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This study aims to provide insights into human–algorithm interaction in revenue management (RM) decision-making and to uncover the underlying heuristics and biases of overriding systems’ recommendations.

Following constructivist traditions, 20 in-depth interviews were conducted with revenue optimisers, analysts, managers and directors with vast experience in over 25 markets and working with different RM systems (RMSs) at the property and corporate levels. The hermeneutics approach was used to interpret and make meaning of the participants’ lived experiences and interactions with RMSs.

The findings explain the nature of the interaction between RM professionals and RMSs, the cognitive mechanism by which the system users judgementally adjust or override its recommendations and the heuristics and biases behind override decisions. Additionally, the findings reveal the individual decision-maker characteristics and organisational factors influencing human–algorithm interactions.

Although the study focused on human–system interaction in hotel RM, it has larger implications for integrating human judgement into computerised systems for optimal decision-making.

The study findings expose human biases in working with RMSs and highlight the influencing factors that can be addressed to achieve effective human–algorithm interactions.

The study offers a holistic framework underpinned by the organisational role and expectation confirmation theories to explain the cognitive mechanisms of human–system interaction in managerial decision-making.

The unforeseen disruption in workplaces triggered by COVID-19 has led many organizations to a sudden transition into virtual or remote working. The change posed various challenges to the project management community in managing their project and team members. The study intends to identify those challenges address the gap in current knowledge and literature and apply them as lessons learned for preparation for current and future remote work settings.

This is a qualitative research case study armed with semi-structured interview questions among nine experienced project managers based in Malaysia.

The qualitative research case study exposed the challenges faced by the project management community during the pandemic lockdown period and how they strived to deliver results despite the surrounding uncertainty. They did face motivation drops, excess workload and other stressors. The study revealed positive variables that was not detected by past literature, for instance how remote work reduces team conflict.

In Malaysia, most of such project management and pandemic-related studies focus on the construction industry. This study opens up research across multiple industries. There are not many articles that take the lessons learned from COVID-19 into future sustainability.

This study aims to investigate the differences in consumer reviews across multiple e-commerce platforms to better assist consumers in making informed decisions. By examining the specific content of these differentiated reviews, the study seeks to provide insights that can enhance e-commerce services and improve consumer satisfaction.

The research utilizes the latent Dirichlet allocation (LDA) method for text analysis to identify the varying concerns of consumers across different e-commerce platforms for the same product. Additionally, the study expands the sentiment dictionary to address polysemy issues, allowing for a more precise capture of sentiment differences among consumers. A non-parametric test is employed to compare reviews across multiple platforms, providing a comprehensive analysis of review disparities.

The findings reveal that consumer concerns and sentiments vary significantly across different e-commerce platforms, even for the same product. The combination of text analysis and non-parametric testing highlights the objectivity of the research, offering valuable evidence and recommendations for improving e-commerce services and enhancing the shopping experience.

This study is original in its approach to combining text analysis with non-parametric testing to examine multi-platform review differences. The research not only contributes to the understanding of consumer behavior in the context of e-commerce but also provides practical suggestions for platforms and consumers, aiming to optimize service quality and consumer satisfaction.

The study explores new aspects of financial investment management with technological involvement, providing detailed knowledge for future research. It identifies gaps in the literature and summarizes key research topics, utilizing a precise data collection framework.

The study is structured using systematic and bibliometric analysis with the antecedents, decisions, outcome-theories, context, and methods (ADO-TCM) framework. Data from Scopus and Web of Science were filtered based on Q1, Q2, social sciences citation index (SSCI) and Australian Business Deans Council (ABDC) criteria, resulting in 128 articles majorly emphasizing the last ten years. The “R” package facilitated bibliometric analysis, starting with data cleaning and import into Biblioshiny for effective results interpretation.

The study found that artificial intelligence detects and mitigates biases in investment decisions through rigorous pattern analysis, including social and ethical biases. The ADO-TCM framework revealed emerging theories, such as robo-advisory theory, offering new directions in behavioral finance for researchers and practitioners. The top authors and articles highlighted existing work in financial management.

The study’s originality is highlighted by its use of unique frameworks for data collection (SPAR-4-SLR) and interpretation (ADO-TCM).

This study aims to investigate the impact of artificial intelligence (AI) on customer loyalty in the luxury fashion market. It explores how AI-driven tools influence customer trust, satisfaction, commitment and engagement, which in turn affect loyalty. By examining these relationships, the study provides insights into the acceptance and effectiveness of AI technologies in enhancing customer loyalty within the luxury fashion sector.

This study employs structural equation modelling (SEM) to analyse data collected from 406 luxury consumers in Iran. The data was gathered using a targeted sampling procedure, leveraging DigiKala’s e-commerce platform. A comprehensive literature review informed the measurement items, and a seven-point Likert scale was used. The methodology includes confirmatory factor analysis (CFA) to assess the reliability and validity of the constructs, followed by hypothesis testing through SEM.

The study reveals that AI significantly enhances customer loyalty in the luxury fashion market by positively influencing trust, satisfaction, commitment and engagement. Satisfaction and engagement were found to be key mediators between AI and loyalty, while trust had no direct impact on loyalty. The results underscore the importance of AI-driven personalized experiences in fostering stronger customer relationships and loyalty.

This study is one of the first to explore the impact of AI on customer loyalty in the luxury fashion market, using a comprehensive model that includes trust, satisfaction, commitment and engagement as mediators. It extends the stimulus-organism-response (SOR) and technology acceptance model (TAM) frameworks, offering valuable insights for luxury brands on how AI can be leveraged to enhance customer relationships and loyalty.

This study aims to analyze the critical factors affecting workplace well-being at construction sites across countries with different income levels. Accordingly, this study’s objectives are to identify: critical factors affecting workplace well-being at construction sites in low-, lower-middle-, upper-middle- and high-income countries, overlapping critical factors across countries with different income levels and agreements on the critical factors across countries with different income levels.

This study identified 19 factors affecting workplace well-being using a systematic literature review and interviews with construction industry professionals. Subsequently, the factors were inserted into a questionnaire survey and distributed among construction industry professionals across Yemen, Zimbabwe, Malaysia and Saudi Arabia, receiving 110, 169, 335 and 193 responses. The collected data were analyzed using descriptive and inferential statistics, including mean, normalized value, overlap analysis and agreement analysis.

This study identified 16 critical factors across all income levels. From those, 3 critical factors overlap across all countries (communication between workers, general safety and health monitoring and timeline of salary payment). Also, 3 critical factors (salary package, working environment and working hours) overlap across low-, low-middle and upper-middle-income countries, and 1 critical factor (project leadership) overlaps across low-middle, upper-middle and high-income countries. The agreements are inclined to be compatible between low- and low-middle-income, and between low- and high-income countries. However, agreements are incompatible across the remaining countries.

This study can serve as a standard for maintaining satisfactory workplace well-being at construction sites.

To the best of the authors’ knowledge, this study is the first attempt to analyze factors affecting workplace well-being at construction sites across countries with different income levels.

Generative artificial intelligence (GenAI) is one of the most diffused AI technologies, capable of generating manifold forms of content, including music, text, images and synthetic data. The purpose of this study is to analyze the determinants that affect GenAI acceptance and its outcomes on both the explorative and exploitative forms of innovation.

The study employs a conceptual framework based on the technology-organization-environment (TOE) paradigm. Through Smart-PLS analysis, it examines empirical data retrieved from an online survey where 302 manufacturing companies took part.

It is found that GenAI has the potential to facilitate both exploratory and exploitative innovation, particularly via the moderating effect of environmental dynamism. Hence the adoption of GenAI has potential to improve organizational performance.

The study is the first empirical project to investigate factors that influence manufacturing firms' adoption of GenAI. As the first project to have integrated the TOE paradigm when examining the impact of environmental dynamism on exploratory and exploitative innovation, the study emphasizes the double innovation potential of GenAI in organizational performance improvement.

Acknowledging the importance of both lean thinking (LT) and Industry 4.0 (I4.0) for successful business performance and organisational success, the present study investigates the critical success factors (CSFs) for the concurrent implementation of both approaches, a field not yet significantly explored.

The study conducts two systematic literature reviews (SLRs), one on LT and the other one on I4.0 to map out the CSFs for the effective implementation of each approach. These CSFs are subsequently prioritised with the use of a Delphi Study. Finally, from the set of the common CSFs recognised through the two approaches, a more condensed list is put forward as the first step towards achieving a successful synergy between LT and I4.0.

The study’s findings suggest the most important CSFs and determine their definition in the context of a concurrent implementation of LT and I4.0. This can provide managers and practitioners with the awareness of crucial factors, enabling them to take the necessary steps for planning and implementing both approaches.

A concise set of CSFs for the concurrent implementation of both LT and I4.0 has been identified, which can be viewed as a starting point for providing top executives and managers with useful insights into enhanced business efficiency and performance. This study furthermore contributes to the overall body of knowledge on LT and I4.0.

The scholarly literature that explores a common set of CSFs for the concurrent implementation of LT and I4.0 is limited. This gap significantly enhances the importance of the present research, contributing to a better understanding amongst both academics and practitioners of the key supporting factors for the integration of the two approaches.

The purpose of the study is to identify the soft skills and abilities that are crucial to success in the fields of operations management (OM) and supply chain management (SCM), using the O*NET database and the classification of a set of professional figures integrating values for task skills and abilities needed to operate successfully in these professions.

The study used the O*NET database to identify the soft skills and abilities required for success in OM and SCM industries. Correlation analysis was conducted to determine the tasks required for the job roles and their characteristics in terms of abilities and soft skills. ANOVA analysis was used to validate the findings. The study aims to help companies define specific assessments and tests for OM and SCM roles to measure individual attitudes and correlate them with the job position.

As a result of the work, a set of soft skills and abilities was defined that allow, through correlation analysis, to explain a large number of activities required to work in the operations and SCM (OSCM) environment.

The work is inherently affected by the database used for the professional figures mapped and the scores that are attributed within O*NET to the analyzed elements.

The information resulting from this study can help companies develop specific assessments and tests for the roles of OM and SCM to measure individual attitudes and correlate them with the requirements of the job position. The study aims to address the need to identify soft skills in the human sphere and determine which of them have the most significant impact on the OM and SCM professions.

The originality of this study lies in its approach to identify the set of soft skills and abilities that determine success in the OM and SCM industries. The study used the O*NET database to correlate the tasks required for specific job roles with their corresponding soft skills and abilities. Furthermore, the study used ANOVA analysis to validate the findings in other sectors mapped by the same database. The identified soft skills and abilities can help companies develop specific assessments and tests for OM and SCM roles to measure individual attitudes and correlate them with the requirements of the job position. In addressing the necessity for enhanced clarity in the domain of human factor, this study contributes to identifying key success factors. Subsequent research can further investigate their practical application within companies to formulate targeted growth strategies and make appropriate resource selections for vacant positions.

This research examines the complex relationship between usage of Chat Generative Pre-Trained Transformer (ChatGPT) amongst student and their creativity, learning and assessment using empirical data collected from postgraduate students. In addition, the study explores the student’s intrinsic motivation for usage to understand student categories. This research seeks to provide further insights into this artificial intelligence tool in enhancing the educational ecosystem for all stakeholders concerned.

The target population of this research – the students of post-graduation in diverse fields of science and management. A five-point Likert scale-structured questionnaire adapted from earlier literature relevant to the research questions was adopted for data collection. The data were collected for two months, resulted in 403 usable responses. Ethical considerations of assurance of confidentiality to the participants were strictly adhered to. Structured equation modelling (SEM) was employed to explore the relationships between the constructs of the study for the assessment of latent relationships. SmartPLS 4 was used to explore these relationships.

Usage has a negative impact on a student’s creativity, but increased usage of ChatGPT encourages a student’s adoption due to its perceived usability. Pedagogical applications of ChatGPT aid students as a learning tool but require controlled usage under supervision.

This study is innovative in the context of postgraduate students, where very little evidence of creativity exists. Through this research, the authors illuminate how ChatGPT use affects academic performance, benefiting educators as a tool but for evaluation and assessment, policymakers and students. The findings of the study provide implications that help to create effective digital education strategies for stakeholders.