Digital Transformation in SMEs: A Comparative Study of Developing and Developed Economies
DOI:
https://doi.org/10.64137/31080080/IJFEMS-V2I1P104Keywords:
Digital transformation, Small and medium-Sized enterprises, Developing economies, Developed economies, Digital adoption, Firm performance, Digital infrastructure, Institutional support, Mixed-methods researchAbstract
Digital transformation has become a critical driver of competitiveness, productivity, and resilience for small and medium-sized enterprises (SMEs). However, the extent and impact of digital transformation vary significantly between developing and developed economies due to differences in infrastructure, skills, institutional support, and resource availability. The purpose of this study is to comparatively examine the drivers, adoption levels, challenges, and performance outcomes of digital transformation in SMEs operating in developing and developed economies.
The study adopts a comparative research design using a mixed-methods approach. Quantitative data were collected through structured surveys administered to SMEs in selected developing and developed countries, while qualitative insights were obtained through semi-structured interviews with SME owners and managers. Statistical techniques, including descriptive analysis and regression modeling, were used to assess the relationship between digital transformation initiatives and firm performance.
The findings reveal that SMEs in developed economies demonstrate higher levels of digital adoption, supported by advanced digital infrastructure, skilled labor, and stronger institutional frameworks. In contrast, SMEs in developing economies face significant barriers such as limited financial resources, inadequate digital skills, and weak technological ecosystems. Despite these challenges, digital transformation positively influences operational efficiency, market reach, and innovation in SMEs across both contexts.
The study concludes that while digital transformation offers substantial benefits for SMEs regardless of economic context, targeted policy interventions, capacity-building initiatives, and affordable digital solutions are essential to bridge the digital divide and enhance SME competitiveness, particularly in developing economies.
References
[1] A. Bharadwaj, O. A. El Sawy, P. A. Pavlou, and N. Venkatraman, “Digital business strategy: Toward a next generation of insights,” MIS Quarterly, vol. 37, no. 2, pp. 471–482, 2013, Available: https://www.jstor.org/stable/43825919
[2] M. L. A. M. Bogers, R. Garud, L. D. W. Thomas, P. Tuertscher, and Y. Yoo, “Digital innovation: transforming research and practice,” Innovation, vol. 24, no. 1, pp. 1–9, Nov. 2021, doi: https://doi.org/10.1080/14479338.2021.2005465.
[3] Y.-Y. K. Chen, Y.-L. Jaw, and B.-L. Wu, “Effect of digital transformation on organisational performance of SMEs,” Internet Research, vol. 26, no. 1, pp. 186–212, Feb. 2016, doi: https://doi.org/10.1108/intr-12-2013-0265.
[4] European Commission, SME strategy for a sustainable and digital Europe, Publications Office of the European Union, 2020. [Online]. Available:https://stip.oecd.org/stip/interactive-dashboards/policy-initiatives/2023%2Fdata%2FpolicyInitiatives%2F99995726
[5] S. Kraus, S. Durst, J. J. Ferreira, P. Veiga, N. Kailer, and A. Weinmann, “Digital Transformation in Business and Management research: an Overview of the Current Status Quo,” International Journal of Information Management, vol. 63, no. 4, pp. 1–18, 2022, doi: https://doi.org/10.1016/j.ijinfomgt.2021.102466.
[6] OECD, “OECD SME and Entrepreneurship Outlook 2019,” OECD, 2019. https://www.oecd.org/en/publications/2019/05/oecd-sme-and-entrepreneurship-outlook-2019_7083aa23.html
[7] T. Ritter and C. L. Pedersen, “Digitization capability and the digitalization of business models in business-to-business firms: Past, present, and future,” Industrial Marketing Management, vol. 86, no. 0019-8501, pp. 180–190, 2020, doi: https://doi.org/10.1016/j.indmarman.2019.11.019.
[8] G. Vial, “Understanding Digital transformation: a Review and a Research Agenda,” The Journal of Strategic Information Systems, vol. 28, no. 2, pp. 118–144, 2019, doi: https://doi.org/10.1016/j.jsis.2019.01.003.
[9] B. M. Omowole, A. Q. Olufemi-Phillips, O. C. Ofodile, N. L. Eyo-Udo, and S. E. Ewim, “Barriers and drivers of digital transformation in SMEs: A conceptual analysis,” International Journal of Scholarly Research in Science and Technology, vol. 5, no. 2, pp. 019–036, Nov. 2024, doi: https://doi.org/10.56781/ijsrst.2024.5.2.0037.
[10] P. Waditwar, “Leading through the Synthetic Media Era: Platform Governance to Curb AI-Generated Fake News, Protect the Public, and Preserve Trust,” Open Journal of Leadership, vol. 14, no. 03, pp. 403–418, 2025, doi: https://doi.org/10.4236/ojl.2025.143020.
[11] J. Chen, and Y. Zhang, “Digital transformation of SMEs: A systematic literature review,” Journal of Small Business Management, vol. 59, no. 4, pp. 1–29, 2021.
[12] OECD, SMEs in the digital age: Opportunities and challenges. OECD Publishing, 2019.
[13] P. Waditwar, “Smart Procurement in the Sports Industry: A Strategic Approach for Efficiency and Performance Enhancement,” Open Journal of Business and Management, vol. 13, no. 03, pp. 1743–1761, 2025, doi: https://doi.org/10.4236/ojbm.2025.133090.
[14] V. Scuotto, M. Del Giudice, and E. G. Carayannis, “The effect of social networking sites and absorptive capacity on SMES’ innovation performance,” The Journal of Technology Transfer, vol. 42, no. 2, pp. 409–424, Nov. 2016, doi: https://doi.org/10.1007/s10961-016-9517-0.
[15] G. Vial, “Understanding Digital transformation: a Review and a Research Agenda,” The Journal of Strategic Information Systems, vol. 28, no. 2, pp. 118–144, 2019, doi: https://doi.org/10.1016/j.jsis.2019.01.003.
[16] E. Autio, S. Nambisan, L. D. W. Thomas, and M. Wright, “Digital affordances, spatial affordances, and the genesis of entrepreneurial ecosystems,” Strategic Entrepreneurship Journal, vol. 12, no. 1, pp. 72–95, Jan. 2018, doi: https://doi.org/10.1002/sej.1266.
[17] A. Bayo‐Moriones, M. Billón, and F. Lera‐López, “Perceived performance effects of ICT in manufacturing SMEs,” Industrial Management & Data Systems, vol. 113, no. 1, pp. 117–135, Mar. 2013, doi: https://doi.org/10.1108/02635571311289700.
[18] P. Waditwar, “Overcoming the AI Data Eclipse: Obstacles to the Full Adoption of Artificial Intelligence in the Procurement Technology Sector,” World Journal of Advanced Research and Reviews, vol. 27, no. 3, pp. 1583–1590, Sep. 2025, doi: https://doi.org/10.30574/wjarr.2025.27.3.3296.
[19] A. Hervé, C. Schmitt, and R. Baldegger, “Digitalization, Entrepreneurial Orientation & Internationalization of Micro-, Small-, and Medium-Sized Enterprises,” Technology Innovation Management Review, vol. 10, no. 4, pp. 5–17, Apr. 2020, doi: https://doi.org/10.22215/timreview/1343.
[20] L. Li, F. Su, W. Zhang, and J.-Y. Mao, “Digital transformation by SME entrepreneurs: A capability perspective,” Information Systems Journal, vol. 28, no. 6, pp. 1129–1157, Jun. 2018, doi: https://doi.org/10.1111/isj.12153.
[21] Prajkta Waditwar, “Transforming Government Procurement through Electronic Bidding—A Case Study on the City of Somerville’s Implementation of BidExpress Infotech,” Open Journal of Leadership, vol. 14, no. 01, pp. 165–175, Jan. 2025, doi: https://doi.org/10.4236/ojl.2025.141007.
[22] A. R. Polu, G. S. B. Narra, N. Vattikonda, V. K. R. Buddula, and H. H. S. Patchipulusu, “Evolution of AI in Software Development and Cybersecurity: Unifying Automation, Innovation, and Protection in the Digital Age,” International Journal of Research in Engineering and Applied Sciences, vol. 11, no. 5, pp. 1–15, Jan. 2025, doi: https://doi.org/10.63665/ijreas.v11i5.01.
[23] A. A. S. Singh et al., “Predictive Modeling for Classification of SMS Spam Using NLP and ML Techniques,” International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 2, no. 4, Dec. 2021, doi: https://doi.org/10.63282/3050-9262.ijaidsml-v2i4p107.
[24] Vaibhav Maniar et al., “Review of Streaming ETL Pipelines for Data Warehousing: Tools, Techniques, and Best Practices,” International Journal of AI, BigData, Computational and Management Studies, vol. 2, no. 3, Oct. 2021, doi: https://doi.org/10.63282/3050-9416.ijaibdcms-v2i3p109.
[25] D. Rajendran, “Anomaly Identification in IoT-Networks Using Artificial Intelligence-Based Data-Driven Techniques in Cloud Environment,” International Journal of Emerging Trends in Computer Science and Information Technology, vol. 2, no. 2, Jun. 2021, doi: https://doi.org/10.63282/3050-9246.ijetcsit-v2i2p110.
[26] Rami Reddy Kothamaram et al., “A Survey of Adoption Challenges and Barriers in Implementing Digital Payroll Management Systems in Across Organizations,” International Journal of Emerging Research in Engineering and Technology, vol. 2, no. 2, Jun. 2021, doi: https://doi.org/10.63282/3050-922x.ijeret-v2i2p109.
[27] A. A. Singh, Vetrivelan Tamilmani, V. Maniar, Rami Reddy Kothamaram, D. Rajendran, and Venkata Deepak Namburi, “Hybrid AI Models Combining Machine-Deep Learning for Botnet Identification,” International Journal of Humanities and Information Technology, no. Special 1, pp. 30–45, 2021, doi: https://doi.org/10.21590/ijhit.spcl.01.04.
[28] Avinash Attipalli et al., “A Review of AI and Machine Learning Solutions for Fault Detection and Self-Healing in Cloud Services,” International Journal of AI, BigData, Computational and Management Studies, vol. 2, 2021, doi: https://doi.org/10.63282/3050-9416.ijaibdcms-v2i3p107.
[29] Avinash Attipalli et al., “Enhancing Cloud Infrastructure Security Through AI-Powered Big Data Anomaly Detection,” International Journal of Emerging Research in Engineering and Technology, vol. 2, 2021, doi: https://doi.org/10.63282/3050-922x.ijeret-v2i2p107.
[30] Raghuvaran Kendyala et al., “A Survey of Artificial Intelligence Methods in Liquidity Risk Management: Challenges and Future Directions,” International Journal of Artificial Intelligence, Data Science, and Machine Learning, vol. 2, 2021, doi: https://doi.org/10.63282/3050-9262.ijaidsml-v2i1p105.
[31] Varun Bitkuri, Raghuvaran Kendyala, Jagan Kurma, Jaya Vardhani Mamidala, Avinash Attipalli, and Sunil Jacob Enokkaren, “A Survey on Hybrid and Multi-Cloud Environments: Integration Strategies, Challenges, and Future Directions,” International Journal of Computer Technology and Electronics Communication, vol. 4, no. 1, pp. 3219–3229, 2021, doi: https://doi.org/10.15680/IJCTECE.2021.0401004.
[32] A. R. Polu et al., “Blockchain Technology as a Tool for Cybersecurity: Strengths, Weaknesses, and Potential Applications,” ACCENT JOURNAL OF ECONOMICS ECOLOGY & ENGINEERING, vol. 7, no. 8, pp. 167-174, 2022.
[33] A. Aggarwal, L. Agarwal, B. P. R. Rella, N. Nagpal, D. Kalla, and M. Sharma, “A Performance Comparison of Machine Learning Models for Rain Prediction,” Lecture Notes in Networks and Systems, pp. 319–328, Oct. 2025, doi: https://doi.org/10.1007/978-3-032-03527-1_25.
[34] D. Rajendran, A. Arjun Singh Singh, V. Maniar, V. Tamilmani, R. R. Kothamaram, and V. D. Namburi, “Data-Driven Machine Learning-Based Prediction and Performance Analysis of Software Defects for Quality Assurance,” Universal Library of Engineering Technology, pp. 59–68, 2022, doi: https://doi.org/10.70315/uloap.ulete.2022.008.
[35] V. D. Namburi et al., “Machine Learning Algorithms for Enhancing Predictive Analytics in ERP-Enabled Online Retail Platform,” International Journal of Advance Industrial Engineering, vol. 10, no. 4, pp. 65-73, 2022.
[36] Venkata Deepak Namburi et al., “Review of Machine Learning Models for Healthcare Business Intelligence and Decision Support,” International Journal of AI, BigData, Computational and Management Studies, vol. 3, no. 3, Jun. 2022, doi: https://doi.org/10.63282/3050-9416.ijaibdcms-v3i3p110.
[37] V. Tamilmani, A. A. Singh Singh, V. Maniar, R. R. Kothamaram, D. Rajendran, and V. D. Namburi, “Forecasting Financial Trends Using Time Series Based ML-DL Models for Enhanced Business Analytics,” SSRN Electronic Journal, 2025, doi: https://doi.org/10.2139/ssrn.5837143.
[38] Varun Bitkuri et al., “Empowering Cloud Security with Artificial Intelligence: Detecting Threats Using Advanced Machine learning Technologies,” International Journal of AI, BigData, Computational and Management Studies, vol. 3, no. 4, 2022, doi: https://doi.org/10.63282/3050-9416.ijaibdcms-v3i4p106.
[39] A. Attipalli, J. V. Mamidala, J. KURMA, V. BITKURI, R. Kendyala, and S. Enokkaren, “Towards the Efficient Management of Cloud Resource Allocation: A Framework Based on Machine Learning,” SSRN Electronic Journal, 2025, doi: https://doi.org/10.2139/ssrn.5741265.
[40] S. J. Enokkaren, A. Attipalli, V. Bitkuri, R. Kendyala, J. Kurma, and J. V. Mamidala, “A Deep-Review based on Predictive Machine Learning Models in Cloud Frameworks for the Performance Management,” Universal Library of Engineering Technology, pp. 43–52, 2022, doi: https://doi.org/10.70315/uloap.ulete.2022.006.
[41] Jagan Kurma, Jaya Vardhani Mamidala, Avinash Attipalli, Sunil Jacob Enokkaren, Varun Bitkuri, and Raghuvaran Kendyala, “A Review of Security, Compliance, and Governance Challenges in Cloud-Native Middleware and Enterprise Systems,” International Journal of Research and Applied Innovations, vol. 5, no. 1, pp. 6434–6443, 2022, doi: https://doi.org/10.15662/IJRAI.2022.0501003.
[42] A. Attipalli, S. Enokkaren, J. KURMA, J. V. Mamidala, R. Kendyala, and V. BITKURI, “A Deep-Review based on Predictive Machine Learning Models in Cloud Frameworks for the Performance Management,” SSRN Electronic Journal, 2025, doi: https://doi.org/10.2139/ssrn.5741282
[43] R. Chalasani, M. S. V. Tyagadurgam, V. N. Gangineni, S. Pabbineedi, M. Penmetsa, and J. R. Bhumireddy, “Leveraging Big Datasets for Machine Learning-Based Anomaly Detection in Cybersecurity Network Traffic,” SSRN Electronic Journal, 2025, doi: https://doi.org/10.2139/ssrn.5538121.
[44] Rahul Vadisetty, Anand Polamarasetti, V. Varadarajan, D. Kalla, and G. K. Ramanathan, “Cyber Warfare and AI Agents: Strengthening National Security Against Advanced Persistent Threats (APTs),” Communications in computer and information science, pp. 578–587, Oct. 2025, doi: https://doi.org/10.1007/978-3-032-07373-0_43.
[45] R. Chalasani, M. S. V. Tyagadurgam, V. N. Gangineni, S. Pabbineedi, M. Penmetsa, and J. R. Bhumireddy, “Leveraging Big Datasets for Machine Learning-Based Anomaly Detection in Cybersecurity Network Traffic,” SSRN Electronic Journal, 2025, doi: https://doi.org/10.2139/ssrn.5538121.
[46] S. K. Chundru, S. R. Vangala, R. M. Polam, B. Kamarthapu, A. B. Kakani, and S. K. K. Nandiraju, “Efficient Machine Learning Approaches for Intrusion Identification of DDoS Attacks in Cloud Networks,” SSRN Electronic Journal, 2025, doi: https://doi.org/10.2139/ssrn.5515262.
[47] V. D. Namburi et al., “Intelligent Network Traffic Identification Based on Advanced Machine Learning Approaches,” International Journal of Emerging Trends in Computer Science and Information Technology, vol. 4, no. 4, pp. 118-128, 2023, doi: https://doi.org/10.63282/3050-9246.ijetcsit-v4i4p113.
[48] D. Rajendran, V. Maniar, Vetrivelan Tamilmani, Venkata Deepak Namburi, A. Arjun, and Rami Reddy Kothamaram, “CNN-LSTM Hybrid Architecture for Accurate Network Intrusion Detection for Cybersecurity,” Journal Of Engineering And Computer Sciences, vol. 2, no. 11, pp. 1–13, 2025, Accessed: Feb. 10, 2026. [Online]. Available: https://sarcouncil.com/2023/11/cnn-lstm-hybrid-architecture-for-accurate-network-intrusion-detection-for-cybersecurity
[49] R. R. Kothamaram et al., “Exploring the Influence of ERP-Supported Business Intelligence on Customer Relationship Management Strategies,” International Journal of Technology, Management and Humanities, vol. 9, no. 4, pp. 179-191, 2023.
[50] D. Rajendran and A. A. Singh, “Exploration of Java-Based Big Data Frameworks: Architecture, Challenges, and Opportunities,” Journal of Artificial Intelligence & Cloud Computing, pp. 1–8, Dec. 2023, doi: https://doi.org/10.47363/jaicc/2023(2)501.
[51] V. Tamilmani, V. D. Namburi, A. A. Singh Singh, V. Maniar, R. R. Kothamaram, and D. Rajendran, “Real-Time Identification of Phishing Websites Using Advanced Machine Learning Methods,” SSRN Electronic Journal, 2025, doi: https://doi.org/10.2139/ssrn.5837142.
[52] J. V. Mamidala et al., “A Survey of Blockchain-Enabled Supply Chain Processes in Small and Medium Enterprises for Transparency and Efficiency,” International Journal of Humanities and Information Technology, vol. 5, no. 4, pp. 84-95, 2023.
[53] Varun Bitkuri et al., “Efficient Resource Management and Scheduling in Cloud Computing: A Survey of Methods and Emerging Challenges,” International Journal of Emerging Trends in Computer Science and Information Technology, vol. 4, no. 3, Oct. 2023, doi: https://doi.org/10.63282/3050-9246.ijetcsit-v4i3p112.
[54] N. Jaya, None Avinash Attipalli, J. Enokkaren, None Varun Bitkuri, None Raghuvaran Kendyala, and None Jagan Kurma, “A Survey on Hybrid and Multi-Cloud Environments: Integration Strategies, Challenges, and Future Directions,” International Journal of Humanities and Information Technology, vol. 5, no. 02, pp. 53–66, May 2023, doi: https://doi.org/10.21590/ijhit.05.02.08.
[55] J. Enokkaren, “Machine Learning Models Powered by Big Data for Health Insurance Expense Forecasting,” International Research Journal of Economics and Management Studies IRJEMS, vol. 2, no. 1, 2023, Accessed: Feb. 10, 2026. [Online]. Available: https://irjems.org/irjems-v2i1p143.html
[56] M. Roshni Thanka et al., “A hybrid approach for melanoma classification using ensemble machine learning techniques with deep transfer learning,” Computer methods and programs in biomedicine update, vol. 3, pp. 100103–100103, Jan. 2023, doi: https://doi.org/10.1016/j.cmpbup.2023.100103.
[57] Prajkta Waditwar, “From Fragmentation to Focus: The Benefits of Centralizing Procurement,” International Journal of Research and Applied Innovations, vol. 06, no. 06, Nov. 2023, doi: https://doi.org/10.15662/ijrai.2023.0606006.
[58] P. Waditwar, “Agentic AI in Contract Analytics Harnessing Machine Learning for Risk Assessment and Compliance in Government Procurement Contracts,” Open Journal of Business and Management, vol. 13, no. 05, pp. 3385–3395, 2025, doi: https://doi.org/10.4236/ojbm.2025.135179.
[59] A. R. N. R, T. Rajasri, R. Praveen, D. Kalla, S. P. Bendale, and N. Venu, “CAC Training - A Unified Cybersecurity Training Program for Military Staff,” 2025 3rd International Conference on Communication, Security, and Artificial Intelligence (ICCSAI), pp. 569–573, Apr. 2025, doi: https://doi.org/10.1109/iccsai64074.2025.11064463.


