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INTEREST CATEGORY: INNOVATION AND TECH
POSTING TYPE: Calls: Journals

Posted by: Carlos Flavián

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Lights and shadows of generative AI for individuals, organizations, and society

Lights and shadows of generative AI for individuals, organizations, and society” explores the multifaceted impacts of generative artificial intelligence (GenAI) and related technologies on various domains. This special issue delves into both the advantages and potential pitfalls of GenAI, touching upon its profound influence on individuals, businesses, and society at large. It examines the integration of AI in business strategies across different sectors, highlighting how AI’s increasing sophistication is transforming marketplaces and decision-making processes. The issue also addresses the ethical implications and challenges arising from AI adoption, underscoring the need for a balanced approach to harness GenAI’s full potential while mitigating its risks.

Guest editors:

1. Dr. Carlos Flavián
   University of Zaragoza, Zaragoza, Spain
2. Dr. Luis V. Casaló
   University of Zaragoza, Zaragoza, Spain
3. Dr. Russell Belk
   York University, Toronto, Canada
4. Dr. Ulrike Gretzel
   University of Southern California, Los Angeles, California, United States of America
5. Dr. Jochen Wirtz
   National University of Singapore, Singapore, Singapore.

Special issue information:

This special issue aims to address not only the key benefits but also the major potential drawbacks for individuals, organizations, and societies of recent advances in generative artificial intelligence (GenAI) and other advanced AI-powered technologies (e.g., autonomous robots, predictive analytics) across varied service and consumption contexts.

Motivation

The recent rapid development of GenAI and its application in various industries has been unstoppable and is expected to have dramatic impact on individuals, organizations, and societies (Dwivedi et al., 2023a). AI has been incorporated into digital business strategies, including applications such as expert systems, machine learning, robotics, natural language processing, machine vision, or speech recognition (Bornet et al., 2021; Collins et al., 2021). Implementation of AI-powered technologies such as service robots, chatbots, smart speakers, and other intelligent assistants already deliver new services (Borges et al., 2021) and increasingly interact with customers in the frontline (Gursoy et al., 2019; Wirtz et al., 2018) in various sectors such as banking (e.g., Flavián et al., 2022), hospitality and tourism (e.g., Schepers et al., 2022; Dwivedi et al. 2023b), or healthcare (e.g., Wirtz et al., 2021). AI is becoming increasingly more sophisticated, according to the levels proposed by Huang and Rust (2018), includes not only mechanical but also analytical, intuitive, and empathetic skills. As a result, the implementation of AI in business is transforming the marketplace (Bock et al., 2020) and affecting decision-making (Akdim et al., 2023; Duan et al., 2019). GenAI is expected to accelerate these developments.

From a company perspective, AI implementation can generate value via process automation, improved decision-making, and customer engagement (Borges et al., 2021; Wirtz et al., 2023a). Therefore, the success of AI initiatives is based on their expected benefits, for example, customer experience, service quality, and productivity (Wirtz et al., 2021). However, previous studies have also noted that users can experience not only benefits, but also costs derived from the interaction with AI applications (Puntoni et al., 2021; Wirtz et al., 2023b). Similarly, the use of AI in business strategy still holds several open questions (Borges et al., 2021). Therefore, it is not surprising that previous studies have suggested that the development of new AI applications presents many challenges (Dwivedi et al., 2021; Belk, et al. 2023; Mariani et al., 2023).

Similarly, recent advances in the field of AI (e.g., generative AI) offer not only new opportunities but have also raised several concerns (e.g., Mustak et al. 2023). GenAI produces new content in the form of human-like discourse (Wong et al., 2023), and the use of GenAI models and systems is becoming widespread. ChatGPT, which is probably the most well-known GenAI tool, generates personalized responses to users’ messages or questions based on the user input (Dwivedi et al., 2023a). GenAI may have great implications for business practice (Peres et al., 2023) in several sectors, such as banking (Dwivedi et al., 2023), travel and tourism (Wong et al., 2023), or health care (The Lancet Regional Health-Europe, 2023), as well as in activities such as marketing and management (Dwivedi et al., 2023a; Kshetri et al., 2023). Therefore, there is a need to understand GenAI applications and their positive and negative consequences for consumers, companies, and the society.

In addition, previous literature has identified several ethical implications (e.g., related to fairness, privacy, security, etc.) in the context of AI use (Ashok et al., 2022; Wirtz et al., 2023b) that still require further analysis from several perspectives.

In summary, the widespread use of GenAI across domains offers opportunities and challenges that need to be effectively understood to take full advantage of its benefits. It also requires implementing measures to mitigate the possible negative effects associated with its potential risks and threats. This dual approach will ensure that the adoption of GenAI remains aligned with ethical, practical and sustainable standards, thereby enabling greater innovation, efficiency, and positive change.

Potential illustrative research questions

This special issue invites proposals focused not only on the challenges and opportunities, but also on the risks and threats associated with the widespread use of GenAI.

Opportunities and challenges

• What is the effect of using GenAI and other new AI applications in business practices? Are there differences across industries and sectors (e.g., banking, hospitality and tourism, healthcare)?
• What are the main challenges and long-term benefits of using GenAI and other new AI applications in the contexts of teaching, academic research, and business practice?
• How can GenAI and other new AI applications be used to address global challenges (e.g., Sustainable Development Goals)?
• What are the main determinants and barriers to consumers’ and managers’ adoption of GenAI and other new AI applications? How does the use of GenAI and other new AI applications affect customer and manager decision making?
• How can GenAI contribute to personalization and efficiency in the communication and advertising processes?
• How does the use of GenAI and other new AI applications affect the different stages of the customer journey (e.g., pre-purchase, purchase, and post-purchase)? How can GenAI and other forms of AI improve the customer or user experience?
• How might reliance on GenAI enhance or limit human creativity and critical thinking in different fields?
• How can specific individuals (e.g., people with disabilities) benefit from the use of GenAI and other new AI applications?

Risks and threats

• What are the key digital ethical issues (e.g., fairness, biases, privacy, security) caused by the use of GenAI and its algorithms? How could these ethical issues be mitigated?
• How can the use of GenAI applications for manipulation of the public opinion and consumer decision making be reduced or even avoided?
• How is GenAI and other new AI applications affecting the labor market? Will employees be replaced by GenAI and how?
• How might differences in access to and use of GenAI exacerbate social and economic inequalities?
• What are potential contextual conditions (e.g., country and organizational cultures) that shape how individuals and organizations use GenAI and other new AI applications?
• What challenges do GenAI applications pose for existing legal and regulatory frameworks, and how might legal frameworks be developed to address GenAI-related risks?

Expected types of analysis and context of study

We welcome submissions focused on varied sectors and environments (e.g., healthcare, education, banking, hospitality, and tourism). We especially welcome papers that include multiple studies (combining at least two studies to overcome the limitations of a quantitative cross-sectional analysis), longitudinal studies, and studies that employ mixed methods (combining qualitative and quantitative). Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers).

Keywords

Generative AI, GenAI, benefits, dark side, consumer-AI interaction, AI in business strategy, AI in information systems, responsible AI.

References

Ashok, M., Madan, R., Joha, A., & Sivarajah, U. (2022). Ethical framework for Artificial Intelligence and Digital technologies. International Journal of Information Management, 62, 102433.

Belanche, D., Casaló, L. V., Flavián, C., & Schepers, J. (2020). Service robot implementation: A theoretical framework and research agenda. The Service Industries Journal,��40(3-4), 203-225.

Belanche, D., Belk, R. W., Casaló, L. V. & Flavián, C. (2024). The dark side of artificial intelligence in services. The Service Industries Journal, In press.

Belk, R. W., Belanche, D., & Flavián, C. (2023). Key concepts in artificial intelligence and technologies 4.0 in services. Service Business,��17(1), 1-9.

Bock, D. E., Wolter, J. S., & Ferrell, O. C. (2020). Artificial intelligence: Disrupting what we know about services. Journal of Services Marketing, 34(3), 317–334.

Borges, A. F., Laurindo, F. J., Spínola, M. M., Gonçalves, R. F., & Mattos, C. A. (2021). The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions. International Journal of Information Management, 57, 102225.

Bornet, P., Barkin, I., & Wirtz, J. (2021). Intelligent Automation: Welcome to the World of Hyperautomation: Learn How to Harness Artificial Intelligence to Boost Business & Make Our World More Human. World Scientific.

Collins, C., Dennehy, D., Conboy, K., & Mikalef, P. (2021). Artificial intelligence in information systems research: A systematic literature review and research agenda. International Journal of Information Management, 60, 102383.

Duan, Y., Edwards, J. S., & Dwivedi, Y. K. (2019). Artificial intelligence for decision making in the era of Big Data–evolution, challenges and research agenda. International Journal of Information Management, 48, 63-71.

Dwivedi, Y. K., Hughes, L., Ismagilova, E., Aarts, G., Coombs, C., Crick, T., … & Williams, M. D. (2021). Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 57, 101994.

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.

Dwivedi, Y. K., Pandey, N., Currie, W., & Micu, A. (2023b). 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.

Flavián, C., Akdim, K., & Casaló, L. V. (2023). Effects of voice assistant recommendations on consumer behavior. Psychology & Marketing, 40(2), 328-346.

Flavián, C., Pérez-Rueda, A., Belanche, D., & Casaló, L. V. (2022). Intention to use analytical artificial intelligence (AI) in services–the effect of technology readiness and awareness. Journal of Service Management, 33(2), 293-320.

Gursoy, D., Chi, O. H., Lu, L., & Nunkoo, R. (2019). Consumers acceptance of artificially intelligent (AI) device use in service delivery. International Journal of Information Management, 49, 157-169.

Huang, M. H., & Rust, R. T. (2018). Artificial intelligence in service. Journal of service research, 21(2), 155-172.Puntoni et al., 2021.

Kshetri, N., Dwivedi, Y. K., Davenport, T. H., & Panteli, N. (2023). Generative artificial intelligence in marketing: Applications, opportunities, challenges, and research agenda. International Journal of Information Management, 102716.

Mariani, M., Machado, I., Magrelli, V., & Dwivedi, Y. (2023). Artificial intelligence in innovation research: a systematic review, conceptual framework, and future research directions. Technovation, 122, 102623.

Mustak, M., Salminen, J., Mäntymäki, M., Rahman, A., & Dwivedi, Y. K. (2023). Deepfakes: Deceptions, mitigations, and opportunities. Journal of Business Research,��154, 113368.

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(2), 269-275.

Schepers, J., Belanche, D., Casaló, L. V., & Flavián, C. (2022). How smart should a service robot be?. Journal of Service Research, 25(4), 565-582.

The Lancet Regional Health – Europe (2023). Editorial: Embracing generative AI in health care. The Lancet Regional Health – Europe, 30, 100677.

Wirtz, J., Hofmeister, J., Chew, P.Y.P, & Ding X.D. (2023). Digital service technologies, service robots, AI, and the strategic pathways to cost-effective service excellence,��Service Industries Journal, 43(15-16), 1173-1196.

Wirtz, J., Kunz, W., Hartley, N., & Tarbit, J. (2023b). Corporate digital responsibility in service firms and their ecosystems. Journal of Service Research. 26(2), 173–190.

Wirtz, J., Kunz, W., & Paluch, S. (2021). The service revolution, intelligent automation and service robots. European Business Review, January, 38-45.

Wirtz, J., Patterson, P.G., Kunz, W.H., Gruber, T., Lu, V.N., Paluch, S. & Martins, A. (2018). Brave new world: service robots in the frontline,��Journal of Service Management, 29 (5), 907-931.

Manuscript submission information:

Submit your manuscript via International Journal of Information Management  system and select the appropriate Special Issue “VSI: Lights and shadows of Gen AI”. Manuscript submissions will be handled as per the editorial policy and  specified by the International Journal of Information Management. Any queries can be directed to the corresponding executive guest editor: cflavian@unizar.es

Submission window: 1 September 2024 – 30 November 2024

Keywords:

Generative AI, GenAI, benefits, dark side, consumer-AI interaction, AI in business strategy, AI in information systems, responsible AI