Mobile Applications Adoption and Use in Strategic Competitive Intelligence: A Structural Equation Modelling Approach

Authors

  • Alexander Maune University of South Africa
  • Milind Thomas Themalil Institute of Engineering & Technology, JK Lakshmipat University, Jaipur

DOI:

https://doi.org/10.37380/jisib.v12i1.922

Keywords:

Strategic Competitive Intelligence, UTAUT, UTAUT2, Adoption, Mobile Applications, Use behaviour, Unified Theory of Acceptance and Use of Technology

Abstract

This article examined the key determinants of mobile applications’ adoptionand use in strategic competitive intelligence. A quantitative research based on a survey of150 participants drawn from strategic competitive intelligence practitioners and analysts wasused to examine and validate the extended UTAUT2 Model to identify the key determinantsof mobile applications` adoption and use in SCI. PLS-SEM algorithm was used to analysedata. Findings show that PE, SI, HT, SE, and BI had significant influence over UB while EE,HM, PV, SN, and PR had an insignificant influence. Adoption and use of mobile applicationswas considered a planned behaviour. Perhaps the most important findings for SCIPs relateto the importance-performance map analysis that showed the greater absolute importance ofself-efficacy on use behaviour. Previous empirical studies have largely ignored the influence ofcognitive psychological perceptive which this study addressed by examining key determinantsof behaviour intention and user behaviour.

References

Abrahão, R. S., Moriguchi, S. N. and 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,Vol. 13, pp. 221–230.

Ajzen, I. (1991). “The theory of planned behavior,” Organisational Behavior and Human Decision Process, Vol. 50(2), pp. 179–211. https://doi.org/10.1080/08870446.2011.613995.

Ajzen, I. (2002). “Residual Effects of Past on Later Behavior: Habituation and Reasoned Action Perspectives,” Personality & Social Psychology Review, Vol. 6(2), pp. 107-122.

Ajzen, I. (2012). The theory of planned behaviour. In P. A.M. Lange, E. T. Higgins and A. W. Kruglanski (Eds.), Handbook of theories of social psychology (Vol. 1, pp. 438-459). London, UK: Sage.

Ajzen, I. and Fishbein, M. (1980). Understanding attitudes and predicting social behaviour, Englewood-Cliffs, NJ: Prentice-Hall.

Ajzen, I. and Fishbein, M. (2005). “The Influence of Attitudes on Behavior,” in The Handbook of Attitudes, D. Albarracín, B. T. Johnson, and M. P. Zanna (eds.), Mahwah, NJ: Erlbaum, pp. 173-221.

Avkiran, N. K. (2018). Rise of the Partial Least Squares Structural Equation Modeling: An Application in Banking. In N. K. Avkiran, C. M. Ringle (Eds.), Partial Least Squares Structural Equation Modeling (pp. 1-29). International Series in Operations Research & Management Science 267. https://doi.org/10.1007/978-3-319-71691-6_1.

Bagozzi, R. P. and Yi, Y. (1988). “On the evaluation of structural equation models,” Journal of the Academy of Marketing Science, Vol. 16(1), pp. 74–94.

Bailey, C. and Austin, M. (2006). “360 Degree Feedback and Developmental Outcomes: The Role of Feedback Characteristics, Self-Efficacy and Importance of Feedback Dimensions to Focal Managers' Current Role,” International Journal of Selection and Assessment, Vol.14, pp. 51-66.

Bandura, A. (1986). Social foundations of thought and action, Englewood Cliffs, NJ Prentice-Hall.

Bandura, A. (1997). Self-efficacy: The exercise of control, New York: Freeman.

Bandura, A. and Locke, E. A. (2003). “Negative self-efficacy and goal effects revisited,” Journal of Applied Psychology, Vol. 88, pp. 87-99.

Barua, Z., Alam, M. Z., and Hu, W. (2018). “Using the UTAUT Model to Determine Factors Affecting Acceptance and Use of Mobile Health (mHealth) Services in Bangladesh,” Journal of Studies in Social Sciences, Vol. 17(2), pp. 137-172.

Briones-Penalver, A. J., Bernal-Conesa, J. A., and Nieves-Nieto, C. (2018). “Analysis of corporate social responsibility in Spanish agribusiness and its influence on innovation and performance,” Corporate Social Responsibility and Environmental Management, Vol. 25(2), pp. 182-193.

Broadhead-Fearn, D. and White, K. M. (2006). “The role of self-efficacy in predicting rule-following behaviors in shelters for homeless youth: a test of the theory of planned behavior,” The Journal of Social Psychology, Vol. 146(3), pp. 307-325.

Brown, S. A. and Venkatesh, V. (2005). “Model of Adoption of Technology in the Household: A Baseline Model Test and Extension Incorporating Household Life Cycle,” MIS Quarterly, Vol. 29(4), pp. 399-426.

Chan, K. Y., Gong, M., Xu, Y., and Thong, J. Y. L. (2008, July 3-7). “Examining User Acceptance of SMS: An Empirical Study in China and Hong Kong.” In Proceedings of 12th Pacific Asia Conference on Information System, Suzhou, China.

Chao, C. M. (2019). “Factors Determining the Behavioral Intention to Use Mobile Learning: An Application and Extension of the UTAUT Model,” Front. Psychol. 10:1652. Doi:10.3389/fpsyg.2019.01652.

Chatzisarantis, N. L. D. and Biddle, S. J. H. (1998). “Functional significance of psychological variables that are included in the Theory of Planned Behaviour: A Self-Determination Theory approach to the study of attitudes, subjective norms, perceptions of control and intentions,” European Journal of Social Psychology, Vol. 28, pp. 303-322.

Childers, T. L., Carr, C. L., Peck, J., and Carson, S. (2001). “Hedonic and Utilitarian Motivations for Online Retail Shopping Behavior,” Journal of Retailing, Vol. 7(4), pp. 511-535.

Dodds, W. B., Monroe, K. B., and Grewal, D. (1991). “Effects of Price, Brand, and Store Information on Buyers,” Journal of Marketing Research, Vol. 28(3), pp. 307-319.

East, R. (1993). “Investment decisions and the theory of planned behaviour,” Journal of Economic Psychology, Vol. 14, pp. 337-375.

Falk, R. F. and Miller, N. B. (1992). A primer for soft modeling. University of Akron Press.

Fishbein, M. and Ajzen, I. (1975). Belief, Attitude, Intention and Behavior: An Introduction to Theory and Research, Addison-Wesley, Reading, MA.

Fornell, C. G., and Larcker, D. F. (1981). “Structural equation models with unobservable variables and measurement error: Algebra and statistics,” Journal of Marketing Research, Vol.18 (3), pp. 328–388.

Gefen, D. and Straub, D. (2005). “A practical guide to factorial validity using PLS-Graph: Tutorial and annotated example,” Communications of the Association for Information Systems, Vol. 16(1), pp. 91-109.

Gharaibeh, N., Gharaibeh, M. K., Gharaibeh, O. and Bdour, W. (2020). “Exploring intention to adopt mobile commerce: Integrating UTAUT2 with social media,” International Journal of Scientific and Technology Research, Vol. 9 (3), pp. 3826-3833.

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, Vol. 25(3), pp. 215-235.

Ha, C. L. (1998). “The theory of reasoned action applied to brand loyalty,” The Journal of Product and Brand Management, Vol. 7(1), pp. 51-61.

Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2nd Ed.). Los Angeles: Sage.

Henseler, J., Ringle, C. M., and Sarstedt, M. (2015). “A new criterion for assessing discriminant validity in variance-based structural equation modeling,” Journal of the Academy of Marketing Science, Vol. 43, pp. 115–135.

Khurana, S. and Jain, D. (2019). “Applying and Extending UTAUT2 Model of Adoption of New Technology in the Context of M-Shopping Fashion Apps,” International Journal of Innovative Technology and Exploring Engineering (IJITEE), Vol. 8(9S), pp. 752-759. DOI: 10.35940/ijitee.I1122.0789S19.

Kim, S. S. and Malhotra, N. K. (2005). “A Longitudinal Model of Continued IS Use: An Integrative View of Four Mechanisms Underlying Post-Adoption Phenomena,” Management Science, Vol. 51(5), pp. 741-755.

Kim, S. S., Malhotra, N. K., and Narasimhan, S. (2005). “Two Competing Perspectives on Automatic Use: A Theoretical and Empirical Comparison,” Information Systems Research, Vol. 16(4), pp. 418-432.

Limayem, M., Hirt, S. G., and Cheung, C. M. K. (2007). “How Habit Limits the Predictive Power of Intentions: The Case of IS Continuance,” MIS Quarterly, Vol. 31(4), pp. 705-737.

Liu, G-S. and Tai, P. T. (2016). “A Study of Factors Affecting the Intention to Use Mobile Payment Services in Vietnam,” Economics World, Vol. 4(6), pp. 249-273. Doi: 10.17265/2328-7144/2016.06.001.

Marcoulides, G. A. and Saunders, C. (2006). “Editor’s Comments – PLS: A Silver Bullet?” MIS Quarterly, Vol. 30(2), pp. iii-ix.

Maune, A. (2021). “Intention to use mobile applications in competitive intelligence: An extended conceptual framework,” Journal of Intelligence Studies in Business, Vol. 11(2), pp. 6-29.

Maune, A., Matanda, E. and Mundonde, J. (2021). “Financial Inclusion as an Intentional Behaviour in Zimbabwe,” Acta Universitatis Danubius. OEconomica, Vol. 17(4), pp. 177-211.

O'connor, R. C. and Armitage, C. J. (2003). “Theory of Planned Behaviour and Parasuicide: An Exploratory Study,” Current Psychology: Developmental, Learning, Personality, Social, Vol. 22(3), pp. 196-205.

Ouellette, J. A. and Wood, W. (1998). “Habit and Intention in Everyday Life: The Multiple Processes by Which Past Behavior Predicts Future Behavior,” Psychological Bulletin, Vol. 124(1), pp. 54-74.

Roy, S. (2017). “App adoption and switching behavior: applying the extended tam in smartphone app usage,” JISTEM - Journal of Information Systems and Technology Management, Vol. 14(2), pp. 239-261. DOI: 10.4301/S1807-17752017000200006.

Shneor, R. and Munimb, Z. H. (2019). “Reward crowdfunding contribution as planned behaviour: An extended framework,” Journal of Business Research, Vol. 103, pp. 56–70.

Solberg K. S. (2019). “The argument that ‘there is nothing new in the competitive intelligence field,’” Journal of Intelligence Studies in Business, Vol. 9(3), p. 4-6.

Tarhini, A., Alalwan, A. A., Shammout, A. B., Al-Badi, A. (2019). “An analysis of the factors affecting mobile commerce adoption in developing countries: Towards an integrated model,” Review of International Business and Strategy, Vol. 29(3), pp. 157-179.

Tenenhaus, M., Vinzi, V. E., Chatelin, Y.-M., and Lauro, C. (2005). “PLS path modeling,” Computational Statistics and Data Analysis, Vol. 48, pp. 159–205.

Thong J. Y. L., Hong, S. J., and Tam, K. Y. (2006). “The Effects of Post-Adoption Beliefs on the Expectation–Confirmation Model for Information Technology Continuance,” International Journal of Human-Computer Studies, Vol. 64(9), pp. 799-810.

van der Heijden, H. (2004). “User Acceptance of Hedonic Information Systems,” MIS Quarterly, Vol. 28(4), pp. 695-704.

Venkatesh, V., Morris, M. G., Davis, G. B., and Davis, F. D. (2003). “User Acceptance of Information Technology: Toward a Unified View,” MIS Quarterly, Vol. 27(3), pp. 425-478.

Venkatesh, V., Thong, J., and Xu, X. (2012). “Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology,” MIS Quarterly, Vol. 36(1), pp. 157-178.

Yadav, R., Chauhan, V. and Pathak, G. S. (2015). “Intention to adopt internet banking in an emerging economy: a perspective of Indian youth,” International Journal of Bank Marketing, Vol. 33(4), pp. 530-544.

Zeithaml, V. A. (1988). “Consumer Perceptions of Price, Quality, and Value: A Means–End Model and Synthesis of Evidence,” Journal of Marketing, Vol. 52(3), pp. 2-22.

Zhao, H., Seibert, S. E. and Hills, G. E. (2005). “The Mediating Role of Self-Efficacy in the Development of Entrepreneurial Intentions,” Journal of Applied Psychology, Vol. 90, pp. 1265-1272.

Zimmerman, B. J., Bandura, A. and Martinez-Pons, M. (1992). “Self-motivation for academic attainment: The role of self-efficacy beliefs and personal goal setting,” American Educational Research Journal, Vol. 29, pp. 663-676.

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Published

2022-12-22