Skip to main navigation menu Skip to main content Skip to site footer

Revolutionary artificial intelligence or rogue technology? The promises and pitfalls of ChatGPT

DOI:

https://doi.org/10.15678/IER.2023.0904.07

Abstract

Objective: The objective of the article is to offer a thorough exploration and comprehension of the obstacles and potential advantages linked to the application of generative artificial intelligence (GAI) in the business realm, particularly emphasizing ChatGPT.

Research Design & Methods: The research utilized a narrative and critical examination of existing literature and constructed a conceptual framework grounded in prior studies. Our theoretical framework was developed through a deductive reasoning approach to ensure the logical and effective organization of the study. Consequently, this work should be considered a conceptual article that sheds light on one hand on the promises and opportunities, and on the other hand on the controversies and risks associated with generative artificial intelligence in the fields of management and economics, using ChatGPT as a specific case study.

Findings: In recent years, artificial intelligence has experienced rapid progress, leading to its widespread applications. The chatbot industry, exemplified by ChatGPT, has garnered considerable attention, with experts and researchers asserting that generative artificial intelligence and ChatGPT could transform our work routines and daily existence. Although these technologies have the potential to revolutionize data analysis and report generation, concerns have been raised about their societal impacts, particularly in areas such as ethics, privacy, and security.

Implications & Recommendations: The regulation of the GAI market is imperative to ensure fairness, competitive balance, and safeguard intellectual property and privacy while addressing potential geopolitical risks. With the evolving job landscape, individuals must continuously acquire new digital skills through education, particularly in response to the growing prominence of AI system training. Ethical considerations, such as prioritizing user privacy and security, are crucial for GAI developers to mitigate risks related to personal data violation and social surveillance, emphasizing responsible AI practices and adherence to ethical guidelines to prevent social manipulation and maintain goodwill.

Contribution & Value Added: The article structures scientific knowledge on the advantages and drawbacks of the generative artificial intelligence in business. The articles attempted to put together the main aspects of this new phenomenon.

Keywords

artificial intelligence (AI), generative artificial intelligence (GAI), ChatGPT, technology adoption, digital transformation, OpenAI, chatbots

(PDF) Save

Author Biography

Marek Sieja

Assistant professor at Cracow University of Technology, the Department of Automation and Computer Science at the Faculty of Electrical and Computer Engineering. He received his PhD in electrotechnics in 2016. His research interests include genetic algorithms and electrical metrology.

Krzysztof Wach

Full Professor at Krakow University of Economics (Poland). Professor of social sciences (2020), Post-Doc De-gree of Habilitated Doctor (dr hab.) in economics (2013), PhD in Management (2006). Member of the Com-mission for Economic Sciences of the Polish Academy of Sciences (PAN). Member of the Commission for Eco-nomic Sciences of the Polish Academy of Arts and Sciences (PAU). Member of the Commission for Organiza-tion and Management of the Polish Academy of Sciences (PAN) – Branch in Kraków. He serves on the editori-al boards of international journals as an editor, including ‘European Journal of International Management’ (SSCI WoS), ‘Sustainable Technology and Entrepreneurship’ (Elsevier), ‘Central European Management Jour-nal’ (Emerald), ‘International Journal of Multinational Corporation Strategy’ (Inderscience), ‘Entrepreneurial Business and Economics Review’ (ESCI/Scopus). He has published over 240 peer-reviewed publications (in-cluding 126 journal articles, 100 chapters and 17 books), and 19 edited volumes. His research interests in-clude entrepreneurship, international business, innovation, and family firms.


References

  1. Aly, H. (2022). Digital transformation, development and productivity in developing countries: is artificial intelligence a curse or a blessing?. Review of Economics and Political Science, 7(4), 238-256. https://doi.org/10.1108/REPS-11-2019-0145
  2. Bengio, Y., Courville, A., & Vincent, P. (2013). Representation learning: A review and new perspectives. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), 1798-1828.
  3. Bonet-Jover, A., Sepúlveda-Torres, R., Saquete, E., & Martínez-Barco, P. (2023). A semi-automatic annotation methodology that combines Summarization and Human-In-The-Loop to create disinformation detection resources. Knowledge-Based Systems, 275, 110723. https://doi.org/10.1016/j.knosys.2023.110723
  4. Cappa, F., Oriani, R., Peruffo, E. ., & McCarthy, I. (2021). Big data for creating and capturing value in the digitalized environment: unpacking the effects of volume, variety, and veracity on firm performance. Journal of Production and Innovation Management, 38 49-67. https://doi.org/10.1111/jpim.12545
  5. Chatterjee, S., Sreenivasulu, N.S., & Hussain, Z. (2022). Evolution of artificial intelligence and its impact on human rights: from sociolegal perspective. International Journal of Law and Management, 64(2), 184-205. https://doi.org/10.1108/IJLMA-06-2021-0156
  6. Costello, E. (2023). ChatGPT and the Educational AI Chatter: Full of Bullshit or Trying to Tell Us Something?. Postdigital Science and Education. https://doi.org/10.1007/s42438-023-00398-5
  7. Derish, P.A, & Annesley, T.M. (2011). How to write a rave review. Clinical Chemistry, 57(3), 388-391. https://doi.org/10.1373/clinchem.2010.160622
  8. Doanh, D.C., Dufek, Z., Ejdys, J., Ginevičius, R., Korzyński, P., Mazurek, G., Paliszkiewicz, J., Wach, K., & Ziemba, E. (2023). Generative AI in the manufacturing process: theoretical considerations. Engineering Management in Production and Services, 15(4), 76-89. https://doi.org/10.2478/emj-2023-0029
  9. Dumrak, J. & Zarghami, S.A. (2023). The role of artificial intelligence in lean construction management. Engineering, Construction and Architectural Management, Ahead-of-Print. https://doi.org/10.1108/ECAM-02-2022-0153
  10. Efe, A. (2022). The Impact of Artificial Intelligence on Social Problems and Solutions: An Analysis on The Context of Digital Divide and Exploitation. Yeni Medya, (13), 247-264. https://doi.org/10.55609/yenimedya.1146586
  11. Ferrari, R. (2015). Writing narrative style literature reviews. Medical Writing, 24(4), 230-235.
  12. Fisher, C. et al. (2010). Researching and Writing a Dissertation. 3rd edition. Harlow: Prentice Hall.
  13. Gao, Y., & Liu, H. (2023). Artificial intelligence-enabled personalization in interactive marketing: a customer journey perspective. Journal of Research in Interactive Marketing, 17(5), 663-680. https://doi.org/10.1108/JRIM-01-2022-0023
  14. Głodowska, A., Maciejewski, M., & Wach, K. (2023). Navigating the digital landscape: A conceptual framework for understanding digital entrepreneurship and business transformation. International Entrepreneurship Review, 9(4), 7-20. https://doi.org/10.15678/IER.2023.0904.01
  15. He, A.-Z., & Zhang, Y. (2023). AI-powered touch points in the customer journey: a systematic literature review and research agenda. Journal of Research in Interactive Marketing, 17(4), 620-639. https://doi.org/10.1108/JRIM-03-2022-0082
  16. Hoque, M.A., Rasiah, R., Furuoka, F., & Kumar, S. (2022). Linkages among automation, job displacement and reshoring: evidence from the Bangladeshi apparel industry. Research Journal of Textile and Apparel, 26(4), 515-531. https://doi.org/10.1108/RJTA-04-2021-0044
  17. Jaiwant, S.V. (2023). The Changing Role of Marketing: Industry 5.0 - the Game Changer (pp. 187-202). In Saini, A. and Garg, V. (Ed.), Transformation for Sustainable Business and Management Practices: Exploring the Spectrum of Industry 5.0. Leeds: Emerald Publishing. https://doi.org/10.1108/978-1-80262-277-520231014
  18. Janssen, M., & Kuk, G. (2016). The challenges and limits of big data algorithms in technocratic governance, Government Information Quarterly, 33(3) 371-377. https://doi.org/10.1016/j.giq.2016.08.011
  19. Janssen, M., Brous, P., Estevez, E., Barbosa, L.S., & Jankowski, T. (2020). Data governance: Organizing data for trustworthy Artificial Intelligence. Government Information Quarterly, 37(3), https://doi.org/10.1016/j.giq.2020.101493
  20. Karinshak, E., & Jin, Y. (2023). AI-driven disinformation: a framework for organizational preparation and response. Journal of Communication Management, 27(4), 539-562. https://doi.org/10.1108/JCOM-09-2022-0113
  21. Kitsara, I. (2022). Artificial Intelligence and the Digital Divide: From an Innovation Perspective. In A. Bounfour (Ed.) Platforms and Artificial Intelligence. Progress in IS (pp. 245-265). Springer, Cham. https://doi.org/10.1007/978-3-030-90192-9_12
  22. Korzynski, P., Kozminski, A.K., & Baczynska, A. (2023a). Navigating leadership challenges with technology: Uncovering the potential of ChatGPT, virtual reality, human capital management systems, robotic process automation, and social media. International Entrepreneurship Review, 9(2), 7-18. https://doi.org/10.15678/IER.2023.0902.01
  23. Korzynski, P., Mazurek, G., Altmann, A., Ejdys, J., Kazlauskaite, R., Paliszkiewicz, J., Wach, K., & Ziemba, E. (2023b). Generative artificial intelligence as a new context for management theories: analysis of ChatGPT. Central European Management Journal, 31(1), 3-13. https://doi.org/10.1108/CEMJ-02-2023-0091
  24. Korzynski, P., Mazurek, G., Krzypkowska, P., & Kurasinski, A. (2023c). Artificial intelligence prompt engineering as a new digital competence: Analysis of generative AI technologies such as ChatGPT. Entrepreneurial Business and Economics Review, 11(3), 25-37. https://doi.org/10.15678/EBER.2023.110302
  25. Korzynski, P., Rook, C., Florent Treacy, E., & Kets de Vries, M. (2021). The impact of self-esteem, conscientiousness and pseudo-personality on technostress. Internet Research, 31(1), 59-79. https://doi.org/10.1108/INTR-03-2020-0141
  26. Kumar, A., Gupta, N., & Bapat, G. (2023a). Who is making the decisions? How retail managers can use the power of ChatGPT. Journal of Business Strategy. Ahead-of-Print. https://doi.org/10.1108/jbs-04-2023-0067
  27. Kumar, A., Krishnamoorthy, B., & Bhattacharyya, S.S. (2023b). Machine learning and artificial intelligence-induced technostress in organizations: a study on automation-augmentation paradox with socio-technical systems as coping mechanisms. International Journal of Organizational Analysis, Ahead-of-Print. https://doi.org/10.1108/IJOA-01-2023-3581
  28. Kwong, C.K., Jiang, H., & Luo, X.G. (2016). AI-based methodology of integrating affective design, engineering, and marketing for defining design specifications of new products. Engineering Applications of Artificial Intelligence, 47(10, 49-60. https://doi.org/10.1016/j.engappai.2015.04.001
  29. Lee, L.W., Dabirian, A., McCarthy, I.P, &. Kietzmann, J. (2020). Making sense of text: artificial intelligence-enabled content analysis. European Journal of Marketing, 54(3), 615-644. https://doi.org/10.1108/EJM-02-2019-0219
  30. Lutz, C. (2019). Digital inequalities in the age of artificial intelligence and big data. Human Behaviour and Emerging Techgnologies, 1(2), 141-148. https://doi.org/10.1002/hbe2.140
  31. Mariani, M.M., Machado, I., Magrelli, V., & Dwivedi, Y.K. (2023). Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions. Technovation, 122, 102623. https://doi.org/10.1016/j.technovation.2022.102623
  32. Mazurek, G. (2023). Artificial Intelligence, Law, and Ethics. Krytyka Prawa, 15(1), 11-14. https://doi.org/10.7206/kp.2080-1084.568
  33. Mazurek, G., & Małagocka, K. (2019). Perception of privacy and data protection in the context of the development of artificial intelligence. Journal of Management Analytics, 6(4), 344-364. https://doi.org/10.1080/23270012.2019.1671243
  34. Nair, K. (2019). Overcoming today’s digital talent gap in organizations worldwide. Development and Learning in Organizations, 33(6), 16-18. https://doi.org/10.1108/DLO-02-2019-0044
  35. Pagallo, U., Ciani Sciolla, J., & Durante, M. (2022). The environmental challenges of AI in EU law: lessons learned from the Artificial Intelligence Act (AIA) with its drawbacks. Transforming Government: People, Process and Policy, 16(3), 359-376. https://doi.org/10.1108/TG-07-2021-0121
  36. Pahl, S. (2023). An emerging divide: Who is benefiting from AI?. IAP-UNIDO. Retrieved from https://iap.unido.org/articles/emerging-divide-who-benefiting-ai#fn-2303-0 on September 23, 2023.
  37. Pautasso, M. (2013). Ten simple rules for writing a literature review. PLoS Computational Biology, 9, 1003149. https://doi.org/10.1371/journal.pcbi.1003149
  38. Peres, R., Schreier, M., Schweidel, D., & Sorescu, A. (2023). On ChatGPT and beyond: How generative artificial intelligence may affect research, teaching, and practice. International Journal of Research in Marketing. https://doi.org/10.1016/j.ijresmar.2023.03.001
  39. Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., & Sutskever, I. (2019). Language models are unsupervised multitask learners. OpenAI Blog, 1(8).
  40. Ratten, V. (2023). Research Methodologies for Business Management. London: Routledge
  41. Rawashdeh, A. (2023). The consequences of artificial intelligence: an investigation into the impact of AI on job displacement in accounting. Journal of Science and Technology Policy Management, Ahead-of-Print. https://doi.org/10.1108/JSTPM-02-2023-0030
  42. Schweidel, D.A., Reisenbichler, M., Reutterer, T., & Zhang, K. (2023). Leveraging AI for Content Generation: A Customer Equity Perspective (pp. 125-145). In Sudhir, K. and Toubia, O. (Ed.), Artificial Intelligence in Marketing (“Review of Marketing Research”, Vol. 20), Leeds: Emerald Publishing. https://doi.org/10.1108/S1548-643520230000020006
  43. Sieja, M., & Wach, K. (2019). The use of evolutionary algorithms for optimization in the modern entrepreneurial economy: interdisciplinary perspective. Entrepreneurial Business and Economics Review, 7(4), 117-130. https://doi.org/10.15678/eber.2019.070407
  44. Singh, A., & Chouhan, T. (2023). Artificial Intelligence in HRM: Role of Emotional–Social Intelligence and Future Work Skill (pp. 175-196). In Tyagi, P., Chilamkurti, N., Grima, S., Sood, K., & Balusamy, B. (Ed.), The Adoption and Effect of Artificial Intelligence on Human Resources Management, Part A. Leeds: Emerald Publishing. https://doi.org/10.1108/978-1-80382-027-920231009
  45. Slapeta, J. (2023). Are ChatGPT and other pretrained language models good parasitologists?. Trends in Parasitology, 39(5), 314-316. https://doi.org/10.1016/j.pt.2023.02.006
  46. Smits, J., & Borghuis, T. (2022). Generative AI and Intellectual Property Rights (pp. 323-344). In B. Custers & E. Fosch-Villaronga (Eds.), Law and Artificial Intelligence: Regulating AI and Applying AI in Legal Practice. T.M.C. Asser Press. https://doi.org/10.1007/978-94-6265-523-2_17
  47. Sundaresan, S., & Zhang, Z. (2022). AI-enabled knowledge sharing and learning: redesigning roles and processes. International Journal of Organizational Analysis, 30(4), 983-999. https://doi.org/10.1108/IJOA-12-2020-2558
  48. Wach, K., Duong, C.D., Ejdys, J., Kazlauskaitė, R., Mazurek, G., Korzyński, P., Paliszkiewicz, J., & Ziemba, E. (2023b). The dark side of generative artificial intelligence: A critical analysis of controversies and risks of ChatGTP. Entrepreneurial Business and Economics Review, 11(2), 7-30. https://doi.org/10.15678/EBER.2023.110201
  49. Wamba-Taguimdje, S.-L., Fosso Wamba, S., Kala Kamdjoug, J.R., & Tchatchouang Wanko, C.E. (2020). Influence of artificial intelligence (AI) on firm performance: the business value of AI-based transformation projects. Business Process Management Journal, 26(7), 1893-1924. https://doi.org/10.1108/BPMJ-10-2019-0411
  50. Yaiprasert, C., & Hidayanto, A.N. (2024). AI-powered ensemble machine learning to optimize cost strategies in logistics business. International Journal of Information Management Data Insights, 4, 100209. https://doi.org/10.1016/j.jjimei.2023.100209
  51. Zhang, F., Pan, Z., & Lu,Y. (2023). AIoT-enabled smart surveillance for personal data digitalization: Contextual personalization-privacy paradox in smart home. Information & Management, 60(2), 103736, https://doi.org/10.1016/j.im.2022.103736

Downloads

Download data is not yet available.

Similar Articles

21-30 of 93

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)

1 2 > >>