Artificial Intelligence as a catalyst for the Sustainability of Small and Medium Scale Businesses (SMEs) in Nigeria

https://doi.org/10.35912/amor.v5i1.1719
Published: Nov 29, 2023

Abstract:

Purpose: This study examines the role of AI in small business operations. Specifically, we identified areas where AI could be deployed, barriers to deployment of AI, identified AI tools in business, and ascertained the number of SMEs that consciously use any form of AI in their business operations.

Research Methodology: This study adopted a descriptive design. The population of the study was 27546 small businesses that were registered under the Cooperate Affairs Commission (CAC), and a sample size of 379 was arrived at by adopting Krejcie and Morgan's 1970 sample size determination formula. The source of data was solely primary through interviews, which later formed a stepping stone for the structured questionnaire used for the study. The instrument was validated and tested for reliability. Data analysis was performed using descriptive statistics consisting of frequencies and percentages.

Results: Most SMEs in Nigeria are still operating manually; hence, they do not enjoy the massive potential of AI deployment and remain perpetually small in size.

Limitations: Descriptive statistics for the analysis were used to reduce the inferrability of the findings.

Contribution: Given the increasing dependence on technology and AI, this study highlights the importance of AI not only for big and multinational corporations, but also for SMEs in Nigeria.

Novelty: A study of this nature has not been undertaken in Nigeria, specifically focusing on southeast Nigeria, which has a large number of small businesses in the region.

Keywords:
1. Artificial Intelligence
2. Small Business
3. Technologies
4. ChatBot and Nigeria
Authors:
1 . Arachie Augustine Ebuka
Department of Business Administration, Nnamdi Azikiwe University, Awka, Nigeria
2 . Dibua Emmanuel
Department of Business Administration, Nnamdi Azikiwe University, Awka, Nigeria
3 . Peter Idigo
Department of Business Administration, Nnamdi Azikiwe University, Awka, Nigeria
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How to Cite
Ebuka, A. A., Emmanuel, D., & Idigo, P. (2023). Artificial Intelligence as a catalyst for the Sustainability of Small and Medium Scale Businesses (SMEs) in Nigeria. Annals of Management and Organization Research, 5(1), 1–11. https://doi.org/10.35912/amor.v5i1.1719
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Issue & Section
Vol. 5 No. 1 (2023): August
Articles
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References

    Ambulkar, S., Blackhurst, J., & Grawe, S. (2015). Firm's resilience to supply chain disruptions: Scale development and empirical examination. Journal of Operations Management, 33, 111-122.

    Amesho, K. T., Edoun, E. I., Naidoo, V., & Pooe, S. (2022). Sustainable competitive advantage through technology and innovation systems in the local government authorities. Africa’s Public Service Delivery & Performance Review, 10(1), 19.

    Arakpogun, E. O., Elsahn, Z., Olan, F., & Elsahn, F. (2021). Artificial intelligence in Africa: Challenges and opportunities. The fourth industrial revolution: Implementation of artificial intelligence for growing business success, 375-388.

    Arzikulov, O. (2021). Artificial intelligence to increase the efficiency of small businesses. ISJ Theoretical & Applied Science, 8(100), 412-415.

    Bandari, V. (2019). The Impact of Artificial Intelligence on the Revenue Growth of Small Businesses in Developing Countries: An Empirical Study. Reviews of Contemporary Business Analytics, 2(1), 33-44.

    Basri, W. (2020). Examining the impact of artificial intelligence (AI)-assisted social media marketing on the performance of small and medium enterprises: toward effective business management in the Saudi Arabian context. International Journal of Computational Intelligence Systems, 13(1), 142.

    Burian, J. (2020). The complex choreography of supply chain resilience. Industry Week.

    Cavalcante, R. C., Brasileiro, R. C., Souza, V. L., Nobrega, J. P., & Oliveira, A. L. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55, 194-211.

    Chalmers, D., MacKenzie, N. G., & Carter, S. (2021). Artificial intelligence and entrepreneurship: Implications for venture creation in the fourth industrial revolution. Entrepreneurship theory and Practice, 45(5), 1028-1053.

    Chan, C. M., Teoh, S. Y., Yeow, A., & Pan, G. (2019). Agility in responding to disruptive digital innovation: Case study of an SME. Information systems journal, 29(2), 436-455.

    Chan, L., Morgan, I., Simon, H., Alshabanat, F., Ober, D., Gentry, J., . . . Cao, R. (2019). Survey of AI in cybersecurity for information technology management. Paper presented at the 2019 IEEE technology & engineering management conference (TEMSCON).

    Commission, E. (2015). User guide to the SME definition. Retrieved from https://data.europa.eu/doi/10.2873/620234

    Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.

    Delponte, L. (2018). European Artificial Intelligence (AI) leadership, the path for an integrated vision.

    Elavarasan, R. M., & Pugazhendhi, R. (2020). Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic. Science of the Total Environment, 725, 138858.

    Ferreira, J., Mueller, J., & Papa, A. (2020). Strategic knowledge management: theory, practice and future challenges. Journal of Knowledge Management, 24(2), 121-126.

    Giuggioli, G., & Pellegrini, M. M. (2023). Artificial intelligence as an enabler for entrepreneurs: a systematic literature review and an agenda for future research. International Journal of Entrepreneurial Behavior & Research, 29(4), 816-837.

    Grover, P., Kar, A. K., & Dwivedi, Y. K. (2022). Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions. Annals of Operations Research, 308(1-2), 177-213.

    Helm, J. M., Swiergosz, A. M., Haeberle, H. S., Karnuta, J. M., Schaffer, J. L., Krebs, V. E., . . . Ramkumar, P. N. (2020). Machine learning and artificial intelligence: definitions, applications, and future directions. Current reviews in musculoskeletal medicine, 13, 69-76.

    Hess, T., Matt, C., Benlian, A., & Wiesböck, F. (2016). Options for formulating a digital transformation strategy. MIS Quarterly Executive, 15(2).

    Iansiti, M., & Lakhani, K. R. (2020). Competing in the age of AI: Strategy and leadership when algorithms and networks run the world: Harvard Business Press.

    Iqbal, T., & Khan, M. N. (2021). The Impact of Artificial Intelligence (AI) on CRM and Role of Marketing Managers.

    Jab?o?ska, M. R., & Pólkowski, Z. (2017). ARTIFICIAL INTELLIGENCE-BASED PROCESSES IN SMES. Studies & Proceedings of Polish Association for Knowledge Management(86).

    Kabir, M. E. (2020). Artificial Intelligence in Business and Future Prospect. IAR Journal of Entrepreneurship, Innovation & Design Thinking, 1(1).

    Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business horizons, 62(1), 15-25.

    Karimova, F. (2016). A survey of e-commerce recommender systems. European Scientific Journal, 12(34), 75-89.

    Kartal, H., Oztekin, A., Gunasekaran, A., & Cebi, F. (2016). An integrated decision analytic framework of machine learning with multi-criteria decision making for multi-attribute inventory classification. Computers & Industrial Engineering, 101, 599-613.

    Kraus, M., Feuerriegel, S., & Oztekin, A. (2020). Deep learning in business analytics and operations research: Models, applications and managerial implications. European Journal of Operational Research, 281(3), 628-641.

    Kumar, A., & Kalse, A. (2021). WITHDRAWN: Usage and adoption of artificial intelligence in SMEs: Elsevier.

    Kumar, A., Pandey, A., Pujari, P., & Arora, A. (2023). Adoption of ai and e-commerce improving marketing performance of smes. Academy of Marketing Studies Journal, 27(5), 1-10.

    Kumar, B. S., & Ravi, V. (2016). A survey of the applications of text mining in financial domain. Knowledge-Based Systems, 114, 128-147.

    Lian, S. B. (2023). The Factors Influencing Digital Technologies Adoption in Green Supply Chain Innovation: Evidence from the Social Enterprises in Malaysia. Electronic Journal of Business and Management, 8(1), 32-39.

    Mahidhar, V., & Davenport, T. H. (2018). Why companies that wait to adopt AI may never catch up. Harvard Business Review, 6(2018), 8-13.

    Meltzer, J. P. (2018). The impact of artificial intelligence on international trade. Brookings Institution, Thursday, December, 13, 2016.

    Obschonka, M., & Audretsch, D. B. (2020). Artificial intelligence and big data in entrepreneurship: a new era has begun. Small business economics, 55, 529-539.

    Pullen, A., De Weerd?Nederhof, P., Groen, A., Song, M., & Fisscher, O. (2009). Successful patterns of internal SME characteristics leading to high overall innovation performance. Creativity and innovation management, 18(3), 209-223.

    Rai, A., Constantinides, P., & Sarker, S. (2019). Next generation digital platforms: toward human-AI hybrids. MIS quarterly, 43(1), iii-ix.

    Stefanuk, V., & Zhozhikashvili, A. (2002). Productions and rules in artificial intelligence. Kybernetes, 31(6), 817-826.

    Sturm, T., Gerlach, J. P., Pumplun, L., Mesbah, N., Peters, F., Tauchert, C., . . . Buxmann, P. (2021). Coordinating Human and Machine Learning for Effective Organizational Learning. MIS quarterly, 45(3).

    Ulas, D. (2019). Digital Transformation Process and SMEs. Procedia computer science, 158, 662-671. doi:https://doi.org/10.1016/j.procs.2019.09.101

    Ulrich, P., Frank, V., & Kratt, M. (2021). Adoption of Artificial Intelligence Technologies in German SMEs-Results from an Empirical Study. Paper presented at the PACIS.

    Weber, M., Beutter, M., Weking, J., Böhm, M., & Krcmar, H. (2022). AI Startup Business Models: Key Characteristics and Directions for Entrepreneurship Research. Business & Information Systems Engineering, 64(1), 91-109.

    West, J., & Bhattacharya, M. (2016). Intelligent financial fraud detection: a comprehensive review. Computers & security, 57, 47-66.

    Xing, F. Z., Cambria, E., & Welsch, R. E. (2018). Natural language based financial forecasting: a survey. Artificial Intelligence Review, 50(1), 49-73.

  1. Ambulkar, S., Blackhurst, J., & Grawe, S. (2015). Firm's resilience to supply chain disruptions: Scale development and empirical examination. Journal of Operations Management, 33, 111-122.
  2. Amesho, K. T., Edoun, E. I., Naidoo, V., & Pooe, S. (2022). Sustainable competitive advantage through technology and innovation systems in the local government authorities. Africa’s Public Service Delivery & Performance Review, 10(1), 19.
  3. Arakpogun, E. O., Elsahn, Z., Olan, F., & Elsahn, F. (2021). Artificial intelligence in Africa: Challenges and opportunities. The fourth industrial revolution: Implementation of artificial intelligence for growing business success, 375-388.
  4. Arzikulov, O. (2021). Artificial intelligence to increase the efficiency of small businesses. ISJ Theoretical & Applied Science, 8(100), 412-415.
  5. Bandari, V. (2019). The Impact of Artificial Intelligence on the Revenue Growth of Small Businesses in Developing Countries: An Empirical Study. Reviews of Contemporary Business Analytics, 2(1), 33-44.
  6. Basri, W. (2020). Examining the impact of artificial intelligence (AI)-assisted social media marketing on the performance of small and medium enterprises: toward effective business management in the Saudi Arabian context. International Journal of Computational Intelligence Systems, 13(1), 142.
  7. Burian, J. (2020). The complex choreography of supply chain resilience. Industry Week.
  8. Cavalcante, R. C., Brasileiro, R. C., Souza, V. L., Nobrega, J. P., & Oliveira, A. L. (2016). Computational intelligence and financial markets: A survey and future directions. Expert Systems with Applications, 55, 194-211.
  9. Chalmers, D., MacKenzie, N. G., & Carter, S. (2021). Artificial intelligence and entrepreneurship: Implications for venture creation in the fourth industrial revolution. Entrepreneurship theory and Practice, 45(5), 1028-1053.
  10. Chan, C. M., Teoh, S. Y., Yeow, A., & Pan, G. (2019). Agility in responding to disruptive digital innovation: Case study of an SME. Information systems journal, 29(2), 436-455.
  11. Chan, L., Morgan, I., Simon, H., Alshabanat, F., Ober, D., Gentry, J., . . . Cao, R. (2019). Survey of AI in cybersecurity for information technology management. Paper presented at the 2019 IEEE technology & engineering management conference (TEMSCON).
  12. Commission, E. (2015). User guide to the SME definition. Retrieved from https://data.europa.eu/doi/10.2873/620234
  13. Davenport, T. H., & Ronanki, R. (2018). Artificial intelligence for the real world. Harvard Business Review, 96(1), 108-116.
  14. Delponte, L. (2018). European Artificial Intelligence (AI) leadership, the path for an integrated vision.
  15. Elavarasan, R. M., & Pugazhendhi, R. (2020). Restructured society and environment: A review on potential technological strategies to control the COVID-19 pandemic. Science of the Total Environment, 725, 138858.
  16. Ferreira, J., Mueller, J., & Papa, A. (2020). Strategic knowledge management: theory, practice and future challenges. Journal of Knowledge Management, 24(2), 121-126.
  17. Giuggioli, G., & Pellegrini, M. M. (2023). Artificial intelligence as an enabler for entrepreneurs: a systematic literature review and an agenda for future research. International Journal of Entrepreneurial Behavior & Research, 29(4), 816-837.
  18. Grover, P., Kar, A. K., & Dwivedi, Y. K. (2022). Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions. Annals of Operations Research, 308(1-2), 177-213.
  19. Helm, J. M., Swiergosz, A. M., Haeberle, H. S., Karnuta, J. M., Schaffer, J. L., Krebs, V. E., . . . Ramkumar, P. N. (2020). Machine learning and artificial intelligence: definitions, applications, and future directions. Current reviews in musculoskeletal medicine, 13, 69-76.
  20. Hess, T., Matt, C., Benlian, A., & Wiesböck, F. (2016). Options for formulating a digital transformation strategy. MIS Quarterly Executive, 15(2).
  21. Iansiti, M., & Lakhani, K. R. (2020). Competing in the age of AI: Strategy and leadership when algorithms and networks run the world: Harvard Business Press.
  22. Iqbal, T., & Khan, M. N. (2021). The Impact of Artificial Intelligence (AI) on CRM and Role of Marketing Managers.
  23. Jab?o?ska, M. R., & Pólkowski, Z. (2017). ARTIFICIAL INTELLIGENCE-BASED PROCESSES IN SMES. Studies & Proceedings of Polish Association for Knowledge Management(86).
  24. Kabir, M. E. (2020). Artificial Intelligence in Business and Future Prospect. IAR Journal of Entrepreneurship, Innovation & Design Thinking, 1(1).
  25. Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business horizons, 62(1), 15-25.
  26. Karimova, F. (2016). A survey of e-commerce recommender systems. European Scientific Journal, 12(34), 75-89.
  27. Kartal, H., Oztekin, A., Gunasekaran, A., & Cebi, F. (2016). An integrated decision analytic framework of machine learning with multi-criteria decision making for multi-attribute inventory classification. Computers & Industrial Engineering, 101, 599-613.
  28. Kraus, M., Feuerriegel, S., & Oztekin, A. (2020). Deep learning in business analytics and operations research: Models, applications and managerial implications. European Journal of Operational Research, 281(3), 628-641.
  29. Kumar, A., & Kalse, A. (2021). WITHDRAWN: Usage and adoption of artificial intelligence in SMEs: Elsevier.
  30. Kumar, A., Pandey, A., Pujari, P., & Arora, A. (2023). Adoption of ai and e-commerce improving marketing performance of smes. Academy of Marketing Studies Journal, 27(5), 1-10.
  31. Kumar, B. S., & Ravi, V. (2016). A survey of the applications of text mining in financial domain. Knowledge-Based Systems, 114, 128-147.
  32. Lian, S. B. (2023). The Factors Influencing Digital Technologies Adoption in Green Supply Chain Innovation: Evidence from the Social Enterprises in Malaysia. Electronic Journal of Business and Management, 8(1), 32-39.
  33. Mahidhar, V., & Davenport, T. H. (2018). Why companies that wait to adopt AI may never catch up. Harvard Business Review, 6(2018), 8-13.
  34. Meltzer, J. P. (2018). The impact of artificial intelligence on international trade. Brookings Institution, Thursday, December, 13, 2016.
  35. Obschonka, M., & Audretsch, D. B. (2020). Artificial intelligence and big data in entrepreneurship: a new era has begun. Small business economics, 55, 529-539.
  36. Pullen, A., De Weerd?Nederhof, P., Groen, A., Song, M., & Fisscher, O. (2009). Successful patterns of internal SME characteristics leading to high overall innovation performance. Creativity and innovation management, 18(3), 209-223.
  37. Rai, A., Constantinides, P., & Sarker, S. (2019). Next generation digital platforms: toward human-AI hybrids. MIS quarterly, 43(1), iii-ix.
  38. Stefanuk, V., & Zhozhikashvili, A. (2002). Productions and rules in artificial intelligence. Kybernetes, 31(6), 817-826.
  39. Sturm, T., Gerlach, J. P., Pumplun, L., Mesbah, N., Peters, F., Tauchert, C., . . . Buxmann, P. (2021). Coordinating Human and Machine Learning for Effective Organizational Learning. MIS quarterly, 45(3).
  40. Ulas, D. (2019). Digital Transformation Process and SMEs. Procedia computer science, 158, 662-671. doi:https://doi.org/10.1016/j.procs.2019.09.101
  41. Ulrich, P., Frank, V., & Kratt, M. (2021). Adoption of Artificial Intelligence Technologies in German SMEs-Results from an Empirical Study. Paper presented at the PACIS.
  42. Weber, M., Beutter, M., Weking, J., Böhm, M., & Krcmar, H. (2022). AI Startup Business Models: Key Characteristics and Directions for Entrepreneurship Research. Business & Information Systems Engineering, 64(1), 91-109.
  43. West, J., & Bhattacharya, M. (2016). Intelligent financial fraud detection: a comprehensive review. Computers & security, 57, 47-66.
  44. Xing, F. Z., Cambria, E., & Welsch, R. E. (2018). Natural language based financial forecasting: a survey. Artificial Intelligence Review, 50(1), 49-73.