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Understanding the restorative potential of built environments is essential for promoting mental well-being. However, existing studies often rely on static image-based methods, which are inherently limited in capturing the temporal and spatial dynamics of environmental perception. These methods frequently introduce biases, such as selective framing and abrupt transitions, failing to reflect natural viewing behavior. Addressing these limitations, this study investigates the restorative qualities of campus environments using dynamic VR stimuli and eye-tracking technology. By providing continuous temporal and spatial information, dynamic VR stimuli offer a more immersive and ecologically valid approach to understanding how specific environmental features contribute to psychological restoration.

This study investigates the restorative qualities of campus environments using VR eye-tracking technology and dynamic stimuli. Campus environments were filmed through walking sequences and paired with PRS audio prompts. About 40 university students participated in the experiment, with eye-tracking data processed using computer vision-based semantic segmentation and the concept of relative areas of interest, followed by correlation analysis with restorative quality scores.

The results revealed that natural elements such as “sky,” “tree,” “waterscape” and “landscape corridor” were significantly positively correlated with the being-away and fascination dimensions, indicating their role in capturing attention and supporting psychological recovery. Conversely, architectural elements like “architectural corridor” and “building facade” were negatively correlated with the extent dimension, while “architectural open space” positively correlated, enhancing spatial perception and exploration.

These findings underscore the importance of natural elements and open spaces in built environments while also revealing the complex influence of architectural features. The study provides valuable insights for optimizing campus design to support students’ mental health and well-being.

Intelligence transformation has hugely influenced business operation in many industries and countries, such as in the emerging market. Generative Artificial Intelligence (GAI) adoption by organisations is a significant result of transformation. However, the influence of GAI adoption on small and medium-sized enterprises (SMEs) has been given less attention in business and management studies. In particular, managing the relationship of employees in GAI adoption is a focal point during the transformation from an ethical, responsible and sustainable perspective. Drawing on organisational socialisation and technology adoption theories, this chapter develops a process of socialising newcomers and/or existing employees in the development of GAI adoption in their workflow with identification of challenges and strategies to the adapt to the change. This discussion can help managers and other key persons to effectively manage the relationship and interactions between employees and technology (GAI) in a more ethical, responsible and sustainable manner.

Artificial intelligence (AI) will transform various processes by utilizing and sharing data and information. This transformation brings new opportunities and challenges to organizations. Effective leadership is essential to handle these changes. However, there is no scientific research on how AI affects the everyday lives of managers. Therefore, this paper aims to identify how AI can affect changes in the skills and personality traits of managers using AI.

A structured literature review identified leadership competencies relevant to the AI era. Three scientific databases were included in the search: (I) Scopus, (II) EBSCO Business Source Complete, and (III) Web of Science. A total of 730 articles were identified from the three databases under the topics “Digital Leadership,” “Leadership” AND “Artificial Intelligence,” “Future Leadership,” “Algorithm Leadership,” “AI Leadership,” “Artificial Leadership,” and “Data-driven Leadership.”

A total of 24 leadership competencies, including 12 personality traits and 12 skills, were identified from the literature. To adapt effectively to AI, leaders should focus on developing communication skills and forming high-performance teams working cross-functionally and in a symbiosis of humans and machines.

The article adds knowledge to leadership theories and provides a basis for future management education.

Previous studies highlighted a dearth of scale to measure algorithm aversion among working professionals. Accordingly, this study tried to adopt and validate an algorithm aversion scale for employees.

The scale was developed and validated in three stages. In the first stage, a five-item xenophobic scale was adopted. Its items were rephrased to match the algorithm aversion-related workplace context. In the second stage, the sample was divided into two subsamples using random sampling. The exploratory factor analysis (EFA) applied on the first subsample. The confirmatory factor analysis (CFA) was conducted on the second sample to check the model’s fitness proposed by EFA. In the third stage, a sample of 378 respondents was collected. The Cronbach’s alpha (CA) and composite reliability (CR) values checked internal consistency reliability of the proposed algorithm aversion scale.

The EFA yielded a one-factor solution with acceptable factor loading (greater than 0.50) of all (five) statements. All values of model indices in CFA were in the acceptable range, which recommended the factorial structure of the scale. The acceptable (greater than 0.70) values of CA and CR concluded the internal consistency reliability. The convergent validity was suggested by appropriate average variance extracted (AVE) values (greater than 0.50). In addition, as expected, the proposed algorithm aversion scale reported a statistically significant positive correlation with neuroticism and a considerable negative relationship with trust. These findings concluded the criterion validity of the scale.

This study uses primary data to adopt and validate algorithm aversion scale.

The study’s objective is to assess the impact of employer branding (EB) on person–job fit (PJF), work engagement (WE), internal communication (IC) and employee performance (EP) in tourism businesses. In addition, the study evaluated the mediating roles of PJF, WE and IC between EB and EP.

To gather data, the questionnaire was distributed to 1,024 employees in travel agencies and 746 questionnaires valid for statistical analysis were received.

The findings indicated the positive influence of EB on PJF, WE, IC and EP. These findings also revealed the positive effects of PJF, WE and IC on EP. The partial mediating functions of PJF, WE and IC in the nexus between EB and EP, and the mediating function of PJF in the nexus between EB and WE are highlighted.

General managers should invest in creating a strong EB that highlights the organization's core values, culture and unique features, such as personal development opportunities and a supportive environment.

This study presents and empirically examines an innovative theoretical model that thoroughly evaluates the effects EB, PJF, WE and IC on EP in the lens of Social Exchange Theory (SET).

This conceptual paper provides an overview of eye-tracking technology (ETT) as a process-tracing method to study children’s consumer behavior. This paper aims to provide a literature review of the child as consumer-focused eye-tracking research, identifying theoretical and research gaps. This paper discusses the benefits, drawbacks and ethical implications of ETT and highlights opportunities and best practices for future research.

This paper details eye movement and how ETT captures it. This paper delivers a typology of different approaches to eye-tracking research and presents information about ETT equipment and metrics. This paper also summarizes previous studies using eye tracking to study children. This paper concludes with research questions and topics for future research where ETT can be used to study child consumers in contexts such as playing advergames, using social media and shopping (in-person and online).

ETT is commonly used to assess children’s behavior in a variety of contexts but has scarcely been used to study children’s consumer behavior. ETT has great potential to be used to better understand children as consumers.

To the best of the authors’ knowledge, this is the first paper to review using ETT to study children’s consumer behavior.

The advent of artificial intelligence (AI) in the accounting landscape marks a significant shift, promising gains in efficiency and accuracy but also eliciting concerns about job displacement (JD) and broader socio-economic implications. This study aims to provide an in-depth understanding of how AI’s integration in accounting contributes to JD, reshapes decision-making processes and reverberates across economic and social dimensions. It also offers evidence-based policy recommendations to mitigate adverse outcomes.

Leveraging a cross-sectional survey disseminated through Facebook, this research used snowball sampling to target a diverse cohort of accounting professionals. The collected data were subjected to meticulous analysis through descriptive and regression models, facilitated by SmartPLS 4 software.

The analysis revealed a significant correlation between AI’s increasing role in accounting and a heightened rate of JD. This study found that this displacement is not isolated; it has tangible repercussions on decision-making paradigms, economic well-being, professional work dynamics and social structures. These insights corroborate existing frameworks, including, but not limited to, theories of technological unemployment and behavioural adjustments.

Although providing valuable insights, this study acknowledges limitations such as the restricted sample size, the cross-sectional nature of the survey and the inherent biases of self-reported data. Future research could aim to extend these initial findings by adopting a longitudinal approach and potentially integrating external data sources.

As AI technology becomes increasingly ingrained in accounting practices, there is an urgent need for coordinated action among stakeholders. Policy recommendations include focused efforts on talent retention, investment in upskilling programs and the establishment of support mechanisms for those adversely affected by AI adoption.

By synthesising a range of theoretical perspectives, this study offers a comprehensive exploration of AI’s multi-dimensional impacts on the accounting profession. It stands out for its nuanced examination of JD and its economic and social implications, thereby contributing to both academic discourse and policy formulation. This work serves as an urgent call to action, highlighting the need for strategies that both exploit AI’s potential benefits and protect the workforce from its disruptive impact.

This study aims to explore emerging trends in mental health and work performance research, identifying key themes and future research directions.

This study analyzed 743 relevant documents downloaded from Scopus database published between 2014 and 2024 using VOSviewer software for co-citation and co-occurrence analyses.

Findings reveal significant growth in research focusing mental health and work performance research. The western countries, such as USA, United Kingdom and Australia, emerge as leading contributors to the field, with medicine and psychology dominating the subject areas. The analysis highlights a complex interplay between employee mental health and work performance, underscoring the need for more interdisciplinary research. The results emphasize the necessity for customized interventions, long-term impact studies, cross-cultural research, technological innovations and policy advocacy to effectively address mental health issues in the workplace.

To the best of the authors’ knowledge, this study contributes to a deeper understanding of current research trends on mental health and work performance for the first time in the literature and provides a foundation for future investigations. This comprehensive analysis offers valuable insights for researchers, practitioners and policymakers seeking to enhance employee well-being and optimize work performance in contemporary organizational settings.