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Despite the merit of extant studies on career decision regrets, they are not well integrated, are developed at different speeds and differ in focus. Specifically, they do not address an important question about the levels and antecedents of regret arising from choosing entrepreneurship instead of paid employment and vice versa. The authors adopted the regret regulation theory as foundation to examining the moderated effect of entrepreneurial potential (EP) on career choice regret (CCR) among employees and entrepreneurs.

The authors surveyed 721 employees and 724 entrepreneurs from a developing country and applied partial least squares-structural equation modelling (PLS-SEM) to test the hypotheses.

Employees regretted their career choice three times more when compared with entrepreneurs. However, the authors failed to conclude that the latter had three times better living conditions when compared with the former. EP negatively influenced the regret of being an entrepreneur in lieu of an employee while it positively influenced the regret of being an employee in lieu of an entrepreneur. The perceived opportunity cost of being a higher EP employee was three times greater when compared with that of being a lower EP entrepreneur. The effect of EP on CCR was mitigated or amplified by duration in the career, former career status, decision justifiability, and perceived environment's supportiveness.

The design was cross-sectional, thus, the findings cannot be interpreted in the strict sense of causality.

The authors rely on an important yet often overlooked context of the choice between entrepreneurship and paid employment to test, clarify, and extend the regret regulation theory. The findings have novel human resource management and entrepreneurship policy implications.

This study aims to answer two research questions: first, to what extent can workplace bullying be explained by ageism? And second, does the likelihood of workplace bullying increase when age interacts with gender and ethnic minority?

The authors report results from a survey carried out in 11 organizations in Estonia (N = 1,614) using the Negative Acts Questionnaire-Revised (Einarsen et al., 2009).

The results show that ageism does not explain bullying in Estonia. As in some earlier studies, older age correlates negatively with negative acts, and women report less work-related bullying than men. These findings were unexpected because Estonia's post-socialist background and the highest gender wage gap in Europe suggested otherwise. However, there is gendered ageism in work-related bullying such that older women report more negative acts in their workplace. Respondents from ethnic minority groups do not experience more bullying in general, nor in combination with age. Surprisingly, managers reported both person- and work-related bullying more than employees with no subordinates.

The study contributes to intersectionality literature with a view to workplace bullying in post-socialist study context.

In this study the authors aim to outline new ways of information extraction for automated valuation models, which in turn would help to increase transparency in valuation procedures and thus contribute to more reliable statements about the value of real estate.

The authors hypothesize that empirical error in the interpretation and qualitative assessment of visual content can be minimized by collating the assessments of multiple individuals and through use of repeated trials. Motivated by this problem, the authors developed an experimental approach for semi-automatic extraction of qualitative real estate metadata based on Comparative Judgments and Deep Learning. The authors evaluate the feasibility of our approach with the help of Hedonic Models.

The results show that the collated assessments of qualitative features of interior images show a notable effect on the price models and thus over potential for further research within this paradigm.

To the best of the authors’ knowledge, this is the first approach that combines and collates the subjective ratings of visual features and deep learning for real estate use cases.

Drawing on the transformational leadership (TL) and knowledge-based view (KBV) theory, the present study investigates the impact of TL on radical innovation (RI) through the mediation of knowledge management capabilities (KMCs) and moderation of competitive intensity (CI) of textile and apparel industries with an Asian context.

Data were collected from the relevant stakeholders of the industry-university collaboration teams with a structured survey questionnaire. Working with 304 textile and apparel industry respondents, structural equation modeling based partial least square (PLS-SEM) is used to test the conceptual framework. PLS-SEM technique was applied to test the hypothesis using Smart-PLS 3.8 packages program.

The results proposed TL has a positive impact on KMC and RI. Furthermore, the study reveals KMC positively mediated the relationship between TL and RI. This mediation is conditional on the moderating role of CI for the KMC (knowledge acquisition capability + knowledge-sharing capability) path to RI. Conversely, moderation of CI is insignificant and does not influence on fostering RI.

Leaders and managers have realized creative and innovative culture is built within the organizations by leader-follower collaboration through actual knowledge acquisition and knowledge sharing. Moreover, industry policymakers and practitioners establish the knowledge management department to enhance the innovation culture among the firms' stakeholders to encourage RI to sustain the global business market.

The study has introduced KMC as a mediator and CI as a moderator in the proposed model between TL-RI and KMC-RI. Further, it explores the linkages between TL, KMC, CI and RI.

The purpose of this paper is to illuminate the ethical concerns associated with the use of artificial intelligence (AI) in the medical sector and to provide solutions that allow deriving maximum benefits from this technology without compromising ethical principles.

This paper provides a comprehensive overview of AI in medicine, exploring its technical capabilities, practical applications, and ethical implications. Based on our expertise, we offer insights from both technical and practical perspectives.

The study identifies several advantages of AI in medicine, including its ability to improve diagnostic accuracy, enhance surgical outcomes, and optimize healthcare delivery. However, there are pending ethical issues such as algorithmic bias, lack of transparency, data privacy issues, and the potential for AI to deskill healthcare professionals and erode humanistic values in patient care. Therefore, it is important to address these issues as promptly as possible to make sure that we benefit from the AI’s implementation without causing any serious drawbacks.

This paper gains its value from the combined practical experience of Professor Elhassan gained through his practice at top hospitals worldwide, and the theoretical expertise of Dr. Arabi acquired from international institutes. The shared experiences of the authors provide valuable insights that are beneficial for raising awareness and guiding action in addressing the ethical concerns associated with the integration of artificial intelligence in medicine.

Machine learning (ML) analytical tools are increasingly being considered as an alternative quantitative methodology in management research. This paper proposes a new approach for uncovering the antecedents behind product and product–service innovation (PSI).

The ML approach is novel in the field of innovation antecedents at the country level. A sample of the Equatorian National Survey on Technology and Innovation, consisting of more than 6,000 firms, is used to rank the antecedents of innovation.

The analysis reveals that the antecedents of product and PSI are distinct, yet rooted in the principles of open innovation and competitive priorities.

The analysis is based on a sample of Equatorian firms with the objective of showing how ML techniques are suitable for testing the antecedents of innovation in any other context.

The novel ML approach, in contrast to traditional quantitative analysis of the topic, can consider the full set of antecedent interactions to each of the innovations analyzed.

The main purpose of this research paper is to generate a holistic bibliometric study of the tourism industry and COVID-19 fields, to further investigate the current interests and trends emerging from scientific collaboration and thematic analysis and to identify research gaps that indicate future research directions.

This study conducts several analyses, which include the co-authorship and social network analysis, co-citation and keyword co-occurrence knowledge structures. The authors generate a knowledge map of the leading articles and link them with previous literature to elucidate the debates and consensus in research on COVID-19 and tourism.

Research interests concentrate in the USA, China, Europe and the Oceania areas, so more cross-continental collaborations are expected among them and with other regions. Popular topics are tourism sustainable transformation, crisis management and multidisciplinary fields like tourism, hospitality, information technology and environmental sciences. This paper also identifies underexplored topics for future investigation on the social, environmental, cultural and governance dimensions of sustainable tourism.

This paper contributes to guiding tourism researchers in identifying and finding publication references and future collaborations. Moreover, the investigation of knowledge structures could be beneficial for scholars hoping to broaden the current understanding of this field and discover potential for future tourism research, especially in the global pandemic and other severe health crises.

This study enriches the existing literature in the fields of tourism and the pandemic and highlights current interests and research trends exploring scientific collaboration, thematic analysis and knowledge mapping.

This perspective article responds to the emergence of artificial intelligence (AI) as a significant opportunity for growth among family businesses, highlighting the need for future research to attain a clear picture of the next generation of family business successors.

This is a literature review of current technology adoption within family businesses. The authors offer some research insight to spur critical thinking and discourse around the impact of AI on family business successors.

Family businesses are initially skeptical of AI technology. However, its use and adoption are crucial for the survival of the family business. To leverage this technology, the authors need to investigate the role of the family business successors as “Gen AI.”

It is challenging but necessary to develop policies and educational support for successors to ensure the survival of family businesses worldwide. The authors propose four key areas of future research.

This paper aims at reflecting on the changing boundaries of public sector auditing. In particular the paper focusses on the opportunities and challenges for performance auditing, for sustainable development auditing and digitalised public sector auditing.

Building from the papers in this special issue, the authors draw on the literature on current development of public sector auditing (such as public sector auditing performance auditing, sustainable development auditing and digitalised public sector auditing) and propose a future research agenda.

The “changing boundaries” of public sector auditing may influence the difficulty for public sector organisations to manage the uncertainties and risks not only associated to corruption but also the public governance development (collaborative, digital and emergency governance). The authors speculate on the role that the “changing boundaries” of public sector auditing can play.

The paper contributes by setting new research avenues for future studies on public sector auditing in a post-new public management context.