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The aim of this paper is to study the relationship between banks' ownership structure and their risk-taking behavior as well as the impact of banking regulation on banks' approach to taking risk, after the 2008 financial crisis.

The empirical analysis considers a sample of listed banks from European Union (EU) countries, over the period of 2011–2016 and uses the generalized least squared (GLS) random effect (RE) method, following Baltagi and Wu (1999) and Pathan (2009).

The authors find that the structure of the board of directors can influence bank risk behavior but not the ownership concentration. No significant relation was found between the influence of the regulatory environment and bank risk, i.e., stricter regulation has no effect on risk taking by banks.

The paper contributes to the literature in risk measures and banks' corporate governance. It also considers the impact of regulatory framework on banks' risk-taking behavior. The aim of this empirical analysis was to examine in greater detail these subjects and the dynamics between them after the significant structural changes in the macroeconomic environment and in the financial system, particularly with regards the regulatory and supervisory framework following the 2008 financial crisis, using data from European Union countries.

The purpose of this paper is to propose a chaotic optimization method for identifying the parameters of the Jiles–Atherton (J-A) hysteresis model.

The J-A model has five parameters which are assigned with physical meaning and whose determination is demanding. To determine these parameters, the fitness function, which represents the difference between the measured and the modeled hysteresis loop, is formed. Optimal parameter values are the values that minimize the fitness function.

The parameters of J-A model for three magnetic materials are determined. The model with the optimal parameters is validated using measured data and comparison with particle swarm optimization algorithm, genetic algorithm, pattern search and simulated annealing algorithm. The results show that the proposed method provides better agreement between measured and modeled hysteresis loop than other methods used for comparison. The proposed method is also suitable for simultaneous optimization of multiple hysteresis loops.

Chaotic optimization method is implemented for the first time for J-A model parameter identification. Numerical comparisons with results obtained with other optimization algorithms demonstrate that this method is a suitable alternative in parameters identification of J-A hysteresis model. Furthermore, this method is easy to implement and set up.

Budget hotels are becoming an emerging industry for convenience and affordability, where consumer sentiments are of paramount importance. Tourism has become increasingly dependent on social media and online platforms to gather travel-related information, purchase travel products, food, lodging, etc., and share views and experiences. The user-generated data helps companies make informed decisions through predictive and behavioural analytics.

Design/Methodology/Approach: This study uses text mining, deep learning, and machine learning techniques for data collection and sentiment analysis based on 117,151 online reviews of the customers posted on the TripAdvisor website from May 2004 to May 2019 from 197 hotels of five prominent budget hotel groups spread across India using Feedforward Neural Network along with Keras package and Softmax activation function.

Findings: The word-of-mouth turns into electronic word-of-mouth through social networking sites, with easy access to information that enables customers to pick a budget hotel. We identified 20 widely used words that most customers use in their reviews, which can help managers optimise operational efficiency by boosting consumer acceptability, satisfaction, positive experiences, and overcoming negative consumer perceptions.

Practical Implications: The analysis of the review patterns is based on real-time data, which is helpful to understand the customer’s requirements, particularly for budget hotels.

Originality/Value: We analysed TripAdvisor reviews posted over the last 16 years, excluding the Corona period due to industry crises. The findings reverberate in consonance with the performance improvement theory, which states feed-forward a neural network enhances organisational, process, and individual-level performance in the hospitality industry based on customer reviews.

Instagram health and wellbeing influencers (HWIs) have been increasingly considered as important sources of information and advice for their followers. This study aims to investigate the key antecedents of followers' attitude towards HWIs as well as their influence on their followers' intent to purchase organic products. The moderating effect of gender is also taken into account.

Based on data collected from 251 Instagram HWIs followers, the authors empirically tested the conceptual model using structural equation modeling.

First, the authors demonstrate that attitude towards HWIs positively impacts followers' attitude towards the promoted brands as well as their intention to purchase organic food brands. Second, followers' attitude towards HWIs is mainly influenced by perceived congruence, influencer credibility, and physical attractiveness. Finally, gender acts as a moderator, e.g. attitude towards HWIs is more likely to be influenced by perceived congruence and physical attractiveness among female followers.

The findings allow organic brands' managers to understand the key antecedents of followers' attitudes toward HWIs, and therefore, better select talented influencers who are able to create purchase intentions among both existing and potential customers.

This original research bridges a gap pertaining to the potential use of HWIs to shape consumer intention to purchase organic products. To the authors' knowledge, this study is the first of its kind to investigate the impact of attitudes toward influencers on both brand attitude and purchase intention in the organic food industry.

Overall, there is a lack of research using online reviews as a proxy of customer experience when addressing the study of tourism in island destinations.

The current investigation aims to fill this gap by focussing on an African small island developing states, i.e. Cape Verde. This paper reports of tourist reviews extracted from TripAdvisor from “two islands of the senses” as coined by this archipelago’s national tourism organization, specifically Santo Antão and Fogo islands. The data analysis was performed through Leximancer software to generate concepts out of words, followed by themes.

The present research focussed on experiences in island tourism to identify their main dimensions based on visitors’ narratives in online reviews. The obtained results are of potential value to the literature by contributing to a better understanding of tourist experience in the context of tourism in islands in an understudied country, Cape Verde.

Results are presented and object of discussion vis-à-vis scientific literature and conclusions put forward in this journal paper.

Examines the tenth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

This study aims to uncover the destination personality of the World Surfing Reserve (WSR) in Europe, Ericeira, from local stakeholders’ perspectives; understand if WSR recognition influences the perception of destination personality; and understand if there is an alignment between the vision of the destination management Organization (DMO) and stakeholders in terms of destination personality.

An extensive literature search was conducted to identify personality traits, which were then filtered and included in a survey of Ericeira's stakeholders and in a DMO interview. A principal components analysis enabled the identification of the most relevant personality traits.

Cool, appealing and self-assured emerged as destination-specific personality traits of Ericeira, indicating that other similar destinations can consider them in future branding actions. The findings indicate that WSR recognition can be a determinant for local tourism but has no impact on destination personality as viewed by local stakeholders. Therefore, one can suggest that personality is embedded in a tourist destination and is somewhat resistant to external WSR recognition. In general, alignment was found between the views of the local stakeholders and the DMO.

This study reinforces the literature on the importance of stakeholder involvement in place brand development. It also suggests that external recognition may have an impact on local tourism but has a limited impact on destination personality. Finally, this research constitutes a baseline for further studies on the destination personality traits of current and prospective WSR.

Examines the ninth published year of the ITCRR. Runs the whole gamut of textile innovation, research and testing, some of which investigates hitherto untouched aspects. Subjects discussed include cotton fabric processing, asbestos substitutes, textile adjuncts to cardiovascular surgery, wet textile processes, hand evaluation, nanotechnology, thermoplastic composites, robotic ironing, protective clothing (agricultural and industrial), ecological aspects of fibre properties – to name but a few! There would appear to be no limit to the future potential for textile applications.

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.