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Global Journal of Flexible Systems Management

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28.09.2023 | ORIGINAL RESEARCH

Unravelling the Impact of Generative Artificial Intelligence (GAI) in Industrial Applications: A Review of Scientific and Grey Literature

verfasst von: Arpan Kumar Kar, P. S. Varsha, Shivakami Rajan

Erschienen in: Global Journal of Flexible Systems Management | Ausgabe 4/2023

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Abstract

The scope of application of generative artificial intelligence (GAI) in industrial functions is gaining high prominence in academic and industrial discourses. In this article, we explore the usage of GAI and large language models (LLMs) in industrial applications. It promises myriad advantages such as greater engagement, cooperation and accessibility. LLMs like ChatGPT are able to evaluate unstructured queries, assess alternatives and offer actionable advice to users. It is being used to produce fast reports, flexible responses, environment scanning capabilities and insights that can enhance organisation flexibility in making better and quicker decisions, improving customer experiences and thereby augmenting firm profitability. This article offers a comprehensive review of scientific and grey literature in GAI and language models. The synthesis of complementary sources of information brings exciting perspectives in this fast evolving field. We provide directions surrounding future use of GAI as well as research directions for management researchers.

Vorheriger Artikel The Viability of Supply Chains with Interpretable Learning Systems: The Case of COVID-19 Vaccine Deliveries

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Abukmeil, M., Ferrari, S., Genovese, A., Piuri, V., & Scotti, F. (2021). A survey of unsupervised generative models for exploratory data analysis and representation learning. ACM Computing Surveys (csur), 54(5), 1–40. https://doi.org/10.1145/3450963CrossRef

Adam, M., Wessel, M., & Benlian, A. (2021). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 31(2), 427–445. https://doi.org/10.1007/s12525-020-00414-7CrossRef

Adams, R. J., Smart, P., & Huff, A. S. (2017). Shades of grey: Guidelines for working with the grey literature in systematic reviews for management and organizational studies. International Journal of Management Reviews, 19(4), 432–454. https://doi.org/10.1111/ijmr.12102CrossRef

Ahani, A., Nilashi, M., Ibrahim, O., Sanzogni, L., & Weaven, S. (2019). Market segmentation and travel choice prediction in Spa hotels through TripAdvisor’s online reviews. International Journal of Hospitality Management, 80, 52–77. https://doi.org/10.1016/j.ijhm.2019.01.003CrossRef

Ahmed, Z., Mohamed, K., Zeeshan, S., & Dong, X. (2020). Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. Database, 2020, 1–35. https://doi.org/10.1093/database/baaa010CrossRef

Ajzen, I. (2011). The theory of planned behaviour: Reactions and reflections. Psychology & Health, 26(9), 1113–1127. https://doi.org/10.1080/08870446.2011.613995CrossRef

Akter, S., Dwivedi, Y. K., Sajib, S., Biswas, K., Bandara, R. J., & Michael, K. (2022). Algorithmic bias in machine learning-based marketing models. Journal of Business Research, 144, 201–216. https://doi.org/10.1016/j.jbusres.2022.01.083CrossRef

Alami, H., Rivard, L., Lehoux, P., Hoffman, S. J., Stéphanie Bernadette, M. C., Savoldelli, M., & Fortin, J. (2020). Artificial intelligence in health care: Laying the foundation for responsible, sustainable, and inclusive innovation in low- and middle-income countries. Globalization and Health, 16(52), 1–6. https://doi.org/10.1186/s12992-020-00584-1CrossRef

Alghamdi, N. A., & Al-Baity, H. H. (2022). Augmented analytics driven by AI: A digital transformation beyond business intelligence. Sensors, 22(20), 8071. https://doi.org/10.3390/s22208071CrossRef

Theodorou, T. I., Zamichos, A., Skoumperdis, M., Kougioumtzidou, A., Tsolaki, K., Papadopoulos, D., & Tzovaras, D. (2021). An AI-enabled stock prediction platform combining news and social sensing with financial statements. Future Internet, 13(6), 138. https://doi.org/10.3390/fi13060138CrossRef

Anas, I., Basri, M., & Musdariah, A. (2022). Digital language teacher professional development from a CALL perspective Perceived knowledge and activeness in ECCR. Computer Assisted Language Learning-Electronic Journal (CALL-EJ), 23(1), 1–21.

Bader, V., & Kaiser, S. (2019). Algorithmic decision-making? The user interface and its role for human involvement in decisions supported by artificial intelligence. Organization, 26(5), 655–672. https://doi.org/10.1177/135050841985571CrossRef

Bagwari, A., Sinha, A., Singh, N. K., Garg, N., & Kanti, J. (2022). CBIR-DSS: Business decision oriented content-based recommendation model for E-commerce. Information, 13(10), 479. https://doi.org/10.3390/info13100479CrossRef

Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Available at SSRN 4337484, 1-22. https://doi.org/10.2139/ssrn.4337484

Baird, A., Furukawa, M. F., & Raghu, T. S. (2012). Understanding contingencies associated with the early adoption of customer-facing web portals. Journal of Management Information Systems, 29(2), 293–324. https://doi.org/10.2753/MIS0742-1222290210CrossRef

Baker, W., Colditz, J. B., Dobbs, P. D., Mai, H., Visweswaran, S., Zhan, J., & Primack, B. A. (2022). Classification of Twitter vaping discourse using BERTweet: Comparative deep learning study. JMIR Medical Informatics, 10(7), e33678. https://doi.org/10.2196/33678CrossRef

Bansal, N., Sharma, A., & Singh, R. K. (2019). Fuzzy AHP approach for legal judgement summarization. Journal of Management Analytics, 6(3), 323–340. https://doi.org/10.1080/23270012.2019.1655672CrossRef

Barlow, M. A., Verhaal, J. C., & Angus, R. W. (2019). Optimal distinctiveness, strategic categorization, and product market entry on the Google Play app platform. Strategic Management Journal, 40(8), 1219–1242. https://doi.org/10.1002/smj.3019CrossRef

Batra, G., Queirolo, A., & Santhanam, N. (2018). Artificial intelligence: The time to act is now, McKinsey. https://www.mckinsey.com/industries/industrials-and-electronics/our-insights/artificial-intelligence-the-time-to-act-is-now. Retrieved on 8^th April 2023.

Bazzan, J., Echeveste, M. E., Formoso, C. T., Altenbernd, B., & Barbian, M. H. (2023). An information management model for addressing residents’ complaints through artificial intelligence techniques. Buildings, 13(3), 737. https://doi.org/10.3390/buildings13030737CrossRef

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency, pp. 610–623. Doi: https://doi.org/10.1145/3442188.3445922

Benzies, K. M., Premji, S., Hayden, K. A., & Serrett, K. (2006). State-of-the-evidence reviews: Advantages and challenges of including grey literature. Worldviews on Evidence-Based Nursing, 3(2), 55–61. https://doi.org/10.1111/j.1741-6787.2006.00051.xCrossRef

Bharathi, S. V. (2017). Prioritizing and ranking the big data information security risk spectrum. Global Journal of Flexible Systems Management, 18, 183–201. https://doi.org/10.1007/s40171-017-0157-5CrossRef

Biderman, S., & Raff, E. (2022). Fooling moss detection with pretrained language models. In Proceedings of the 31st ACM international conference on information & knowledge management, pp. 2933–2943. Doi: https://doi.org/10.1145/3511808.3557079

Birasnav, M., Mittal, R., & Loughlin, S. (2015). Linking leadership behaviors and information exchange to improve supply chain performance: A conceptual model. Global Journal of Flexible Systems Management, 16, 205–217. https://doi.org/10.1007/s40171-015-0092-2CrossRef

Biswas, S. S. (2023a). Role of chat GPT in public health. Annals of Biomedical Engineering, 51(5), 868–869. https://doi.org/10.1007/s10439-023-03172-7CrossRef

Biswas, S. S. (2023b). Potential use of chat GPT in global warming. Annals of Biomedical Engineering, 51(6), 1126–1127. https://doi.org/10.1007/s10439-023-03171-8CrossRef

Bittencourt, I. I., Costa, E., Silva, M., & Soares, E. (2009). A computational model for developing semantic web-based educational systems. Knowledge-Based Systems, 22(4), 302–315. https://doi.org/10.1145/3511808.3557079CrossRef

Bjork, C. (2023). ChatGPT threatens language diversity. More needs to be done to protect our differences in the age of AI, available at https://theconversation.com/chatgpt-threatens-language-diversity-more-needs-to-be-done-to-protect-our-differences-in-the-age-of-ai-198878. Retrieved on 5^th April 2023.

Blackman, R. (2020).A practical guide to building ethical AI. https://hbr.org/2020/10/a-practical-guide-to-building-ethical-ai. Retrieved on 6^th April 2023.

Bochkay, K., Brown, S. V., Leone, A. J., & Tucker, J. W. (2023). Textual analysis in accounting: What’s next? Contemporary Accounting Research, 40(2), 765–805. https://doi.org/10.1111/1911-3846.12825CrossRef

Booth, P., Chaperon, S. A., Kennell, J. S., & Morrison, A. M. (2020). Entrepreneurship in island contexts: A systematic review of the tourism and hospitality literature. International Journal of Hospitality Management, 85(102438), 1–12. https://doi.org/10.1016/j.ijhm.2019.102438CrossRef

Brown Roger (2023). How can logistics companies benefit with the usage of Chat GTP? https://original.newsbreak.com/@roger-brown-1587674/2925661721748-how-can-logistics-companies-benefit-with-the-usage-of-chat-gtp. Retrieved on 3^rd April 2023.

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., & Amodei, D. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901. https://doi.org/10.48550/arXiv.2005.14165CrossRef

Businesswire (2023). New GPT AI-powered large language model for banking increases financial services institutions' competitiveness and enables accelerated digital transformation in weeks, not years. https://www.businesswire.com/news/home/20220223005437/en/New-GPT-AI-Powered-Large-Language-Model-for-Banking-Increases-Financial-Services-Institutions-Competitiveness-and-Enables-Accelerated-Digital-Transformation-in-Weeks-Not-Years. Retrieved on 3^rd April 2023.

Carlson, K., Kopalle, P. K., Riddell, A., Rockmore, D., & Vana, P. (2023). Complementing human effort in online reviews: A deep learning approach to automatic content generation and review synthesis. International Journal of Research in Marketing, 40(1), 54–74. https://doi.org/10.1016/j.ijresmar.2022.02.004CrossRef

Cetinic, E., & She, J. (2022). Understanding and creating art with AI: Review and outlook. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), 18(2), 1–22. https://doi.org/10.1145/3475799CrossRef

Chakraborty, A., & Kar, A. K. (2017). Swarm intelligence: A review of algorithms. In S. Patnaik, X. S. Yang, & K. Nakamatsu (Eds.), Nature-inspired computing and optimization. Modeling and optimization in science and technologies (Vol. 10, pp. 475–494). Cham: Springer.CrossRef

Chan, A. (2023). GPT-3 and InstructGPT: Technological dystopianism, utopianism, and “Contextual” perspectives in AI ethics and industry. AI and Ethics, 3(1), 53–64. https://doi.org/10.1007/s43681-022-00148-6CrossRef

Chanda, U., & Goyal, P. (2020). A Bayesian network model on the interlinkage between socially responsible HRM, employee satisfaction, employee commitment and organizational performance. Journal of Management Analytics, 7(1), 105–138. https://doi.org/10.1080/23270012.2019.1650670CrossRef

Chatterjee, A. (2022). Art in an age of artificial intelligence. Frontiers in Psychology, 13, 1–9. https://doi.org/10.3389/fpsyg.2022.1024449CrossRef

Chatterji, A., Delecourt, S., Hasan, S., & Koning, R. (2019). When does advice impact startup performance? Strategic Management Journal, 40(3), 331–356. https://doi.org/10.1002/smj.2987CrossRef

Chen, X., & Kim, H. M. (2021). Antecedents and consequence of the consumer’s psychic distance in cross-border e-commerce. International Trade, Politics and Development, 5(2), 156–173. https://doi.org/10.1108/itpd-08-2021-0008CrossRef

Chi-Hsien, K., & Nagasawa, S. (2019). Applying machine learning to market analysis: Knowing your luxury consumer. Journal of Management Analytics, 6(4), 404–419. https://doi.org/10.1080/23270012.2019.1692254CrossRef

Chiu, K. L., Collins, A., & Alexander, R. (2021). Detecting hate speech with gpt-3. arXiv preprint arXiv:2103.12407, pp. 1–29. Doi: https://doi.org/10.48550/arXiv.2103.12407

Colasanto, F., Grilli, L., Santoro, D., & Villani, G. (2022). AlBERTino for stock price prediction: A Gibbs sampling approach. Information Sciences, 597, 341–357. https://doi.org/10.1016/j.ins.2022.03.051CrossRef

Conn, V. S., Valentine, J. C., Cooper, H. M., & Rantz, M. J. (2003). Grey literature in meta-analyses. Nursing Research, 52(4), 256–261. https://doi.org/10.1097/00006199-200307000-00008CrossRef

Cui, R., Gallino, S., Moreno, A., & Zhang, D. J. (2018). The operational value of social media information. Production and Operations Management, 27(10), 1749–1769. https://doi.org/10.1111/poms.12707CrossRef

Dantas, 2023. Ethics in chatGPT and other AI’s. https://medium.com/@adilmarcoelhodantas/ethics-in-chatgpt-and-other-ais-ee31ce8e9f09. Retrieved on 5^th April 2023.

De Cremer, D., Kasparov, G. (2021). AI should augment human intelligence, not replace it. https://hbr.org/2021/03/ai-should-augment-human-intelligence-not-replace-it. Retrieved on 6^th April 2023.

de Lima-Santos, M. F., & Ceron, W. (2021). Artificial intelligence in news media: Current perceptions and future outlook. Journalism and Media, 3(1), 13–26. https://doi.org/10.3390/journalmedia3010002CrossRef

Dehouche, N., & Dehouche, K. (2023). What’s in a text-to-image prompt? The potential of stable diffusion in visual arts education. Heliyon, 9(6), 1–12. https://doi.org/10.1016/j.heliyon.2023.e16757CrossRef

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint, vol 1, pp. 4171–4186. Doi: https://doi.org/10.48550/arXiv.1810.04805

Dhanaraj, R. K., Rajkumar, K., & Hariharan, U. (2020). Enterprise IoT modeling: Supervised, unsupervised, and reinforcement learning. Business Intelligence for Enterprise Internet of Things. https://doi.org/10.1007/978-3-030-44407-5_3CrossRef

Dhillon, M. K., Rafi-Ul-Shan, P. M., Amar, H., Sher, F., & Ahmed, S. (2023). Flexible green supply chain management in emerging economies: A systematic literature review. Global Journal of Flexible Systems Management, 24(1), 1–28. https://doi.org/10.1007/s40171-022-00321-0CrossRef

Dickmanns, E. D. (2004). Dynamic vision-based intelligence. AI Magazine, 25(2), 10–10.

Durairaj, A. K., & Chinnalagu, A. (2021). Transformer based contextual model for sentiment analysis of customer reviews: A fine-tuned BERT. International Journal of Advanced Computer Science and Applications, 12(11), 474–480. https://doi.org/10.14569/IJACSA.2021.0121153CrossRef

Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., & Wright, R. (2023a). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642CrossRef

Dwivedi, R., Nerur, S., & Balijepally, V. (2023b). Exploring artificial intelligence and big data scholarship in information systems: A citation, bibliographic coupling, and co-word analysis. International Journal of Information Management Data Insights, 3(2), 100185. https://doi.org/10.1016/j.jjimei.2023.100185CrossRef

Dwivedi, Y. K., Pandey, N., Currie, W., & Micu, A. (2023c). Leveraging ChatGPT and other generative artificial intelligence (AI)-based applications in the hospitality and tourism industry: Practices, challenges and research agenda. International Journal of Contemporary Hospitality Management. https://doi.org/10.1108/IJCHM-05-2023-0686CrossRef

Edwards (2022). DALL-E image generator is now open to everyone. https://arstechnica.com/information-technology/2022/09/openai-image-generator-dall-e-now-available-without-waitlist/ Retrieved on 2^nd April, 2023.

Else, H. (2023). Abstracts written by ChatGPT fool scientists. Nature, 613(7944), 423–423. https://doi.org/10.1038/d41586-023-00056-7CrossRef

Endrele Rob (2023).GAI isn’t about what you think it is. https://www.computerworld.com/article/3686093/generative-ai-isnt-about-what-you-think-it-is.html Retrieved on 22^nd March, 2023.

Ernst, D., & Dolnicar, S. (2018). How to avoid random market segmentation solutions. Journal of Travel Research, 57(1), 69–82. https://doi.org/10.1177/004728751668497CrossRef

Euchner, J. (2023). Generative AI. Research-Technology Management, 66(3), 71–74. https://doi.org/10.1080/08956308.2023.2188861CrossRef

Flanagin, A., Bibbins-Domingo, K., Berkwits, M., & Christiansen, S. L. (2023). Nonhuman “Authors” and implications for the integrity of scientific publication and medical knowledge. JAMA, 329(8), 637–639. https://doi.org/10.1001/jama.2023.1344CrossRef

Floridi, L. et al. (2018). AI4People’s ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. https://www.eismd.eu/wp-content/uploads/2019/03/AI4People’s-Ethical-Framework-for- a-Good-AI-Society.pdf. Retrieved on 6^th April 2023.

Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30, 681–694. https://doi.org/10.1007/s11023-020-09548-1CrossRef

Forbes (2023). Is it true that generative AI ChatGPT Will flood the internet with infinite content, asks AI ethics and AI law. Link: https://www.forbes.com/sites/lanceeliot/2023/02/23/is-it-true-that-generativeai-chatgpt-will-flood-the-internet-with-infinite-content-asks-ai-ethics-and-ai-law/ Retrieved on 3^rd July, 2023.

Forero, V.G., & Rennes, F (2022). How do new ventures use AI to enhance creative marketing?. https://repository.urosario.edu.co/bitstreams/b9efd274-f9c7-4d16-a821-64a38cecfb5a/download. Retrieved on 2^nd April 2023.

Gareth, J., Daniela, W., & Trevor, H. Robert., T. (2013). An introduction to statistical learning: With applications. In R. Current medicinal chemistry, springer texts in statistics. Springer New York, New York, NY. 1, pp. 14–426.

Garg, S., Sinha, S., Kar, A. K., & Mani, M. (2022). A review of machine learning applications in human resource management. International Journal of Productivity and Performance Management, 71(5), 1590–1610. https://doi.org/10.1108/IJPPM-08-2020-0427CrossRef

Garousi, V., Felderer, M., Mäntylä, M. V., & Rainer, A. (2020). Benefitting from the grey literature in software engineering research. In Contemporary Empirical methods in software engineering. Cham: Springer International Publishing. pp. 385–413. Doi: https://doi.org/10.1007/978-3-030-32489-6_14

Genest, P. E., & Lapalme, G. (2011). Framework for abstractive summarization using text-to-text generation. In Proceedings of the workshop on monolingual text-to-text generation (pp. 64–73). https://aclanthology.org/W11-1608.pdf Retrieved on 2^nd April 2023.

George, A. S., & George, A. H. (2023). A review of ChatGPT AI’s impact on several business sectors. Partners Universal International Innovation Journal, 1(1), 9–23. https://doi.org/10.5281/zenodo.7644359CrossRef

Ghahramani, Z. (2001). An introduction to hidden Markov models and Bayesian networks. International Journal of Pattern Recognition and Artificial Intelligence, 15(01), 9–42.CrossRef

Goh, O. S., Depickere, A., Fung, C. C., & Wong, K. W. (2006). Top-down natural language query approach for embodied conversational agent. In International multi conference of engineers and computer scientists (IMECS 2006). International Association of Engineers (IAENG), pp. 470–475 https://researchportal.murdoch.edu.au/esploro/outputs/conferencePaper/Top-down-natural-language-query-approach-for/991005540048807891#file-0 Retrieved on 2^nd April 2023

Greengard Samuel (2023a). ChatGPT: Understanding the ChatGPT AI Chatbot. https://www.eweek.com/big-data-and-analytics/chatgpt/ Retrieved on 22^nd March, 2023a.

Greengard Samuel (2023b). What is GAI.(2023b). https://www.eweek.com/big-data-and-analytics/what-is-generative-ai/#:~:text=Samuel%20Greengard,learning%20and%20deep%20learning%20algorithms Retrieved on 22^nd March, 2023b.

Grosan, C., Abraham, A. (2011). Rule-based expert systems. In: Intelligent systems. Intelligent systems reference Library, vol 17. Springer, Berlin, Heidelberg, pp. 149–185. Doi: https://doi.org/10.1007/978-3-642-21004-4_7

Grover, P., & Kar, A. K. (2017). Big data analytics: A review on theoretical contributions and tools used in literature. Global Journal of Flexible Systems Management, 18, 203–229. https://doi.org/10.1007/s40171-017-0159-3CrossRef

Guan, C., Wang, X., Zhang, Q., Chen, R., He, D., & Xie, X. (2019). Towards a deep and unified understanding of deep neural models in nlp. In International conference on machine learning, PMLR, pp. 2454–2463. https://proceedings.mlr.press/v97/guan19a.html

Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2021). A review on generative adversarial networks: Algorithms, theory, and applications. IEEE Transactions on Knowledge and Data Engineering, 35(4), 3313–3332. https://doi.org/10.1109/TKDE.2021.3130191CrossRef

Haenlein, M., & Kaplan, A. (2019). A brief history of artifcial intelligence: On the past, present, and future of artifcial intelligence. California Management Review, 61(4), 5–14. https://doi.org/10.1177/0008125619864925CrossRef

Haleem, A., Javaid, M., & Singh, R. P. (2022). An era of ChatGPT as a significant futuristic support tool: A study on features, abilities, and challenges. BenchCouncil Transactions on Benchmarks, Standards and Evaluations, 2(4), 100089. https://doi.org/10.1016/j.tbench.2023.100089CrossRef

Hao Karen (2023). What Is ChatGPT? What to know about the AI Chatbot; OpenAI's chatbot and Microsoft's conversational Bing have triggered a new AI race that may reshape the future of work. https://www.wsj.com/articles/chatgpt-ai-chatbot-app-explained-11675865177. Retrieved on 8^th April 2023.

Hartmann, J., & Netzer, O. (2023). Natural language processing in marketing. In Sudhir, K. and Toubia, O. (Ed.) artificial intelligence in marketing (Review of Marketing Research, Vol. 20), Emerald Publishing Limited, Bingley, 191–215. https://doi.org/10.1108/S1548-643520230000020011

Hassan, A., & Albayari, A. (2022). The usage of artificial intelligence in journalism. In Future of organizations and work after the 4th industrial revolution: The role of artificial intelligence, big data, automation, and robotics (pp. 175–197). Cham: Springer International Publishing.

Hasselbalch (2022). Testing ChatGPT’s ethical readiness. https://dataethics.eu/testing-chatgpts-ethical-readiness/. Retrieved on 5^th April 2023.

Heising, L., & Angelopoulos, S. (2022). Operationalising fairness in medical AI adoption: Detection of early Alzheimer’s disease with 2D CNN. BMJ Health & Care Informatics, 29(1), 1–7. https://doi.org/10.1136/bmjhci-2021-100485CrossRef

Helps K (2023). How to use ChatGPT to power up your customer communication. https://blog.clicksend.com/chat-gpt-communications/. Retrieved on 5^th April 2023.

Hemanand, D., Mishra, N., Premalatha, G., Mavaluru, D., Vajpayee, A., Kushwaha, S., & Sahile, K. (2022). Applications of intelligent model to analyze the green finance for environmental development in the context of artificial intelligence. Computational Intelligence and Neuroscience, 2022, 1–8. https://doi.org/10.1155/2022/2977824CrossRef

Herhausen, D., Ludwig, S., Grewal, D., Wulf, J., & Schoegel, M. (2019). Detecting, preventing, and mitigating online firestorms in brand communities. Journal of Marketing, 83(3), 1–21. https://doi.org/10.1177/0022242918822300CrossRef

Herath, H. M. K. K. M. B., & Mittal, M. (2022). Adoption of artificial intelligence in smart cities: A comprehensive review. International Journal of Information Management Data Insights, 2(1), 100076. https://doi.org/10.1016/j.jjimei.2022.100076CrossRef

Hirosawa, T., Harada, Y., Yokose, M., Sakamoto, T., Kawamura, R., & Shimizu, T. (2023). Diagnostic accuracy of differential-diagnosis lists generated by generative pretrained transformer 3 Chatbot for clinical vignettes with common chief complaints: A pilot study. International Journal of Environmental Research and Public Health, 20(4), 3378. https://doi.org/10.3390/ijerph20043378CrossRef

Hiter S (2023) Top 9 generative AI applications and tools. https://www.eweek.com/artificial-intelligence/generative-ai-apps-tools/ Retrieved on 3^rd July, 2023.

Hoberg, G., & Phillips, G. (2018). Conglomerate industry choice and product language. Management Science, 64(8), 3735–3755. https://doi.org/10.1287/mnsc.2016.2693CrossRef

Hope, J. (2021). Take advantage of assistive technology available free of charge. Disability Compliance for Higher Education, 26(7), 4–5. https://doi.org/10.1002/dhe.30986CrossRef

Hose, K., Amaral, A., Götze, U., & Peças, P. (2023). Manufacturing flexibility through industry 4.0 technological concepts—impact and assessment. Global Journal of Flexible Systems Management, 24, 1–19. https://doi.org/10.1007/s40171-023-00339-yCrossRef

Hou, J., Zhao, H., Zhao, X., & Zhang, J. (2016). Predicting mobile users’ behaviors and locations using dynamic Bayesian networks. Journal of Management Analytics, 3(3), 191–205. https://doi.org/10.1080/23270012.2016.1198242CrossRef

Hu, L. (2022). GAI and Future. https://pub.towardsai.net/generative-ai-and-future-c3b1695876f2. Retrieved on 16^th April 2023.

Huang, A. H., Wang, H., & Yang, Y. (2023). FinBERT: A large language model for extracting information from financial text. Contemporary Accounting Research, 40(2), 806–841.CrossRef

Huang, M. H., Rust, R., & Maksimovic, V. (2019). The feeling economy: Managing in the next generation of artificial intelligence (AI). California Management Review, 61(4), 43–65. https://doi.org/10.1177/0008125619863436CrossRef

Huang, Y., & Ye, Y. (2022). The impacts of internet + rural financial industry on county economy and industrial growth algorithm. Discrete Dynamics in Nature and Society, 2022, 1–12. https://doi.org/10.1155/2022/6842974CrossRef

Jain, N., Girotra, K., & Netessine, S. (2014). Managing global sourcing: Inventory performance. Management Science, 60(5), 1202–1222. https://doi.org/10.1287/mnsc.2013.1816CrossRef

Jakesch, M., Bhat, A., Buschek, D., Zalmanson, L., & Naaman, M. (2023). Co-writing with opinionated language models affects users’ views. In Proceedings of the 2023 CHI conference on human factors in computing systems, pp. 1–15. Doi: https://doi.org/10.1145/3544548.3581196

Jin, F. (2019). Output analysis in voice interaction in AI environment. Informatica, 43(3), 321–324. https://doi.org/10.31449/inf.v43i3.2799CrossRef

Joshbersin, (2023). The role of GAI and large language models in HR. https://joshbersin.com/2023/03/the-role-of-generative-ai-and-large-language-models-in-hr/. Retrieved on 3^rd April 2023.

Jovanović, M. (2022). Generative artificial intelligence: trends and prospects. https://www.computer.org/csdl/magazine/co/2022/10/09903869/1H0G6xvtREk. 0.1109/MC.2022.3192720. Retrieved on 16^th April 2023

Kalla, D., & Smith, N. (2023). Study and analysis of chat GPT and its impact on different fields of study. International Journal of Innovative Science and Research Technology, 8(3), 827–833.

Kalliamvakou, E. (2022). Research: quantifying GitHub Copilot’s impact on developer productivity and happiness. The GitHub Blog, Sep, 7. https://github.blog/2022-09-07-research-quantifying-github-copilots-impact-on-developer-productivity-and-happiness/ Retrieved on 27^th April 2023.

Kang, Y., Cai, Z., Tan, C. W., Huang, Q., & Liu, H. (2020). Natural language processing (NLP) in management research: A literature review. Journal of Management Analytics, 7(2), 139–172. https://doi.org/10.1080/23270012.2020.1756939CrossRef

Kaplan, S., & Vakili, K. (2015). The double-edged sword of recombination in breakthrough innovation. Strategic Management Journal, 36(10), 1435–1457. https://doi.org/10.1002/smj.2294CrossRef

Kar, A. K., & Kushwaha, A. K. (2021). Facilitators and barriers of artificial intelligence adoption in business–insights from opinions using big data analytics. Information Systems Frontiers, 25, 1351–13741. https://doi.org/10.1007/s10796-021-10219-4CrossRef

Kar, A. K., & Navin, L. (2021). Diffusion of blockchain in insurance industry: An analysis through the review of academic and trade literature. Telematics and Informatics, 58(101532), 1–13. https://doi.org/10.1016/j.tele.2020.101532CrossRef

Kar, A. K., Choudhary, S. K., & Singh, V. K. (2022). How can artificial intelligence impact sustainability: A systematic literature review. Journal of Cleaner Production. https://doi.org/10.1016/j.jclepro.2022.134120CrossRef

Kar, A. K., & Rakshit, A. (2015). Flexible pricing models for cloud computing based on group decision making under consensus. Global Journal of Flexible Systems Management, 16, 191–204. https://doi.org/10.1007/s40171-015-0093-1CrossRef

Kar, A. K., Angelopoulos, S., & Rao, H. R. (2023). Big data-driven theory building: Philosophies, guiding principles, and common traps. International Journal of Information Management, 71, 102661. https://doi.org/10.1016/j.ijinfomgt.2023.102661CrossRef

Kar, A. K. (2016). Bio inspired computing–a review of algorithms and scope of applications. Expert Systems with Applications, 59, 20–32. https://doi.org/10.1016/j.eswa.2016.04.018CrossRef

Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274CrossRef

Kidd, J., & Nieto McAvoy, E. (2023). Deep Nostalgia: Remediated memory, algorithmic nostalgia and technological ambivalence. Convergence, 29(3), 620–640. https://doi.org/10.1177/13548565221149839CrossRef

Kim (2022). The future of creativity, brought to you by artificial intelligence https://venturebeat.com/datadecisionmakers/the-future-of-creativity-brought-to-you-by-artificial-intelligence/ Retrieved on 2^nd April, 2023.

Ko, D. G., Mai, F., Shan, Z., & Zhang, D. (2019). Operational efficiency and patient-centered health care: A view from online physician reviews. Journal of Operations Management, 65(4), 353–379. https://doi.org/10.1002/joom.1028CrossRef

Kong, F., Yuan, L., Houssam, N., Tanner, F., Shreya, C., and Ricardo, H. (2023). Neural insights for digital marketing content design. In Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining (KDD '23). Association for Computing Machinery, New York, NY, USA, PP. 4320–4332. Doi: https://doi.org/10.1145/3580305.3599875

Korzynski, P., Mazurek, G., Altmann, A., Ejdys, J., Kazlauskaite, R., Paliszkiewicz, J., & Ziemba, E. (2023). 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-02-2023-009CrossRef

Kristian (2023). GPT-4 + Midjourney V5: The future of photography? https://www.allabtai.com/gpt-4-midjourney-v5-the-future-of-photography/ Retrieved on 4^th April 2023.

Kumar, P., Hollebeek, L. D., Kar, A. K., & Kukk, J. (2023). Charting the intellectual structure of customer experience research. Marketing Intelligence & Planning, 41(1), 31–47. https://doi.org/10.1108/MIP-05-2022-0185CrossRef

Kumar, V., Rajan, B., Venkatesan, R., & Lecinski, J. (2019). Understanding the role of artificial intelligence in personalized engagement marketing. California Management Review, 61(4), 135–155. https://doi.org/10.1177/0008125619859317CrossRef

Kushwaha, A. K., & Kar, A. K. (2021). MarkBot–a language model-driven chatbot for interactive marketing in post-modern world. Information Systems Frontiers. https://doi.org/10.1007/s10796-021-10184-yCrossRef

Kushwaha, A. K., Kumar, P., & Kar, A. K. (2021). What impacts customer experience for B2B enterprises on using AI-enabled chatbots? Insights from Big data analytics. Industrial Marketing Management, 98, 207–221. https://doi.org/10.1016/j.indmarman.2021.08.011CrossRef

Law, K. S., & Chung, F. L. (2020). Knowledge-driven decision analytics for commercial banking. Journal of Management Analytics, 7(2), 209–230. https://doi.org/10.1080/23270012.2020.1734879CrossRef

Lee, M., Liang, P., & Yang, Q. (2022a). Coauthor: Designing a human-ai collaborative writing dataset for exploring language model capabilities. In Proceedings of the 2022a CHI conference on human factors in computing systems, association for computing machinery, New York, NY, USA, Article 388, pp. 1–19 doi: https://doi.org/10.1145/3491102.3502030

Lee, Y., Kim, T. S., Chang, M., & Kim, J. (2022b). Interactive children’s story rewriting through parent-children interaction. In Proceedings of the first workshop on intelligent and interactive writing assistants (In2Writing 2022b), Association for Computational Linguistics. Dublin, Ireland, pp. 62–71. doi: https://doi.org/10.18653/v1/2022.in2writing-1.9

Li, J., Larsen, K., & Abbasi, A. (2020a). TheoryOn: A design framework and system for unlocking behavioral knowledge through ontology learning. MIS Quarterly, 44(4), 1733–1772.CrossRef

Li, T., Sahu, A. K., Talwalkar, A., & Smith, V. (2020b). Federated learning: Challenges, methods, and future directions. IEEE Signal Processing Magazine, 37(3), 50–60. https://doi.org/10.1109/MSP.2020.2975749CrossRef

Lim, S., Tucker, C. S., & Kumara, S. (2017). An unsupervised machine learning model for discovering latent infectious diseases using social media data. Journal of Biomedical Informatics, 66, 82–94. https://doi.org/10.1016/j.jbi.2016.12.007CrossRef

Liu, V., Vermeulen, J., Fitzmaurice, G., & Matejka, J. (2023). 3DALL-E: Integrating text-to-image AI in 3D design workflows. In Proceedings of the 2023 ACM designing interactive systems conference, pp. 1955–1977.

Liu, X., Tong, Y., Xu, A., & Akkiraju, R. (2020). Using language models to pre-train features for optimizing information technology operations management tasks. In International conference on service-oriented computing Cham: Springer International Publishing, pp. 150–161. Doi: https://doi.org/10.1007/978-3-030-76352-7_18

Liu, J., Shen, D., Zhang, Y., Dolan, B., Carin, L., & Chen, W. (2021). What makes good in-context examples for GPT-$3 $?. arXiv preprint arXiv:2101.06804., pp. 1–12. Doi: https://doi.org/10.48550/arXiv.2101.0680

Lorente, M. P. S., Lopez, E. M., Florez, L. A., Espino, A. L., Martínez, J. A. I., & de Miguel, A. S. (2021). Explaining deep learning-based driver models. Applied Sciences, 11(8), 3321. https://doi.org/10.3390/app11083321CrossRef

Lucy, L., & Bamman, D. (2021). Gender and representation bias in GPT-3 generated stories. In Proceedings of the third workshop on narrative understanding, pp. 48–55.

Luitse, D., & Denkena, W. (2021). The great transformer: Examining the role of large language models in the political economy of AI. Big Data & Society, 8(2), 1–14. https://doi.org/10.1177/20539517211047734CrossRef

Lutkevich Ben (2020). BERT language model, https://www.techtarget.com/searchenterpriseai/definition/BERT-language-model Retrieved on 22nd Mar 2023.

Lyu, Y., Wang, X., Lin, R., & Wu, J. (2022). Communication in human–AI co-creation: Perceptual analysis of paintings generated by text-to-image system. Applied Sciences, 12(22), 11312. https://doi.org/10.3390/app122211312CrossRef

Macey-Dare, R. (2023). How ChatGPT and generative AI systems will revolutionize legal services and the legal profession. Available at SSRN, 1–50. https://doi.org/10.2139/ssrn.4366749

Maettig, B., & Foot, H. (2020). Approach to improving training of human workers in industrial applications through the use of intelligence augmentation and human-in-the-loop. In 2020 15th international conference on computer science & education (ICCSE) IEEE, pp. 283–288.

Marr, B (2023a). Beyond ChatGPT: 14 mind-blowing AI tools everyone should be trying out now. https://www.forbes.com/sites/bernardmarr/2023a/02/28/beyond-chatgpt-14-mind-blowing-ai-tools-everyone-should-be-trying-out-now/?sh=5c8f1a497a1b Retrieved on 2nd Apr 2023a.

Marr, B. (2023b). Revolutionizing retail: How ChatGPT Is changing the shopping experience. https://www.forbes.com/sites/bernardmarr/2023b/03/21/revolutionizing-retail-how-chatgpt-is-changing-the-shopping-experience/?sh=68979f8d2540. Retrieved on 6th Apr 2023b.

Marr, B. (2023c). The 7 best examples of how ChatGPT can be used in human resources (HR). https://www.forbes.com/sites/bernardmarr/2023b03/07/the-7-best-examples-of-how-chatgpt-can-be-used-in-human-resources-hr/. Retrieved on 6th Apr 2023b

Marr Bernard (2023d). Auto-GPT may be the strong AI tool that surpasses ChatGPT. https://www.forbes.com/sites/bernardmarr/2023c/04/24/auto-gpt-may-be-the-strong-ai-tool-that-surpasses-chatgpt/?sh=662681c27640. Retrieved on 27th Apr 2023c.

Martyrosian, M. “Blog | 2023 Marketing Toolbox: MarTech and AdTech, CDP, AI, SuperApp.” Viber, 20 Sept. 2022, https://www.forbusiness.viber.com/en/blog/post/2023-marketing-toolbox-martech-and-adtech-cdp-ai-superapp/. Retrieved on 3rd Apr 2023.

McAuley, L., Tugwell, P., & Moher, D. (2000). Does the inclusion of grey literature influence estimates of intervention effectiveness reported in meta-analyses? The Lancet, 356(9237), 1228–1231. https://doi.org/10.1016/S0140-6736(00)02786-0CrossRef

McDonald, D. D. (2010). Natural language generation. Handbook of Natural Language Processing, 2, 121–144. https://doi.org/10.1016/S0140-6736(00)02786-0CrossRef

MD (2023). About MidJourney. Link: https://www.midjourney.com/home/ Retrieved on 3rd Jul 2023.

Mearian, L. (2023). Job seekers are using ChatGPT to write resumes—and nabbing jobs. https://www.computerworld.com/article/3688336/job-seekers-are-using-chatgpt-to-write-resumes-and-nabbing-jobs.html Retrieved on 22nd Mar 2023.

Melissa (2023). Why chat GPT is a must use business tool in 2023. https://www.linkedin.com/pulse/why-chatgpt-must-use-business-tool-2023-melissa-mitchell?trk=pulse-article. Retrieved on 22nd Mar 2023.

Metz, A. (2022). 6 exciting ways to use ChatGPT: From coding to poetry. TechRadar. https://www.techradar.com/features/6-exciting-ways-to-use-chatgpt-from-coding-to-poetry. Retrieved on 8th Apr 2023.

Mikalef, P., Conboy, K., Lundström, J. E., & Popovič, A. (2022). Thinking responsibly about responsible AI and ‘the dark side’of AI. European Journal of Information Systems, 31(3), 257–268. https://doi.org/10.1080/0960085X.2022.2026621CrossRef

Mogaji, E., Olaleye, S., & Ukpabi, D. (2020a). Using AI to personalise emotionally appealing advertisement. Digital and Social Media Marketing: Emerging Applications and Theoretical Development, 1, 137–150. https://doi.org/10.1007/978-3-030-24374-6_10CrossRef

Mogaji, E., Soetan, T., & Kieu, T. (2020b). The implications of artificial intelligence on the digital marketing of financial services to vulnerable customers. Australasian Marketing Journal, 29(3), 235–242. https://doi.org/10.1016/j.ausmj.2020.05.003CrossRef

Mollick, E. R., & Mollick, L. (2023). Using AI to implement effective teaching strategies in classrooms: Five strategies, including prompts, 1–23. https://doi.org/10.2139/ssrn.4391243

Naser, N. (2022). Frown, step back, wrinkle and sigh for DALL·E 2. https://www2.deloitte.com/uk/en/pages/deloitte-analytics/articles/dalle-2.html Retrieved on 2^nd April, 2023.

Nass, C., & Moon, Y. (2000). Machines and mindlessness: Social responses to computers. Journal of Social Issues, 56(1), 81–103. https://doi.org/10.1111/0022-4537.00153CrossRef

Nass, C., Steuer, J., & Tauber, E. R. (1994). Computers are social actors. In Proceedings of the SIGCHI conference on Human factors in computing systems, pp. 72–78. Doi: https://doi.org/10.1145/191666.191703

Nguyen, H., Calantone, R., & Krishnan, R. (2020). Influence of social media emotional word of mouth on institutional investors’ decisions and firm value. Management Science, 66(2), 887–910. https://doi.org/10.1287/mnsc.2018.3226CrossRef

Nisar, S., & Aslam, M. S. (2023). Is ChatGPT a good tool for T&CM students in studying pharmacology?. Available at SSRN 4324310, 1-16. https://doi.org/10.2139/ssrn.4324310

Northey, G., Hunter, V., Mulcahy, R., & Choong, K. (2022). Man vs machine: How artificial intelligence in banking influences consumer belief in financial advice. International Journal of Bank Marketing, 40(6), 1182–1199. https://doi.org/10.1108/IJBM-09-2021-043CrossRef

Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Available at SSRN 4375283, pp. 1-15. https://doi.org/10.2139/ssrn.4375283

OpenAI, T. B. (2022). Chatgpt: Optimizing language models for dialogue. OpenAI. https://openai.com/blog/chatgpt. Retrieved on 8th Apr 2023.

OpenAI.(2023a).ChatGPT https://openai.com/blog/chatgpt Retrieved on 3rd Jul 2023.

Oppenlaender, J. (2022). A taxonomy of prompt modifiers for text-to-image generation. arXiv:2204.13988, pp. 1 -18. Doi: https://doi.org/10.48550/arXiv.2204.13988

Overgoor, G., Chica, M., Rand, W., & Weishampel, A. (2019). Letting the computers take over: Using AI to solve marketing problems. California Management Review, 61(4), 156–185. https://doi.org/10.1177/00081256198593CrossRef

Oviedo-Trespalacios, O., Peden, A. E., Cole-Hunter, T., Costantini, A., Haghani, M., Rod, J. E., & Reniers, G. (2023). The risks of using chatgpt to obtain common safety-related information and advice. Safety Science, 167, 106244. https://doi.org/10.1016/j.ssci.2023.106244CrossRef

Palanisamy, R., & Foshay, N. (2013). Impact of user’s internal flexibility and participation on usage and information systems flexibility. Global Journal of Flexible Systems Management, 14, 195–209. https://doi.org/10.1007/s40171-013-0044-7CrossRef

Paul ML (2022) “Alexa has a new voice—your dead relative’s.” Washington Post: https://www.washingtonpost.com/nation/2022/06/23/alexa-amazon-voice-dead-people/ Retrieved on 2nd Apr 2023.

Paez, A. (2017). Gray literature: An important resource in systematic reviews. Journal of Evidence-Based Medicine, 10(3), 233–240. https://doi.org/10.1111/jebm.12266CrossRef

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, 40, 269–275. https://doi.org/10.1016/j.ijresmar.2023.03.001CrossRef

Perkins, M. (2023). Academic integrity considerations of AI Large Language Models in the post-pandemic era: ChatGPT and beyond. Journal of University Teaching & Learning Practice, 20(2), 7. https://doi.org/10.53761/1.20.02.07CrossRef

Pophal, L. (2022). AI for content creation. Information Today, 39, 37–38.

Pothula V (2023). Algo Trading with ChatGPT. https://blog.quantinsti.com/algorithmic-trading-chatgpt/. Retrieved on 4th Apr 2023.

Pournader, M., Ghaderi, H., Hassanzadegan, A., & Fahimnia, B. (2021). Artificial intelligence applications in supply chain management. International Journal of Production Economics, 241, 108250. https://doi.org/10.1016/j.ijpe.2021.108250CrossRef

Prentice, C., & Nguyen, M. (2020). Engaging and retaining customers with AI and employee service. Journal of Retailing and Consumer Services, 56(102186), 1–13. https://doi.org/10.1016/j.jretconser.2020.102186CrossRef

Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical text-conditional image generation with clip latents. arXiv preprint arXiv:2204.06125, 1–27. Doi:https://doi.org/10.48550/arXiv.2204.06125

Reed, L. (2022). ChatGPT for automated testing: From conversation to code. Sauce lab. https://saucelabs.com/resources/blog/chatgpt-automated-testing-conversation-to-code. Retrieved on 22^nd March, 2023.

Reisenbichler, M., Reutterer, T., Schweidel, D. A., & Dan, D. (2022). Frontiers: Supporting content marketing with natural language generation. Marketing Science, 41(3), 441–452. https://doi.org/10.1287/mksc.2022.1354CrossRef

Rogers, E. M. (2004). A prospective and retrospective look at the diffusion model. Journal of Health Communication, 9(S1), 13–19. https://doi.org/10.1080/10810730490271449CrossRef

Rohrbach, M., Qiu, W., Titov, I., Thater, S., Pinkal, M., & Schiele, B. (2013). Translating video content to natural language descriptions. In Proceedings of the IEEE international conference on computer vision, pp. 433–440. https://doi.org/10.1109/ICCV.2013.61

Ruan, S., Sun, X., Yao, R., & Li, W. (2021). Deep learning based on hierarchical self-attention for finance distress prediction incorporating text. Computational Intelligence and Neuroscience, 2021, 1–11. https://doi.org/10.1155/2021/1165296CrossRef

Sabharwal, N., Agrawal, A., Sabharwal, N., & Agrawal, A. (2021). Future of BERT models. Hands-on Question Answering Systems with BERT: Applications in Neural Networks and Natural Language Processing. https://doi.org/10.1007/978-1-4842-6664-9CrossRef

Salaberria, A., Azkune, G., de Lacalle, O. L., Soroa, A., & Agirre, E. (2023). Image captioning for effective use of language models in knowledge-based visual question answering. Expert Systems with Applications, 212, 118669. https://doi.org/10.1016/j.eswa.2022.118669CrossRef

Santos, R. S., & Qin, L. (2019). Risk capital and emerging technologies: Innovation and investment patterns based on artificial intelligence patent data analysis. Journal of Risk and Financial Management, 12(4), 189. https://doi.org/10.3390/jrfm12040189CrossRef

Schank, R. C. (1972). Conceptual dependency: A theory of natural language understanding. Cognitive Psychology, 3(4), 552–631. https://doi.org/10.1016/0010-0285(72)90022-9CrossRef

Sharma, G. D., Erkut, B., Jain, M., Kaya, T., Mahendru, M., Srivastava, M., Uppal, R. S., & Singh, S. (2020). Sailing through the COVID-19 Crisis by using AI for financial market predictions. Mathematical Problems in Engineering, 2020, 1–18. https://doi.org/10.1155/2020/1479507CrossRef

Sharma, M., Savage, C., Nair, M., Larsson, I., Svedberg, P., & Nygren, J. M. (2022). Artificial intelligence applications in health care practice: Scoping review. Journal of Medical Internet Research, 24(10), e40238. https://doi.org/10.2196/40238CrossRef

Sheth, J. N., Jain, V., Roy, G., & Chakraborty, A. (2022). AI-driven banking services: The next frontier for a personalised experience in the emerging market. International Journal of Bank Marketing, 40(6), 1248–1271. https://doi.org/10.1108/IJBM-09-2021-0449CrossRef

Shrestha, Y. R., Ben-Menahem, S. M., & Von Krogh, G. (2019). Organizational decision-making structures in the age of artificial intelligence. California Management Review, 61(4), 66–83.CrossRef

Singh, V., Chen, S. S., Singhania, M., Nanavati, B., & Gupta, A. (2022a). How are reinforcement learning and deep learning algorithms used for big data based decision making in financial industries–A review and research agenda. International Journal of Information Management Data Insights, 2(2), 100094. https://doi.org/10.1016/j.jjimei.2022.100094CrossRef

Singh, V., Nanavati, B., Kar, A. K., & Gupta, A. (2022b). How to maximize clicks for display advertisement in digital marketing? A reinforcement learning approach. Information Systems Frontiers, 25, 1621–1638. https://doi.org/10.1007/s10796-022-10314-0CrossRef

Sinha Prabhakant, Arun Shastri, Sally E Lorimer (2023). How GAI Will Change Sales. https://hbr.org/2023/03/how-generative-ai-will-change-sales Retrieved on 3rd Apr 2023.

Skinner, R. E. (2012). Building the Second Mind, 1961–1980: From the ascendancy of ARPA to the advent of commercial expert systems. https://escholarship.org/uc/item/82h464gg Retrieved on 3rd Jul 2023.

Smyth, P. (1994). Hidden Markov models for fault detection in dynamic systems. Pattern Recognition, 27(1), 149–164. https://doi.org/10.1016/0031-3203(94)90024-8CrossRef

Softlink Global (2023) Unlock the potential of smarter supply chain with ChatGPT. https://softlinkglobal.com/unlock-the-potential-of-a-smarter-supply-chain-with-chat-gpt/. Retrieved on 4th Apr 2023.

Sovrano, F., & Vitali, F. (2023). An objective metric for explainable AI: How and why to estimate the degree of explainability. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2023.110866CrossRef

Stable Diffusion (2023). Stable diffusion online https://stablediffusionweb.com/ Retrieved on 3rd Jul 2023.

Stackploe Beth(2023).Are you ready for ChatGPT for 3-D Printing? https://www.digitalengineering247.com/article/are-you-ready-for-chatgpt-for-3d-printing/

Stokel-Walker, C. J. N. (2023). ChatGPT listed as author on research papers: Many scientists disapprove. Nature, 613, 620–621. https://doi.org/10.1038/d41586-023-00107-zCrossRef

Su, J., & Yang, W. (2023). Unlocking the power of ChatGPT: A framework for applying generative AI in education. ECNU Review of Education. https://doi.org/10.1177/20965311231168423CrossRef

Sushil. (2017). Multi-criteria valuation of flexibility initiatives using integrated TISM–IRP with a big data framework. Production Planning & Control, 28(11-12), 999-1010. https://doi.org/10.1080/09537287.2017.1336794

Syam, N., & Sharma, A. (2018). Waiting for a sales renaissance in the fourth industrial revolution: Machine learning and artificial intelligence in sales research and practice. Industrial Marketing Management, 69, 135–146. https://doi.org/10.1016/j.indmarman.2017.12.019CrossRef

Tan, C. F., Wahidin, L. S., Khalil, S. N., Tamaldin, N., Hu, J., & Rauterberg, G. W. M. (2016). The application of expert system: A review of research and applications. ARPN Journal of Engineering and Applied Sciences, 11(4), 2448–2453.

Tang, D., & Zhang, M. (2018). Deep learning in sentiment analysis. Deep Learning in Natural Language Processing. https://doi.org/10.1007/978-981-10-5209-5_8CrossRef

Teubner, T., Flath, C. M., Weinhardt, C., van der Aalst, W., & Hinz, O. (2023). Welcome to the era of chatgpt et al. the prospects of large language models. Business & Information Systems Engineering, 65(2), 95–101. https://doi.org/10.1007/s12599-023-00795-xCrossRef

TGL(2023). Leading logistics into the future: The role of Chat GPT in the Industry. https://www.linkedin.com/pulse/leading-logistics-future-role-chat-gpt-industry-/. Retrieved on 5th Apr 2023.

Thorp, H. H. (2023). ChatGPT is fun, but not an author. Science, 379(6630), 313–313. https://doi.org/10.1126/science.adg7879CrossRef

Tiwary, N. K., Kumar, R. K., Sarraf, S., Kumar, P., & Rana, N. P. (2021). Impact assessment of social media usage in B2B marketing: A review of the literature and a way forward. Journal of Business Research, 131, 121–139. https://doi.org/10.1016/j.jbusres.2021.03.028CrossRef

Tomych (2023). ChatGPT for Fintech: How to use ChatGPT in banking. https://dashdevs.com/blog/chatgpt-for-fintech-how-to-use-chatgpt-in-banking/. Retrieved on 5th Apr 2023.

Trivedi (2023). ChatGPT and its role in logistics & supply chain management. https://managemententhusiast.com/chatgpt-and-its-role-in-logistics-supply-chain-management/. Retrieved on 3rd Apr 2023.

Tung, L. (2023). ChatGPT can write code. Now researchers say it’s good at fixing bugs, too. ZDNet. https://www.zdnet.com/article/chatgpt-can-write-code-now-researc hers-say-its-good-at-fixing-bugs-too/ Retrieved on 8th Apr 2023.

UmaMaheswaran, S. K., Kaur, G., Pankajam, A., Firos, A., Vashistha, P., Tripathi, V., & Mohammed, H. S. (2022). Empirical analysis for improving food quality using artificial intelligence technology for enhancing healthcare sector. Journal of Food Quality, 2022, 1–13.CrossRef

Van Assen, M., Muscogiuri, E., Tessarin, G., & De Cecco, C. N. (2022). Artificial intelligence: A century-old story. In Artificial intelligence in cardiothoracic imaging, Cham: Springer International Publishing, pp. 3–13.

Varsha, P. S. (2023). How can we manage biases in artificial intelligence systems–A systematic literature review. International Journal of Information Management Data Insights, 3(1), 100165. https://doi.org/10.1016/j.jjimei.2023.100165CrossRef

Varsha, P. S., & Shree, S. N. (2023). Embracing data-driven analytics (DDA) in human resource management to measure the organization performance. Handbook of Big Data Research Methods. https://doi.org/10.4337/9781800888555.00017CrossRef

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30, 1–15.

Verma, N., Malhotra, D., & Singh, J. (2020). Big data analytics for retail industry using MapReduce-Apriori framework. Journal of Management Analytics, 7(3), 424–442. https://doi.org/10.1080/23270012.2020.1728403CrossRef

Vincent(2021). This free web tool is a fast and easy way to remove objects from images. https://www.theverge.com/2021/10/25/22744431/remove-edit-objects-images-photos-ai-free-cleanup-pictures Retrieved on 2nd Apr 2023.

Votto, A. M., Valecha, R., Najafirad, P., & Rao, H. R. (2021). Artificial intelligence in tactical human resource management: A systematic literature review. International Journal of Information Management Data Insights, 1(2), 100047. https://doi.org/10.1016/j.jjimei.2021.100047CrossRef

Wai Foong (2022). Stable diffusion 2: The good, the bad and the ugly. https://towardsdatascience.com/stable-diffusion-2-the-good-the-bad-and-the-ugly-bd44bc7a1333. Retrieved on 2nd Apr 2023.

Wen, Y., Liang, Y., & Zhu, X. (2023). Sentiment analysis of hotel online reviews using the BERT model and ERNIE model—Data from China. PLoS ONE, 18(3), e0275382. https://doi.org/10.1371/journal.pone.0275382CrossRef

Weed, M. (2006). Sports tourism research 2000–2004: A systematic review of knowledge and a meta-evaluation of methods. Journal of Sport & Tourism, 11(1), 5–30. https://doi.org/10.1080/14775080600985150CrossRef

Wessel, M., Adam, M., Benlian, A., & Thies, F. (2023). Generative AI and its transformative value for digital platforms. https://www.jmis-web.org/cfps/JMIS_SI_CfP_Generative_AI.pdf Retrieved on 3rd Jul 2023

Wichmann, P., Brintrup, A., Baker, S., Woodall, P., & McFarlane, D. (2020). Extracting supply chain maps from news articles using deep neural networks. International Journal of Production Research, 58(17), 5320–5336. https://doi.org/10.1080/00207543.2020.1720925CrossRef

Wiggers Kyle(2022). A brief history of diffusion, the tech at the heart of modern image-generating AI. https://techcrunch.com/2022/12/22/a-brief-history-of-diffusion-the-tech-at-the-heart-of-modern-image-generating-ai/. Retrieved on 2nd Apr 2023.

Xu, T., Zhang, P., Huang, Q., Zhang, H., Gan, Z., Huang, X., & He, X. (2018). Attngan: Fine-grained text to image generation with attentional generative adversarial networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1316 -1324. Doi: https://doi.org/10.48550/arXiv.1711.10485

Yadav, A., & Sagar, M. (2021). Modified Total Interpretive Structural Modeling of Marketing Flexibility Factors for Indian Telecommunications Service Providers. Global Journal of Flexible Systems Management, 22(4), 307–330.

Yang, J., & Chew, E. (2021). The design model for robotic waitress. International Journal of Social Robotics, 13(7), 1541–1551. https://doi.org/10.1007/s12369-021-00745-1CrossRef

Yen, C., & Chiang, M. C. (2021). Trust me, if you can: A study on the factors that influence consumers’ purchase intention triggered by chatbots based on brain image evidence and self-reported assessments. Behaviour & Information Technology, 40(11), 1177–1194. https://doi.org/10.1080/0144929X.2020.1743362CrossRef

Yoshikuni, A. C., Dwivedi, R., Dultra-de-Lima, R. G., Parisi, C., & Oyadomari, J. C. T. (2023). Role of emerging technologies in accounting information systems for achieving strategic flexibility through decision-making performance: An exploratory study based on North American and South American Firms. Global Journal of Flexible Systems Management, 24, 199–218. https://doi.org/10.1007/s40171-022-00334-9CrossRef

Zanon, L. G., Arantes, R. F. M., Calache, L. D. D. R., & Carpinetti, L. C. R. (2020). A decision making model based on fuzzy inference to predict the impact of SCOR® indicators on customer perceived value. International Journal of Production Economics, 223(107520), 1–17.

Zhai, X. (2023). Chatgpt for next generation science learning. XRDS: Crossroads, the ACM Magazine for Students, 29(3), 42–46. https://doi.org/10.1145/3589649CrossRef

Zweben, M., & Sweeney, M. (2021). An expert systems journey—red pepper and blue martini. IEEE Annals of the History of Computing, 44(1), 73–86. https://doi.org/10.1109/MAHC.2021.3123100CrossRef

Titel: Unravelling the Impact of Generative Artificial Intelligence (GAI) in Industrial Applications: A Review of Scientific and Grey Literature
verfasst von: Arpan Kumar Kar
P. S. Varsha
Shivakami Rajan
Publikationsdatum: 28.09.2023
Verlag: Springer India
Erschienen in: Global Journal of Flexible Systems Management / Ausgabe 4/2023
Print ISSN: 0972-2696
Elektronische ISSN: 0974-0198
DOI: https://doi.org/10.1007/s40171-023-00356-x

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