TBS Education

Transformative artificially intelligent tools, such as ChatGPT, designed to generate sophisticated text indistinguishable from that produced by a human, are applicable across a wide range of contexts. The technology presents opportunities as well as, often ethical and legal, challenges, and has the potential for both positive and negative impacts for organisations, society, and individuals. Offering multi-disciplinary insight into some of these, this article brings together 43 contributions from experts in fields such as computer science, marketing, information systems, education, policy, hospitality and tourism, management, publishing, and nursing. The contributors acknowledge ChatGPT’s capabilities to enhance productivity and suggest that it is likely to offer significant gains in the banking, hospitality and tourism, and information technology industries, and enhance business activities, such as management and marketing. Nevertheless, they also consider its limitations, disruptions to practices, threats to privacy and security, and consequences of biases, misuse, and misinformation. However, opinion is split on whether ChatGPT’s use should be restricted or legislated. Drawing on these contributions, the article identifies questions requiring further research across three thematic areas: knowledge, transparency, and ethics; digital transformation of organisations and societies; and teaching, learning, and scholarly research. The avenues for further research include: identifying skills, resources, and capabilities needed to handle generative AI; examining biases of generative AI attributable to training datasets and processes; exploring business and societal contexts best suited for generative AI implementation; determining optimal combinations of human and generative AI for various tasks; identifying ways to assess accuracy of text produced by generative AI; and uncovering the ethical and legal issues in using generative AI across different contexts.

The metaverse has the potential to extend the physical world using augmented and virtual reality technologies allowing users to seamlessly interact within real and simulated environments using avatars and holograms. Virtual environments and immersive games (such as, Second Life, Fortnite, Roblox and VRChat) have been described as antecedents of the metaverse and offer some insight to the potential socio-economic impact of a fully functional persistent cross platform metaverse. Separating the hype and “meta…” rebranding from current reality is difficult, as “big tech” paints a picture of the transformative nature of the metaverse and how it will positively impact people in their work, leisure, and social interaction. The potential impact on the way we conduct business, interact with brands and others, and develop shared experiences is likely to be transformational as the distinct lines between physical and digital are likely to be somewhat blurred from current perceptions. However, although the technology and infrastructure does not yet exist to allow the development of new immersive virtual worlds at scale - one that our avatars could transcend across platforms, researchers are increasingly examining the transformative impact of the metaverse. Impacted sectors include marketing, education, healthcare as well as societal effects relating to social interaction factors from widespread adoption, and issues relating to trust, privacy, bias, disinformation, application of law as well as psychological aspects linked to addiction and impact on vulnerable people. This study examines these topics in detail by combining the informed narrative and multi-perspective approach from experts with varied disciplinary backgrounds on many aspects of the metaverse and its transformational impact. The paper concludes by proposing a future research agenda that is valuable for researchers, professionals and policy makers alike.

This paper explores the definitions and usage of competence, especially in the context of training and development initiatives in the USA, UK, France and Germany, seeking to clarify the concept by incorporating knowledge, skills and competences within a holistic competence typology. One-dimensional frameworks of competence are inadequate and are giving way to multi-dimensional frameworks. Functional and cognitive competences are increasingly being added to behavioural competencies in the USA, while in the UK cognitive and behavioural competences are being added to the occupational functional competence model. France, Germany, and Austria entering the arena more recently, adopted from the outset more holistic, but different, approaches. After comparing these approaches, we argue that a holistic framework is useful in identifying the combination of competences that are necessary for particular occupations and to promote labour mobility.

Purpose The main purpose of our study is to analyze the influence of Artificial Intelligence (AI) on firm performance, notably by building on the business value of AI-based transformation projects. This study was conducted using a four-step sequential approach: (1) analysis of AI and AI concepts/technologies; (2) in-depth exploration of case studies from a great number of industrial sectors; (3) data collection from the databases (websites) of AI-based solution providers; and (4) a review of AI literature to identify their impact on the performance of organizations while highlighting the business value of AI-enabled projects transformation within organizations. Design/methodology/approach This study has called on the theory of IT capabilities to seize the influence of AI business value on firm performance (at the organizational and process levels). The research process (responding to the research question, making discussions, interpretations and comparisons, and formulating recommendations) was based on a review of 500 case studies from IBM, AWS, Cloudera, Nvidia, Conversica, Universal Robots websites, etc. Studying the influence of AI on the performance of organizations, and more specifically, of the business value of such organizations’ AI-enabled transformation projects, required us to make an archival data analysis following the three steps, namely the conceptual phase, the refinement and development phase, and the assessment phase. Findings AI covers a wide range of technologies, including machine translation, chatbots and self-learning algorithms, all of which can allow individuals to better understand their environment and act accordingly. Organizations have been adopting AI technological innovations with a view to adapting to or disrupting their ecosystem while developing and optimizing their strategic and competitive advantages. AI fully expresses its potential through its ability to optimize existing processes and improve automation, information and transformation effects, but also to detect, predict and interact with humans. Thus, the results of our study have highlighted such AI benefits in organizations, and more specifically, its ability to improve on performance at both the organizational (financial, marketing and administrative) and process levels. By building on these AI attributes, organizations can, therefore, enhance the business value of their transformed projects. The same results also showed that organizations achieve performance through AI capabilities only when they use their features/technologies to reconfigure their processes. Research limitations/implications AI obviously influences the way businesses are done today. Therefore, practitioners and researchers need to consider AI as a valuable support or even a pilot for a new business model. For the purpose of our study, we adopted a research framework geared toward a more inclusive and comprehensive approach so as to better account for the intangible benefits of AI within organizations. In terms of interest, this study nurtures a scientific interest, which aims at proposing a model for analyzing the influence of AI on the performance of organizations, and at the same time, filling the associated gap in the literature. As for the managerial interest, our study aims to provide managers with elements to be reconfigured or added in order to take advantage of the full benefits of AI, and therefore improve organizations’ performance, the profitability of their investments in AI transformation projects, and some competitive advantage. This study also allows managers to consider AI not as a single technology but as a set/combination of several different configurations of IT in the various company’s business areas because multiple key elements must be brought together to ensure the success of AI: data, talent mix, domain knowledge, key decisions, external partnerships and scalable infrastructure. Originality/value This article analyses case studies on the reuse of secondary data from AI deployment reports in organizations. The transformation of projects based on the use of AI focuses mainly on business process innovations and indirectly on those occurring at the organizational level. Thus, 500 case studies are being examined to provide significant and tangible evidence about the business value of AI-based projects and the impact of AI on firm performance. More specifically, this article, through these case studies, exposes the influence of AI at both the organizational and process performance levels, while considering it not as a single technology but as a set/combination of the several different configurations of IT in various industries.

The coronavirus (COVID-19) outbreak shows that pandemics and epidemics can seriously wreak havoc on supply chains (SC) around the globe. Humanitarian logistics literature has extensively studied epidemic impacts; however, there exists a research gap in understanding of pandemic impacts in commercial SCs. To progress in this direction, we present a systematic analysis of the impacts of epidemic outbreaks on SCs guided by a structured literature review that collated a unique set of publications. The literature review findings suggest that influenza was the most visible epidemic outbreak reported, and that optimization of resource allocation and distribution emerged as the most popular topic. The streamlining of the literature helps us to reveal several new research tensions and novel categorizations/classifications. Most centrally, we propose a framework for operations and supply chain management at the times of COVID-19 pandemic spanning six perspectives, i.e., adaptation, digitalization, preparedness, recovery, ripple effect, and sustainability. Utilizing the outcomes of our analysis, we tease out a series of open research questions that would not be observed otherwise. Our study also emphasizes the need and offers directions to advance the literature on the impacts of the epidemic outbreaks on SCs framing a research agenda for scholars and practitioners working on this emerging research stream.

Supply chain resilience and data analytics capability have generated increased interest in academia and among practitioners. However, existing studies often treat these two streams of literature independently. Our study model reconciles two different streams of literature: data analytics capability as a means to improve information-processing capacity and supply chain resilience as a means to reduce a ripple effect in supply chain or quickly recover after disruptions in the supply chain. We have grounded our theoretical model in the organisational information processing theory (OIPT). Four research hypotheses are tested using responses from 213 Indian manufacturing organisations collected via a pre-tested survey-based instrument. We further test our model using variance-based structural equation modelling, popularly known as PLS-SEM. All of the hypotheses were supported. The findings of our study offer a unique contribution to information systems (IS) and operations management (OM) literature. The findings further provide numerous directions to the supply chain managers. Finally, we note our study limitations and provide further research directions.

Inter-firm networks, as an inter-organizational form, are increasingly perceived as a model for entrepreneurial firm growth. We study egocentric networks of high-growth entrepreneurial firms in the IT industry and explore how these firms grow through the Use of external relations and become competitive. Based on case study research, we identify that Firms are using relations for a variety of purposes and that every firm has an individual relational mix. This relational mix changes with the development of the firms. While the relative importance of social and reputational networks decrease with the firms' development, co-opetition networks increase over time. Knowledge and innovation networks are a function of reputation and management capacity while the development of marketing networks depends on the firm's culture and management style. Both weak ties and strong tics are important for the growth of the firm since they fulfil different functions. Firm growth is determined by path-dependent relational capability that eventually reaches its limits and leads to the reconfiguration of a rather stable network. Additionally, firm growth depends not only on the building of egocentric networks but also on the existence and development of healthy sociocentric networks.

Research into and practice of Higher Education for Sustainable Development (HESD) have been increasing during the last two decades. These have focused on providing sustainability education to future generations of professionals. In this context, there has been considerable progress in the incorporation of SD in universities’ curricula. Most of these efforts have focussed on the design and delivery of sustainability-oriented competences. Some peer-reviewed articles have proposed different pedagogical approaches to better deliver SD in these courses; however, there has been limited research on the connection between how courses are delivered (pedagogical approaches) and how they may affect sustainability competences. This paper analyses competences and pedagogical approaches, using hermeneutics to connect these in a framework based on twelve competences and twelve pedagogical approaches found in the literature. The framework connects the course aims to delivery in HESD by highlighting the connections between pedagogical approaches and competences in a matrix structure. The framework is aimed at helping educators in creating and updating their courses to provide a more complete, holistic, and systemic sustainability education to future leaders, decision makers, educators, and change agents. To better develop mind-sets and actions of future generations, we must provide students with a complete set of sustainability competences.

Abstract ChatGPT and its variants that use generative artificial intelligence (AI) models have rapidly become a focal point in academic and media discussions about their potential benefits and drawbacks across various sectors of the economy, democracy, society, and environment. It remains unclear whether these technologies result in job displacement or creation, or if they merely shift human labour by generating new, potentially trivial or practically irrelevant, information and decisions. According to the CEO of ChatGPT, the potential impact of this new family of AI technology could be as big as “the printing press”, with significant implications for employment, stakeholder relationships, business models, and academic research, and its full consequences are largely undiscovered and uncertain. The introduction of more advanced and potent generative AI tools in the AI market, following the launch of ChatGPT, has ramped up the “AI arms race”, creating continuing uncertainty for workers, expanding their business applications, while heightening risks related to well‐being, bias, misinformation, context insensitivity, privacy issues, ethical dilemmas, and security. Given these developments, this perspectives editorial offers a collection of perspectives and research pathways to extend HRM scholarship in the realm of generative AI. In doing so, the discussion synthesizes the literature on AI and generative AI, connecting it to various aspects of HRM processes, practices, relationships, and outcomes, thereby contributing to shaping the future of HRM research.

Although digital transformation offers a number of opportunities for today’s organizations, information systems scholars and practitioners struggle to grasp what digital transformation really is, particularly in terms of how it differs from the well-established concept of information technology (IT)-enabled organizational transformation. By integrating literature from organization science and information systems research with two longitudinal case studies—one on digital transformation, the other on IT-enabled organizational transformation—we develop an empirically grounded conceptualization that sets these two phenomena apart. We find that there are two distinctive differences: (1) digital transformation activities leverage digital technology in (re)defining an organization’s value proposition, while IT-enabled organizational transformation activities leverage digital technology in supporting the value proposition, and (2) digital transformation involves the emergence of a new organizational identity, whereas IT-enabled organizational transformation involves the enhancement of an existing organizational identity. We synthesize these arguments in a process model to distinguish the different types of transformations and propose directions for future research.

In a short span of time since its introduction, generative artificial intelligence (AI) has garnered much interest at both personal and organizational levels. This is because of its potential to cause drastic and widespread shifts in many aspects of life that are comparable to those of the Internet and smartphones. More specifically, generative AI utilizes machine learning, neural networks, and other techniques to generate new content (e.g. text, images, music) by analyzing patterns and information from the training data. This has enabled generative AI to have a wide range of applications, from creating personalized content to improving business operations. Despite its many benefits, there are also significant concerns about the negative implications of generative AI. In view of this, the current article brings together experts in a variety of fields to expound and provide multi-disciplinary insights on the opportunities, challenges, and research agendas of generative AI in specific industries (i.e. marketing, healthcare, human resource, education, banking, retailing, the workplace, manufacturing, and sustainable IT management).

Recent research on the microfoundations of corporate social responsibility (CSR) has highlighted the need for improved measures to evaluate how stakeholders perceive and subsequently react to CSR initiatives. Drawing on stakeholder theory and data from five samples of employees (N = 3,772), the authors develop and validate a new measure of corporate stakeholder responsibility (CStR), which refers to an organization’s context-specific actions and policies designed to enhance the welfare of various stakeholder groups by accounting for the triple bottom line of economic, social, and environmental performance; it is conceptualized as a superordinate, multidimensional construct. Results from exploratory factor analyses, first- and second-order confirmatory factor analyses, and structural equation modeling provide strong evidence of the convergent, discriminant, incremental, and criterion-related validities of the proposed CStR scale. Two-wave longitudinal studies further extend prior theory by demonstrating that the higher-order CStR construct relates positively and directly to organizational pride and perceived organizational support, as well as positively and indirectly to organizational identification, job satisfaction, and affective commitment, beyond the contribution of overall organizational justice, ethical climate, and prior measures of perceived CSR.

This article explores how individual entrepreneurial orientation dimensions influence the relationship between competitive strategy and firm performance. The findings show the different impacts of individual entrepreneurial orientation dimensions on competitive strategy and the effects of cost leadership and differentiation on performance. Innovativeness is related most highly to differentiation strategy. Risk-taking and competitive aggressiveness are negatively associated with both differentiation and cost leadership strategies. Both differentiation and cost leadership strategies are positively related to performance. This study refines our understanding of the effects of entrepreneurial orientation on small firm performance.

The emergence of multivariate analysis techniques transforms empirical validation of theoretical concepts in social science and business research. In this context, structural equation modelling (SEM) has emerged as a powerful tool to estimate conceptual models linking two or more latent constructs. This paper shows the suitability of the partial least squares (PLS) approach to SEM (PLS-SEM) in estimating a complex model drawing on the philosophy of verisimilitude and the methodology of soft modelling assumptions. The results confirm the utility of PLS-SEM as a promising tool to estimate a complex, hierarchical model in the domain of big data analytics quality.

Abstract We examine announcement period abnormal returns to acquirers of listed and unlisted targets in 17 Western European countries over the interval 1996–2001. Acquirers of listed targets earn an insignificant average abnormal return of –0.38%, while acquirers of unlisted targets earn a significant average abnormal return of 1.48%. This listing effect in acquirers' returns persists through time and across countries and remains after controlling for the method of payment for the target, the acquirer's size and Tobin's Q, pre-announcement leakage of information about the transaction, whether the acquisition created a blockholder in the acquirer's ownership structure, whether the acquisition was a cross-border deal, and other variables. The fundamental factors that give rise to this listing effect, which has also been documented in U.S. acquisitions, remain elusive.

The UN's sustainable development goals underscore engaging supply-chain stakeholders with environmentally friendly practices. Small- and medium-size enterprises (SMEs) are key participants in several supply chains, but their operations often produce a significant environmental impact. Their transition to sustainable practices is challenging because they operate with constrained resources, which are mostly invested in pressing activities. Therefore, evidence is needed that shows the benefits of investing limited resources in sustainable activities to support decision-making in SMEs. Research has neglected to connect circular economy and sustainable-oriented innovation whilst accounting for external factors affecting the implementation of sustainable processes and technology within SMEs in developing countries. This paper fills that gap by analyzing the impact of external factors on the implementation of circular economy and technology, and their influence on sustainable-oriented innovation and sustainable performance. Responses from 165 Mexican SMEs have been collected and analyzed using structural equation modeling to test direct and indirect effects between constructs. Findings reveal that while both governmental support and customer pressure facilitate the adoption of circular economy, only governmental support contributes directly to technology implementation. They also highlight the value of circular economy to support the adoption of sustainable-oriented innovation and to mediate the relationship between technology implementation and sustainable-oriented innovation. The overarching finding is that circular economy promoting sustainability-oriented innovation has a positive impact on financial, environmental, and social performance. This is a key implication to inform managers in SMEs on the potential benefits of investing in sustainable solutions.

This paper aims to bridge the knowledge gap in the existing literature on Bitcoin, Blockchain and Fintech. It begins by clarifying the definition of these concepts. Through a systematic review and case studies in the supply chain industry, this paper brings out the applications, the benefits/value, and the challenges/issues of Bitcoin, Blockchain and Fintech in several industries. It also presents the research methodologies/approaches used during such research. The classification framework developed and used to perform an analysis of 141 articles from five top academic databases serves as a baseline study. It offers the opportunity to evaluate the level of knowledge on Bitcoin, Blockchain and Fintech, and their evolution over time. The findings show that these technologies are evolving, and organizations are embracing them for competitive advantage. Thus, organizations need to leverage research on these technologies to better understand them, optimize their business strategies, and develop critical insights for decision-making.

Abstract The dawn of generative artificial intelligence (AI) has the potential to transform logistics and supply chain management radically. However, this promising innovation is met with a scholarly discourse grappling with an interplay between the promising capabilities and potential drawbacks. This conversation frequently includes dystopian forecasts of mass unemployment and detrimental repercussions concerning academic research integrity. Despite the current hype, existing research exploring the intersection between AI and the logistics and supply chain management (L&SCM) sector remains limited. Therefore, this editorial seeks to fill this void, synthesizing the potential applications of AI within the L&SCM domain alongside an analysis of the implementation challenges. In doing so, we propose a robust research framework as a primer and roadmap for future research. This will give researchers and organizations comprehensive insights and strategies to navigate the complex yet promising landscape of AI integration within the L&SCM domain.

The covid-19 pandemic crisis presents unprecedented challenges and has profound implications for the way people live and work. Information and communication technologies have been playing a crucial role in ensuring business continuity as lockdown measures have suddenly forced employees from across the globe to telework, often leaving them unprepared and ill-equipped. This paper develops an epidemic-induced telework adjustment model derived from the theory of Work Adjustment and the Interactional Model of Individual Adjustment. It is tested on a sample of 1574 teleworkers in France. The results demonstrate the superiority of the influence of crisis-specific variables that are professional isolation, telework environment, work increase and stress. Implications for research are discussed while concrete and actionable recommendations for organisations are provided.

Artificial Intelligence (AI) offers a promising solution for building and promoting more resilient supply chains. However, the literature is highly dispersed regarding the application of AI in supply-chain management. The literature to date lacks a decision-making framework for identifying and applying powerful AI techniques to build supply-chain resilience (SCRes), curbing advances in research and practice on this interesting interface. In this paper, we propose an integrated Multi-criteria decision-making (MCDM) technique powered by AI-based algorithms such as Fuzzy systems, Wavelet Neural Networks (WNN) and Evaluation based on Distance from Average Solution (EDAS) to identify patterns in AI techniques for developing different SCRes strategies. The analysis was informed by data collected from 479 manufacturing companies to determine the most significant AI applications used for SCRes. The findings show that fuzzy logic programming, machine learning big data, and agent-based systems are the most promising techniques used to promote SCRes strategies. The study findings support decision-makers by providing an integrated decision-making framework to guide practitioners in AI deployment for building SCRes.