Analysis Acceptance of XYZ Company Digital Membership Using Technology Acceptance Model
DOI:
https://doi.org/10.35870/ijsecs.v5i1.3496Keywords:
Technology Acceptance, Digital Membership, TAM, Perceived Usefulness, Perceived Ease of Use, Behavioral IntentionAbstract
The current study explores the acceptance of XYZ Company’s digital membership program in the light of the Technology Acceptance Model (TAM). The objective is to find some key values that predict user adoption, focusing mainly on the predictors of Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) in behavioral intention (BI). Data was collected from 378 respondents and Structural Equation Modeling (SEM) was used on the data obtained. The data provides evidence for the significant effects of PU and PEOU on BI as expected, underlining how important these constructions are to influence user acceptance. The study extends the TAM with other variables such as Trust, data privacy, and user experience (UX) to provide a broader understanding. These variables are important, especially in the case of a digital membership program operated by a company, for the technological requirements that need to satisfy multiple customers and match those needs with industry constraints. The study findings reveal a significant mediating effect of UX on the relationship between PEOU and PU, as well as a moderating impact of trust and data privacy on the relationship between PEOU and PU, which in turn creates a more satisfying level of assurance and satisfaction. While the findings inform, in a very specific way what Company XYZ can do from a management perspective to improve the user experience and enhance the benefits of its digital membership program and user adoption/engagement. In line with the academic literature, this study places TAM into the context of corporate digital membership offering relevant knowledge and practical recommendations for organizations to replicate similar initiatives. The strategic implications of these results demonstrate the importance for companies to design their digital solutions according to user expectations and needs, to consistently deliver optimal customer value and satisfaction in a highly competitive sales environment.
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References
El-Masri, M., & Tarhini, A. (2017). Factors affecting the adoption of e-learning systems in Qatar and USA: Extending the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2). Education Tech Research Dev, 65(4), 743–763. https://doi.org/10.1007/s11423-016-9508-8
Cheng, Y., Wang, Q., & Li, Z. (2021). Trust, social influence, and perceived risk in technology adoption: A meta-analytic review. Journal of Retailing and Consumer Services, 63, 102713. https://doi.org/10.1016/j.jretconser.2021.102713
Alalwan, A. A., Baabdullah, A. M., Rana, N. P., Tamilmani, K., & Dwivedi, Y. K. (2018). Examining adoption of mobile internet in Saudi Arabia: Extending TAM with perceived enjoyment, innovativeness and trust. Technology in Society, 55, 100–110. https://doi.org/10.1016/j.techsoc.2018.06.007
Kim, H., & Park, S. (2021). Enhancing user engagement through personalized recommendations in loyalty programs: A TAM perspective. Journal of Business Research, 129, 468-478. https://doi.org/10.1016/j.jbusres.2021.02.029
Dwivedi, Y. K., Hughes, L., Baabdullah, A. M., Ribeiro-Navarrete, S., Giannakis, M., Al-Debei, M. M., & Wamba, S. F. (2022). Metaverse beyond the hype: Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 66, 102542. https://doi.org/10.1016/j.ijinfomgt.2022.102542
Andersson, J., Baresi, L., Bencomo, N., Lemos, R., Gorla, A., Inverardi, P., & Vogel, T. (2013). Software engineering processes for self-adaptive systems. In R. de Lemos, H. Giese, H. A. Müller, & M. Shaw (Eds.), Software Engineering for Self-Adaptive Systems II (Vol. 7475). Springer. https://doi.org/10.1007/978-3-642-35813-5_3
Kim, D., & Park, S. (2009, May). Reinforcement learning-based dynamic adaptation planning method for architecture-based self-managed software. In 2009 ICSE Workshop on Software Engineering for Adaptive and Self-Managing Systems (pp. 76-85). IEEE. https://doi.org/10.1109/SEAMS.2009.5069076
Ho, H. N., & Lee, E. (2015, January). Model-based reinforcement learning approach for planning in self-adaptive software system. In Proceedings of the 9th International Conference on Ubiquitous Information Management and Communication (pp. 1-8). https://doi.org/10.1145/2701126.2701191
Elkhodary, A., Esfahani, N., & Malek, S. (2010, November). FUSION: A framework for engineering self-tuning self-adaptive software systems. In Proceedings of the Eighteenth ACM SIGSOFT International Symposium on Foundations of Software Engineering (pp. 7-16). https://doi.org/10.1145/1882291.1882296
Esfahani, N., Elkhodary, A., & Malek, S. (2013). A learning-based framework for engineering feature-oriented self-adaptive software systems. IEEE Transactions on Software Engineering, 39(11), 1467-1493. https://doi.org/10.1109/TSE.2013.37
Computing, A. (2006). An architectural blueprint for autonomic computing. IBM White Paper. Retrieved from https://www.ibm.com/support/pages/architectural-blueprint-autonomic-computing
Gamma, E. (1995). Design patterns: Elements of reusable object-oriented software. Pearson Education India.
Weyns, D., de Lemos, R., Giese, H., Müller, H. A., & Shaw, M. (2013). On patterns for decentralized control in self-adaptive systems. In R. de Lemos, H. Giese, H. A. Müller, & M. Shaw (Eds.), Software Engineering for Self-Adaptive Systems II (Vol. 7475). Springer. https://doi.org/10.1007/978-3-642-35813-5_4
Cheng, S. W., Poladian, V. V., Garlan, D., & Schmerl, B. (2009). Improving architecture-based self-adaptation through resource prediction. In B. H. C. Cheng, R. de Lemos, H. Giese, P. Inverardi, & J. Magee (Eds.), Software Engineering for Self-Adaptive Systems (Vol. 5525). Springer. https://doi.org/10.1007/978-3-642-02161-9_4
Luckey, M., Nagel, B., Gerth, C., & Engels, G. (2011, May). Adapt cases: Extending use cases for adaptive systems. In Proceedings of the 6th International Symposium on Software Engineering for Adaptive and Self-Managing Systems (pp. 30-39). https://doi.org/10.1145/1988008.1988014
Cheng, S. W. (2008). Rainbow: Cost-effective software architecture-based self-adaptation. Carnegie Mellon University.
Briand, L. C., Daly, J. W., & Wust, J. K. (1999). A unified framework for coupling measurement in object-oriented systems. IEEE Transactions on Software Engineering, 25(1), 91-121. https://doi.org/10.1109/32.748920
Hitz, M., & Montazeri, B. (1995). Measuring coupling and cohesion in object-oriented systems (pp. 25-27). na.
Fenton, N., & Bieman, J. (2014). Software metrics: A rigorous and practical approach. CRC Press.
Gui, G., & Scott, P. D. (2009). Measuring software component reusability by coupling and cohesion metrics. J. Comput., 4(9), 797-805.
J. Bates. (n.d.). Slashdot. BizX. Retrieved from https://slashdot.org
Floch, J., Hallsteinsen, S., Stav, E., Eliassen, F., Lund, K., & Gjorven, E. (2006). Using architecture models for runtime adaptability. IEEE Software, 23(2), 62-70. https://doi.org/10.1109/MS.2006.61
Gui, N., & De Florio, V. (2012, September). Towards meta-adaptation support with reusable and composable adaptation components. In 2012 IEEE Sixth International Conference on Self-Adaptive and Self-Organizing Systems (pp. 49-58). IEEE. https://doi.org/10.1109/SASO.2012.11
Litoiu, M., Woodside, M., & Zheng, T. (2005, May). Hierarchical model-based autonomic control of software systems. In Proceedings of the 2005 Workshop on Design and Evolution of Autonomic Application Software (pp. 1-7). https://doi.org/10.1145/1083063.1083071
Müller, H. A., Pezzè, M., & Shaw, M. (2008, May). Visibility of control in adaptive systems. In ULSSIS@ ICSE (pp. 23-26).
Dowling, J., & Cahill, V. (2001). The K-Component Architecture Meta-Model for Self-Adaptive Software. In A. Yonezawa & S. Matsuoka (Eds.), Metalevel Architectures and Separation of Crosscutting Concerns. Reflection 2001 (Vol. 2192). Springer. https://doi.org/10.1007/3-540-45429-2_6
David, P. C., & Ledoux, T. (2006). An Aspect-Oriented Approach for Developing Self-Adaptive Fractal Components. In W. Löwe & M. Südholt (Eds.), Software Composition. SC 2006 (Vol. 4089). Springer. https://doi.org/10.1007/11821946_6
Wu, Y., Wu, Y., Peng, X., & Zhao, W. (2010, July). Implementing self-adaptive software architecture by reflective component model and dynamic AOP: A case study. In 2010 10th International Conference on Quality Software (pp. 288-293). IEEE. https://doi.org/10.1109/QSIC.2010.56
B. S. Shang-Wen Cheng. (2018, December 28). Model Problem: Znn.com. [Online]. Available: https://www.hpi.uni-potsdam.de/giese/public/selfadapt/exemplars/model-problem-znn-com/.
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