Main Article Content
Abstract
Purpose: This study examines the challenges of digital technology adaptation faced by corporate online businesses, focusing on the interplay between growth opportunities and security requirements. The study aims to comprehensively understand technical, security, and organizational barriers while offering actionable strategies to overcome these obstacles.
Research Design and Methodology: The study employs a qualitative systematic literature review (SLR) methodology to synthesize findings from recent scholarly contributions. The review integrates theoretical frameworks such as the Technology Acceptance Model (TAM) and organizational innovation theories to contextualize digital adaptation's challenges and potential solutions.
Findings and Discussion: The findings reveal that successful digital adaptation is hindered by technical complexities such as integrating AI and IoT, cybersecurity threats, including ransomware and phishing, and organizational resistance due to limited digital literacy and employee readiness. A holistic approach that combines robust security measures, employee training programs, and cultural transformation is critical for addressing these challenges. The discussion highlights the importance of aligning organizational strategies with advanced technologies to foster operational resilience and market competitiveness.
Implications: This study provides valuable insights for business leaders and policymakers. Practically, it emphasizes the need for proactive security integration, workforce upskilling, and leveraging AI for enhanced operational efficiency. From a managerial perspective, fostering innovation-supportive cultures and aligning technological strategies with organizational goals is critical. Policymakers are encouraged to create regulatory frameworks that balance technological innovation with robust security measures to promote sustainable business growth.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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- Al-Emran, M., & Shaalan, K. (2021). Recent advances in technology acceptance models and theories. Springer. https://doi.org/10.1007/978-3-030-64987-6
- Al-Qaysi, N., Mohamad-Nordin, N., & Al-Emran, M. (2020). Employing the technology acceptance model in social media: A systematic review. Education and Information Technologies, 25(6), 4961–5002. https://doi.org/10.1007/s10639-020-10197-1
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- Catal, C., Ozcan, A., Donmez, E., & Kasif, A. (2023). Analysis of cyber security knowledge gaps based on cyber security body of knowledge. Education and Information Technologies, 28(2), 1809–1831. https://doi.org/10.1007/s10639-022-11261-8
- Cetindamar Kozanoglu, D., & Abedin, B. (2021). Understanding the role of employees in digital transformation: conceptualization of digital literacy of employees as a multi-dimensional organizational affordance. Journal of Enterprise Information Management, 34(6), 1649–1672. https://doi.org/10.1108/JEIM-01-2020-0010
- Chandra, B., & Rahman, Z. (2024). Artificial intelligence and value co-creation: a review, conceptual framework and directions for future research. Journal of Service Theory and Practice, 34(1), 7–32. https://doi.org/10.1108/JSTP-03-2023-0097
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- Creese, S., Dutton, W. H., & Esteve-González, P. (2021). The social and cultural shaping of cybersecurity capacity building: a comparative study of nations and regions. Personal and Ubiquitous Computing, 25(5), 941–955. https://doi.org/10.1007/s00779-021-01569-6
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340. https://doi.org/10.2307/249008
- Florenthal, B. (2019). Young consumers’ motivational drivers of brand engagement behavior on social media sites. Journal of Research in Interactive Marketing, 13(3), 351–391. https://doi.org/10.1108/JRIM-05-2018-0064
- Ishengoma, F. (2024). Revisiting the TAM: adapting the model to advanced technologies and evolving user behaviours. The Electronic Library, 42(6), 1055–1073. https://doi.org/10.1108/EL-06-2024-0166
- Kowalski, M., Bernardes, R. C., Gomes, L., & Borini, F. M. (2024). Microfoundations of dynamic capabilities for digital transformation. European Journal of Innovation Management, ahead-of-print(ahead-of-print). https://doi.org/10.1108/EJIM-12-2023-1074
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- Rane, N. L., Paramesha, M., Choudhary, S. P., & Rane, J. (2024). Artificial intelligence, machine learning, and deep learning for advanced business strategies: a review. Partners Universal International Innovation Journal, 2(3), 147–171. https://doi.org/10.5281/zenodo.12208298
- Ritz, W., Wolf, M., & McQuitty, S. (2019). Digital marketing adoption and success for small businesses. Journal of Research in Interactive Marketing, 13(2), 179–203. https://doi.org/10.1108/JRIM-04-2018-0062
- Saravanos, A., Zervoudakis, S., & Zheng, D. (2022). Extending the technology acceptance model 3 to incorporate the phenomenon of warm-glow. Information, 13(9), 429. https://doi.org/10.3390/info13090429
- Schiuma, G., Schettini, E., Santarsiero, F., & Carlucci, D. (2022). The transformative leadership compass: six competencies for digital transformation entrepreneurship. International Journal of Entrepreneurial Behavior & Research, 28(5), 1273–1291. https://doi.org/10.1108/IJEBR-01-2021-0087
- Singh, C., Dash, M. K., Sahu, R., & Kumar, A. (2024). Artificial intelligence in customer retention: a bibliometric analysis and future research framework. Kybernetes, 53(11), 4863–4888. https://doi.org/10.1108/K-02-2023-0245
- Syahnur, H. (2024). Leveraging Technology and Innovation for Effective E-Business Management. Advances in Business & Industrial Marketing Research, 2(2 SE-Articles), 83–95. https://doi.org/10.60079/abim.v2i2.285
- Wang, S., Asif, M., Shahzad, M. F., & Ashfaq, M. (2024). Data privacy and cybersecurity challenges in the digital transformation of the banking sector. Computers & Security, 147, 104051. https://doi.org/https://doi.org/10.1016/j.cose.2024.104051
- Weerasinghe, S., & Hindagolla, M. C. B. (2018). Technology acceptance model and social network sites (SNS): a selected review of literature. Global Knowledge, Memory and Communication, 67(3), 142–153. https://doi.org/10.1108/GKMC-09-2017-0079
- Willcocks, L. P. (2024). Automation, digitalization and the future of work: A critical review. Journal of Electronic Business & Digital Economics, 3(2), 184–199. https://doi.org/10.1108/JEBDE-09-2023-0018
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- Xue, M., Xiu, G., Saravanan, V., & Montenegro-Marin, C. E. (2021). Cloud computing with AI for banking and e-commerce applications. The Electronic Library, 39(4), 539–552. https://doi.org/10.1108/EL-07-2020-0207
- Yang, X. (2023). The effects of AI service quality and AI function-customer ability fit on customer’s overall co-creation experience. Industrial Management & Data Systems, 123(6), 1717–1735. https://doi.org/10.1108/IMDS-08-2022-0500
- Zhang, Z., Ning, H., Shi, F., Farha, F., Xu, Y., Xu, J., Zhang, F., & Choo, K.-K. R. (2022). Artificial intelligence in cyber security: research advances, challenges, and opportunities. Artificial Intelligence Review, 55(2), 1029–1053. https://doi.org/10.1007/s10462-021-09976-0
- Zhao, X., Li, X., Li, Y., & Wang, Z. (2024). The impact of digital transformation on firm performance. Industrial Management & Data Systems, 124(8), 2567–2587. https://doi.org/10.1108/IMDS-09-2023-0661
References
Akter, S., Wamba, S. F., Gunasekaran, A., Dubey, R., & Childe, S. J. (2016). How to improve firm performance using big data analytics capability and business strategy alignment? International Journal of Production Economics, 182, 113–131. https://doi.org/10.1016/j.ijpe.2016.08.018
Al-Emran, M., & Shaalan, K. (2021). Recent advances in technology acceptance models and theories. Springer. https://doi.org/10.1007/978-3-030-64987-6
Al-Qaysi, N., Mohamad-Nordin, N., & Al-Emran, M. (2020). Employing the technology acceptance model in social media: A systematic review. Education and Information Technologies, 25(6), 4961–5002. https://doi.org/10.1007/s10639-020-10197-1
Ardito, L., Filieri, R., Raguseo, E., & Vitari, C. (2024). Artificial intelligence adoption and revenue growth in European SMEs: synergies with IoT and big data analytics. Internet Research, ahead-of-print(ahead-of-print). https://doi.org/10.1108/INTR-02-2024-0195
Berman, T., Schallmo, D., & Kraus, S. (2024). Strategies for digital entrepreneurship success: the role of digital implementation and dynamic capabilities. European Journal of Innovation Management, 27(9), 198–222. https://doi.org/10.1108/EJIM-01-2024-0081
Botta, A., de Donato, W., Persico, V., & Pescapé, A. (2016). Integration of Cloud computing and Internet of Things: A survey. Future Generation Computer Systems, 56, 684–700. https://doi.org/https://doi.org/10.1016/j.future.2015.09.021
Catal, C., Ozcan, A., Donmez, E., & Kasif, A. (2023). Analysis of cyber security knowledge gaps based on cyber security body of knowledge. Education and Information Technologies, 28(2), 1809–1831. https://doi.org/10.1007/s10639-022-11261-8
Cetindamar Kozanoglu, D., & Abedin, B. (2021). Understanding the role of employees in digital transformation: conceptualization of digital literacy of employees as a multi-dimensional organizational affordance. Journal of Enterprise Information Management, 34(6), 1649–1672. https://doi.org/10.1108/JEIM-01-2020-0010
Chandra, B., & Rahman, Z. (2024). Artificial intelligence and value co-creation: a review, conceptual framework and directions for future research. Journal of Service Theory and Practice, 34(1), 7–32. https://doi.org/10.1108/JSTP-03-2023-0097
Cosa, M. (2024). Business digital transformation: strategy adaptation, communication and future agenda. Journal of Strategy and Management, 17(2), 244–259. https://doi.org/10.1108/JSMA-09-2023-0233
Creese, S., Dutton, W. H., & Esteve-González, P. (2021). The social and cultural shaping of cybersecurity capacity building: a comparative study of nations and regions. Personal and Ubiquitous Computing, 25(5), 941–955. https://doi.org/10.1007/s00779-021-01569-6
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340. https://doi.org/10.2307/249008
Florenthal, B. (2019). Young consumers’ motivational drivers of brand engagement behavior on social media sites. Journal of Research in Interactive Marketing, 13(3), 351–391. https://doi.org/10.1108/JRIM-05-2018-0064
Ishengoma, F. (2024). Revisiting the TAM: adapting the model to advanced technologies and evolving user behaviours. The Electronic Library, 42(6), 1055–1073. https://doi.org/10.1108/EL-06-2024-0166
Kowalski, M., Bernardes, R. C., Gomes, L., & Borini, F. M. (2024). Microfoundations of dynamic capabilities for digital transformation. European Journal of Innovation Management, ahead-of-print(ahead-of-print). https://doi.org/10.1108/EJIM-12-2023-1074
Kuzlu, M., Fair, C., & Guler, O. (2021). Role of Artificial Intelligence in the Internet of Things (IoT) cybersecurity. Discover Internet of Things, 1(1), 7. https://doi.org/10.1007/s43926-020-00001-4
Lai, Z. J., Leong, M. K., Khoo, K. L., & Sidhu, S. K. (2025). Integrating technology acceptance model and value-based adoption model to determine consumers’ perception of value and intention to adopt AR in online shopping. Asia Pacific Journal of Marketing and Logistics, 37(1), 1–19. https://doi.org/10.1108/APJML-03-2024-0386
Prajapati, S., & Singh, A. (2022). Cyber-Attacks on Internet of Things (IoT) Devices, Attack Vectors, and Remedies: A Position Paper BT - IoT and Cloud Computing for Societal Good (J. K. Verma, D. Saxena, & V. González-Prida (eds.); pp. 277–295). Springer International Publishing. https://doi.org/10.1007/978-3-030-73885-3_17
Radanliev, P., De Roure, D., Page, K., Nurse, J. R. C., Mantilla Montalvo, R., Santos, O., Maddox, L., & Burnap, P. (2020). Cyber risk at the edge: current and future trends on cyber risk analytics and artificial intelligence in the industrial internet of things and industry 4.0 supply chains. Cybersecurity, 3(1), 13. https://doi.org/10.1186/s42400-020-00052-8
Rane, N. L., Paramesha, M., Choudhary, S. P., & Rane, J. (2024). Artificial intelligence, machine learning, and deep learning for advanced business strategies: a review. Partners Universal International Innovation Journal, 2(3), 147–171. https://doi.org/10.5281/zenodo.12208298
Ritz, W., Wolf, M., & McQuitty, S. (2019). Digital marketing adoption and success for small businesses. Journal of Research in Interactive Marketing, 13(2), 179–203. https://doi.org/10.1108/JRIM-04-2018-0062
Saravanos, A., Zervoudakis, S., & Zheng, D. (2022). Extending the technology acceptance model 3 to incorporate the phenomenon of warm-glow. Information, 13(9), 429. https://doi.org/10.3390/info13090429
Schiuma, G., Schettini, E., Santarsiero, F., & Carlucci, D. (2022). The transformative leadership compass: six competencies for digital transformation entrepreneurship. International Journal of Entrepreneurial Behavior & Research, 28(5), 1273–1291. https://doi.org/10.1108/IJEBR-01-2021-0087
Singh, C., Dash, M. K., Sahu, R., & Kumar, A. (2024). Artificial intelligence in customer retention: a bibliometric analysis and future research framework. Kybernetes, 53(11), 4863–4888. https://doi.org/10.1108/K-02-2023-0245
Syahnur, H. (2024). Leveraging Technology and Innovation for Effective E-Business Management. Advances in Business & Industrial Marketing Research, 2(2 SE-Articles), 83–95. https://doi.org/10.60079/abim.v2i2.285
Wang, S., Asif, M., Shahzad, M. F., & Ashfaq, M. (2024). Data privacy and cybersecurity challenges in the digital transformation of the banking sector. Computers & Security, 147, 104051. https://doi.org/https://doi.org/10.1016/j.cose.2024.104051
Weerasinghe, S., & Hindagolla, M. C. B. (2018). Technology acceptance model and social network sites (SNS): a selected review of literature. Global Knowledge, Memory and Communication, 67(3), 142–153. https://doi.org/10.1108/GKMC-09-2017-0079
Willcocks, L. P. (2024). Automation, digitalization and the future of work: A critical review. Journal of Electronic Business & Digital Economics, 3(2), 184–199. https://doi.org/10.1108/JEBDE-09-2023-0018
Wirtz, B. W. (2021). Artificial Intelligence, Big Data, and Cloud Computing BT - Digital Business and Electronic Commerce: Strategy, Business Models and Technology (B. W. Wirtz (ed.); pp. 217–258). Springer International Publishing. https://doi.org/10.1007/978-3-030-63482-7_7
Xue, M., Xiu, G., Saravanan, V., & Montenegro-Marin, C. E. (2021). Cloud computing with AI for banking and e-commerce applications. The Electronic Library, 39(4), 539–552. https://doi.org/10.1108/EL-07-2020-0207
Yang, X. (2023). The effects of AI service quality and AI function-customer ability fit on customer’s overall co-creation experience. Industrial Management & Data Systems, 123(6), 1717–1735. https://doi.org/10.1108/IMDS-08-2022-0500
Zhang, Z., Ning, H., Shi, F., Farha, F., Xu, Y., Xu, J., Zhang, F., & Choo, K.-K. R. (2022). Artificial intelligence in cyber security: research advances, challenges, and opportunities. Artificial Intelligence Review, 55(2), 1029–1053. https://doi.org/10.1007/s10462-021-09976-0
Zhao, X., Li, X., Li, Y., & Wang, Z. (2024). The impact of digital transformation on firm performance. Industrial Management & Data Systems, 124(8), 2567–2587. https://doi.org/10.1108/IMDS-09-2023-0661