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This study investigates the relationships between environmental, social, and governance (ESG) controversies and insolvency risk in the insurance sector. Drawing from legitimacy and stakeholder theories, the authors explore the impact of ESG controversies on insurers’ insolvency risk and the moderating effect of ESG practices on this relationship.

This study utilises a dataset comprising 120 stock insurance firms spanning from 2011 to 2022. The authors employed system-GMM estimations to control for potential endogeneity and conducted several robustness checks.

ESG controversy positively influences insurers’ insolvency risk, with ESG practices mitigating these positive effects. The Governance (G) component of ESG practices plays a key role in counteracting the effects of ESG controversies on insurance companies’ insolvency risk.

This is the first study to investigate the direct relationship between ESG controversies and insolvency risk in the insurance industry. It underscores the critical influence of stakeholders’ perceptions of the company’s legitimacy, which is determined by the number of ESG controversies undertaken by the insurer company, on its insolvency risk. Additionally, by examining the three components of ESG practices individually, the authors offer insights into how managers can gain a competitive edge, particularly by utilising governance practices as safeguards against the adverse effects of ESG controversies on their financial risk.

This study aims to explore the role of sustainable fashion knowledge in shaping individual sustainable responsibility within the dynamic landscape of the fashion industry from a novel perspective, by exploring the intricate interplay between sustainable fashion knowledge, emotional and spiritual sustainable capacities.

A quantitative study was used, and a causal model with partial least squares structural equation modeling was developed. A total of 211 valid responses were obtained, and data were analysed to confirm the proposed hypotheses.

The findings confirm the positive impact of sustainable fashion knowledge on individual sustainable responsibility, mediated by both spiritual and emotional sustainable capacities. This study underscores the significance of individuals in influencing societal norms, prompting fashion companies to adopt sustainable practices.

The proposed conceptual framework integrates insights from the emotional and spiritual knowledge dynamics. This study uncovers the pathways through which individuals contribute to a more sustainable society.

The study not only advances the understanding of sustainable fashion practices but also provides actionable insights for policymakers, businesses and individuals seeking to foster a culture of sustainability in the fashion ecosystem.

Organisational slack has been widely considered in strategic management, but there is a gap in understanding the process of accumulation and application of slack resources. From a dynamic perspective and over an extended period of time, this paper analyses the management of slack resources and evaluates whether the different behaviours, in relation to the accumulation and consumption of slack resources, have any effect on performance.

The resource-based view and the dynamic extension of this theory, i.e. resource management and resource orchestration, were analysed in order to evaluate how slack resources can be managed and generate value. Assuming a configurational approach, the analysis was structured into two stages to answer the proposed hypothesis. The first stage studied whether there were different patterns of management of slack resources over time using the DistatisR package. The second stage evaluated which behaviours had the greatest impact in terms of profitability by using a dynamic panel data regression.

Three different types of slack resource management were found in companies: efficient, effective and erratic. Different types do not have the same impact on performance.

The dynamic management of slack resources has scarcely been considered, even during periods of crisis and economic expansion. This research advances the understanding of how firms transform slack resources into performance from a dynamic perspective.

Drawing on resource and capability theory, this study aimed to analyze the relationship between the dynamic capabilities (DC), the knowledge management (KM) process (KMP) and results in customers and people. More specifically, the study argues that the KM process mediates the relationship between DC and the results outlined above. In addition, a predictive analysis is carried out that demonstrates the relevance of the KM process in the model.

The study sample is made up of 118 Spanish organizations that have some kind of recognition of excellence awarded by the European Foundation for Quality Management (EFQM). Partial least squares methodology is used to validate the research model, the hypothesis testing and the predictive analysis.

The results show that organizations which leverage the DC through the KMP improve customer and people outcomes. Moreover, the predictive power is higher when the KMPmediates the relationship between the DC and the results.

There is no consensus in the literature on the relationship between DC, KM and performance. Moreover, there are also not enough papers that study KM or DC through the dimensions that define these constructs or variables. Given this need, this work considers the KMP according to the stages of knowledge creation, storage, transfer and application. Similarly, DC is dimensioned in sensing, learning, integrating and coordinating capabilities. These, as reconfigurators of knowledge assets, influence the KMP. Accordingly, the empirical model connects these knowledge domains and analyses their link to outcomes.

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.

The purpose of this study is to explore brand customer erosion at both the category and brand levels while considering consumer socio-demographic characteristics and weight of purchase factors.

Data from 3,563 buyers encompassing 20,601 purchases were collected from a prominent household data panel.

Brand customer erosion varies depending on socio-demographic factors (householder age, family size, life cycle and social class) and weight of purchase; variations are evident depending on the specific brand.

The paper makes a substantial contribution to the established fields of marketing and consumer behavior literature by opening a new line of research. It does so by demonstrating, the impact of socio-demographic factors on customer erosion. Simultaneously, it presents results that contradict the limited existing research on the influence of weight of purchase on brand customer erosion.

Urban centers, with their dense populations and evolving mobility patterns, are pivotal in addressing global sustainability challenges. This study focuses on identifying the key elements driving the adoption of sustainable urban mobility innovations, with a renewed emphasis on cycling as a core component.

Employing the Service Dominant Logic framework, this research examines how various conditions associated with the cycling ecosystem influence the adoption or negation of bicycles as a sustainable mode of urban transportation. The study conducts a comprehensive analysis across 60 cities to unravel these dynamics.

The investigation reveals that five distinct combinations of conditions facilitate the adoption of bicycles, while two specific combinations lead to its negation. Importantly, the study uncovers the presence of a “lock-in” mechanism, a critical factor in hindering bicycle adoption in urban settings.

This research contributes significantly to the field of sustainable urban mobility by integrating Service-Dominant Logic with empirical findings from a diverse set of global cities. It provides valuable insights into the complex interplay of factors influencing cycling adoption, offering a nuanced understanding of the barriers and drivers in this domain. The identification of a “lock-in” mechanism as a key impediment to cycling adoption adds a novel dimension to existing literature, presenting actionable pathways for policymakers and urban planners to foster more sustainable and bike-friendly urban environments.

This study aims to investigate the influence of the theory of planned behavior (TPB), innovation and sustainability, through environmental awareness, in the formation of entrepreneurial intention (EI).

Based on the alignment between theoretical axes, such as the theory of planned behavior, adaptation–innovation theory and the environmental axis of sustainability, quantitative research was developed with students from Brazilian and Spanish universities. We obtained a sample of 686 university students enrolled in courses related to management in both countries. Data analysis included exploratory factor analysis and logistic regression.

Our results revealed that the constructs of TPB (personal attitudes and perceived behavioral control) influenced EI, excluding subjective norms. Innovation had an influence on the EI of students from both countries. In Brazilians, valuing change predicts EI, highlighting the importance of flexibility. Conversely, Spaniards emphasize originality, valuing uniqueness and creativity as drivers of EI. Sustainability, via environmental awareness, did not influence EI.

With this study, we aim to encourage policies and practices aimed at higher education institutions globally, to broaden the understanding about the importance of environmental, innovative and social studies and practices aligned with the context of entrepreneurship. The final aim is to enhance social and environmental benefits generated by future entrepreneurs.

The Metaverse is rapidly reshaping the understanding of tourism, yet the public perception of this new domain remains largely uncharted empirically. This paper aims to build on the technology acceptance model (TAM) and diffusion of innovations theory (DIT) to fill this gap, offering crucial insights that could inform scholars and practitioners in both the tourism and technology sectors.

Using a big-data approach, the authors applied machine learning to scrape comments made by social media users on recent popular posts or videos related to tourism in the Metaverse from three prominent social media platforms. The cleaning process narrowed down 15,461 comments to 2,650, which were then analysed using thematic, emotion and sentiment analysis techniques.

The thematic analysis revealed that virtual tourism evokes a complex range of public beliefs. While many express awe and excitement toward its immersive capabilities, others remain sceptical about authenticity compared to physical travel. Additional themes show people draw comparisons to real-world tourism, discuss technology’s role and note educational value and novelty. However, some comments raise concerns about potential societal harms, exploitation and mental health impacts. Sentiment analysis found over half of the comments positive, though some were negative. Emotion analysis showed contentment, happiness and excitement as most frequent, though sadness, worry and loneliness also featured. Overall, perceptions of Metaverse tourism encompass enthusiasm yet substantial ambivalence.

To the best of the authors’ knowledge, this study is one of the first to comprehensively analyse public discussions on Metaverse tourism. It takes TAM and Rogers’s DIT a step further and provides fresh insights into how these theories can be employed in the emerging field of Metaverse tourism. The themes revealed new conceptual insights into multidimensional factors shaping public beliefs about Metaverse tourism and thus informing scholarly research on virtual interaction and technology acceptance regarding Metaverse tourism. In addition, the results can help tourism providers, platforms and marketers address salient public beliefs and sentiments/attitudes in developing marketing offerings, experiences and communications. Over time, this analysis methodology can be used to track the evolving public perceptions of Metaverse tourism.

Natural disasters represent an increasing threat to businesses, putting at risk their continuity in light of sustainable performance conditions. The present chapter explores the role of organizational resilience and of human capital in manufacturing companies hit by a natural disaster, an earthquake in the current study, by considering performance in the long run. In doing so, a survey has been performed on a sample of 131 manufacturing companies hit by the Emilia earthquake (Italy) in 2012, considering both perceptual data and balance sheet data. This represents a key contribution of this chapter, as extant literature on the impact of resilience on business performance has mainly used perceptual data; conversely, our study, considering balance sheet data, enables a more comprehensive and realistic view of the phenomenon. The sample was selected from the AIDA database, as it includes revenue data that we could add to the perceptual measures obtained by administering a structured questionnaire. Partial least squares structural equation modeling (PLS-SEM) was then employed. The results show the importance of developing adaptive processes that leverage on the organization’s human capital and resilience to respond to adverse exogenous events. More specifically, it has been found that human capital and organizational resilience are profitable to post-disaster economic performance in the long run, supporting the economic sustainability of affected businesses. The implications are related to reinforcing new business solutions and adaptive strategies, looking at both organizational resilience and human capital investment to reach a stable economic business performance in the long-run after a detrimental event.