The Role of Convenience and Usability in the Adoption of Electronic Management Systems: A Theoretical and Empirical Review
DOI:
https://doi.org/10.55927/ajabm.v3i4.12185Keywords:
Convenience, Usability, Electronic Management System, TAM, Technology AdoptionAbstract
This study aims to analyze the role of convenience and usability in the adoption of electronic management systems (EMS) through a scientometric approach. Using a bibliometric analysis of 1,112 articles published in the Scopus database between 2014 and 2024, this study maps the thematic evolution, trends, and main clusters in the related literature. Topic network visualization techniques using VOSviewer and RStudio were used to identify dominant themes and reveal relationships between topics, such as technology acceptance model (TAM), artificial intelligence (AI), blockchain, and other emerging technologies. The results show that convenience and ease of use continue to be key factors in EMS adoption, especially in sectors such as e-commerce, education, and healthcare. The emergence of new issues such as trust, risk, and privacy signals a shift in the focus of recent research, reflecting the need to consider psychological and social factors in modern technology. The theoretical implications suggest that while traditional models such as TAM are still relevant, the integration of new factors such as user satisfaction and privacy is becoming increasingly important.
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Abbas, A., Ekowati, D., Suhariadi, F., Anwar, A., & Fenitra, R. M. (2023). Technology acceptance and COVID-19: a perspective for emerging opportunities from crisis. Technology Analysis and Strategic Management, 1–13. https://doi.org/10.1080/09537325.2023.2214642
Abd Majid, F., & Mohd Shamsudin, N. (2019). Identifying factors affecting acceptance of virtual reality in classrooms based on Technology Acceptance Model (TAM). Asian Journal of University Education, 15(2), 52–60. https://doi.org/10.24191/ajue.v15i2.7556
Abuhay, T. M., Nigatie, Y. G., & Kovalchuk, S. V. (2018). Towards Predicting Trend of Scientific Research Topics using Topic Modeling. Procedia Computer Science, 136, 304–310. https://doi.org/10.1016/j.procs.2018.08.284
Aishwarya, R., & Vivek Anand, M. (2023). Blockchain Framework For Securing Autonomous Vehicles. 2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023. https://doi.org/10.1109/ICAECA56562.2023.10200036
Akram, U., Fülöp, M. T., Tiron-Tudor, A., Topor, D. I., & Căpușneanu, S. (2021). Impact of digitalization on customers’ well-being in the pandemic period: Challenges and opportunities for the retail industry. International Journal of Environmental Research and Public Health, 18(14). https://doi.org/10.3390/ijerph18147533
Awa, H. O., Ojiabo, O. U., & Emecheta, B. C. (2015). Integrating TAM, TPB and TOE frameworks and expanding their characteristic constructs for e-commerce adoption by SMEs. Journal of Science and Technology Policy Management, 6(1), 76–94. https://doi.org/10.1108/JSTPM-04-2014-0012
Barbosa, A. M., De Silva, K. S. F., Lagares, M. H., Rodrigues, D. A., Martins, J. V. M., Da Costa, I. R., & Moura, K. K. V. O. (2018). Scientometric analysis: Five years of genetic polymorphisms. Genetics and Molecular Research, 17(2). https://doi.org/10.4238/gmr16039913
Ben Mansour, K. (2016). An analysis of business’ acceptance of internet banking: an integration of e-trust to the TAM. Journal of Business and Industrial Marketing, 31(8), 982–994. https://doi.org/10.1108/JBIM-10-2016-271
Chen, H. H., Kang, H. Y., & Lee, A. H. I. (2017). A decision making model for selecting environmental management system (EMS) project contractor. Environmental Engineering and Management Journal, 16(7), 1583–1594. https://doi.org/10.30638/eemj.2017.172
Cobelli, N., & Blasi, S. (2024). Combining topic modeling and bibliometric analysis to understand the evolution of technological innovation adoption in the healthcare industry. European Journal of Innovation Management, 27(9), 127–149. https://doi.org/10.1108/EJIM-06-2023-0497
Ding, Y., & Chai, K. H. (2015). Emotions and continued usage of mobile applications. Industrial Management and Data Systems, 115(5), 833–852. https://doi.org/10.1108/IMDS-11-2014-0338
Hamzah, H., Wahab, S. N., Othman, N., & Ferguson, G. (2024). Greening the hospitality industry: examining institutional influences and perceived benefits of EMS in Malaysian SME hotels. Journal of Hospitality and Tourism Insights. https://doi.org/10.1108/JHTI-12-2023-0922
Hikmah, H., Ratnawati, A. T., & Darmanto, S. (2023). Role of Attitude and Intention on the Relationship between Perceived Ease of Use, Perceived Usefulness, Trust, and E-Tax System Behavior. Global Business and Finance Review, 28(7), 89–104. https://doi.org/10.17549/gbfr.2023.28.7.89
Hsieh, M.-Y. (2020). Interdisciplinarily Exploring the Most Potential IoT Technology Determinants in the Omnichannel E-Commerce Purchasing Decision-Making Processes. Applied Sciences, 10(2), 603. https://doi.org/10.3390/app10020603
Hussain, S., Qazi, S., Ahmed, R. R., Vveinhardt, J., & Streimikiene, D. (2019). Innovative user engagement and playfulness on adoption intentions of technological products: evidence from SEM-based multivariate approach. Economic Research-Ekonomska Istrazivanja , 32(1), 555–577. https://doi.org/10.1080/1331677X.2018.1558086
Jayashree, S., Malarvizhi, C. A., Mayel, S., & Rasti, A. (2016). Impact of integration of management system on IS014000 EMS towards corporate sustainability. Information (Japan), 19(6B), 2137–2144.
Kamarudzaman, Z. A., & Jambari, D. I. (2021). Change Management Framework for Managing Information Systems Post Adoption in Public Sector. Proceedings of the International Conference on Electrical Engineering and Informatics. https://doi.org/10.1109/ICEEI52609.2021.9611150
Kumari, S., & Muthulakshmi, P. (2023). Artificial Intelligence—Blockchain Enabled Technology for Internet of Things: Research Statements, Open Issues, and Possible Applications in the Near Future. In Privacy Preservation of Genomic and Medical Data (pp. 433–480). Taylor and Francis.
Lee, S. L., Ainin, S., Dezdar, S., & Mallasi, H. (2015). Electronic data interchange adoption from technological, organisational and environmental perspectives. International Journal of Business Information Systems, 18(3), 299–320. https://doi.org/10.1504/IJBIS.2015.068166
Li, J., Goerlandt, F., & Reniers, G. (2021). An overview of scientometric mapping for the safety science community: Methods, tools, and framework. Safety Science, 134. https://doi.org/10.1016/j.ssci.2020.105093
Li, Q., Zhu, C., & Shi, T. (2021). Augmented reality advertising in an e-commerce model with competition. Electronic Commerce Research and Applications, 49, 101092. https://doi.org/10.1016/j.elerap.2021.101092
Makmor, N., Aziz, N. A., & Alam, S. S. (2019). Social commerce an extended technology acceptance model: The mediating effect of perceived ease of use and perceived usefulness. Malaysian Journal of Consumer and Family Economics, 22, 119–136.
Marakarkandy, B., Yajnik, N., & Dasgupta, C. (2017). Enabling internet banking adoption: An empirical examination with an augmented technology acceptance model (TAM). Journal of Enterprise Information Management, 30(2), 263–294. https://doi.org/10.1108/JEIM-10-2015-0094
Mathew, V., & Soliman, M. (2021). Does digital content marketing affect tourism consumer behavior? An extension of technology acceptance model. Journal of Consumer Behaviour, 20(1), 61–75. https://doi.org/10.1002/cb.1854
Mendhurwar, S., & Mishra, R. (2023). ‘Un’-blocking the industry 4.0 value chain with cyber-physical social thinking. Enterprise Information Systems, 17(2). https://doi.org/10.1080/17517575.2021.1930189
Mingers, J., & Leydesdorff, L. (2015). A review of theory and practice in scientometrics. European Journal of Operational Research, 246(1), 1–19. https://doi.org/10.1016/j.ejor.2015.04.002
Misran, Syaifuddin, Muhammad, A. N., & Khadafi, R. (2022). A Meta-Analysis of Big Data Security : Using Blockchain for One Data Governance , Case Study of Local Tax Big Data in Indonesia. Proceedings of the International Conference on Public Organization, 209(Iconpo 2021), 198–206.
Munir, A. R., & Ilyas, G. B. (2017). Extending the technology acceptance model to predict the acceptance of customer toward mobile banking service in Sulawesi Selatan. International Journal of Economic Research, 14(4), 365–375.
Nam, S. T., Lee, H. C., Shin, S. Y., & Jin, C. Y. (2014). A meta-analysis of relationship between constructs on the theory of reasoned action. Information (Japan), 17(7A), 3129–3134.
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
Osman, I. H., Anouze, A. L., Irani, Z., Lee, H., Medeni, T. D., & Weerakkody, V. (2019). A cognitive analytics management framework for the transformation of electronic government services from users’ perspective to create sustainable shared values. European Journal of Operational Research, 278(2), 514–532. https://doi.org/10.1016/j.ejor.2019.02.018
Othman, B., Harun, A., Ismail, D. A., Sadq, Z. M., Ali, S., & Ramsey, T. S. (2019). Malaysian consumer behaviour towards internet banking: An application of technology acceptance model. International Journal of Psychosocial Rehabilitation, 23(2), 689–703. https://doi.org/10.37200/IJPR/V23I2/PR190324
Ozturk, A. B., Bilgihan, A., Nusair, K., & Okumus, F. (2016). What keeps the mobile hotel booking users loyal? Investigating the roles of self-efficacy, compatibility, perceived ease of use, and perceived convenience. International Journal of Information Management, 36(6), 1350–1359. https://doi.org/10.1016/j.ijinfomgt.2016.04.005
Padua, D. (2021). An Unpredictable Era at the Time of Covid-19. In Innovation, Technology and Knowledge Management (pp. 19–37). Springer. https://doi.org/10.1007/978-3-030-83803-4_2
Pavlov, A., Pavlov, D., Umarov, A., & Gordeev, A. (2022). Method of Structural-Parametric Synthesis of Configuration Multi-Mode Object. Informatics and Automation, 21(4), 812–845. https://doi.org/10.15622/ia.21.4.7
Phani Bhaskar, P., & Prasanna Kumar, D. (2017). A study on factors influence towards e-commerce. International Journal of Mechanical Engineering and Technology, 8(9), 478–494.
Rese, A., Schreiber, S., & Baier, D. (2014). Technology acceptance modeling of augmented reality at the point of sale: Can surveys be replaced by an analysis of online reviews? Journal of Retailing and Consumer Services, 21(5), 869–876. https://doi.org/10.1016/j.jretconser.2014.02.011
Rundle-Thiele, S., Kubacki, K., Tkaczynski, A., & Parkinson, J. (2015). Using two-step cluster analysis to identify homogeneous physical activity groups. Marketing Intelligence and Planning, 33(4), 522–537. https://doi.org/10.1108/MIP-03-2014-0050
Siagian, H., Tarigan, Z. J. H., Basana, S. R., & Basuki, R. (2022). The effect of perceived security, perceived ease of use, and perceived usefulness on consumer behavioral intention through trust in digital payment platform. International Journal of Data and Network Science, 6(3), 861–874. https://doi.org/10.5267/j.ijdns.2022.2.010
Suardi, W., Nurmandi, A., Mutiarin, D., Purnomo, E. P., Pribadi, U., Purwaningsih, T., Misran, M., Zulkifli, Z., & Younus, M. (2023). A Historical Review for City Branding: Hyper Competition, Challenges, and Improvement Opportunities. Jurnal Bina Praja, 15(1), 85–99. https://doi.org/10.21787/jbp.15.2023.85-99
Sun, Y., & Jung, H. (2024). Machine Learning (ML) Modeling, IoT, and Optimizing Organizational Operations through Integrated Strategies: The Role of Technology and Human Resource Management. Sustainability (Switzerland), 16(16). https://doi.org/10.3390/su16166751
Tan, Z., Liu, C., Mao, Y., Guo, Y., Shen, J., & Wang, X. (2016). AceMap: A Novel Approach towards Displaying Relationship among Academic Literatures. WWW 2016 Companion - Proceedings of the 25th International Conference on World Wide Web, 437–442. https://doi.org/10.1145/2872518.2890514
Tandon, U., Kiran, R., & Sah, A. N. (2016). Analysing the complexities of website functionality, perceived ease of use and perceived usefulness on customer satisfaction of online shoppers in India. International Journal of Electronic Marketing and Retailing, 7(2), 115–140. https://doi.org/10.1504/IJEMR.2016.077118
Tiwari, P., Tiwari, S. K., & Gupta, A. (2021). Examining the Impact of Customers’ Awareness, Risk and Trust in M-Banking Adoption. FIIB Business Review, 10(4), 413–423. https://doi.org/10.1177/23197145211019924
Troisi, O., Fenza, G., Grimaldi, M., & Loia, F. (2022). Covid-19 sentiments in smart cities: The role of technology anxiety before and during the pandemic. Computers in Human Behavior, 126, 106986. https://doi.org/10.1016/j.chb.2021.106986
Venkatesh, V., Thong, J. Y. L., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376. https://doi.org/10.17705/1jais.00428
Wang, X., Chen, Y., Liu, Y., Yao, L., Estill, J., Bian, Z., Wu, T., Shang, H., Lee, M. S., Wei, D., Tian, J., Ma, B., Wang, Y., Tian, G., & Yang, K. (2019). Reporting items for systematic reviews and meta-analyses of acupuncture: The PRISMA for acupuncture checklist. BMC Complementary and Alternative Medicine, 19(1), 1–10. https://doi.org/10.1186/s12906-019-2624-3
Wang, Y., Deng, Q., Rod, M., & Ji, S. (2021). A thematic exploration of social media analytics in marketing research and an agenda for future inquiry. Journal of Strategic Marketing, 29(6), 471–491. https://doi.org/10.1080/0965254X.2020.1755351
Wu, H. C., & Cheng, C. C. (2018). What Drives Experiential Loyalty Toward Smart Restaurants? The Case Study of KFC in Beijing. Journal of Hospitality Marketing and Management, 27(2), 151–177. https://doi.org/10.1080/19368623.2017.1344952
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