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Cited by 416Impact of AI and Big Data on Business and SocietyArtificial Intelligence ApplicationsEthics and Social Impacts of AI

Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study

Open access
Cubric, Marija·2020·Technology in Society
DOI 10.1016/j.techsoc.2020.101257
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Abstract

The number of academic papers in the area of Artificial Intelligence (AI) and its applications across business and management domains has risen significantly in the last decade, and that rise has been followed by an increase in the number of systematic literature reviews . The aim of this study is to provide an overview of existing systematic reviews in this growing area of research and to synthesise the findings related to drivers, barriers and social implications of the AI adoption in business and management. The methodology used for this tertiary study is based on Kitchenham and Charter's guidelines [14], resulting in a selection of 30 reviews published between 2005 and 2019 which are reporting results of 2021 primary studies. These reviews cover the AI adoption across various business sectors (healthcare, information technology, energy, agriculture , apparel industry , engineering, smart cities, tourism and transport), management and business functions (HR, customer services, supply chain , health and safety, project management, decision-support, systems management and technology adoption). While the drivers for the AI adoption in these areas are mainly economic, the barriers are related to the technical aspects (e.g. availability of data, reusability of models) as well as the social considerations such as, increased dependence on non-humans, job security, lack of knowledge, safety, trust and lack of multiple stakeholders’perspectives. Very few reviews outside of the healthcare management domain consider human, organisational and wider societal factors of the AI adoption. In addition to increased focus on social implications of AI, the reviews are recommending more rigorous evaluation, increased use of hybrid solutions (AI and non-AI) and multidisciplinary approach to AI design and evaluation. Furthermore, this study found that there is a lack of systematic reviews in some of the early AI adoption sectors such as financial industry and retail.

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  • The nature of AI development-requiring volumes of training data, specialised hardware, and technical expertise-presents a significant challenge for organisations wishing to integrate AI into their business processes.

    “However, the nature of AI development-requiring volumes of training data, specialised hardware for building and training models, and technical expertise in machine learning-presents a signifcant challenge for organisations wishing to integrate AI into their business processes [35,44].”

    From Out of Context: Investigating the Bias and Fairness Concerns of “Artificial Intelligence as a Service” (2023) · cites this paper as [35]

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Research works printed but not linked: 31

  1. Reviews included P1. AFZAL, W., & TORKAR, R. (2011). On the application of genetic programming for software engineering predictive modelling: A systematic review. Expert Systems with Applications, 38(9), 11984-11997.
  2. AZEEM, M.I., PALOMBA, F., SHI, L., & WANG, Q. (2019). Machine learning techniques for code smell detection: A systematic literature review and meta-analysis. Information and Software Technology, 108, 115-138.
  3. P3. BARYANNIS, G., VALIDI, S., DANI, S., & ANTONIOU, G. (2019). Supply chain risk management and artificial intelligence: state of the art and future research directions. International Journal of Production Research, 57(7), 2179-2202.
  4. P4. BROADBENT, E., STAFFORD. R., & MACDONALD, B. (2009). Acceptance of healthcare robots for the older population: Review and future directions. International Journal of Social Robotics, 1(4):319-330.
  5. P5. CALATAYUD, A., MANGAN, J., & CHRISTOPHER, M. (2019). The self-thinking supply chain. Supply Chain Management: An International Journal, 24(1), 22-38.
  6. DE SOUZA, J.T., DE FRANCISCO, A.C., PIEKARSKI, C.M., & DO PRADO, G.F. (2019). Data mining and machine learning to promote smart cities: A systematic review from 2000 to 2018. Sustainability (Switzerland), 11(4), 1077.
  7. DIAS, R.D., GUPTA, A., & YULE, S.J. (2019). Using Machine Learning to Assess Physician Competence: A Systematic Review. Academic Medicine, 94(3), 427-439.
  8. P9. GUO, Z.X., WONG, W.K., LEUNG, S., & LI, M. (2011). Applications of artificial intelligence in the apparel industry: a review. Textile Research Journal, 81(18), 1871-1892.
  9. KAARTEMO, V., & HELKKULA, A. (2018). A Systematic Review of Artificial Intelligence and Robots in Value Co-creation: Current Status and Future Research Avenues. Journal of Creating Value, 4(2), 211- 228.
  10. KACHOUIE, R., SEDIGHADELI, S. & ABKENAR, A.B. (2017). The role of socially assistive robots in elderly wellbeing: A systematic review. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10281, 669-682.
  11. KACHOUIE, R., SEDIGHADELI, S., KHOSLA, R., & CHU, M. (2014). Socially Assistive Robots in Elderly Care: A Mixed-Method Systematic Literature Review. International Journal of Human-Computer Interaction, 30(5), 369-393.
  12. KHAN, S., & YAIRI, T. (2018). A review on the application of deep learning in system health management. Mechanical Systems and Signal Processing, 107, 241-265.
  13. KUSTER, C., REZGUI, Y., & MOURSHED, M. (2017). Electrical load forecasting models: A critical systematic review. Sustainable Cities and Society, 35, 257-270.
  14. LARANJO, L., DUNN, A. G., TONG, H. L., KOCABALLI, A. B., CHEN, J., BASHIR, R., ... & COIERA, E. (2018). Conversational agents in healthcare: a systematic review. Journal of the American Medical Informatics Association, 25(9), 1248-1258.
  15. MAALOUF, N., SIDAOUI, A., ELHAJJ, I.H., & ASMAR, D. (2018). Robotics in Nursing: A Scoping Review. Journal of Nursing Scholarship, 50(6), 590-600.
  16. MAITA, A.R.C., MARTINS, L.C., LÓPEZ PAZ, C.R., PERES, S.M., & FANTINATO, M., (2015a). Process mining through artificial neural networks and support vector machines. Business Process Management Journal, 21(6), 1391-1415.
  17. MALHOTRA, R. (2015). A systematic review of machine learning techniques for software fault prediction. Applied Soft Computing Journal, 27, 504-518.
  18. MEMETI, S., PLLANA, S., BINOTTO, A., KOLODZIEJ, J., & BRANDIC, I. (2018). Using Meta-heuristics and Machine Learning for Software Optimization of Parallel Computing Systems: A Systematic Literature Review. Computing, 101, 8(2019), 893-936.
  19. MORA, M., FORGIONNE, G., GUPTA, J.N.D., CERVANTES, F., & GELMAN, O. (2005). Intelligent Decision- Making Support Systems. A Strategic Research Agenda. Journal of Decision Systems, 14(1-2), 179-196.
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Authors
Cubric, M.
Venue
Technology in Society
Publisher
Elsevier BV
Published
2020-08
Volume
62
Pages
101257
ISSN
0160-791X (ISSN-L)
DOI
10.1016/j.techsoc.2020.101257
OpenAlex
W3017620100
Cited by
416
References
30
Citations by year
2026: 100 · 2025: 117 · 2024: 69 · 2023: 60 · 2022: 38 · 2021: 28 · 2020: 4
FWCI
20.67
Type
journal article
Language
en
Open access
Licence
Locations
  • Technology in Society (publishedVersion, other-oa)
  • University of Hertfordshire Research Archive (University of Hertfordshire) (submittedVersion, other-oa)
  • RePEc: Research Papers in Economics (submittedVersion)
  • University of Hertfordshire Research Archive (University of Hertfordshire) (submittedVersion, cc-by-nc-nd)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
  • Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
  • Social Sciences › Safety Research › Ethics and Social Impacts of AI
Keywords
artificial intelligence adoption, business and management, AI adoption barriers, societal impact of AI, trust in AI, job security, data availability

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