Broadband Mobile Applications’ Adoption by SMEs in Taiwan—A Multi-Perspective Study of Determinants
Source abstract
Abstract
The purpose of this study is to explore what determinants affect small and medium enterprises regarding the adoption of broadband mobile applications. Today, broadband mobile applications are changing the business landscape rapidly, which presents a vital issue for enterprises to understand and tackle. Both qualitative and quantitative methods were applied in this study in order to analyze the issues enterprises may face with broadband mobile applications. Qualitatively, a preliminary study was conducted based on a review of literature to explore the factors that impact small and medium enterprises. Quantitatively, Structural Equation Modeling and AMOS were deployed to further examine the potential factors. As broadband mobile applications are mostly installed in the personal mobile device and operated by individuals, this study integrated the perspective of internal users along with the Technology-Organization-Environment framework to develop an ITOE research model to provide a more comprehensive view on the determinants and factors. The practicality and feasibility of the ITOE research model were then verified by the study results through the fifteen determinants identified. Based on the findings, implications and future research directions are proposed.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Technology Adoption and User Behaviour · Digital Platforms and Economics · Digital Marketing and Social Media
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Department of Industrial Engineering and Management, School of Management, National Taipei University of Technology, Taipei City 106, Taiwan
Graduate School of Creative Industry Design, National Taiwan University of Arts, New Taipei City 220, Taiwan
Department of Banking and Finance, College of Business, Chinese Culture University, Taipei City 111, Taiwan
References (71)
Show source-provided references
1. GSMA (2022, February 02). The Mobile Economy 2022. Available online: https://www.gsma.com/mobileeconomy/wp-content/uploads/2022/02/280222-The-Mobile-Economy-2022.pdf.
2. Differences between LTE and 3G service customers: Business and policy implications10.1016/j.tele.2015.03.001 ↗
3. Business-to-business marketing responses to COVID-19 crisis: A business process perspective10.1108/MIP-05-2020-0217 ↗
4. Stjepić, A.-M., Bach, M.P., and Vukšić, V.B. (2021). Exploring Risks in the Adoption of Business Intelligence in SMEs Using the TOE Framework. J. Risk Financ. Manag., 14.10.3390/jrfm14020058 ↗
5. Mobile marketing technology adoption in service SMEs: A multi-perspective framework10.1108/JSTPM-11-2018-0105 ↗
6. Mapping SME productivity research: A systematic review of empirical evidence and future research agenda10.1007/s11187-021-00450-3 ↗
7. Social Media Adoption in SMEs Impacted by COVID-19: The TOE Model10.13106/jafeb.2020.vol7.no11.915 ↗
8. Small and Medium Enterprise Administration MOEA Taiwan (2021). 2021 White Paper on Small and Medium Enterprises in Taiwan, Small and Medium Enterprise Administration MOEA. [1st ed.].
9. (2022, April 02). Speedchecker, Speedchecker’s Mobile Coverage Datasets. Available online: https://www.speedchecker.com/products/cellular-coverage-datasets.html.
10. International Telecommunication Union (ITU) (2022, June 30). Requirements Related to Technical Performance for IMT-Advanced Radio Interface(s). Available online: https://www.itu.int/pub/R-REP-M.2134-2008/en.
11. Chauhan, K. (2022, April 02). Handset Sales by Model 2019Q1-January 2022. Available online: https://report.counterpointresearch.com/posts/report_view/Hit/2768.
12. 5G Evolution: A View on 5G Cellular Technology Beyond 3GPP Release 1510.1109/ACCESS.2019.2939938 ↗
13. IHS Markit (2020). The 5G Economy in a Post-COVID-19 Era: The Role of 5G in a Post Pandemic World Economy, Qualcomm.
14. Development perspectives, firm strategies and applications in mobile commerce10.1016/S0148-2963(02)00429-0 ↗
15. The Enterprise Mobile Applications Development Framework10.1109/MITP.2010.45 ↗
16. Effectiveness of online behavioral targeting: A psychological perspective10.1016/j.elerap.2018.11.006 ↗
17. Malik, S., Chadhar, M., Vatanasakdakul, S., and Chetty, M. (2021). Factors Affecting the Organizational Adoption of Blockchain Technology: Extending the Technology–Organization–Environment (TOE) Framework in the Australian Context. Sustainability, 13.10.3390/su13169404 ↗
18. Ramdani, B., and Kawalek, P. (2007, January 14–16). SME adoption of enterprise systems in the Northwest of England. Proceedings of the IFIP International Working Conference on Organizational Dynamics of Technology-Based Innovation, Manchester, UK.
19. Ali Qalati, S., Li, W., Ahmed, N., Ali Mirani, M., and Khan, A. (2021). Examining the Factors Affecting SME Performance: The Mediating Role of Social Media Adoption. Sustainability, 13.10.3390/su13010075 ↗
20. Factors Influencing SMEs’ Adoption of Cloud Computing Services in Lebanon: An Empirical Analysis Using TOE and Contextual Theory10.1109/ACCESS.2020.2987331 ↗
21. Bhuiyan, Y., Othman, S.H., and Radzi, R.Z.R.M. (2019). An Enhancement of TOE Model by Investigating the Influential Factors of Cloud Adoption Security Objectives. Int. J. Innov. Comput., 9.10.11113/ijic.v9n1.192 ↗
22. Cloud computing adoption by SMEs in the northeast of England: A multi-perspective framework10.1108/17410391311325225 ↗
23. Adoption of e-commerce applications in SMEs10.1108/02635571111170785 ↗
24. SMEs’ e-commerce adoption: Perspectives from Denmark and Australia10.1108/17410390910932803 ↗
25. The Determinants of Adoption of Cloud-Based ERP of Nigerian’s SMES Manufacturing Sector Using Toe Framework and Doi Theory
26. The Investigation of E-Marketplace Adoption by Small Medium Enterprises Using Individual-Technology-Organization-Environment (ITOE) Framework: A Case Study in Yogyakarta Province Indonesia
27. Factors Influencing Audit Technology Acceptance by Audit Firms: A New I-TOE Adoption Framework
28. Tornatzky, L.G., and Fleischer, M. (1990). The context for change: Organization, technology and environment. The Processes of Technological Innovation, Lexington Books.
29. An Integrated Model of Information Systems Adoption in Small Businesses10.1080/07421222.1999.11518227 ↗
30. Big data analytics adoption model for small and medium enterprises10.1108/JSTPM-02-2020-0018 ↗
31. Innovation diffusion in global contexts: Determinants of post-adoption digital transformation of European companies10.1057/palgrave.ejis.3000650 ↗
32. Antecedents of mobile marketing adoption by SMEs: Does industry variance matter?10.1080/10919392.2021.1956847 ↗
33. A systematic review and analysis of determinants impacting adoption and assimilation of e-commerce in small and medium enterprises10.1504/IJEB.2018.098128 ↗
34. SLRA: Challenges faced by SMEs in the adoption of E-commerce and sustainability in Industry 4.010.32725/acta.2021.010 ↗
35. Determinants of the adoption of Information and Communication Technologies (ICT): An empirical analysis based on firm-level data for the Swiss business sector10.1016/j.strueco.2004.01.003 ↗
36. Analysis of the adoption of customer facing InStore technologies in retail SMEs10.1016/j.jretconser.2020.102225 ↗
37. The economics of mobile payments: Understanding stakeholder issues for an emerging financial technology application10.1016/j.elerap.2006.12.004 ↗
38. Factors Influencing SMEs Adoption of Social Media Marketing10.1016/j.sbspro.2014.07.025 ↗
39. The impact of technological-organizational-environmental (TOE) factors on firm performance: Merchant’s perspective of mobile payment from Thailand’s retail and service firms10.1108/JABS-01-2020-0012 ↗
40. Understanding AI adoption in manufacturing and production firms using an integrated TAM-TOE model10.1016/j.techfore.2021.120880 ↗
41. User acceptance of information technology: Toward a unified view10.2307/30036540 ↗
42. Customers’ acceptance intention of self-service technology of restaurant industry: Expanding UTAUT with perceived risk and innovativeness10.1007/s11628-020-00425-6 ↗
43. The qualitative interview in IS research: Examining the craft10.1016/j.infoandorg.2006.11.001 ↗
44. Key success factors influencing SME managers’ information behaviour on emerging ICT (EICT) adoption decision-making in UK SMEs10.1108/BL-02-2018-0008 ↗
45. Electronic business adoption by European firms: A cross-country assessment of the facilitators and inhibitors10.1057/palgrave.ejis.3000475 ↗
46. Vasiljeva, T., Kreituss, I., and Lulle, I. (2021). Artificial Intelligence: The Attitude of the Public and Representatives of Various Industries. J. Risk Financ. Manag., 14.10.3390/jrfm14080339 ↗
47. Extremes of acceptance: Employee attitudes toward artificial intelligence10.1108/JBS-12-2018-0204 ↗
48. Rigdon, E.E. (2014). Structural Equation Modeling: Nontraditional Alternatives, John Wiley & Sons, Ltd.. Wiley Online Library, Statistics Reference Online.10.1002/9781118445112.stat06528 ↗
49. Jöreskog, K.G., and Sörbom, D. (1993). LISREL 8: Structural Equation Modeling with the SIMPLIS Command Language, Scientific Software International.
50. Education Research Quantitative Analysis for Little Respondents: Comparing of Lisrel, Tetrad, GSCA, Amos, SmartPLS, WarpPLS, and SPSS10.30605/jsgp.4.2.2021.1326 ↗
51. Hair, J.F., Anderson, R.E., Tatham, R.L., and Black, W.C. (1998). Multivariate Data Analysis, Prentice Hall International. [5th ed.].
52. On the evaluation of structural equation models10.1007/BF02723327 ↗
53. Structural equation models with unobservable variables and measurement error: Algebra and statistics10.1177/002224378101800313 ↗
54. Bollen, K.A., and Long, J.S. (1993). Alternative ways of assessing model fit. Testing Structural Equation Models, Sage Publishing.
55. Exploratory and confirmatory factor analytic investigations of the Illness Attitudes Scale in a nonclinical sample10.1016/S0005-7967(98)00159-4 ↗
56. Significance tests and goodness of fit in the analysis of covariance structures10.1037/0033-2909.88.3.588 ↗
57. Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives10.1080/10705519909540118 ↗
58. Xie, A., and Huang, X. (2012). The Impact of Use Context on Mobile Payment Acceptance: An Empirical Study in China. Advances in Computer Science and Education, Springer.
59. How Habit Limits the Predictive Power of Intention: The Case of Information Systems Continuance10.2307/25148817 ↗
60. Rogers, E.M. (1995). Diffusion of Innovations, Free Press. [4th ed.].
61. Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors10.1016/j.im.2014.03.006 ↗
62. Issues in Adopting Agile Development Principles for Mobile Cloud Computing Applications10.1016/j.procs.2015.05.131 ↗
63. Better the devil you know: Inter-organizational information technology and network social capital in coopetition networks10.1016/j.im.2020.103344 ↗
64. Lack of access to external finance and SME labor productivity: Does project quality matter?10.1007/s11187-018-0082-9 ↗
65. Cusmano, L., Koreen, M., and Pissareva, L. (2022, July 01). 2018 OECD ministerial conference on SMEs: Key Issues Paper. Available online: https://www.oecd-ilibrary.org/content/paper/90c8823c-en.
66. Porter, M.E. (1998). Competitive Strategy: Techniques for Analyzing Industries and Competitors, Free Press.
67. Lutfi, A., Alsyouf, A., Almaiah, M.A., Alrawad, M., Abdo, A.A.K., Al-Khasawneh, A.L., Ibrahim, N., and Saad, M. (2022). Factors Influencing the Adoption of Big Data Analytics in the Digital Transformation Era: Case Study of Jordanian SMEs. Sustainability, 14.10.3390/su14031802 ↗
68. Intention to Use Intelligent Conversational Agents in e-Commerce among Malaysian SMEs: An Integrated Conceptual Framework Based on Tri-theories including Unified Theory of Acceptance, Use of Technology (UTAUT), and T-O-E
69. Li, Y.-H. (2008, January 10–12). An empirical investigation on the determinants of e-procurement adoption in Chinese manufacturing enterprises. Proceedings of the 2008 International Conference on Management Science and Engineering 15th Annual Conference Proceedings, Long Beach, CA, USA.10.1109/ICMSE.2008.4668890 ↗
70. Opportunities and challenges of cloud computing to improve health care services10.2196/jmir.1867 ↗
71. Ahmadi, H., Sadoughi, F., GholamHosseini, L., Azadi, T., Sheikhtaheri, A., Nilashi, M., and Dehnad, A. (2019, January 24–25). Organizational factors affecting the adoption of a technological innovation: A study of the hospital information system (HIS). Proceedings of the 2019 5th International Conference on Web Research (ICWR), Tehran, Iran.10.1109/ICWR.2019.8765270 ↗