Skip to main content Skip to main navigation menu Skip to site footer
Articles
Published: 2023-05-22

The impact of artificial intelligence on consumer behaviour and changes in business activity due to pandemic effects

ISCTE Business School, Portugal
Piaget
BRU-Business Research Unit
ISCTE
ISCTE
COVID-19 Consumers Companies E-commerce Intelligent Systems Artificial intelligence

Abstract

The COVID-19 pandemic has impacted the world economy, and the restrictions have shaken business models. E-commerce has skyrocketed as the only way to purchase products and AI has received closer consideration as social distancing has become imperative. This research aims to find whether the COVID-19 has translated into an opportunity for the use of AI by companies. A survey incorporating consumers and companies was conducted to analyse the positioning of consumers regarding the use of AI, as well as the perception of companies regarding their possible use of AI. It was concluded that due to COVID-19 there was an increase in the relevance that companies give to AI, the main drivers being the companies' views on AI and the benefits from its use. Regarding consumer behaviour, consumers are more receptive to AI use, favouring a fully automated experience, with half of the sample preferring to buy online.

Metrics

Metrics Loading ...

References

  1. Anderson, J. C., & Gerbing, D. W. (1988). Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin, 103(3), 411–423. https://doi.org/10.1037/0033-2909.103.3.411 DOI: https://doi.org/10.1037/0033-2909.103.3.411
  2. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Journal of the Academy of Marketing Science, 16(1), 74–94. https://doi.org/10.1007/BF02723327 DOI: https://doi.org/10.1007/BF02723327
  3. Burgess, A. (2018). The Executive Guide to Artificial Intelligence. In The Executive Guide to Artificial Intelligence. Springer International Publishing. https://doi.org/10.1007/978-3-319-63820-1 DOI: https://doi.org/10.1007/978-3-319-63820-1
  4. Butzmann, L., Daweke, E., Geimer, J., Kolev, N., & Stiller, M. (2017). From mystery to mastery: Unlocking the business value of Artificial Intelligence in the insurance industry. Deloitte Digital, November, 1–45. https://www2.deloitte.com/content/dam/Deloitte/ru/Documents/financial-services/artificial-intelligence-in-insurance.pdf
  5. Carmo, H., & Ferreira, M. (1998). Metodologia da Investigação: Guia para Auto-aprendizagem. Universidade Aberta, Lisboa.
  6. Chintalapati, S., & Pandey, S. K. (2022). Artificial intelligence in marketing: A systematic literature review. International Journal of Market Research, 64(1), 38-68. DOI: https://doi.org/10.1177/14707853211018428
  7. Coombs, C. (2020). Will COVID-19 be the tipping point for the Intelligent Automation of work? A review of the debate and implications for research. International Journal of Information Management, June, 102182. https://doi.org/10.1016/j.ijinfomgt.2020.102182 DOI: https://doi.org/10.1016/j.ijinfomgt.2020.102182
  8. Coombs, C., Hislop, D., Taneva, S. K., & Barnard, S. (2020). The strategic impacts of Intelligent Automation for knowledge and service work: An interdisciplinary review. Journal of Strategic Information Systems, July 2017, 101600. https://doi.org/10.1016/j.jsis.2020.101600 DOI: https://doi.org/10.1016/j.jsis.2020.101600
  9. Davenport, T., & Kalakota, R. (2019). DIGITAL TECHNOLOGY The potential for artificial intelligence in healthcare. Future Healthcare Journal, 6(2), 94–102. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6616181/ DOI: https://doi.org/10.7861/futurehosp.6-2-94
  10. De Bruyn, A., Viswanathan, V., Beh, Y. S., Brock, J. K. U., & Von Wangenheim, F. (2020). Artificial intelligence and marketing: Pitfalls and opportunities. Journal of Interactive Marketing, 51(1), 91-105. DOI: https://doi.org/10.1016/j.intmar.2020.04.007
  11. Denning, S. (2018). What Is Strategic Agility? Forbes. https://www.forbes.com/sites/stevedenning/2018/01/28/what-is-strategic-agility/amp/
  12. Dew, N., & Sarasvathy, S. D. (2016). Exaptation and niche construction: Behavioral insights for an evolutionary theory. Industrial and Corporate Change, 25(1), 167–179. https://doi.org/10.1093/icc/dtv051 DOI: https://doi.org/10.1093/icc/dtv051
  13. Dickson, B. (2020). 3 ways AI is transforming the insurance industry. https://thenextweb.com/growth-quarters/2020/02/24/3-ways-ai-is-transforming-the-insurance-industry/
  14. Ehiorobo, O. A. (2020). STRATEGIC AGILITY AND AI-ENABLED RESOURCE CAPABILITIES FOR BUSINESS SURVIVAL IN POST-COVID-19 GLOBAL ECONOMY. International Journal of Information, Business and Management, 12(4), 201–214. https://search.proquest.com/docview/2438206567?pq-origsite=gscholar&fromopenview=true
  15. El-Sheikh, A. A., Abonazel, M. R., & Gamil, N. (2017). A review of software packages for structural equation modeling:A Comparative Study. Applied Mathematics and Physics, 5(3), 85–94. https://doi.org/10.12691/amp-5-3-2
  16. Finlay, S. (2018). Artificial Intelligence and Machine Learning for Business. In Artificial Intelligence and Machine Learning for Business for Non-Engineers (3rd ed.). Relativistic. https://www.goodreads.com/book/show/35270840-artificial-intelligence-and-machine-learning-for-business
  17. Fornell, C., & Larcker, F. D. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(3), 39–50. DOI: https://doi.org/10.1177/002224378101800104
  18. Gavetti, G., Helfat, C. E., & Marengo, L. (2017). Searching, Shaping, and the Quest for Superior Performance. Strategy Science, 2(3), 194–209. https://doi.org/10.1287/stsc.2017.0036 DOI: https://doi.org/10.1287/stsc.2017.0036
  19. Güngör, H. (2020). Creating Value with Artificial Intelligence: A Multi-stakeholder Perspective. Journal of Creating Value, 6(1), 72–85. https://doi.org/10.1177/2394964320921071 DOI: https://doi.org/10.1177/2394964320921071
  20. Günther, W. A., Rezazade Mehrizi, M. H., Huysman, M., & Feldberg, F. (2017). Debating big data: A literature review on realizing value from big data. Journal of Strategic Information Systems, 26(3), 191–209. https://doi.org/10.1016/j.jsis.2017.07.003 DOI: https://doi.org/10.1016/j.jsis.2017.07.003
  21. Haenlein, M., & Kaplan, A. (2019). A brief history of artificial intelligence: On the past, present, and future of artificial intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925 DOI: https://doi.org/10.1177/0008125619864925
  22. Hair, J. F., Hult, G. M., Ringle, C., & Sarstedt, M. (2017). A primer on partial least squares structural equation modeling. In Sage Publication. DOI: https://doi.org/10.15358/9783800653614
  23. Hao, F., Xiao, Q., & Chon, K. (2020). COVID-19 and China’s Hotel Industry: Impacts, a Disaster Management Framework, and Post-Pandemic Agenda. International Journal of Hospitality Management, 90(June), 102636. https://doi.org/10.1016/j.ijhm.2020.102636 DOI: https://doi.org/10.1016/j.ijhm.2020.102636
  24. Haque, A., Fernando, M., & Caputi, P. (2019). The Relationship Between Responsible Leadership and Organisational Commitment and the Mediating Effect of Employee Turnover Intentions: An Empirical Study with Australian Employees. Journal of Business Ethics, 156(3), 759–774. https://doi.org/10.1007/s10551-017-3575-6 DOI: https://doi.org/10.1007/s10551-017-3575-6
  25. Harris, P., Dall’Olmo Riley, F., Riley, D., & Hand, C. (2017). Online and store patronage: a typology of grocery shoppers. International Journal of Retail and Distribution Management, 45(4), 419–445. https://doi.org/10.1108/IJRDM-06-2016-0103 DOI: https://doi.org/10.1108/IJRDM-06-2016-0103
  26. Henke, N., & Kaka, N. (2018). McKinsey: Analytics comes of age. McKinsey Analytics, January, 1–100. https://www.mckinsey.com/~/media/McKinsey/Business Functions/McKinsey Analytics/Our Insights/Analytics comes of age/Analytics-comes-of-age.ashx
  27. Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8
  28. Huang, M. H., & Rust, R. T. (2021). A strategic framework for artificial intelligence in marketing. Journal of the Academy of Marketing Science, 49, 30-50. DOI: https://doi.org/10.1007/s11747-020-00749-9
  29. Johne, A. (1999). Successful market innovation. European Journal of Innovation Management, 2(1), 6–11. https://doi.org/10.1108/14601069910248838 DOI: https://doi.org/10.1108/14601069910248838
  30. Judeh, M. (2014). What is your concept of strategic agility? ResearchGate. https://www.researchgate.net/post/What_is_your_concept_of_strategic_agility
  31. Kim, W., & Mauborgne, R. (2005). Blue Ocean Strategy: FROM THEORY TO PRACTICE. In E. Nofzinger, P. Maquet, & M. Thorpy (Eds.), Neuroimaging of Sleep and Sleep Disorders (Vol. 47, Issue 3). https://journals.sagepub.com/doi/pdf/10.1177/000812560504700301
  32. Kjellberg, H., Azimont, F., & Reid, E. (2015). Market innovation processes: Balancing stability and change. Industrial Marketing Management, 44, 4–12. https://doi.org/10.1016/j.indmarman.2014.10.002 DOI: https://doi.org/10.1016/j.indmarman.2014.10.002
  33. Lalmuanawma, S., Hussain, J., & Chhakchhuak, L. (2020). Applications of machine learning and artificial intelligence for Covid-19 (SARS-CoV-2) pandemic: A review. Chaos, Solitons and Fractals, 139. https://doi.org/10.1016/j.chaos.2020.110059 DOI: https://doi.org/10.1016/j.chaos.2020.110059
  34. Lu, H., Li, Y., Chen, M., Kim, H., & Serikawa, S. (2017). Brain Intelligence: Go beyond Artificial Intelligence. Mobile Networks and Applications, 23(2), 368–375. https://doi.org/10.1007/s11036-017-0932-8 DOI: https://doi.org/10.1007/s11036-017-0932-8
  35. Luksha, P. (2008). NICHE CONSTRUCTION: THE PROCESS OF OPPORTUNITY CREATION IN THE ENVIRONMENT. Strategic Entrepreneurship Journal, 2(4), 269–283. https://doi.org/10.1002/sej DOI: https://doi.org/10.1002/sej.57
  36. Maritz, A. (2020). A multi-disciplinary business approach to COVID-19: La Trobe Business School perspectives. IJOI The International Journal of Organizational Innovation, 13(1), 2020–1095. http://www.ijoi-online.org/
  37. McGrath, R. (2019). Seeing Around Corners: How to Spot Inflection Points before They Happen. Houghton Mifflin Harcourt. https://www.goodreads.com/book/show/43261121-seeing-around-corners
  38. Morton, J., Stacey, P., & Mohn, M. (2018). Building and maintaining strategic agility: An agenda and framework for executive IT leaders. California Management Review, 61(1), 94–113. https://doi.org/10.1177/0008125618790245 DOI: https://doi.org/10.1177/0008125618790245
  39. Naudé, W. (2020). Artificial Intelligence against COVID-19: An Early Review. IZA Discussion Papers, 13110, 1–14. https://www.iza.org/publications/dp/13110/artificial-intelligence-against-covid-19-an-early-review DOI: https://doi.org/10.2139/ssrn.3568314
  40. Nenonen, S, & Storbacka, K. (2018). Smash: Using Market Shaping to Design New Strategies for Innovation, Value Creation, and Growth. In Emerald Publishing (1st ed.). Emerald Publishing Limited. https://books.google.pt/books?id=2vBJDwAAQBAJ&printsec=copyright&redir_esc=y#v=onepage&q&f=false DOI: https://doi.org/10.1108/9781787437975
  41. Nenonen, Suvi, Kjellberg, H., Pels, J., Cheung, L., Lindeman, S., Mele, C., Sajtos, L., & Storbacka, K. (2014). A new perspective on market dynamics: Market plasticity and the stability–fluidity dialectics. Marketing Theory, 14(3), 269–289. https://doi.org/10.1177/1470593114534342 DOI: https://doi.org/10.1177/1470593114534342
  42. Nenonen, Suvi, Storbacka, K., & Windahl, C. (2019). Capabilities for market-shaping: triggering and facilitating increased value creation. Journal of the Academy of Marketing Science, 47, 617–639. https://doi.org/10.1007/s11747-019-00643-z DOI: https://doi.org/10.1007/s11747-019-00643-z
  43. Nielsen. (2020). COVID-19: The Unexpected Catalyst for Tech Adoption. Nielsen CPG, FMCG & Retail. https://www.nielsen.com/za/en/insights/article/2020/covid-19-the-unexpected-catalyst-for-tech-adoption/
  44. Panch, T., Szolovits, P., & Atun, R. (2018). Artificial intelligence, machine learning and health systems. Journal of Global Health, 8(2), 1–8. https://doi.org/10.7189/jogh.08.020303 DOI: https://doi.org/10.7189/jogh.08.020303
  45. Pantano, E., Pizzi, G., Scarpi, D., & Dennis, C. (2020). Competing during a pandemic? Retailers’ ups and downs during the COVID-19 outbreak. Journal of Business Research, 116(May), 209–213. https://doi.org/10.1016/j.jbusres.2020.05.036 DOI: https://doi.org/10.1016/j.jbusres.2020.05.036
  46. Patrick, B. (2020). What is artificial intelligence? Journal of Accountancy. https://www.journalofaccountancy.com/issues/2020/feb/what-is-artificial-intelligence.html
  47. Patvardhan, S., & Ramachandran, J. (2020). Shaping the future: Strategy making as artificial evolution. Organization Science. https://doi.org/10.1287/orsc.2019.1321 DOI: https://doi.org/10.1287/orsc.2019.1321
  48. Perrault, R., Shoham, Y., Brynjolfsson, E., Clark, J., Etchemendy, J., Grosz, B., Lyons, T., Manyika, J., Mishra, S., & Niebles, J. C. (2019). Artificial Intelligence Index 2019 Annual Report. AI Index Steering Committee, Human-Centered AI Institute, Stanford University, Stanford, CA, 291. https://hai.stanford.edu/sites/g/files/sbiybj10986/f/ai_index_2019_report.pdf
  49. Pettersen, L. (2019). Why Artificial Intelligence Will Not Outsmart Complex Knowledge Work. Work, Employment and Society, 33(6), 1058–1067. https://doi.org/10.1177/0950017018817489 DOI: https://doi.org/10.1177/0950017018817489
  50. Preacher, K. J., & Hayes, S. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. DOI: https://doi.org/10.3758/BRM.40.3.879
  51. Priem, R. L., Butler, J. E., & Li, S. (2013). Toward reimagining strategy research: Retrospection and prospection on the 2011 amr decade award article. Academy of Management Review, 38(4), 471–489. https://doi.org/10.5465/amr.2013.0097 DOI: https://doi.org/10.5465/amr.2013.0097
  52. Provdanov, C. C., & Freitas, E. C. De. (2013). Metodologia do trabalho científico: métodos e técnicas da pesquisa e do trabalho acadêmico. In Novo Hamburgo: Feevale. https://doi.org/10.1017/CBO9781107415324.004 DOI: https://doi.org/10.1017/CBO9781107415324.004
  53. Ringle, C. M., Henseler, J., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8 DOI: https://doi.org/10.1007/s11747-014-0403-8
  54. Scor. (2018). The Impact of Artificial Intelligence on the (re)insurance sector. Risk Analysis, 7(3), 277–280. https://doi.org/10.1111/j.1539-6924.1987.tb00460.x DOI: https://doi.org/10.1111/j.1539-6924.1987.tb00460.x
  55. Shabbir, J., & Anwer, T. (2015). Artificial Intelligence and its Role in Near Future. Journal of Latex Class Files, 14(8), 1–11. http://arxiv.org/abs/1804.01396
  56. Sheth, J. (2020). Impact of Covid-19 on consumer behavior: Will the old habits return or die? Journal of Business Research, 117, 280–283. https://doi.org/10.1016/j.jbusres.2020.05.059 DOI: https://doi.org/10.1016/j.jbusres.2020.05.059
  57. Sreeharsha, V. (2020). Computer Vision Could Help Enforce Social- Distancing in the Workplace. The Wall Street Journal PRO Artificial Intelligence. https://www.wsj.com/articles/computer-vision-could-help-enforce-social-distancing-in-the-workplace-11587720601
  58. Tarka, P. (2018). An overview of structural equation modeling: its beginnings, historical development, usefulness and controversies in the social sciences. Quality and Quantity, 52(1), 313–354. https://doi.org/10.1007/s11135-017-0469-8 DOI: https://doi.org/10.1007/s11135-017-0469-8
  59. Tarski, A. (1977). Introducción a la Lógica y a la Metodología de las Ciencias. Investigación en Ciencias Sociales, Interamericana: México, D. F.
  60. Vergara, S. (2006). Projectos e relatórios de pesquisa em administração. São Paulo: Atlas.
  61. Vilelas, J. (2009). Investigação – o processo de construção do conhecimento. Lisboa: Sílabo. www.silabo.pt
  62. Wang, Y., & Wang, H. (2022). Reinventing the Wheel of Marketing: Assessing the Impact of Artificial Intelligence (AI) on Digital Marketing and Consumer Buying Behavior. In Innovative Computing: Proceedings of the 4th International Conference on Innovative Computing (IC 2021) (pp. 827-834). Springer Singapore.
  63. Willcocks, L. P., & Lacity, M. C. (2016). A New Approach to Automating Services. MIT Sloan Management Review, 58(1), 40–49. http://eprints.lse.ac.uk/68135/1/Willcocks_New approach_2016.pdf
  64. Williams, C. (2007). Research methods. Journal of Business & Economic Research, 5(3), 65–72.
  65. Yin, R. (1994). Case Study Research Design and Methods (Sage (ed.); 2a edição).

How to Cite

Dias, T., Gonçalves, R., Lopes da Costa, R., F. Pereira, L., & Dias, Álvaro. (2023). The impact of artificial intelligence on consumer behaviour and changes in business activity due to pandemic effects. Human Technology, 19(1), 121–148. https://doi.org/10.14254/1795-6889.2023.19-1.8