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This paper introduces executive functions (EFs)–i.e. high-level cognitive processes that are elicited in novel and non-routinised situations–into discussions within entrepreneurship education (EE). By reviewing the existing literature, it highlights how EFs are important for the entrepreneur, their role in the entrepreneurial process and implications for improving EE.

We conduct a literature review bridging cognitive psychology, EE and entrepreneurship fields to clarify the role of EFs in the entrepreneurial process. To do so, we define EFs and then propose a model of the entrepreneurial process to frame our review and identify knowledge and gaps in current research.

This review shows why EFs are valuable for EE and calls for more focus on them to better prepare students for entrepreneurship and general life challenges. The findings underscore the importance of EFs in understanding key aspects of the entrepreneurial process. Although EFs are studied in the entrepreneurship and EE fields, they are rarely conceptualised from a cognitive psychology perspective, with research often focusing on isolated EF components instead of examining them as a whole.

This review is the first to highlight the role of EFs in the entrepreneurial process in a structured way. Integrating cognitive psychology insights on EFs can enrich EE for both venture creation and value creation approaches while also supporting the development of more effective programs. This focus on EFs also provides a fresh perspective and a valuable lens for understanding complex phenomena such as cognition, learning and the factors behind success and failure in entrepreneurship.

Many studies have examined pre-diploma students' backgrounds and academic performance with results showing that some did not achieve the expected level of competence. This study aims to examine the relationship between students’ demographic characteristics and their academic achievement at the pre-diploma level using machine learning.

Secondary data analysis was used in this study, which involved collecting information about 1,052 pre-diploma students enrolled at Universiti Teknologi MARA (UiTM) Pahang Branch between 2017 and 2021. The research procedure was divided into two parts: data collecting and pre-processing, and building the machine learning algorithm, pre-training and testing.

Gender, family income, region and achievement in the national secondary school examination (Sijil Pelajaran Malaysia [SPM]) predict academic performance. Female students were 1.2 times more likely to succeed academically. Central region students performed better with a value of 1.26. M40-income students were more likely to excel with an odds ratio of 2.809. Students who excelled in SPM English and Mathematics had a better likelihood of succeeding in higher education.

This research was limited to pre-diploma students from UiTM Pahang Branch. For better generalizability of the results, future research should include pre-diploma students from other UiTM branches that offer this programme.

This study is expected to offer insights for policymakers, particularly, the Ministry of Higher Education, in developing a comprehensive policy to improve the tertiary education system by focusing on the fourth Sustainable Development Goal.

These pre-diploma students were found to originate mainly from low- or middle-income families; hence, the programme may help them acquire better jobs and improve their standard of living. Most students enrolling on the pre-diploma performed below excellent at the secondary school level and were therefore given the opportunity to continue studying at a higher level.

This predictive model contributes to guidelines on the minimum requirements for pre-diploma students to gain admission into higher education institutions by ensuring the efficient distribution of resources and equal access to higher education among all communities.

The purpose of this paper is to provide an in-depth analysis of the challenges associated with using artificial intelligence (AI) in academic research and suggest various preventive measures that can be taken to address these issues and transform them into opportunities.

To develop measurement items and constructs, the authors collected 248 responses through an online survey. These responses were then used to establish the structural model and determine discriminant validity through the use of structural equation modeling with SmartPLS 4.0.9.9. Additionally, the authors used SPSS (Version 29) to create graphs and visual representations of the challenges faced and the most commonly used AI tools. These techniques allowed them to explore data and draw meaningful conclusions for future research.

This research shows that AI has a positive impact on higher education, improving learning outcomes and data security. However, issues such as plagiarism and academic integrity can destroy students. The study highlights AI’s potential in education while emphasizing the need to address challenges.

This paper emphasizes the preventive measures to tackle academic challenges and suggests enhancing academic work.

This study examines how AI can be used to personalize learning and overcome challenges in this area. It emphasizes the importance of academic institutions in promoting academic integrity and transparency to prevent plagiarism. Additionally, the study stresses the need for technology advancement and exploration of new approaches to further improve personalized learning with AI.

The purpose of this paper is to review existing research on burnout in the audit profession using the job demands-resources theory (JD-R) with attention to the post-COVID-19 era.

Consistent with prior studies, this paper adopts a systematic review methodology, incorporating a comprehensive synthesis of diverse archival materials. Using relevant keywords, the authors systematically retrieve papers on burnout from reputable databases, such as Google Scholar and Web of Science. Following rigorous selection criteria, the authors identified and analysed 43 academic and practitioner papers. Through this process, the authors contextualise the findings within the JD-R theory framework, which offers valuable insights into the interplay between job characteristics and burnout. Additionally, the authors explore the gender perspective, specifically examining the impact of work-home conflict on the burnout levels of female individuals. This dual focus enhances the understanding of burnout dynamics, considering both theoretical underpinnings and gender-specific experiences in the workplace.

The review reveals that lower-ranked accounting professionals face a greater risk of burnout compared to their higher-ranked counterparts. Additionally, female professionals tend to experience heightened levels of burnout, primarily attributed to work–home conflict, as they often shoulder more domestic and familial responsibilities than their male counterparts. Flexible working arrangements have been shown to mitigate burnout among auditors. However, the transition to remote work during the pandemic yielded mixed outcomes, with professionals exhibiting increased susceptibility to burnout symptoms in some cases.

The study provides new insights into the relevance of flexible work arrangements in the accounting profession in the post-COVID-19 era. The paper also makes suggestions for further research on burnout within the context of the accounting profession.

Incivility can be used to target minority groups as a form of discrimination. This paper aimed to assess the extent to which older workers are particularly targeted by cyber incivility.

Study 1 used a cross-sectional design via an online crowdsourcing platform (N = 208). Study 2 (N = 227) employed a daily diary approach with an age diverse sample.

Age does not directly affect perceptions of cyber incivility, but moderates the relationships between cyber incivility and vitality and vigor. In Study 1, older workers experienced a weaker relationship between perceptions of cyber incivility and increased reports of vigor. Conversely, in Study 2, older workers who experienced cyber incivility reported reduced daily vitality both on the same day and the following day.

The discussion explores the nuances of vigor and vitality in older workers. Additionally, despite research on selective incivility, these studies suggest that while older workers may not be selectively targeted for cyber incivility, they struggle more with its repercussions.

This paper aims to develop and implement a machine learning recommendation engine – an adaptive learning engine that drives business revenue through the ranking and recommendation of offers at a granular customer level across the inbound marketing channels.

A data set of over 300,000 unique sample of mobile customers was extracted and prepared. The gradient boosting machine (GBM) algorithm was developed, consolidated, deployed and experimented on two inbound marketing channels.

Research examining machine learning implementation and operationalisation within the large consumer base is seemingly silent. This paper bridges this gap by developing and implementing a machine learning adaptive engine across two inbound marketing channels. The performance of the inbound channels revealed the significant importance of digital campaigns that are driven by machine learning algorithms. Machine learning techniques can be well positioned as an integral part of a large consumer base marketing operations with real-time one-to-one marketing capability.

The study explores the use of machine learning, a cutting-edge subset of artificial intelligence (AI), to drive consumer business revenue across different marketing channels. Further research should explore these marketing channels in greater depth by considering other branches of AI in driving consumer business revenue.

This study demonstrates the value, ease and application of a machine learning deployment in a consumer business with a large customer base in driving business revenue. It also shows customers' practical response to offerings across channels and the importance of the digital channel to firms with a large customer base.

The paper defines how machine learning extracts can be deployed and operationalised by marketers to drive business revenue. This approach is unique, realistic, easy to deploy and will guide future research in this space.

Extensive research has made it possible for service climate (SC) to establish itself as “a pivotal construct from a practical and theoretical perspective in the services marketing and management literatures” (Auh et al., 2011, p. 427). Key to that interest is the role granted to SC in several important outcomes, such as customer satisfaction and loyalty. A closer look at the theoretical arguments and empirical evidence supporting such role reveals, however, several fragilities. The main purpose of this paper is to present some of those fragilities considered to be particularly relevant to identify possible ways to avoid them.

An extensive review of SC literature was conducted leading to the identification of several fragilities regarding the role of SC in customers experiences.

The literature review conducted revealed several limitations that warrant some caution regarding the general consensus concerning the role of SC in customers experiences.

The originality of this paper lies in the identification of several important issues regarding the arguments and empirical support that have been ignored in the literature regarding the role of SC in customers experiences.

Although Hispanics represent a large and growing proportion of the US workforce, little is known regarding the possible effects of their somewhat unique cultural values, beliefs, and practices on their experiences of work-family conflict or job satisfaction. This research tested theoretically derived hypotheses regarding the protective effect of a component of familism values, family as a source of social support, on these outcomes.

A moderated mediation model was tested using survey data from Hispanic professionals (N = 103).

As predicted, family support was negatively related to work interfering with family (WIF) and positively related to job satisfaction. WIF mediated the relationship between family support and job satisfaction. Additionally, gender moderated this mediated relationship such that the effects were stronger for Hispanic women than men.

Generalizability of the results should be explored by employing larger samples that include longer tenured workers, employees with higher degrees of management experience, and additional Hispanic subgroups. Results contribute to a growing body of research demonstrating beneficial effects of familism values for Hispanics.

Results suggest that organizations may benefit from taking active steps to support familism values among Hispanic workers.

This is the first empirical study to explore the possible benefits of family support values on workplace outcomes.

The purpose of this study was to validate the psychometric properties of the Wise Leadership Questionnaire (WLQ).

Data were collected from three independent samples from Canada, China and Morocco (n = 616). Factor analysis, first- and second-order confirmatory factor analyses, structural equation modeling and Bayesian approach were used.

Study 1 confirmed that the WLQ higher-order factor structure is the most adequate theoretical model to capture the four-factor structure of the wise leadership scale, namely, intellectual shrewdness, spurring action, moral conduct and cultivating humility which are essential for a leader to qualify as wise. Study 2 assessed and supported the criterion-related validity by approving that the higher-order wise leadership construct constituted a predictor of work outcomes such as followers’ subordinates’ performance and job satisfaction. Confirmatory factor analysis results yielded a second-order factor of the wise leadership construct with four first-order factors, namely, the four wise leadership dimensions. The correlations between the four first-order factors (i.e. dimensions) and the second-order factor of the wise leadership are positive and statistically significant in both the China and Morocco samples. They are, respectively, as follows: intellectual shrewdness (β = 0.74; 0.62, p < 0.01), spurring action (β = 0.52; 0.76, p < 0.01), moral conduct (β = 0.76; 0.62, p < 0.01) and cultivating humility (β = 0.78; 0.69, p < 0.01).

Results suggest that the new wise leadership construct is positively associated with followers’ subordinates’ job performance and job satisfaction directly and indirectly through supervisory support, emphasizing the added value and relevance of the WLQ.

This study aims to examine the relationship between leadership behavior, organizational justice, person–organization fit and organizational citizenship behavior in the context of Vietnamese academic libraries. Using social exchange theory, this research indicates the impact of leadership behavior on organizational citizenship behavior. This study also examines the mediating role of organizational justice and person–organization fit in the relationship between leadership behavior and organizational citizenship behavior.

A total of 248 responses are obtained from academic library personnel in Vietnam, which are used to examine the research hypotheses.

The findings partially support the hypotheses because two leadership dimensions significantly influence organizational citizenship behavior, and organizational justice mediates the relationship between relationship-oriented leadership and organizational citizenship behavior. However, person–organization fit does not mediate the relationship between leadership behavior and organizational citizenship behavior.

Results indicate that two types of leadership behavior can significantly impact the organizational citizenship behavior of the librarians. Academic libraries should provide opportunities to librarians to engage in citizenship behavior by implementing organizational justice intervention.

This research contributes to the social exchange theory by integrating leadership behaviors, organizational justice, person–organization fit and organizational citizenship behaviors. Given that no prior studies have investigated the associations among four constructs, the obtained findings are a new exploration.