The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
Small business awareness and adoption of state-of-the-art technologies in emerging and developing markets, and lessons from the COVID-19 pandemic
Abstract
Many challenges continue to hinder digital technologies' adoption by small and medium scale enterprises (SMEs) in developing economies. Comparatively, there are more success stories by SMEs in emerging markets. However, most SMEs operating in the informal sector in the emerging markets and developing economies (EMDEs) face similar challenges that inhibit the adoption of advanced technologies and innovations needed to improve business operations and re-engineer processes. This article evaluates the implementation and use of state-of-the-art technologies by SMEs in EMDEs to improve operations performance and create sustainable competitive advantages. Further, the papers in this Special Issue identify FinTech and analytical algorithms as some of the current technologies employed by SMEs in EMDEs to improve operations and processes in the manufacturing and service industries. The recognized technologies and technical innovations that seem novel in EMDEs have long existed in the advanced economies. Most state-of-the-art technologies, including cloud computing, 'big data', and predictive analytics that can improve operations and strategic decisions, are yet to make inroads in most EMDEs. Also, disruptive computing technologies, data analytics, and the Internet of Things (IoT) required to engineer new business models, reduce overheads, enhance competitive advantages, and digitize SMEs' business operations remain untapped. The absence and non-adoption of digital technologies in EMDEs explain why business activities in most EMDEs remain shut during the outbreak of SARS-CoV-2 and the community lockdown to contain the COVID-19 pandemic. The strategies to survive the 'new normal' imposed by COVID-19 and fierce global competition includes a successful adoption of advanced technologies.
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Cites
Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China
Huang, C., Wang, Y., Li, X., Ren, L., Zhao, J., Hu, Y., Zhang, L., Fan, G., Xu, J., Gu, X., et al. (2020) · The Lancet · doi:10.1016/s0140-6736(20)30183-5 · Cited by 48,884
Armbrust, M., Fox, A., Griffith, R., Joseph, A. D., Katz, R., Konwinski, A., Lee, G., Patterson, D., Rabkin, A., Stoica, I., et al. (2010) · Communications of the ACM · doi:10.1145/1721654.1721672 · Cited by 9,464
Lasi, H., Fettke, P., Kemper, H.-G., Feld, T., & Hoffmann, M. (2014) · Business & Information Systems Engineering · doi:10.1007/s12599-014-0334-4 · Cited by 4,276 · cited 2×
“…include artificial intelligence, the Industrial Internet of Things (IIoT), and blockchain technologies to drive business activities and enhance business process innovation (Papadopoulos, Baltas, and Balta 2020; Sadiku et al. 2017; McKnight 2017; Lasi et al. 2014; Viriyasitavat et al. 2019).”
BackgroundBeyond computation: Information technology, organizational transformation and business performance
Brynjolfsson, E., & Hitt, L. M. (2000) · Journal of Economic Perspectives · doi:10.1257/jep.14.4.23 · Cited by 3,335
Chen, M., Mao, S., & Liu, Y. (2014) · Mobile Networks and Applications · doi:10.1007/s11036-013-0489-0 · Cited by 2,913 · cited 1×
“Big data analytics is the technology designed to economically extract value from massive volumes of a wide variety of data by enabling high-velocity capture, discovery, and analysis (Chen, Mao, and Liu 2014; Hu et al. 2014).”
BackgroundTelehealth for global emergencies: Implications for coronavirus disease 2019 (COVID-19)
Smith, A. C., Thomas, E., Snoswell, C. L., Haydon, H., Mehrotra, A., Clemensen, J., & Caffery, L. J. (2020) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x20916567 · Cited by 1,828
Deep learning for smart manufacturing: Methods and applications
Wang, J., Ma, Y., Zhang, L., Gao, R. X., & Wu, D. (2018) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2018.01.003 · Cited by 1,740
Wollschlaeger, M., Sauter, T., & Jasperneite, J. (2017) · IEEE Industrial Electronics Magazine · doi:10.1109/mie.2017.2649104 · Cited by 1,726
Digital technology and COVID-19
Ting, D. S. W., Carin, L., Dzau, V., & Wong, T. Y. (2020) · Nature Medicine · doi:10.1038/s41591-020-0824-5 · Cited by 1,413
International Journal of Information Management
Hills, P. (2006) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2005.10.001 · Cited by 1,188
Cited by
Drydakis, N. (2022) · Information Systems Frontiers · doi:10.1007/s10796-022-10249-6 · Cited by 180
Sánchez, E., Calderón, R., & Herrera, F. (2025) · Applied Sciences · doi:10.3390/app15126465 · Cited by 123
Asante Antwi, H., Zhou, L., Xu, X., & Mustafa, T. (2021) · Healthcare · doi:10.3390/healthcare9040453 · Cited by 62
Driving Factors of Industry 4.0 Readiness among Manufacturing SMEs in Malaysia
Wong, A. P. H., & Kee, D. M. H. (2022) · Information · doi:10.3390/info13120552 · Cited by 35
Damoah, O. B. O. (2025) · Journal of International Entrepreneurship · doi:10.1007/s10843-025-00372-1 · Cited by 7
Metadata
- Authors
- Akpan, I. J., Udoh, E. A. P., & Adebisi, B.
- Venue
- Journal of Small Business & Entrepreneurship
- Publisher
- Informa UK Limited
- Published
- 2020-09-29
- Volume
- 34
- Issue
- 2
- Pages
- 123-140
- ISSN
- 0827-6331 (ISSN-L)
- 2169-2610
- OpenAlex
- W3089228746
- Cited by
- 435
- References
- 64
- FWCI
- 96.15
- Type
- journal article
- Language
- en
- Open access
- closed
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- Akpan, I. J., Udoh, E. A. P., & Adebisi, B. (2022). Small business awareness and adoption of state-of-the-art technologies in emerging and developing markets, and lessons from the COVID-19 pandemic. Journal of Small Business & Entrepreneurship. https://doi.org/10.1080/08276331.2020.1820185
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
