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This study builds on previous discussion of an important area for both academics and academic journals – the issue of reviewers inappropriately asking for (or “coercing”) citation of their own work. That situation creates an opportunity for (hopefully a small number of) academics to engage in unethical behaviour, often with the goal of increasing their citation count. This study aims to draw attention to this often-overlooked issue, critically considering potential reviewer motivations and offering possible remedies.

This study reviews literature and critically discusses this issue, offering a typology for coercive citation suggestions and sharing previously unpublished commentary from Editors of leading journals.

This study provides a typology of reviewer motivations for coercing citations, suggests potential remedies and considers the positive and negative impacts of these suggestions.

This study identifies an area known from multiple discussions to be important to academics and Editors, where many want changes in journals’ practices. In response, this study provides recommendations for easy changes that would decrease the opportunity for unethical behaviour by reviewers and also, for some journals, improve the quality of reviews.

This study investigates the service quality dimensions of hotel resorts in renowned beach destinations of Thailand. It also explores the relationship between review text sentiment expressed in online platforms and the satisfaction ratings provided for those reviews.

The study employs a two-step analysis approach: first, supervised and unsupervised machine learning via support vector machine (SVM) and latent Dirichlet allocation (LDA) are used to identify service quality dimensions, and second, SmartPLS with PROCESS macro is applied to analyze the moderating roles of quality signals and reviewer’s experience on the relationship between sentiment and satisfaction rating. The dataset comprises 102,179 online reviews from TripAdvisor, focusing on 187 selected hotels rated from 3 to 5 stars.

Eight service quality dimensions were identified, including leisure activities, tangibles and surroundings, reliability, responsiveness, service process, food, empathy and ambience. The study underscores that the service process stands as the sole dimension exhibiting negative sentiment. Furthermore, the analysis revealed a robust positive association between sentiment of review texts and satisfaction, and reviewers’ experience and brand affiliation influenced the relationship between customer sentiment and satisfaction.

Hotel managers should focus efforts on maintaining tangible aspects while enhancing existing service quality level of other dimensions, particularly those related to intangible elements. Independent hotels might implement quality audit to ensure that service quality gaps are monitored.

This study contributes an examination of the moderating roles of quality signals and reviewer’s experience on the relationship between review sentiment and satisfaction rating in online reviews.

Engaging in webcare, i.e. responding to online reviews, can positively affect consumer attitudes, intentions and behavior. Research is often scarce or inconsistent regarding the effects of specific webcare strategies on business performance. Therefore, this study tests whether and how several webcare strategies affect hotel bookings.

We apply machine learning classifiers to secondary data (webcare messages) to classify webcare variables to be included in a regression analysis looking at the effect of these strategies on hotel bookings while controlling for possible confounds such as seasonality and hotel-specific effects.

The strategies that have a positive effect on bookings are directing reviewers to a private channel, being defensive, offering compensation and having managers sign the response. Webcare strategies to be avoided are apologies, merely asking for more information, inviting customers for another visit and adding informal non-verbal cues. Strategies that do not appear to affect future bookings are expressing gratitude, personalizing and having staff members (rather than managers) sign webcare.

These findings help managers optimize their webcare strategy for better business results and develop automated webcare.

We look into several commonly used and studied webcare strategies that affect actual business outcomes, being that most previous research studies are experimental or look into a very limited set of strategies.

Tourism, Technology and Climate Change: The tourism industry is indispensable both for its socio-cultural offerings and its profound economic implications. The economic multiplier effects inherent in the drivers of tourism can stimulate the regional economy even before these areas emerge as tourism meccas. While vast amounts of research have detailed tourism's overarching significance, there is an evident void in understanding its multifaceted impacts, particularly where technological advances, environmental performance (EP) and economic benefits converge. A thorough examination of 907 research records led to this chapter, which identifies these gaps by referencing nine observational and 11 intervention studies. Achieving a Cohen's kappa value of 0.75, the authors note a strong consensus among reviewers, adhering to Cohen's (1940) standards. The findings from the first quarter highlight several areas within the tourism industry that have been under-researched. Particularly, the integration of technology, from ATM infrastructures enhancing tourist financial experiences to digital platforms elevating traveller education and awareness, and tech-driven solutions addressing demographic and ethical considerations in tourism, remains insufficiently explored. Additionally, the authors recognise an existing gap in knowledge regarding the nexus between tourism development and its climatic repercussions, especially before tourism ventures are fully realized. This chapter aims to channel future research into these lesser-trodden areas, fostering a comprehensive grasp of tourism's evolution in the face of rapid technological advancements and its interplay with environmental shifts.

This study explores the link between additional reviews, credibility, and consumers’ online purchasing behavior.

We employ a 2 × 2 between-subjects design to measure subjects’ purchasing behavior with versus without additional reviews and with important versus non-important attributes. A total of 529 valid questionnaires are collected from university students across 30 Chinese provinces.

The addition of negative reviews to a positive initial review enhances consumers’ perceived credibility of the reviewer and the overall review content. This effect is positively moderated by the attribute importance in additional reviews. Moreover, we find that as the time interval increases, consumers’ perceived credibility gradually increases but eventually decreases after reaching a certain threshold. In addition, the attribute importance in additional reviews negatively moderates the impact of perceived credibility on consumer purchasing behavior.

Existing studies on first and subsequent reviews mainly focus on the difference in perceived usefulness between the two. They do not examine how additional reviews affect potential customers’ perceived credibility and their purchase decision-making. This study bridges the gap between the word-of-mouth literature and marketing practices.

The objective of this research is to investigate the capabilities of the ChatGPT GPT-4 model, a form of artificial intelligence (AI), in comparison to human experts in the context of construction project risk management.

Employing a mixed-methods approach, the study draws a qualitative and quantitative comparison between 16 human risk management experts from Finnish construction companies and the ChatGPT AI model utilizing anonymous peer reviews. It focuses primarily on the areas of risk identification, analysis, and control.

ChatGPT has demonstrated a superior ability to generate comprehensive risk management plans, with its quantitative scores significantly surpassing the human average. Nonetheless, the AI model's strategies are found to lack practicality and specificity, areas where human expertise excels.

This study marks a significant advancement in construction project risk management research by conducting a pioneering blind-review study that assesses the capabilities of the advanced AI model, GPT-4, against those of human experts. Emphasizing the evolution from earlier GPT models, this research not only underscores the innovative application of ChatGPT-4 but also the critical role of anonymized peer evaluations in enhancing the objectivity of findings. It illuminates the synergistic potential of AI and human expertise, advocating for a collaborative model where AI serves as an augmentative tool, thereby optimizing human performance in identifying and managing risks.

The aim of this study is to review the literature on how intellectual capital (IC) contributes to the decarbonization efforts of firms. It explores how carbon accounting can measure the components of IC in decarbonization efforts to balance profitability with environmental and social goals, particularly in promoting decent work and economic growth (Sustainable Development Goal [SDG] 8 and its targets [2, 5, 6, 8]). Moreover, it emphasises the importance of multi-stakeholder partnerships for sharing knowledge, expertise, technology, and financial resources (SDG17-Target 17.G) to meet SDG8.

As a consolidated methodological approach, a systematic literature review (SLR) was used in this study to fill the existing research gaps in sustainability accounting. To consolidate and clarify scholarly research on IC towards decarbonization, 149 English articles published in the Scopus database and Google Scholar between 1990 and 2024 were reviewed.

The results highlight that the current research does not sufficiently cover the intersection of carbon accounting and IC in the analysis of decarbonization practices. Stakeholders and regulatory bodies are increasingly pressuring firms to implement development-focused policies in line with SDG8 and its targets, requiring the integration of IC and its measures in decarbonization processes, supported by SDG17-Target 17.G. This integration is useful for creating business models that balance profitability and social and environmental responsibilities.

The integration of social dimension to design sustainable business models for emission reduction and provide a decent work environment by focusing on SDG17-Target 17.G has rarely been investigated in terms of theory and practice. Through carbon accounting, IC can be a key source of SDG8-Targets 8.[2, 5, 6, 8] and SDG17-Target 17.G. Historically, these major issues are not easily aligned with accounting research or decarbonization processes.

The purpose of this study is to identify and synthesise the best evidence on health-care leaders’ and professionals’ experiences and perceptions of compassionate leadership.

A mixed-methods systematic review was conducted in accordance with the Joanna Briggs Institute methodology for mixed-methods systematic reviews using a convergent integrated approach. A systematic search was done in January 2023 in PubMed, CINAHL, Scopus, Medic and MedNar databases. The results were reported based on Preferred Reporting Items for Systematic Reviews and Meta-analyses. The data was analysed using thematic analysis.

Ten studies were included in the review (five qualitative and five quantitative). The thematic analysis identified seven analytical themes as follows: treating professionals as individuals with an empathetic and understanding approach; building a culture for open and safe communication; being there for professionals; giving all-encompassing support; showing the way as a leader and as a strong professional; building circumstances for efficient work and better well-being; and growing into a compassionate leader.

Compassionate leadership can possibly address human resource-related challenges, such as health-care professionals’ burnout, turnover and the lack of patient safety. It should be taken into consideration by health-care leaders, their education and health-care organisations when developing their effectiveness.

This review synthesised the knowledge of compassionate leadership in health care and its benefits by providing seven core elements of health-care leaders’ and professionals’ experiences and perceptions of compassionate leadership.

While founder characteristics have been studied extensively as a factor in attracting employees, organizational attributes of startups have received little attention. To fill the void, this study aims to examine organizational attributes of startups to attract employees.

This research collected 8,817 online employer reviews of 117 startups from Glassdoor.com. This research conducted two studies. In Study 1, a topic modeling approach was applied to identify startup attributes to attract employees having actual employer experience and to examine the importance and relationship of the attributes, drawing the person–organization fit theory. Study 2 used logistic regression analysis to explore the effect of identified attributes on employer attractiveness based on the signaling theory.

Study 1 found six attributes: high-quality coworkers, entrepreneurial culture, development, company outlook, physical office and stereotypical startup perks. This study also found high-quality coworkers, entrepreneurial culture and development are not only related to each other but also more critical attributes than other attributes. The core value of the three attributes can be conceptualized as “learning opportunity.” Study 2 found that employees attracted to “learning opportunity” are more likely to recommend their employers than instrumental attributes, including physical office and stereotypical startup perks.

This research elaborates on employer attributes to attract employees in the context of startup based on the real voices of employees having actual employer experience. Furthermore, this research extends the understanding of employer attributes contributing to employer attractiveness by combining qualitative review data with quantitative rating data.

The current study has a two-fold purpose. Firstly, it aims to analyze the extent to which knowledge management (KM) affects the performance of individuals (task and contextual) on the one hand and that of organizations (product or service, perceived and financial) on the other hand. Secondly, it proposes to investigate the mediating effect of motivation and innovation in the relationship between KM and individual and organizational performance.

Partial least squares structural equation modeling (PLS-SEM) was employed in this study, with mediation analysis performed using advanced PLS-SEM techniques. A total of 1,284 respondents from organizations in both the public and private sectors were included in the sample.

The findings emphasize that KM has a more significant direct effect on individual performance compared to organizational performance. Concurrently, in terms of indirect influence, it is found that KM, through motivation and innovation, has a positive and significant effect on both individual and organizational performances, with a higher influence on the organizational one.

The originality of the work can be noted in designing two different structural models to represent the proposed relationships at the individual and organizational levels. These findings could provide organizational decision makers with empirical evidence, helping them (1) internalize the significance of the KM process in organizations as well as its subsequent effects on individual and organizational performance and (2) identify factors that mediate variable relationships.