Research, 57(3), 571–590. https://doi.org/10.1177/0735633118757732

Mahatanankoon, P., & Vila-Ruiz, J. (2007). Why won’t consumers adopt m-commerce? An exploratory study. Journal of Internet Commerce, 6(4), 113–128. https://doi.org/10.1080/15332860802086367

Maheshwari, P., Seth, N., & Gupta, A. K. (2018). An interpretive structural modeling approach to advertisement effectiveness in the Indian mobile phone industry. Journal of Modelling in Management, 13(1), 190–210. https://doi.org/10.1108/JM2-04-2016-0040

Mallat, N., & Tuunainen, V. K. (2008). Exploring merchant adoption of mobile payment systems: An empirical study. e-Service Journal, 6(2), 24–57. https://doi.org/10.2979/esj.2008.6.2.24

Mallat, N. (2007). Exploring consumer adoption of mobile payments – A qualitative study. Journal of Strategic Information Systems, 16(4), 413–432. https://doi.org/10.1016/j.jsis.2007.08.001

Malone, D. W. (1975). An introduction to the application of interpretive structural modelling. Proceedings of the IEEE, 63(3), 397–404. https://doi.org/10.1109/PROC.1975.9765

Mandal, A., & Deshmukh, S. G. (1994). Vendor selection using interpretive structural modelling (ISM). International Journal of Operations & Production Management, 14(6), 52–59. https://doi.org/10.1108/01443579410062086

Mangla, S. K., Luthra, S., Mishra, N., Singh, A., Rana, N. P., Dora, M., & Yogesh, D. (2018). Barriers to effective circular supply chain management in a developing country context. Production Planning & Control, 29(6), 551–569. https://doi.org/10.1080/09537287.2018.1449265

Marett, K., Pearson, A. W., Pearson, R. A., & Bergiel, E. (2015). Using mobile devices in a high risk context: The role of risk and trust in an exploratory study in Afghanistan. Technology in Society, 41, 54–64. https://doi.org/10.1016/j.techsoc.2014.11.002

Merhi, M., Hone, K., & Tarhini, A. (2019). A cross-cultural study of the intention to use mobile banking between Lebanese and British consumers: Extending UTAUT2 with security, privacy and trust. Technology in Society, 59, 101151. https://doi.org/10.1016/j.techsoc.2019.101151

Mishra, N., Singh, A., Rana, N. P., & Dwivedi, Y. K. (2017). Interpretive structural modelling and fuzzy MICMAC approaches for customer centric beef supply chain: Application of a big data technique. Production Planning & Control, 28(11–12), 945–963. https://doi.org/10.1080/09537287.2017.1336789

Moore, G. C., & Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology innovation. Information Systems Research, 2(3), 192–222. https://doi.org/10.1287/isre.2.3.192

Moorthy, K., Ling, C. S., Fatt, Y. W., Yee, C. M., Yin, E. C. K., Yee, K. S., & Wei, L. K. (2017). Barriers of mobile commerce adoption intention: Perceptions of generation X in Malaysia. Journal of Theoretical and Applied Electronic Commerce Research, 12(2), 37–53. https://doi.org/10.4067/S0718-18762017000200004

Morar, D. D. (2013). An overview of the consumer value literature – perceived value, desired value. Marketing from Information to Decision, 6, 169–186.

Mudgal, R. K., Shankar, R., Talib, P., & Raj, T. (2009). Greening the supply chain practices: An Indian perspective of enablers’ relationships. International Journal of Advanced Operations Management, 1(2–3), 151–176. https://doi.org/10.1504/IJAOM.2009.030671

Norman, D. A. (1988). The psychology of everyday things. Basic Books.

National Payment Corporation of India. (2021a). Digital Payments well entrenched in Indian households across income groups, reveals PRICE and NPCI pan India Survey. Retrieved June 23, 2021, from https://www.npci.org.in/PDF/npci/press-releases/2021/NPCI-Press-Release-Digital-Payments-well-entrenched-in-Indian-household.pdf

National Payment Corporation of India. (2021b). SBI and NPCI launch UPI awareness campaign for YONO users. Retrieved June 23, 2021, from https://www.npci.org.in/PDF/npci/press-releases/2021/NPCI-Press-Release-SBI-and-NPCI-launch-UPI-awareness-campaign-for-YONO-users.pdf

Oktavianus, J., Oviedo, H., Gonzalez, W., Putri, A. P., & Lin, T. T. C. (2017). Why do Taiwanese young adults not jump on the bandwagon of Pokémon Go? Exploring barriers of innovation resistance. In 14th Asia-Pacific Regional Conference of the International Telecommunications Society (ITS): “Mapping ICT into Transformation for the Next Information Society” (pp. 1–42). Kyoto, Japan. http://hdl.handle.net/10419/168529

Oliveira, T., Thomas, M., Baptista, G., & Campos, F. (2016). Mobile payment: Understanding the determinants of customer adoption and intention to recommend the technology. Computers in Human Behavior, 61, 404–414. https://doi.org/10.1016/j.chb.2016.03.030

Opensignal. (2021). Mobile network experience report March 2021. Retrieved June 24, 2021, from https://www.opensignal.com/reports/2021/03/india/mobile-network-experience

Ozturk, A. B., Bilgihan, A., Salehi-Esfahani, S., & Hua, N. (2017). Understanding the mobile payment technology acceptance based on valence theory: A case of restaurant transactions. International Journal of Contemporary Hospitality Management, 29(8), 2027–2049. https://doi.org/10.1108/IJCHM-04-2016-0192

Pal, A., Herath, T., De, R. & Rao, H. R. (2021). Why do people use mobile payment technologies and why would they continue? An examination and implications from India. Research Policy, 50(6), 104228. https://doi.org/10.1016/j.respol.2021.104228

Pop, R. A., Dabija, D. C., Pelau, C., & Dinu, V. (2022). Usage intentions, attitudes, and behaviours towards energy-efficient applications during the COVID-19 pandemic. Journal of Business Economics and Management, 23(3), 668–689. https://doi.org/10.3846/jbem/2022/16959

Rafiq, M., Naz, S., Martins, J. M., Mata, M. N., Mata, P. N., & Maqbool, S. (2021). A study on emerging management practices of renewable energy companies after the outbreak of Covid-19: Using an interpretive structural modeling (ISM) approach. Sustainability, 13(6), 3420. https://doi.org/10.3390/su13063420

Rahman, M. M. (2013). Barriers to m-commerce adoption in developing countries-a qualitative study among the stakeholders of Bangladesh. The International Technology Management Review, 3(2), 80–91. https://doi.org/10.2991/itmr.2013.3.2.2

Ram, S., & Sheth, J. N. (1989). Consumer resistance to innovations: The marketing problem and its solutions. Journal of Consumer Marketing, 6(2), 5–14. https://doi.org/10.1108/EUM0000000002542

Ram, S. (1987). A model of innovation resistance. ACR North American Advances.

Ramos de Luna, I., Liébana-Cabanillas, F., Sánchez-Fernández, J., & Muñoz-Leiva, F. (2019). Mobile payment is not all the same: The adoption of mobile payment systems depending on the technology applied. Technological Forecasting & Social Change, 146, 931–944. https://doi.org/10.1016/j.techfore.2018.09.018

Rana, N. P., Barnard, D. J., Baabdullah, A.M.A., Rees, D., & Roderick, S. (2019). Exploring barriers of m-commerce adoption in SMEs in the UK: Developing a framework using ISM. International Journal of Information Management, 44, 141–153. https://doi.org/10.1016/j.ijinfomgt.2018.10.009

Rana, N. P., Luthra, S., & Rao, H. R. (2022). Assessing challenges to the mobile wallet usage in India: an interpretive structural modelling approach. Information Technology & People. https://doi.org/10.1108/ITP-07-2021-0535

Ravi, V., Shankar, R., & Tiwari, M. K. (2005). Productivity improvement of a computer hardware supply chain. International Journal of Productivity and Performance Management, 54(4), 239–255. https://doi.org/10.1108/17410400510593802

Reserve Bank of India. (2021). Annual Report 2020-21. Retrieved June 30, 2021, from https://rbidocs.rbi.org.in/rdocs/AnnualReport/PDFs/0RBIAR202021_F49F9833694E84C16AAD01BE48F53F6A2.PDF

Rogers, E. M. (1962). Diffusion of innovations. Free Press of Glencoe.

Rogers, E. M. (1995). Diffusion of innovations (4th ed.). The Free Press.

Rogers, E. M. (2003). Diffusion of innovations (5th ed.). The Free Press.

Sadiq, M., Adil, M., & Paul, J. (2021). An innovation resistance theory perspective on purchase of eco-friendly cosmetics. Journal of Retailing and Consumer Services, 59, 102369. https://doi.org/10.1016/j.jretconser.2020.102369

Sage, A. P. (1977). Interpretive structural modeling: Methodology for large-scale systems. McGraw-Hill College.

Sahu, G. P., & Singh, N. K. (2018). Identifying Critical Success Factor (CSFs) for the adoption of digital payment systems: A study of Indian national banks. In Y. Dwivedi et al. (Eds.), Emerging markets from a multidisciplinary perspective, advances in theory and practice of emerging markets (pp. 61–73). Springer, Cham. https://doi.org/10.1007/978-3-319-75013-2_6

Singh, C., Singh, D., & Khamba, J. S. (2020). Developing a conceptual model to implement green lean practices in Indian manufacturing industries using ISM-MICMAC approach. Journal of Science and Technology Policy Management, 12(4), 587–608. https://doi.org/10.1108/JSTPM-08-2019-0080

Singh, M. D., Shankar, R., Narain, R., & Agarwal, A. (2003). An interpretive structural modeling of knowledge management in engineering industries. Journal of Advances in Management Research, 1(1), 28–40. https://doi.org/10.1108/97279810380000356

Singh, N. K., Sahu, G. P., Rana, N. P., Patil, P. P., & Gupta, B. (2018). Critical success factors of the digital payment infrastructure for developing economies. In A. Elbanna, Y. Dwivedi, D. Bunker, & D. Wastell (Eds.), Smart working, living and organising. TDIT 2018. IFIP advances in information and communication technology (pp. 113–125). Springer, Cham. https://doi.org/10.1007/978-3-030-04315-5_9

Sivathanu, B. (2018). Adoption of digital payment systems in the era of demonetization in India: An empirical study. Journal of Science and Technology Policy Management, 10(1), 143–171. https://doi.org/10.1108/JSTPM-07-2017-0033

Song, Y. (2011). What are the affordances and constraints of handheld devices for learning in higher education?. British Journal of Educational Technology, 42(6), 163–166. https://doi.org/10.1111/j.1467-8535.2011.01233.x

Sun, S., Law, R., & Schuckert, M. (2020). Mediating effects of attitude, subjective norms and perceived behavioural control of mobile payment-based hotel reservations. International Journal of Hospitality Management, 84, 102331. https://doi.org/10.1016/j.ijhm.2019.102331

Szmigin, I., & Foxall, G. (1998). Three forms of innovation resistance: The case of retail payment methods. Technovation, 18(6–7), 459–468. https://doi.org/10.1016/S0166-4972(98)00030-3

Talwar, S., Dhir, A., Kaur, P., & Mäntymäki, M. (2020). Barriers toward purchasing from online travel agencies. International Journal of Hospitality Management, 89, 102593. https://doi.org/10.1016/j.ijhm.2020.102593

Tamtam, F., & Tourabi, A. (2021). Interpretive structural modeling of supply chain leagility during COVID-19. IFAC-Papers OnLine, 54(17), 12–17. https://doi.org/10.1016/j.ifacol.2021.11.019

Tansuhaj, P., Gentry, J. W., John, J., Lee Manzer, L., & Cho, B. J. (1991). A cross-national examination of innovation resistance. International Marketing Review, 8(3), 7–20. https://doi.org/10.1108/02651339110000135

Teo, A. C., Tan, G. W., Ooi, K. B., Hew, T. S., & Yew, K. T. (2015). The effects of convenience and speed in m-payment. Industrial Management and Data Systems, 115(2), 311–331. https://doi.org/10.1108/IMDS-08-2014-0231

Tiwari, N., & Singh, N. K. (2019). Factor affecting consumer satisfaction in cashless payment systems in India with respect to Paytm and BHIM. International Journal of Recent Technology and Engineering, 8(3S2), 10–15. https://doi.org/10.35940/ijrte.C1002.0782S719

United Nations Industrial Development Organization. (2020). Coronavirus: The Economic impact 10 July 2020. Retrieved May 26, 2020, from https://www.unido.org/stories/coronavirus-economic-impact-10-july-2020

Van Slyke, C., Ilie, V., Lou, H., & Stafford, T. (2007). Perceived critical mass and the adoption of a communication technology. European Journal of Information Systems, 16(3), 270–283. https://doi.org/10.1057/palgrave.ejis.3000680

Venkatesh, V. G., Rathi, S., & Patwa, S. (2015). Analysis on supply chain risks in Indian apparel retail chains and proposal of risk prioritization model using Interpretive structural modeling. Journal of Retailing and Consumer Services, 26, 153–167. https://doi.org/10.1016/j.jretconser.2015.06.001

Verma, S., Chaurasia, S. S., & Bhattacharyya, S. S. (2019). The effect of government regulations on continuance intention of in-store proximity mobile payment services. International Journal of Bank Marketing, 38(1), 34–62. https://doi.org/10.1108/IJBM-10-2018-0279

Vimalkumar, M., Sharma, S. K., Singh, J. B., & Dwivedi, Y. K. (2021). Okay google, what about my privacy?’: User’s privacy perceptions and acceptance of voice based digital assistants. Computers in Human Behavior, 120, 106763. https://doi.org/10.1016/j.chb.2021.106763

Vinerean, S., Budac, C., Baltador, L. A., & Dabija, D. C. (2022). Assessing the effect of COVID-19 pandemic on m-commerce adoption: An adapted UTAUT2 approach. Electronics, 11(8), 1269. https://doi.org/10.3390/electronics11081269

Warfield, J. N. (1974). Developing interconnected matrices in structural modelling. IEEE Transcript on Systems, Men and Cybernetics, 4(1), 81–87. https://doi.org/10.1109/TSMC.1974.5408524

Warren, S. D., & Brandeis, L. D. (1890). The right to privacy. Harvard Law Review, 4(5), 193–220. https://doi.org/10.2307/1321160

World Health Organization. (2020). Coronavirus disease (COVID-19) weekly epidemiological update and Weekly Operational Update. Retrieved June 23, 2021, from https://www.who.int/emergencies/diseases/novel-coronavirus-2019/situation-reports/

World Health Organization. (2021). COVID-19 weekly epidemiological update. Retrieved June 23, 2021, from https://www.who.int/publications/m/item/weekly-epidemiological-update-on-covid-19---22-june-2021

Xiao, Y., & Fan, Z. (2020). 10 technology trends to watch in the COVID-19 pandemic. World Economic Forum. Retrieved May 26, 2021, from https://www.weforum.org/agenda/2020/04/10-technology-trends-coronavirus-covid19-pandemic-robotics-telehealth/

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. https://doi.org/10.1016/j.chb.2011.08.019

Yang, Y., Liu, Y., Li, H., & Yu, B. (2015). Understanding perceived risks in mobile payment acceptance. Industrial Management & Data Systems, 115(2), 253–269. https://doi.org/10.1108/IMDS-08-2014-0243

Yayboke, E., Carter, W. A., & Crumpler, W. (2020). The need for a leapfrog strategy. Global S&T Development Trend Analysis Platform of Resources and Environment. http://119.78.100.173/C666/handle/2XK7JSWQ/250097

Yu, C. S., & Chantatub, W. (2016). Consumers’ resistance to using mobile banking: evidence from Thailand and Taiwan. International Journal of Electronic Commerce Studies, 7(1), 21–38. https://doi.org/10.7903/ijecs.1375

Yu, C. S., Li, C. K., & Chantatub, W. (2015). Analysis of consumer e-lifestyles and their effects on consumer resistance to using mobile banking: empirical surveys in Thailand and Taiwan. International Journal of Business & Information, 10(2), 198–232. http://search.ebscohost.com/login.aspx?direct=true&db=bsu&AN=103203606&site=ehost-live

Zhou, T. (2013). An empirical examination of continuance intention of mobile payment services. Decision Support Systems, 54(2), 1085–1091. https://doi.org/10.1016/j.dss.2012.10.034

Zhou, T. (2015). An empirical examination of users’ switch from online payment to mobile payment. International Journal of Technology and Human Interaction, 11(1), 55–66. https://doi.org/10.4018/ijthi.2015010104