South Asian Journal of Management

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

A Study of Factors Affecting Mobile Governance (mGov) Adoption Intention in India using an Extension of the Technology Acceptance Model (TAM)


Affiliations
1 Marketing Area, Indian Institute of Management Vishakapatnam, Andhra Bank School of Business Building, Andhra University Campus, Visakhapatnam 530 003. Andhra Pradesh, India
2 Finance & Accounting Area, FORE School of Management, #B-18, Qutub Institutional Area, New Delhi 110016, National Capital Territory of Delhi, India
     

   Subscribe/Renew Journal


Due to the advancement in Information and Communication Technology (ICT) and mobile phone penetration, the Indian government has initiated mobile governance (mGov) for delivering public services to citizens and businesses over mobile devices. However, despite making several efforts, the pace of mGov adoption has been rather slow. This study aims to explore factors affecting the intention to adopt mGov. A total of 379 responses were collected and analyzed. To determine the reliability and validity of the proposed framework, Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) were used. The findings show that perceived usefulness, perceived ease of use, perceived security, and perceived compatibility are crucial determinants affecting the mGov adoption intention. Additionally, the results also indicate that the trust significantly mediates the relationship between perceived usefulness, perceived ease of use and mGov adoption intention. Through the results obtained, we propose which specific determinants government should focus on in order to encourage citizens to adopt and use mGov services.

Keywords

Adoption Intention, Electronic Governance, Mobile Governance, Technology Acceptance Model (TAM), Trust.
User
Subscription Login to verify subscription
Notifications
Font Size

  • Abu-Shanab, E. (2014). Antecedents of trust in e-government services: an empirical test in Jordan, Transforming Government: People, Process and Policy, 8(4), 480-499.

  • Abu-Shanab, E. A. (2016). E-government familiarity influence on Jordanians’ perceptions, Telematics and Informatics, 34(1), 103-113.

  • Abu-Shanab, E. A., & Baker, A. A. N. A. (2011). Evaluating Jordan’s e-government website: A case study. Electronic Government, an International Journal, 8(4), 271-289.

  • Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of information technology. Information Systems Research, 9(2), 204-215.

  • Aggelidis, V. P., & Chatzoglou, P. D. (2009). Using a modified technology acceptance model in hospitals. International Journal of Medical Informatics, 78(2), 115-126.

  • Ahmad Al-Hawari, M. (2014). Does customer sociability matter? Differences in e-quality, e-satisfaction, and e-loyalty between introvert and extravert online banking users. Journal of Services Marketing, 28(7), 538-546.

  • Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In N. J. Kuhl., & J. Beckmann. (Eds.), Action control from cognition to behavior (11-39). Springer, Berlin Heidelberg.

  • Alenezi, H., Tarhini, A., & Masa’deh, R. (2015). Investigating the strategic relationship between information quality and e-government benefits: A literature review, International Review of Social Sciences and Humanities, 9(1), 33-50.

  • Al-Hujran, O., Al-Debei, M. M., Chatfield, A., & Migdadi, M. (2015). The imperative of influencing citizen attitude toward e-government adoption and use. Computers in Human Behavior, 53, 189-203.

  • Almarabeh, T., & AbuAli, A. (2010). A general framework for e-government: definition maturity challenges, opportunities, and success. European Journal of Scientific Research, 39(1), 29-42.

  • Alomari, M., Woods, P., & Sandhu, K. (2012). Predictors for e-government adoption in Jordan: Deployment of an empirical evaluation based on a citizen-centric approach. Information Technology & People, 25(2), 207-234.

  • Aloudat, A., Michael, K., Chen, X., & Al-Debei, M. M. (2014). Social acceptance of location-based mobile government services for emergency management. Telematics and Informatics, 31(1), 153-171.

  • Barney, J. B., & Hansen, M. H. (1994). Trustworthiness as a source of competitive advantage. Strategic Management Journal, 15(S1), 175-190.

  • Bélanger, F., & Carter, L. (2008). Trust and risk in e-government adoption. The Journal of Strategic Information Systems, 17(2), 165-176.

  • Beldad, A., van der Geest, T., de Jong, M., & Steehouder, M. (2012). A cue or two and I’ll trust you: Determinants of trust in government organizations in terms of their processing and usage of citizens’ personal information disclosed online. Government Information Quarterly, 29(1), 41-49.

  • Bestavros, A. (2000). Banking industry walks ‘tightrope’in personalization of web services. Bank Systems and Technology, 37(1), 54-56.

  • Browne, M. W., & Cudeck, R. (1993). Alternative ways of assessing model fit, in Testing structural equation models, K. A. Bollen., & J. S. Long. (Eds.). Newbury Park, CA: Sage Publications, 136-162.

  • Bryer, T. A. (2007). Toward a relevant agenda for a responsive public administration. Journal of Public Administration Research and Theory, 17(3), 479-500.

  • Bwalya, K. J. (2009). Factors affecting adoption of e-government in Zambia, The Electronic Journal of Information Systems in Developing Countries, 38 (4), 1-13.

  • Byrne, B. M. (2009). Structural Equation Modeling With AMOS: Basic Concepts, Applications, and Programming (2 ed.), Taylor & Francis, New York, NY.

  • Carmines, E. G., & McIver, J. P. (1981). Analyzing models with unobserved variables: Analysis of covariance structures, in Social Measurement, ed. George W. Bohrnstedt., & Edgar, F. Borgatta, Beverly Hills, CA: Sage, 65-116.

  • Carter, L., & Campbell, R. (2011). The impact of trust and relative advantage on internet voting diffusion, Journal of Theoretical and Applied Electronic Commerce Research, 6(3), 28-42.

  • Chandra, S., Srivastava, S. C., & Theng, Y. L. (2010). Evaluating the role of trust in consumer adoption of mobile payment systems: An empirical analysis, Communications of the Association for Information Systems, 27(29), 562-588.

  • Chen, J. V., Jubilado, R. J. M., Capistrano, E. P. S., & Yen, D. C. (2015). Factors affecting online tax filing–An application of the IS Success Model and trust theory. Computers in Human Behavior, 43, 251-262.

  • Chen, Y. R. (2008). Corporate governance and cash holdings: Listed new economy versus old economy firms. Corporate Governance: An International Review, 16(5), 430-442.

  • Cheng, T. E., Lam, D. Y., & Yeung, A. C. (2006). Adoption of internet banking: an empirical study in Hong Kong, Decision Support Systems, 42(3), 1558-1572.

  • Cheung, C. M., & Thadani, D. R. (2012). The impact of electronic word-ofmouth communication: A literature analysis and integrative model, Decision Support Systems, 54(1), 461-470.

  • Cheung, G. W., & Lau, R. S. (2008). Testing mediation and suppression effects of latent variables: Bootstrapping with structural equation models, Organizational Research Methods, 11(2), 296-325.

  • Chiou, J. S., & Shen, C. C. (2012). The antecedents of online financial service adoption: the impact of physical banking services on Internet banking acceptance, Behaviour & Information Technology, 31(9), 859-871.

  • Daniel, E. (1999). Provision of electronic banking in the UK and the Republic of Ireland, International Journal of Bank Marketing, 17(2), 72-83.

  • Davis, F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly, 13(3), 319-339.

  • Deci, E. L., & Ryan, R. M. (1985). The general causality orientations scale: Selfdetermination in personality, Journal of Research in Personality, 19(2), 109-134.

  • Delone, W. H., and McLean, E. R. (2003). The DeLone and McLean model of information systems success: a ten-year update, Journal of Management Information Systems, 19(4), 9-30.

  • Evangelidis, A., Akomode, J., Taleb-Bendiab, A., & Taylor, M. (2002). Risk assessment & success factors for e-government in a UK establishment, Electronic Government, 395-402.

  • Goyal, A., Maity, M., Thakur, R., & Srivastava, M. (2013). Customer usage intention of mobile commerce in India: an empirical study. Journal of Indian Business Research. 5(1), 52-72.

  • Groß, M. (2015). Exploring the acceptance of technology for mobile shopping: an empirical investigation among Smartphone users. The International Review of Retail, Distribution and Consumer Research, 25(3), 215-235.

  • 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.

  • Gupta, J. P., & Suri, P. (2017). Measuring public value of e-governance projects in India: citizens’ perspective, Transforming Government: People, Process and Policy, 11(2), 236-261.

  • Hair, J. F., Anderson, R. E., Tatham, R. L., & Black, W. C. (1998). Multivariate Data Analysis, 5th edn Prentice Hall International, New York, NY.

  • Heeks, R., and Bailur, S. (2007). Analyzing e-government research: Perspectives, philosophies, theories, methods, and practice. Government Information Quarterly, 24(2), 243-265.

  • Hung, M. C., & Jen, W. Y. (2012). The adoption of mobile health management services: an empirical study. Journal of Medical Systems, 36(3), 1381-1388.

  • Hussien, I. M., & Abd El Aziz, R. (2013). Investigating e-banking service quality in one of Egypt’s banks: a stakeholder analysis. The TQM Journal, 25(5), 557-576.

  • Iqbal, S., & Bhatti, Z. A. (2015). An investigation of university student readiness towards m-learning using technology acceptance model. The International Review of Research in Open and Distributed Learning, 16(4), 83-103.

  • Irani, Z., Dwivedi, Y. K., & Williams, M. D. (2009). Understanding consumer adoption of broadband: an extension of the technology acceptance model, Journal of the Operational Research Society, 60(10), 1322-1334.

  • Islam, H., Jebarajakirthy, C., & Shankar, A. (2019). An experimental based investigation into the effects of website interactivity on customer behavior in online purchase context. Journal of Strategic Marketing, 1-24.

  • Jaeger, P. T. (2003). The endless wire: E-government as global phenomenon. Government Information Quarterly, 20(4), 323-331.

  • Jiang, P. (2009). Consumer adoption of mobile internet services: An exploratory study, Journal of Promotion Management, 15(3), 418-454.

  • Kalsi, S. N., & Kiran, R. (2013). E-governance success factors: an analysis of egovernance initiatives of ten major states of India, International Journal of Public Sector Management, 26(4), 320-336.

  • 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.

  • Kline, R. B. (2010). Principles and Practice of Structural Equation Modeling, 3rd edn Guilford Press. New York, NY.

  • Kumar, M., & Sinha, O. P. (2007). M-government–mobile technology for e-government. In International conference on e-government, India, 294-301.

  • Kumar, V. R., Lall, A., & Mane, T. (2017). Extending the TAM Model: Intention of Management Students to Use Mobile Banking: Evidence from India, Global Business Review, 18(1), 238-249.

  • Kumar, V., Mukerji, B., Butt, I., & Persaud, A. (2007). Factors for successful egovernment adoption: a conceptual framework, The Electronic Journal of eGovernment, 5(1), 63-76.

  • Kuo, Y. F., & Yen, S. N. (2009). Towards an understanding of the behavioral intention to use 3G mobile value-added services, Computers in Human Behavior, 25(1), 103-110.

  • Kushchu, I. (2007). Mobile Government: An Emerging Direction in E-Government. IGI Publishing.

  • Lean, O. K., Zailani, S., Ramayah, T., & Fernando, Y. (2009). Factors influencing intention to use e-government services among citizens in Malaysia. International Journal of Information Management, 26(6), 458-475.

  • Lee, K. C., & Chung, N. (2009). Understanding factors affecting trust in and satisfaction with mobile banking in Korea: A modified DeLone and McLean’s model perspective. Interacting with Computers, 21(5-6), 385-392.

  • Lin, F., Fofanah, S. S., & Liang, D. (2011). Assessing citizen adoption of eGovernment initiatives in Gambia: A validation of the technology acceptance model in information systems success. Government Information Quarterly, 28(2), 271-279.

  • Liu, Y., Li, H., Kostakos, V., Goncalves, J., Hosio, S., & Hu, F. (2014). An empirical investigation of mobile government adoption in rural China: A case study in Zhejiang province, Government Information Quarterly, 31(3), 432-442.

  • Maranguniæ, N., & Graniæ, A. (2015). Technology acceptance model: A literature review from 1986 to 2013, Universal Access in the Information Society, 14(1), 81-95.

  • Marsh, H. W., & Hocevar, D. (1985). Application of confirmatory factor analysis to the study of self-concept: First-and higher order factor models and their invariance across groups. Psychological Bulletin, 97(3), 562-582.

  • Misuraca, G. C. (2009). e-Government 2015: exploring m-government scenarios, between ICT-driven experiments and citizen-centric implications. Technology Analysis & Strategic Management, 21(3), 407-424.

  • Olasina, G., & Mutula, S. (2015). The influence of national culture on the performance expectancy of e-parliament adoption. Behaviour & Information Technology, 34(5), 492-505.

  • Park, E., Baek, S., Ohm, J., & Chang, H. J. (2014). Determinants of player acceptance of mobile social network games: An application of extended technology acceptance model. Telematics and Informatics, 31 (1), 3-15.

  • Peng, R., Xiong, L., & Yang, Z. (2012). Exploring tourist adoption of tourism mobile payment: An empirical analysis. Journal of Theoretical and Applied Electronic Commerce Research. 7 (1), 21-33.

  • Poblet, M. (2011). Rule of law on the go: New developments of mobile governance. Journal of Universal Computer Science, 17(3), 498-512.

  • Quach, T. N., Thaichon, P., & Jebarajakirthy, C. (2016). Internet service providers’ service quality and its effect on customer loyalty of different usage patterns. Journal of Retailing and Consumer Services, 29(2), 104-113.

  • Rana, N. P., Dwivedi, Y. K., Williams, M. D., & Weerakkody, V. (2016). Adoption of online public grievance redressal system in India: Toward developing a unified view. Computers in Human Behavior, 5(9), 265-282.

  • Rehman, M., Esichaikul, V., & Kamal, M. (2012). Factors influencing egovernment adoption in Pakistan. Transforming Government: People, Process and Policy, 6(3), 258-282.

  • Rogers, E. M. (1995). Diffusion of Innovations: Modifications of a Model for Telecommunications (pp. 25-38). Springer, Berlin Heidelberg.

  • Rogers, E. M. (2003). Diffusion of Innovation, The Free Press New York, NY.

  • Romano Jr, N. C., Pick, J. B., & Roztocki, N. (2010). A motivational model for technology-supported cross-organizational and cross-border collaboration. European Journal of Information Systems, 19(2), 117-133.

  • Schneider, B., Ehrhart, M. G., Mayer, D. M., Saltz, J. L., & Niles-Jolly, K. (2005). Understanding organization-customer links in service settings. Academy of Management Journal, 48(6), 1017-1032.

  • Shaikh, A. A., & Karjaluoto, H. (2015). Mobile banking adoption: A literature review. Telematics and Informatics, 32(1), 129-142.

  • Shankar, A., & Datta, B. (2018). Factors Affecting Mobile Payment Adoption Intention: An Indian Perspective. Global Business Review, 0972150918757870.

  • Shankar, A., & Jebarajakirthy, C. (2019). The influence of e-banking service quality on customer loyalty: A moderated mediation approach. International Journal of Bank Marketing, 37(5), 1119-1142.

  • Shankar, A., & Kumari, P. (2016). Factors Affecting Mobile Banking Adoption Behavior in India. Journal of Internet Banking and Commerce, 21(1), 1-24.

  • Shankar, A., Jebarajakirthy, C., & Ashaduzzaman, M. (2020). How do electronic word of mouth practices contribute to mobile banking adoption?. Journal of Retailing and Consumer Services, 52, 101920.

  • Shareef, M. A., Dwivedi, Y. K., Stamati, T., & Williams, M. D. (2014). SQ mGov: a comprehensive service-quality paradigm for mobile government, Information Systems Management, 31 (2), 126-142.

  • Soni, V., Dey, P. K., Anand, R., Malhotra, C., & Banwet, D. K. (2017). Digitizing grey portions of e-governance, Transforming Government: People, Process and Policy, 11(3), 419-455.

  • Tajfel, H. (1972). Experiments in a vacuum. In J. Israel., & H. T. Triandis. (Eds.). The context of social psychology: A critical assessment (pp. 69-119). London: Academic Press.

  • The National Mobile Governance Initiative (2018). Retrieved 18 March 2018, from http://www.trai.gov.in/sites/default/files/TSDReportJan23032018.pdf

  • Tomer, G., Chauhan, G. S., & Panigrahi, P. K. (2016). Feasibility of m-governance in agriculture: insights from a multimodal study in rural India. Transforming Government: People, Process and Policy, 10 (3), 434-456.

  • TRAI. (2018). Telecom Subscription data as on 31st December 2017. Retrieved 18 March 2018, from http://www.trai.gov.in/sites/default/files/TSDReportJan23032018.pdf

  • Trimi, S., & Sheng, H. (2008). Emerging trends in M-government. Communications of the ACM, 51(5), 53-58.

  • Vaikunthavasan, S., Jebarajakirthy, C., & Shankar, A. (2019). How to make higher education institutions innovative: An application of market orientation practices. Journal of Nonprofit & Public Sector Marketing, 31(3), 274-302.

  • Venkatesh, V. (2000). Determinants of perceived ease of use: Integrating control, intrinsic motivation, and emotion into the technology acceptance model, Information Systems Research, 11(4), 342-365.

  • Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies, Management Science, 46(2), 186-204.

  • Venkatesh, V., & Zhang, X. (2010). Unified theory of acceptance and use of technology: US vs. China. Journal of Global Information Technology Management, 13(1), 5-27.

  • Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view, MIS Quarterly, 27(3), 425-478.

  • 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, MIS Quarterly, 36(1), 157-178.

  • Vincent, J., & Harris, L. (2008). Effective use of mobile communications in egovernment: How do we reach the tipping point?, Information, Community and Society, 11(3), 395-413.

  • Weerakkody, V., El-Haddadeh, R., Al-Sobhi, F., Shareef, M. A., & Dwivedi, Y. K. (2013). Examining the influence of intermediaries in facilitating e-government adoption: An empirical investigation, International Journal of Information Management, 33(5), 716-725.

  • 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.

  • Yang, S. (2016). Role of transfer-based and performance-based cues on initial trust in mobile shopping services: a cross-environment perspective, Information Systems and e-Business Management, 14(1), 47-70.

  • 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.

  • Zarmpou, T., Saprikis, V., Markos, A., & Vlachopoulou, M. (2012). Modeling users’ acceptance of mobile services. Electronic Commerce Research, 12(2), 225-248.


Abstract Views: 278

PDF Views: 0




  • A Study of Factors Affecting Mobile Governance (mGov) Adoption Intention in India using an Extension of the Technology Acceptance Model (TAM)

Abstract Views: 278  |  PDF Views: 0

Authors

Amit Shankar
Marketing Area, Indian Institute of Management Vishakapatnam, Andhra Bank School of Business Building, Andhra University Campus, Visakhapatnam 530 003. Andhra Pradesh, India
Pooja Kumari
Finance & Accounting Area, FORE School of Management, #B-18, Qutub Institutional Area, New Delhi 110016, National Capital Territory of Delhi, India

Abstract


Due to the advancement in Information and Communication Technology (ICT) and mobile phone penetration, the Indian government has initiated mobile governance (mGov) for delivering public services to citizens and businesses over mobile devices. However, despite making several efforts, the pace of mGov adoption has been rather slow. This study aims to explore factors affecting the intention to adopt mGov. A total of 379 responses were collected and analyzed. To determine the reliability and validity of the proposed framework, Confirmatory Factor Analysis (CFA) and Structural Equation Model (SEM) were used. The findings show that perceived usefulness, perceived ease of use, perceived security, and perceived compatibility are crucial determinants affecting the mGov adoption intention. Additionally, the results also indicate that the trust significantly mediates the relationship between perceived usefulness, perceived ease of use and mGov adoption intention. Through the results obtained, we propose which specific determinants government should focus on in order to encourage citizens to adopt and use mGov services.

Keywords


Adoption Intention, Electronic Governance, Mobile Governance, Technology Acceptance Model (TAM), Trust.

References