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Despite the considerable increase in research on entrepreneurship education, few studies examine the role of entrepreneurship educators. Similarly, most frameworks from entrepreneurship education recognize the educator’s importance in facilitating instruction and assessment, but the factors influencing the educator role are not well understood. According to the identity theory, personal factors including self-efficacy, job satisfaction and personal values influence the perspective of self, significance and anticipations that an individual in this role associates with it, determining their planning and actions. The stronger the role identity the more likely entrepreneurship educators will be in effectively developing their entrepreneurial skills as well as the overall learning experience of their students. The objective of this study is to pinpoint the factors that affect entrepreneurial role identity.

Drawing upon the identity theory, this study developed a theoretical framework and carried out an empirical investigation involving a survey of 289 entrepreneurship educators across the globe. Structural equation modeling (SEM) technique was applied to analyze and explore the factors that impact the identity of the educators in their role as entrepreneurship teachers.

The findings show that the role identity of entrepreneurship educators is significantly influenced by their self-efficacy, job satisfaction and personal values. Among these factors, self-efficacy and job satisfaction have the most significant impacts on how educators perceive their role. The implications of these results and directions for future research are also discussed.

The novelty of the current study is derived from its conceptualization of the antecedents of role perception among entrepreneurship educators. This study stands out as one of the earliest attempts to investigate the factors that shape an individual’s scene of self and professional identity as an entrepreneurship educator. The significance of comprehending the antecedents of role perception lies in the insights it can offer into how educators undertake and execute their role, and consequently, their effectiveness in teaching entrepreneurship.

Firms have already begun integrating artificial intelligence (AI) as a replacement for conventional performance management systems owing to its technological superiority. This transition has sparked a growing interest in determining how employees perceive and respond to performance feedback provided by AI as opposed to human supervisors.

A 2 x 2 between-subject experimental design was employed that was manipulated into four experimental conditions: AI algorithms, AI data, highly experienced human supervisors and low-experience human supervisor conditions. A one-way ANOVA and Welch t-test were used to analyze data.

Our findings revealed that with a predefined fixed formula employed for performance feedback, employees exhibited higher levels of trust in AI algorithms, had greater performance expectations and showed stronger intentions to seek performance feedback from AI algorithms than highly experienced human supervisors. Conversely, when performance feedback was provided by human supervisors, even those with less experience, in a discretionary manner, employees' perceptions were higher compared to similar feedback provided by AI data. Moreover, additional analysis findings indicated that combined AI-human performance feedback led to higher levels of employees' perceptions compared to performance feedback solely by AI or humans.

The findings of our study advocate the incorporation of AI in performance management systems and the implementation of AI-human combined feedback approaches as a potential strategy to alleviate the negative perception of employees, thereby increasing firms' return on AI investment.

Our study represents one of the initial endeavors exploring the integration of AI in performance management systems and AI-human collaboration in providing performance feedback to employees.

As more firms adopted AI-related services in recent years, AI service failures have increased. However, the potential costs of AI implementation are not well understood, especially the effect of AI service failure events. This study examines the influences of AI service failure events, including their industry, size, timing, and type, on firm value.

This study will conduct an event study of 120 AI service failure events in listed companies to evaluate the costs of such events.

First, AI service failure events have a negative impact on the firm value. Second, small firms experience more share price declines due to AI service failure events than large firms. Third, AI service failure events in more recent years have a more intensively negative impact than those in more distant years. Finally, we identify different types of AI service failure and find that there are order effects on firm value across the service failure event types: accuracy > safety > privacy > fairness.

First, this study is the initial effort to empirically examine market reactions to AI service failure events using the event study method. Second, this study comprehensively considers the effect of contextual influencing factors, including industry type, firm size and event year. Third, this study improves the understanding of AI service failure by proposing a novel classification and disclosing the detailed impacts of different event types, which provides valuable guidance for managers and developers.

With the recent proliferation of AI, organisations are transforming not only their organisational design but also the input and output operational processes of the hiring process. The purpose of this paper is to explore the organisational and operational dimensions resulting from the deployment of AI during talent acquisition process.

The authors conducted semi-structured interviews and meetings with human resources (HRs) professionals, recruiters and AI hiring platform providers in Sweden. Using an inductive data analysis rooted in the principles of grounded theory, the study uncovered four aggregate dimensions critical to understanding the role of AI in talent acquisition.

With insights from algorithmic management and ambidexterity theory, the study presents a comprehensive theoretical framework that highlights four aggregate dimensions describing AI’s transformative role in talent recruitment. The results provide a cautionary perspective, advising against an excessive emphasis on operational performance driven solely by algorithmic management.

The study is limited in scope and subject to several constraints. Firstly, the sample size and diversity are restricted, as the findings are based on a limited number of semi-structured interviews and meetings with HRs professionals, recruiters, and AI hiring platform providers. Secondly, the rapid evolution of AI technologies means that the study’s findings may quickly become outdated as new advancements and applications emerge.

The results provide managers with actionable information that can lead to more precise and strategic management practices, ultimately contributing to improved organizational performance and outcomes. Plus, enhancing their ability to make informed decisions, optimize processes and address challenges effectively.

The results signal both positive and negative impacts on employment opportunities. On the positive side, AI can streamline recruitment processes, making it easier for qualified candidates to be identified and hired quickly. However, AI systems can also perpetuate existing biases present in the data they are trained on, leading to unfair hiring practices where certain groups are systematically disadvantaged.

By examining the balance between transactional efficiency and relational engagement, the research addresses a crucial trade-off that organizations face when implementing AI in recruitment. The originality lies in its critique of the prevailing emphasis on e-recruiting.

This study aims to investigate the relationship between the adoption of human resource (HR) analytics and managerial decision-making (DM), with attitude toward artificial intelligence (AI) as a potential moderator.

This study was conducted in three phases. In Phase I, a comprehensive scale to measure the “Adoption of HR analytics” was conceptualized and developed. In Phase II, the scale was validated and operationalized. Finally, in Phase III, a survey of 377 managers was conducted, and a conceptual model was validated using structural equation modeling.

This study reveals that the adoption of HR analytics (HRA) and a positive attitude toward AI significantly influence DM. The findings suggest that the structural factors play the most important role in the adoption of HRA, followed by individual factors, value and system support.

These findings hold valuable implications for managers seeking integration of HRA and AI within organizational systems and processes. HR practitioners can evaluate their organization’s readiness for HRA, enabling them to build a future-proof workforce with the necessary skills. It can help managers make the adoption of AI-enabled HRA a reality. The study also helps to remove inhibitions and concerns of HR managers and employees related to AI.

This paper addresses the methodological, practical knowledge and evidence gap in the area of adoption of HRA and DM. It sheds light on the “future of work” in HR, highlighting a potential shift toward human-AI collaboration.

This study examines the impact of outcome uncertainty on the National Football League (NFL) secondary ticket market prices. As a demand-driven market, it is essential to comprehend how resellers respond to outcome uncertainty, one of the consumer demand factors in sports.

Using real-time ticket prices and money lines as a proxy of the probabilities of winning, this study employs a regression analysis and examines 33,554 price observations from the NFL’s secondary ticket market partner, StubHub.

The result shows a positive relationship between outcome uncertainty and secondary market ticket prices, indicating that resellers adjust the prices in response to the level of outcome uncertainty and put more value on games with greater uncertainty. This finding confirms the demand-driven nature of the secondary ticket market, as outcome uncertainty is one of the demand factors in sports.

This study links the uncertainty of outcome hypothesis with secondary ticket market pricing and fills a gap in the literature by providing an important perspective on games with uncertainty in the secondary ticket market. Outcome uncertainty has limited understanding in relation to secondary ticket market pricing despite its relationship with consumer demand. The positive relationship between outcome uncertainty and the ticket prices, grounded in real-time price data and win probability from sport betting markets, enhances our understanding of price determinations in the secondary ticket market.

The purpose of the paper is to analyze the supply and demand of marketing and communication consulting services and evaluate actual and perceived gaps.

The paper uses two different datasets to assess the gap. The supply database comes from desk research carried out in the province of Bergamo (n. 159 consulting agencies). The demand dates are the results of 100 structured interviews with local companies that requested marketing and communication consulting services both inside and outside the province.

Findings show that there is no significant shortage in local service supply. Nonetheless, a limited gap exists between the provision of specific services and their overall quality. Conversely, the perceived gap is wider, leading to an impression of scarce availability – a notion disproven by the analysis of the actual supply.

The study suggests that local agencies may overcome their “myopic” attitude and need to increase their visibility, competencies and expertise by investing in these areas and improving networking.

There are no previous studies that compare the supply and demand for marketing and communication consulting services. The paper also provides insights into actual and perceived gaps in a hypercompetitive environment.

This study aims to deepen our understanding of how inclusionary practices are used within organizations and how they satisfy specific inclusion needs.

We adopt a qualitative research design, reporting on data from semi-structured interviews conducted with 15 diversity and inclusion (D&I) directors/leads and using a thematic approach to analysis.

Our study expands understanding of inclusion practices, showing that they are not uniformly implemented and that practices may satisfy both needs to belong and differences valued, with interviewees prioritizing belonging. Well-being and career development are seen as important inclusion practices demonstrating support and appreciation of difference, thus as inputs, not outputs, of inclusion challenging existing assumptions. Inclusionary practices are malleable, and their impact depends critically on the leaders involved and their commitment to EDI.

Our study shows how practices satisfy inclusion needs and that the implementation of practices varies depending on the leaders involved.

In this study, students’ perceptions of the effective practices of their research mentors were examined. The research mentors implemented the practices informed by the Center for the Improvement of Mentored Experiences in Research (CIMER) mentorship competencies to mentor underrepresented students in engineering education research in a virtual environment.

This research experience for undergraduates (REU) program, situated in the United States of America, consisted of undergraduate students (i.e. mentees), graduate students and faculty mentors who all had at least one underrepresented identity in engineering (i.e. Black, Latiné/x, and/or women). Using qualitative methods, we used data from reflection surveys and follow-up interviews with REU mentees to understand the outcomes of the mentorship strategies employed by the mentors in the program. The data were analyzed thematically using CIMER model constructs and social capital theory as guiding frameworks.

The results indicated the identified strategies students perceived as the most impactful for mentorship throughout the program. Students in the REU gained knowledge on how to activate social capital in mentorship relationships and how to better mentor others.

The findings provide insight on how to operationalize the CIMER mentorship competencies to skillfully mentor underrepresented students in engineering. Given the size of the REU and the nature of qualitative research, the sample size was limited.

The results help inform mentorship practices for underrepresented individuals in engineering education and the workforce. Further, they add to the practical knowledge of implementing CIMER best practices virtually, at a time when the world has transitioned to more hybrid and virtual working and learning environments.

This study identifies impactful strategies for operationalizing mentorship strategies informed by theory- and evidence-based CIMER mentorship competencies. In addition, this study extends knowledge about how to implement mentoring best practices and engage mentorship in a virtual environment.

In recent decades, school discipline has become increasingly characterized by zero-tolerance policies that mandate predetermined punitive consequences for specific offenses. Zero-tolerance policies have not been shown to improve student behavioral outcomes or school climate. Further, these disciplinary policies are applied unevenly across schools and student populations. Despite the well-documented research base that demonstrates that these practices are ineffective, they remain commonplace in K-12 school across the United States. Transformative and culturally responsive educational leadership requires school leaders to examine the historical, societal, and institutional factors that contribute to the racial-discipline gap within their particular schools. This process requires committing to leading for racial justice, self-reflexive practice, and having the courage to boldly name and dismantle practices that do not create equitable outcomes for students on the margins. Drawing on tenets of Critical Race Theory and Culturally Responsive School Leadership to situate the history and proliferation of harmful disciplinary practices, this chapter discusses how critically reflexive school leaders can mobilize restorative practices to dismantle the systems, structures, and practices that reproduce inequities in schools. The chapter provides aspiring and practicing school leaders with the knowledge needed to reform existing school discipline policies and implement practices that support racial justice.