Skip to main content
SpringerLink
Log in

Impact of Artificial Intelligence on Firm Performance: Exploring the Mediating Effect of Process-Oriented Dynamic Capabilities

  • Conference paper
  • First Online:
Digital Business Transformation

Part of the book series: Lecture Notes in Information Systems and Organisation ((LNISO,volume 38))

Abstract

Organizations still dependent on information technology innovation have already adopted the in AI subfields and techniques to adapt or disrupt the market while improvement their performance. Other research has examined the relationship between computing capabilities and organizational performance, with a mediating effect on dynamic process-driven capabilities. We extend this flow of literature and examine the same relationship by taking into account the capabilities of artificial intelligence (AI). Our conceptual framework is based on the paradox of productivity, resource-based view and dynamic capabilities. We relied on an in-depth review of 150 case studies collected on websites related to the integration of AI into organizations. Our study highlights the added value of AI capabilities, in terms of organizational performance, with a focus on improving organizational performance (financial, marketing, and administrative). Our analyses also show that companies improve their performance when they use capabilities of AI to reconfigure their dynamic process-oriented capabilities.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Hopfield, J. J. (1984). Neurons with graded response have collective computational properties like those of two-state neurons. Proceedings of the National Academy of Sciences, 81(10), 3088–3092.

    Google Scholar 

  2. Shortliffe, E. H. (1974). MYCIN: A rule-based computer program for advising physicians regarding antimicrobial therapy selection. Stanford University California Department of Computer Science.

    Google Scholar 

  3. McCulloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The Bulletin of Mathematical Biophysics, 5(4), 115–133.

    Google Scholar 

  4. Turing, A. M. (1950). Can a machine think. The world of Mathematics, 59(236), 433–460.

    Google Scholar 

  5. Turing, A. M. (1937). On computable numbers, with an application to the Entscheidungs problem. Proceedings of the London Mathematical Society, 2(1), 230–265.

    Google Scholar 

  6. Aghion, P., Jones, B. F., & Jones, C. I. (2017). Artificial intelligence and economic growth (No. w23928). National Bureau of Economic Research.

    Google Scholar 

  7. Agrawal, A., Gans, J., & Goldfarb, A. (2018). The economics of artificial intelligence. McKinsey Quarterly.

    Google Scholar 

  8. Greenblatt, R. D., Eastlake, D. E., & Crocker, S. D. (1988). The Greenblatt chess program. In Computer chess compendium (pp 56–66). Springer.

    Google Scholar 

  9. von Krogh, G. (2018). artificial intelligence in organizations: New opportunities for phenomenon-based theorizing. Academy of Management Discoveries.

    Google Scholar 

  10. Russell, S. J., & Norvig, P. (2016). Artificial intelligence: A modern approach. Malaysia: Pearson Education Limited.

    Google Scholar 

  11. PWC. (2019). Sizing the prize: Exploiting the AI revolution, what’s the real value of AI for your business and how can you capitalise? In PwC’s Global Artificial Intelligence Study. Cited 2019, 31 Mar 2019. Available from: https://www.pwc.com/gx/en/issues/data-and-analytics/publications/artificial-intelligence-study.html.

  12. Scanner, V. (2019). Artificial intelligence market report and data. Cited 2019, 02 Aug 2019; Venture Scanner is your analyst and technology powered research firm. Gain deep insights with our carefully crafted executive summaries. Analyze our extensive data on startups, investors, and exits to complete your research. Available from: https://www.venturescanner.com/artificial-intelligence.

  13. Microsoft. (2019). Les 5 chiffres à absolument connaître sur l’IA. Cited 2019, 02 Aug 2019. Available from: https://experiences.microsoft.fr/business/intelligence-artificielle-ia-business/ia-chiffres-cles/.

  14. Françoise Mercadal-Delasalles, K. V. (2017). Les enjeux de mise en œuvre opérationnelle de l’intelligence artificielle dans les grandes entreprises (Vol. 36). CIGREF, réussir le numérique ed. CIGREF, ed. l.i.a.d.l.g. entreprises. CIGREF, réussir le numérique.

    Google Scholar 

  15. Brynjolfsson, E., Rock, D., & Syverson, C. (2018). Artificial intelligence and the modern productivity paradox: A clash of expectations and statistics. In The economics of artificial intelligence: An agenda. University of Chicago Press.

    Google Scholar 

  16. Triplett, J. E. (1999). The Solow productivity paradox: What do computers do to productivity? The Canadian Journal of Economics/Revue Canadienne d’Economique, 32(2), 309–334.

    Google Scholar 

  17. Brynjolfsson, E. (1993). The productivity paradox of information technology: Review and assessment. Communications of the ACM, 36(12), 66–77.

    Google Scholar 

  18. Soh, C., & Markus, M. L. (1995). How IT creates business value: A process theory synthesis. In ICIS 1995 Proceedings (p 4).

    Google Scholar 

  19. Kim, G., et al. (2011). IT capabilities, process-oriented dynamic capabilities, and firm financial performance. Journal of the Association for Information Systems, 12(7), 487.

    Google Scholar 

  20. Mooney, J. G., Gurbaxani, V., & Kraemer, K. L. (1996). A process oriented framework for assessing the business value of information technology. CM SIGMIS Database: The DATABASE for Advances in Information Systems, 27(2), 68–81.

    Google Scholar 

  21. Peteraf, M. A., & Barney, J. B. (2003). Unraveling the resource-based tangle. Managerial and decision economics., 24(4), 309–323.

    Google Scholar 

  22. Bharadwaj, A. S. (2000). A resource-based perspective on information technology capability and firm performance: An empirical investigation. Management Information Systems Quarterly, 24(1), 169–196.

    Google Scholar 

  23. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.

    Google Scholar 

  24. Eriksson, T. (2014). Processes, antecedents and outcomes of dynamic capabilities. Scandinavian Journal of Management, 30(1), 65–82.

    Google Scholar 

  25. Hamet, P., & Tremblay, J. (2017). Artificial intelligence in medicine. Metabolism, 69, S36–S40.

    Google Scholar 

  26. Jorfi, S., Nor, K. M., & Najjar, L. (2011). The relationships between IT flexibility, IT-business strategic alignment, and IT capability. International Journal of Managing Information Technology, 3(1), 16–31.

    Google Scholar 

  27. Terry Anthony Byrd, D. E. T. (2000). Measuring the flexibility of information technology infrastructure: Exploratory analysis of a construct. Journal of Management Information Systems, 17(1), 167–208.

    Google Scholar 

  28. Tallon, P., & Kraemer, K.L. (1999). A process-oriented assessment of the alignment of information systems and business strategy: Implications for IT business value. Unpublished Ph.D. Dissertation (UC Irvine).

    Google Scholar 

  29. Wamba, S. F., et al. (2017). Big data analytics and firm performance: Effects of dynamic capabilities. Journal of Business Research, 70, 356–365.

    Google Scholar 

  30. David, P. A. (1989). The dynamo and the computer: An historical perspective on the modern productivity paradox. The American Economic Review, 80(2), 355–361.

    Google Scholar 

  31. Pilat, D. (2004). Le paradoxe de la productivité: l’apport des micro-données. Revue Économique de l’OCDE, 38(1), 41–73.

    Google Scholar 

  32. Wachter, R. M., & Howell, M. D. (2018). Resolving the productivity paradox of health information technology: A time for optimism. JAMA, 320(1), 25–26.

    Google Scholar 

  33. Dehning, B., Richardson, V. J., & Zmud, R. W. (2007). The financial performance effects of IT-based supply chain management systems in manufacturing firms. Journal of Operations Management., 25(4), 806–824.

    Google Scholar 

  34. Delen, D., Kuzey, C., & Uyar, A. (2013). Measuring firm performance using financial ratios: A decision tree approach. Expert Systems with Applications., 40(10), 3970–3983.

    Google Scholar 

  35. Ittner, C. D., et al. (2003). Performance implications of strategic performance measurement in financial services firms. Accounting, Organizations and Society, 28(7–8), 715–741.

    Google Scholar 

  36. Benner, M. J. (2009). Dynamic or static capabilities? Process management practices and response to technological change. Journal of Product Innovation Management., 26(5), 473–486.

    Google Scholar 

  37. Dosi, G., Nelson, R., & Winter, S. (2001). The nature and dynamics of organizational capabilities. OUP Oxford.

    Google Scholar 

  38. Teece, D. J. (2007). Explicating dynamic capabilities: The nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal., 28(13), 1319–1350.

    Google Scholar 

  39. Breslow, N. E., & Day, N. E. (1980). Statistical methods in cancer research. Vol. 1. The analysis of case-control studies (Vol. 1). Distributed for IARC by WHO, Geneva, Switzerland.

    Google Scholar 

  40. Merriam, S. B. (1998). Qualitative research and case study applications in education. Revised and expanded from “Case Study Research in Education”. ERIC.

    Google Scholar 

  41. Rowley, J. (2002). Using case studies in research. Management Research News, 25(1), 16–27.

    Google Scholar 

  42. Siggelkow, N. (2007). Persuasion with case studies. Academy of Management Journal, 50(1), 20–24.

    Google Scholar 

  43. George, A. L., et al. (2005). Case studies and theory development in the social sciences. MIT Press.

    Google Scholar 

  44. Walsham, G. (1995). Interpretive case studies in IS research: Nature and method. European Journal of Information Systems, 4(2), 74–81.

    Google Scholar 

  45. Kelly, J. E. III, & Hamm, S. (2013). Smart machines: IBM’s Watson and the era of cognitive computing. Columbia University Press.

    Google Scholar 

  46. Noël, J. G. (2009). Isabelle Gaboury, Ph.D. Josée Guignard Noël, M.Sc. Éric Forgues, Ph.D. Louise Bouchard, Ph.D.

    Google Scholar 

  47. Center, A. H., et al. (2008). Public relations practices: Managerial case studies and problems. Pearson Prentice Hall.

    Google Scholar 

  48. Madill, A., Jordan, A., & Shirley, C. (2000). Objectivity and reliability in qualitative analysis: Realist, contextualist and radical constructionist epistemologies. British Journal of Psychology, 91(1), 1–20.

    Google Scholar 

  49. Kracauer, S. J. (1952). The challenge of qualitative content analysis. Public Opinion Quarterly, 631–642.

    Google Scholar 

  50. Grbich, C. (2012). Qualitative data analysis: An introduction (2nd edn). London: Sage Publications.

    Google Scholar 

  51. Mauthner, N. S., & Doucet, A. J. S. (2003). Reflexive accounts and accounts of reflexivity in qualitative data analysis. Sociology, 37(3), 413–431.

    Google Scholar 

  52. Ratner, C. (2002, September). Subjectivity and objectivity in qualitative methodology. In Forum Qualitative Sozialforschung/Forum: Qualitative Social Research, 3(3).

    Google Scholar 

  53. Nonaka, I., & Toyama, R. (2005). The theory of the knowledge-creating firm: Subjectivity, objectivity and synthesis. Industrial and Corporate Change., 14(3), 419–436.

    Google Scholar 

  54. Lockwood, C. M., & MacKinnon, D. P. (1998, March) Bootstrapping the standard error of the mediated effect. In Proceedings of the 23rd Annual Meeting of SAS Users Group International (pp 997–1002). Citeseer.

    Google Scholar 

  55. Bollen, K. A., & Stine, R. A. (1992). Bootstrapping goodness-of-fit measures in structural equation models. Sociological Methods and Research., 21(2), 205–229.

    Google Scholar 

  56. Balambo, M. A., & Baz, J. (2014). De l’intérêt de l’analyse des modèles des équations structurelles par la méthode PLS dans les recherches sur les relations inter organisationnelles: Le cas des recherches en Logistique. In 7ème Edition du colloque international LOGISTIQUA.

    Google Scholar 

  57. Chin, W. W. (1998). The partial least squares approach to structural equation modeling. Modern Methods for Business Research, 295(2), 295–336.

    Google Scholar 

  58. Seddon, P. B., Calvert, C., & Yang, S. J. M. Q. (2010). A multi-project model of key factors affecting organizational benefits from enterprise systems. Management Information Systems Quarterly, 34(2), 305–328.

    Google Scholar 

  59. Bhattacharya, P. J., Seddon, P. B., & Scheepers, R. (2010, December). Enabling strategic transformations with enterprise systems: Beyond operational efficiency. In ICIS (p 55).

    Google Scholar 

  60. Kala Kamdjoug, J. R., Nguegang Tewamba, H. J., & Fosso Wamba, S. (2018). IT capabilities, firm performance and the mediating role of ISRM: A case study from a developing country. Business Process Management Journal.

    Google Scholar 

  61. Balint, B., Forman, C., & Slaughter, S. (2010). Process standardization, task variability, and internal performance in IT business services outsourcing. Working paper. http://www.devsmith.umd.edu/doit/events/pdfs….

  62. Guest, D. (2014). Employee engagement: A sceptical analysis. Journal of Organizational Effectiveness: People and Performance, 1(2), 141–156.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Catholic University of Central Africa, GRIAGES, BP 11,628, Yaoundé, Cameroon

    Serge-Lopez Wamba-Taguimdje, Jean Robert Kala Kamdjoug & Chris Emmanuel Tchatchouang Wanko

  2. Toulouse Business School, Midi-Pyérées, France

    Samuel Fosso Wamba

Authors
  1. Serge-Lopez Wamba-Taguimdje
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Samuel Fosso Wamba
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jean Robert Kala Kamdjoug
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Chris Emmanuel Tchatchouang Wanko
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Serge-Lopez Wamba-Taguimdje .

Editor information

Editors and Affiliations

  1. Department of Business and Economics, Parthenope University of Naples, Naples, Italy

    Rocco Agrifoglio

  2. Department of Business and Economics, Parthenope University of Naples, Naples, Italy

    Rita Lamboglia

  3. Faculty of Law, University of Teramo, Teramo, Italy

    Daniela Mancini

  4. Department of Management, University of Turin, Turin, Italy

    Francesca Ricciardi

Appendix

Appendix

Available upon request. Contact one of the authors to access the list of case studies, and the links.

Rights and permissions

Reprints and permissions

Copyright information

© 2020 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Wamba-Taguimdje, SL., Wamba, S.F., Kamdjoug, J.R.K., Wanko, C.E.T. (2020). Impact of Artificial Intelligence on Firm Performance: Exploring the Mediating Effect of Process-Oriented Dynamic Capabilities. In: Agrifoglio, R., Lamboglia, R., Mancini, D., Ricciardi, F. (eds) Digital Business Transformation. Lecture Notes in Information Systems and Organisation, vol 38. Springer, Cham. https://doi.org/10.1007/978-3-030-47355-6_1

Download citation

Publish with us

Policies and ethics

Search

Navigation