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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.

The purpose of this paper is to explore how GenAI can help companies achieve a higher level of hyper-segmentation and hyper-personalization in the tourism industry, as well as show the importance of this disruptive tool for tourism marketing.

This paper used the Web of Science and Google Scholar databases to provide updated studies and expert authors to explore GenAI in the tourism industry. Analysing hyper-segmentation and hyper-personalization modalities through GenAI and their new challenges for tourists, tourism cities and companies.

Findings reveal that GenAI technology exponentially improves consumers’ segmentation and personalization of products and services, allowing tourism cities and organizations to create tailored content in real-time. That is why the concept of hyper-segmentation is substantially focused on the customer (understood as a segment of one) and his or her preferences, needs, personal motivations and purchase antecedents, and it encourages companies to design tailored products and services with a high level of individual scalability and performance called hyper-personalization, never before seen in the tourism industry. Indeed, contextualizing the experience through GenAI is an important way to enhance personalization.

This paper also contributes to enhancing and bootstrapping the literature on GenAI in the tourism industry because it is a new field of study, and its functional operability is in an incubation stage. Moreover, this viewpoint can facilitate researchers and companies to successfully integrate GenAI into different tourism and travel activities without expecting utopian results. Recently, there have been no studies that tackle hyper-segmentation and hyper-personalization methodologies through GenAI in the tourism industry.

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 present research aims to explore the drivers of generative artificial intelligence (GEN AI)-based innovation adoption in the hospitality industry in Jordan.

To address the research gap and achieve the research work objectives, the Technology-Organization-Environment (TOE) lens and the structural equation modeling (SEM) approach were employed to analyze the sample data collected (n = 221) from the hospitality industry.

The findings indicate that relative advantage, top management support, organizational readiness, organizational culture, competitive pressures, government regulations support and vendor support significantly influence the GEN-AI-based innovation adoption, while the technological complexity is negatively associated with GEN-AI-based innovation adoption. Furthermore, the results showed there is no significant effect of cost on GEN-AI-based innovation adoption.

The paper analyses the TOE framework in a new technological setting. The paper also provides information about how GEN-AI-based innovation adoption may influence hospitality industry performance. Overall, this article provides new insights into the literature concerning AI technologies and through the TOE lens.

This study addresses the gap in understanding text analytics within the service domain, focusing on new service development to provide insights into key research themes and trends in text analytics approaches to service development. It explores the benefits and challenges of implementing these approaches and identifies potential research opportunities for future service development. Importantly, this study offers insights to assist service providers to make data-driven decisions for developing new services and optimising existing ones.

This research introduces the hybrid thematic analysis with a systematic literature review (SLR-TA). It delves into the various aspects of text analytics in service development by analysing 124 research papers published from 2012 to 2023. This approach not only identifies key practical applications but also evaluates the benefits and difficulties of applying text analytics in this domain, thereby ensuring the reliability and validity of the findings.

The study highlights an increasing focus on text analytics within the service industry over the examined period. Using the SLR-TA approach, it identifies eight themes in previous studies and finds that “Service Quality” had the most research interest, comprising 42% of studies, while there was less emphasis on designing new services. The study categorises research into four types: Case, Concept, Tools and Implementation, with case studies comprising 68% of the total.

This study is groundbreaking in conducting a thorough and systematic analysis of a broad collection of articles. It provides a comprehensive view of text analytics approaches in the service sector, particularly in developing new services and service innovation. This study lays out distinct guidelines for future research and offers valuable insights to foster research recommendations.

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.

The sustainable development goals (SDGs) devised by the United Nations (UN) call on countries – whether rich or poor – to solve global issues, improve lives and save the planet for future generations. However, the UN predicts that between $5 and $7tn will need to be spent annually between now and 2030 to accomplish these goals, posing a major financial hurdle. Islamic social finance, if used ethically, seeks to realise SDGs through fairness, justice and equity. Thus, this study aims to determine how Islamic social finance instruments such as Zakat, Waqf, Sadaqat and Qard-hasan contribute to realising SDGs.

This study used a preferred reporting items for systematic reviews and meta-analyses-based systematic literature review. Scopus and Google Scholar were chosen for the qualitative and meta-analysis of studies. The topic was reviewed in 178 academic papers from 2000 to 2022. The required articles were analysed after careful review.

Islamic social financing mechanisms have the capacity to solve many social issues and create better welfare conditions by ensuring economic, social and environmental sustainability in line with the SDGs. Indonesia and Malaysia lead Islamic social finance research, the survey found. The review revealed that Islamic social funding can achieve 11 out of 17 SDGs. Islamic commercial finance can be used for the remaining goals. The paper highlights Islamic social funding research limitations and opportunities.

The review study shows that Islamic social finance can fill the SDG funding gap, especially considering the post-pandemic financial crisis that has increased global income inequality and social disparities.

To the best of the authors’ knowledge, this article is the first of its kind to review the potential of Islamic social financing instruments to help achieve the SDGs.

The purpose of this study is to investigate the effect of coaxial swirlers on acoustic emission and reduction of potential core length in jet engines.

The swirlers are introduced in the form of curved vanes with angles varied from 0° to 130°, corresponding to swirl numbers of 0–1.5. These swirlers are fixed in the annular chamber and tested at different nozzle pressure ratios of 2, 4 and 6.

The study finds that transonic tones exist for the nonswirl jet, creating an unfavorable effect. However, these screech tones are eliminated by introducing a swirl jet at the nozzle exit. Weak swirl shows a greater reduction in noise than strong swirl at subsonic conditions. In addition, the introduction of swirl jets at all pressure ratios significantly reduces jet noise and core length in supersonic conditions, mitigating the noise created by shockwaves and leading to screech tone-free jet mixing.

The paper provides valuable insights into the use of coaxial swirlers for noise reduction and core length reduction in jet engines, particularly in supersonic conditions.