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This article investigates the application of generative artificial intelligence (GenAI) in experiential learning for authentic assessment in higher education. Recognized for its human-like content generation, GenAI has garnered widespread interest, raising concerns regarding its reliability, ethical considerations and overall impact. The purpose of this study is to explore the transformative capabilities and limitations of GenAI for experiential learning.

The study uses “thing ethnography” and “incremental prompting” to delve into the perspectives of ChatGPT 3.5, a prominent GenAI model. Through semi-structured interviews, the research prompts ChatGPT 3.5 on critical aspects such as conceptual clarity, integration of GenAI in educational settings and practical applications within the context of authentic assessment. The design examines GenAI’s potential contributions to reflective thinking, hands-on learning and genuine assessments, emphasizing the importance of responsible use.

The findings underscore GenAI’s potential to enhance experiential learning in higher education. Specifically, the research highlights GenAI’s capacity to contribute to reflective thinking, hands-on learning experiences and the facilitation of genuine assessments. Notably, the study emphasizes the significance of responsible use in harnessing the capabilities of GenAI for educational purposes.

This research showcases the application of GenAI in operations management education, specifically within lean health care. The study offers insights into its capabilities by exploring the practical implications of GenAI in a specific educational domain through thing ethnography and incremental prompting. Additionally, the article proposes future research directions, contributing to the originality of the work and opening avenues for further exploration in the integration of GenAI in education.

This study aims to investigate the combined use of System Dynamics (SD) applications in Enterprise Engineering (EE) research and practice. SD application in EE is becoming widely accepted as a tool to support decision-making processes and for capturing relationships within enterprises.

A systematic literature review (SLR) is conducted using a standard SLR method to provide a comprehensive review of existing literature. The search was conducted on ten platforms identifying 30 publications which were analysed through the use and development of a codebook.

The SLR showed that 90% of the result set consisted of peer-reviewed academic conferences and journal papers. The SLR identified a highly dispersed author set of 83 authors. Amongst these authors, Vinay Kulkarni was an active author who has co-authored up to four publications in this research area. The analysis further revealed that the combined use of SD applications and EE is an emerging research area that still needs to develop in maturity. While all phases of EE have received attention, the current research work is more focused on the design phase. The important gap between model development and implementation is identified.

The study elucidates the existing status of interdisciplinary research combining techniques from the SD and EE disciplines, suggesting future research topics that combine the strengths of these existing disciplines.

This study aims to conceptualize the characteristics of artificial intelligence (AI) dogs while exploring their applications in tourism and hospitality settings.

The total of 30 in-depth interviews were conducted, and data were analyzed through thematic analysis.

This study proposed differences between AI dogs and real dogs and human-like robots, core characteristics of AI dogs’ functions, a matrix of appearance and expectation regarding intelligence for AI dogs and human-like robots, the relationship between ethical barriers and task complexity, adoptions of AI dogs in different user segments and practical applications in hospitality and tourism settings, such as restaurants, city tour guides, extended-stay resorts and event organizations.

This research advances the field of tourism and hospitality studies by introducing the new concept of AI dogs and their practical applications. This present study adds new insights into the opportunities and contexts of human–robot interaction in the field of tourism and hospitality.

To the best of the authors’ knowledge, this research is one of the first studies of AI dogs in tourism and hospitality.

This study aims to identify the factors influencing the creation of a toxic workplace and to examine how to improve a toxic workplace in the electronics industry.

First, the factors that influence the creation of a toxic work environment were identified by reviewing the research literature. Then, the current status of each identified factor in Iran’s electronics industry was evaluated by developing a questionnaire. Based on the survey data, a relationship map between the factors influencing the toxic workplace was created using the fuzzy cognitive mapping technique. Finally, scenarios were designed to improve the toxic workplace.

The results show that the “workplace bullying” factor is the factor with the highest centrality in relation to other factors. The results of the scenario design indicate the effectiveness of the “unrealistic expectations at work” factor as a scenario trigger.

This study helps reduce the toxic workplace in the organization, which plays an important role in improving the employees’ work performance and the organization’s development.

A major obstacle regarding the measurement of an organization's sustainability and accountability in the space economy is defining the context and boundaries of commercial activity in outer space. Here, we introduce an ecosystem framework to address this obstacle. We utilize this framework to analyze the space mining sector. Our ecosystem framework sets the space mining sector's boundaries and helps a firm identify key stakeholders, activities, policies, norms and common pool resources in that sector and the interactions between them; a significant step in structuring how to measure space sustainability and accountability.

Borrowing theories and perspectives from a wide range of academic fields, this paper conducts a comprehensive context analysis of the space mining ecosystem.

Using our ecosystem framework to define the context and set boundaries for the space mining sector allowed us to identify sustainability-related issues in the sector and offer roadmaps to develop sustainability measures and standards.

To the best of the authors’ knowledge, this is one of the first papers to introduce a framework to define boundaries in the global space economy and provides a tool to understand, measure and evaluate the space mining sector's environmental, social and economic issues.

The purpose of the article is to shed light on how COVID-19 affects employee involvement in environmental responsibility and innovative performance in the banking industry, and whether employee engagement mediates the relationship between the variables. Thus, this study tries to understand bank employees’ perspectives in relation to the variables.

The study was collected during Time lag (1) and Time lag (2) from 156 to 216 bank employees, respectively. The study applied two types of analysis, to comprehend the impact of COVID-19 on employees, descriptive analysis and the partial least squares (PLS) are used.

The study's findings focused mainly on the influence of COVID-19 in Jordanian banks on employee innovative performance (EIP) due to pandemic, in addition to its effect on environmental responsibility engagement (ERE). The findings indicated a positive significant relationship between the variables. Meanwhile, employee engagement (EE) mediated the effect between the exogenous and endogenous variables.

The current research provide light on the value of employees' innovative performance and banks' commitment to environmental responsibility for those working in the banking industry, particularly during a pandemic. The findings have significant ramifications for the banking industry and in raising employee engagement.

This study aims to comprehensively address the challenge of delineating traffic scenarios in video footage captured by an embedded camera within an autonomous vehicle.

This methodology involves systematically elucidating the traffic context by leveraging data from the object recognition subsystem embedded in vehicular road infrastructure. A knowledge base containing production rules and logical inference mechanism was developed. These components enable real-time procedures for describing traffic situations.

The production rule system focuses on semantically modeling entities that are categorized as traffic lights and road signs. The effectiveness of the methodology was tested experimentally using diverse image datasets representing various meteorological conditions. A thorough analysis of the results was conducted, which opens avenues for future research.

Originality lies in the potential integration of the developed methodology into an autonomous vehicle’s control system, working alongside other procedures that analyze the current situation. These applications extend to driver assistance systems, harmonized with augmented reality technology, and enhance human decision-making processes.

This study aims to assess previously developed Electronic Health Records System (EHRS) implementation models and identify successful models for decision support.

A systematic review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The data sources used were Scopus, PubMed and Google Scholar. The review identified peer-reviewed papers published in the English Language from January 2010 to April 2023, targeting well-defined implementation of EHRS with decision-support capabilities in healthcare. To comprehensively address the research question, we ensured that all potential sources of evidence were considered, and quantitative and qualitative studies reporting primary data and systematic review studies that directly addressed the research question were included in the review. By including these studies in our analysis, we aimed to provide a more thorough and reliable evaluation of the available evidence.

The findings suggest that the success of EHRS implementation is determined by organizational and human factors rather than technical factors alone. Successful implementation is dependent on a suitable implementation framework and management of EHRS. The review identified the capabilities of Clinical Decision Support (CDS) tools as essential in the effectiveness of EHRS in supporting decision-making.

This study contributes to the existing literature on EHRS implementation models and identifies successful models for decision support. The findings can inform future implementations and guide decision-making in healthcare facilities.

This study aims to modify the human factors analysis and classification system (HFACS) to make it suitable for collapse accident analysis in construction. Based upon the modified HFACS, distribution patterns of causal factors across multiple levels were discerned among causal factors of various stakeholders at construction sites. It explored the correlations between two causal factors from different levels and further determined causation paths from two perspectives of level and stakeholder.

The main research framework consisted of data collection, coding and analysis. Collapse accident reports were collected with adequate causation information. The modified HFACS was utilized for coding causal factors across all five levels in each case. A hybrid approach with two perspectives of level and stakeholder was proposed for frequency analysis, correlation analysis and path identification between causal factors.

Eight causal factors from external organizations at the fifth level were added to the original HFACS. Level-based correlation analyses and path identification provided safety managers with a holistic view of inter-connected causal factors across five levels. Stakeholder-based correlation analyses between causal factors from the fifth level and its non-adjacent levels were implemented based on client, government and third parties. These identified paths were useful for different stakeholders to develop specific safety plans for avoiding construction collapse accidents.

This paper fulfils an identified need to modify and utilize the HFACS model for correlation analysis and path identification of causal factors resulting in collapse accidents, which can provide opportunities for tailoring preventive and protective measures at construction sites.

This study aims to provide a systemic and integrated view of how Industry 4.0 and its enabling technologies affect companies' internal and external environments. It offers a comprehensive view of the contribution about ten years after the start of the Fourth Industrial Revolution.

The study performs a systematic literature review based on Industry 4.0 management literature. Analyzing the results of the 308 final papers in the sample made it possible to build a theoretical model to explain the contribution of Industry 4.0 to the internal and external environment of the company.

The results highlight the contribution of Industry 4.0 to the processes and environment of the company by providing a systemic and integrated view, highlighting the most applied enabling technologies and their internal, external, and combined usefulness in business processes.

Finally, the paper provides a broad view of the Industry 4.0 topic ten years after its origin through an extensive literature analysis that allows us to highlight the significant studies and the areas still under-researched by researchers and opens the debate on the Industry 5.0 scenario.

The model makes it possible to appreciate the role of Industry 4.0 and its enabling technologies in companies in a broad and systemic view and to understand, from a managerial point of view, the interactions, synergies, and possibilities within processes and the reflection on the external environment.