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The use of artificial intelligence (AI) in management research is still nascent and has primarily focused on content analyses of text data. Some method scholars have begun to discuss the potential benefits of far broader applications; however, these discussions have not led yet to a wave of corresponding AI applications by management researchers. This chapter explores the feasibility and the potential value of using AI for a very specific methodological task: the reliable and efficient capturing of higher-level psychological constructs in management research. It introduces the capturing of basic emotions and emotional authenticity of entrepreneurs based on their macro- and microfacial expressions during pitch presentations as an illustrative example of related AI opportunities and challenges. Thus, this chapter provides both motivation and guidance to management scholars for future applications of AI to advance management research.

The impact of artificial intelligence (AI) and extended reality (XR, including virtual reality [VR], augmented reality [AR], and mixed reality [MR]) on marketing in Industry 5.0 and Society 5.0 is explored with systematic literature review in this chapter. AIXR is becoming a necessary aspect of marketing, driven by efficiency, productivity, and innovation. Despite AI's capabilities, the human touch in marketing is preferred due to superior adaptive, creative, and innovative abilities. The use of fully automated marketing systems is limited to specific tasks. This research will benefit both practitioners and academics focusing on AIXR in marketing and is limited by the number of included literature.

This study aims to examine the influence of the derived attributes of embedded artificial intelligence-mobile smart speech recognition (AI-MSSR) technology, namely perceived usefulness, perceived ease of use (PEOU) and perceived enjoyment (PE) on consumer purchase intention (PI) through the chain relationships of attitudes to AI and consumer smart experience, with the moderating effect of consumer innovativeness and Generation (Gen) X and Gen Y in fashion retail.

The study employed a quantitative survey strategy, drawing a sample of 836 respondents from Sri Lanka and India representing Gen X and Gen Y. The data analysis was carried out using smart partial least squares structural equation modelling (PLS-SEM).

The findings show a positive relationship between the perceived attributes of MSSR and consumer PI via attitudes towards AI (AAI) and smart consumer experiences. In addition, consumer innovativeness and Generations X and Y have a moderating impact on the aforementioned relationship. The theoretical and managerial implications of the study are discussed with a note on the research limitations and further research directions.

To multiply the effects of embedded AI-MSSR and consumer PI in fashion retail marketing, managers can develop strategies that strengthen the links between awareness, knowledge of the derived attributes of embedded AI-MSSR and PI by encouraging innovative consumers, especially Gen Y consumers, to engage with embedded AI-MSSR.

This study advances the literature on embedded AI-MSSR and consumer PI in fashion retail marketing by providing an integrated view of the technology acceptance model (TAM), the diffusion of innovation (DOI) theory and the generational cohort perspective in predicting PI.

Artificial intelligence (AI) is shifting the way of how customers interact with organisations by blending both physical and digital environments, thereby creating a new paradigm of customer-organisation relationship. The new relationship boundary driven by AI principally challenges as well as creates opportunities for relationship marketing theories and practices. The main objective of this chapter is to present a framework named ‘physical-digital space’ to demonstrate how AI can merge the physical and digital world. To broaden the understanding, this chapter uses the lens of customer experience in relationship marketing. The framework extends the existing understanding and provides managerial implications on how an organisation can develop strategies so that a customer consciously or subconsciously develops a positive relationship with the organisation.

Background: Artificial intelligence (AI) is a booming sector that has profoundly influenced every walk of life, and the education sector is no exception. In education, AI has helped to develop novel teaching and learning solutions that are currently being tested in various contexts. Businesses and governments across the globe have been pouring money into a wide array of implementations, and dozens of EdTech start-ups are being funded to capitalise on this technological force. The penetration of AI in classroom teaching is also a profound matter of discussion. These have garnered massive amounts of student big data and have a significant impact on the life of both students and educators alike.

Purpose: The prime focus of this chapter is to extensively review and analyse the vast literature available on the utilities of AI in health care, learning, and development. The specific objective of thematic exploration of the literature is to explicate the principal facets and recent advances in the development and employment of AI in the latter. This chapter also aims to explore how the EdTech and healthcare–education sectors would witness a paradigm shift with the advent and incorporation of AI.

Design/Methodology/Approach: To provide context and evidence, relevant publications were identified on ScienceDirect, PubMed, and Google Scholar using keywords like AI, education, learning, health care, and development. In addition, the latest articles were also thoroughly reviewed to underscore recent advances in the same field.

Results: The implementation of AI in the learning, development, and healthcare sector is rising steeply, with a projected expansion of about 50% by 2022. These algorithms and user interfaces economically facilitate efficient delivery of the latter.

Conclusions: The EdTech and healthcare sector has great potential for a spectrum of AI-based interventions, providing access to learning opportunities and personalised experiences. These interventions are often economic in the long run compared to conventional modalities. However, several ethical and regulatory concerns should be addressed before the complete adoption of AI in these sectors.

Originality/Value: The value in exploring this topic is to present a view on the potential of employing AI in health care, medical education, and learning and development. It also intends to open a discussion of its potential benefits and a remedy to its shortcomings.

This case is designed to enable students to understand the role of women in artificial intelligence (AI); understand the importance of ethics and diversity in the AI field; discuss the ethical issues of AI; study the implications of unethical AI; examine the dark side of corporate-backed AI research and the difficult relationship between corporate interests and AI ethics research; understand the role played by Gebru in promoting diversity and ethics in AI; and explore how Gebru can attract more women researchers in AI and lead the movement toward inclusive and equitable technology.

The case discusses how Timnit Gebru (She), a prominent AI researcher and former co-lead of the Ethical AI research team at Google, is leading the way in promoting diversity, inclusion and ethics in AI. Gebru, one of the most high-profile black women researchers, is an influential voice in the emerging field of ethical AI, which identifies issues based on bias, fairness, and responsibility. Gebru was fired from Google in December 2020 after the company asked her to retract a research paper she had co-authored about the pitfalls of large language models and embedded racial and gender bias in AI. While Google maintained that Gebru had resigned, she said she had been fired from her job after she had raised issues of discrimination in the workplace and drawn attention to bias in AI. In early December 2021, a year after being ousted from Google, Gebru launched an independent community-driven AI research organization called Distributed Artificial Intelligence Research (DAIR) to develop ethical AI, counter the influence of Big Tech in research and development of AI and increase the presence and inclusion of black researchers in the field of AI. The case discusses Gebru’s journey in creating DAIR, the goals of the organization and some of the challenges she could face along the way. As Gebru seeks to increase diversity in the field of AI and reduce the negative impacts of bias in the training data used in AI models, the challenges before her would be to develop a sustainable revenue model for DAIR, influence AI policies and practices inside Big Tech companies from the outside, inspire and encourage more women to enter the AI field and build a decentralized base of AI expertise.

This case is meant for MBA students.

Teaching Notes are available for educators only.

CCS 11: Strategy

The expected learning outcomes are to understand the complexities involved in the integration of two carriers with different business strategies and approaches, the merger of two brands with distinct personas and identities and the confluence of two different cultures; figure out the strategic options in front of the Tata Group and how it can deal with various macro- and micro-level business challenges, defy the financial hiccups and manoeuvre the operational complexities to accomplish mission Vihaan.AI; and develop a pragmatic approach to macro and micro business environmental scanning for making strategic business decisions.

In November 2022, Tata Group, the salt to software conglomerate, announced the merger of Air India (AI) and Vistara. This would lead to the formation of the full-service airline under the brand name “Air India”. The obvious reason behind this was the higher recognition, salience and recall of the brand AI as compared with Vistara in the global market. The Tata Group envisaged the brand AI to be a significant international aviation player with the heritage, persona and ethos of the brand Vistara in the renewed manifestation of AI. To realise these goals, Tata Group laid down an ambitious plan called “Vihaan.AI”, which was aimed at capturing a domestic market share of 30% by 2027.

This case study can be taught as part of undergraduate- and postgraduate-level management programmes.

Teaching notes are available for educators only.

CSS 11: Strategy.

The number of candidates applying to public contests (PC) is increasing compared to the number of human resources employees required for selecting them for the Police Force (PF). This work intends to perceive how those public institutions can evaluate and select their candidates efficiently during the different phases of the recruitment process. To achieve this purpose, artificial intelligence (AI) was studied. This paper aims to focus on analysing the AI technologies most used and appropriate to the PF as a complementary recruitment strategy of the National Criminal Investigation police agency of Portugal – Polícia Judiciária.

Using design science research as a methodological approach, the authors suggest a theoretical framework in pair with the segmentation of the candidates and comprehend the most important facts facing public institutions regarding the usage of AI technologies to make decisions about evaluating and selecting candidates. Following the preferred reporting items for systematic reviews and meta-analyses methodology guidelines, a systematic literature review and meta-analyses method was adopted to identify how the usage and exploitation of transparent AI positively impact the recruitment process of a public institution, resulting in an analysis of 34 papers between 2017 and 2021.

Results suggest that the conceptual pairing of evaluation and selection problems of candidates who apply to PC with applicable AI technology such as K-means, hierarchical clustering, artificial neural network and convolutional neural network algorithms can support the recruitment process and could help reduce the workload in the entire process while maintaining the standard of responsibility. The combination of AI and human decision-making is a fair, objective and unbiased process emphasising a decision-making process free of nepotism and favouritism when carefully developed. Innovative and modern as a category, group the statements that emphasise the innovative and contemporary nature of the process.

There are two main limitations in this study that should be considered. Firstly, the difficulty regarding the timetable, privacy and legal issues associated with public institutions. Secondly, a small group of experts served as the validation group for the new framework. Individual semi-structured interviews were conducted to alleviate this constraint. They provide additional insights into an interviewee’s opinions and beliefs.

Ensure that the system is fair, transparent and facilitates their application process.

The main contribution is the AI-based theoretical framework, applicable within the analysis of literature papers, focusing on the problem of how the institutions can gain insights about their candidates while profiling them, how to obtain more accurate information from the interview phase and how to reach a more rigorous assessment of their emotional intelligence providing a better alignment of moral values. This work aims to improve the decision-making process of a PF institution recruiter by turning it into a more automated and evidence-based decision when recruiting an adequate candidate for the job vacancy.