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This paper's purpose is to develop a meaning-based framework for customer loyalty by examining how consumers make sense of customer loyalty through meanings and metaphors.

A qualitative study based on in-depth interviews and focus group data in the retail context was conducted with Finnish customers. The data were analysed with qualitative data analysis techniques such as the constant comparative technique.

The empirical findings comprise eight loyalty meanings characterised by two dimensions. The first dimension is reflexive vs. routinised, and the second dimension is private vs. social. The loyalty types are dimensionalised through four metaphors: loyalty as freedom of choice; as being conventional and binding; and as belongingness.

The findings improve the way customer loyalty currently is understood in the retail setting. The paper proposes that customer insight that utilises thick data can be used to grasp loyalty meanings. These data are rich in context and detail, and they take into account customers' everyday lives. Utilising thick data in the form of storytelling fuels customers' meaning-making related to customer loyalty, potentially enriching their relationship with the retailer.

Customer loyalty has been driven largely by a transactional and company-centric perspective. This article presents an alternative view of customer loyalty that accounts for the variety of meanings that customers may assign to their loyalty-related thoughts and behaviours.

Since the beginning of 2020, economies faced many changes as a result of coronavirus disease 2019 (COVID-19) pandemic. The effect of COVID-19 on the Egyptian Exchange (EGX) is investigated in this research.

To explore the impact of COVID-19, three periods were considered: (1) 17 months before the spread of COVID-19 and the start of the lockdown, (2) 17 months after the spread of COVID-19 and the during the lockdown and (3) 34 months comprehending the whole period (before and during COVID-19). Due to the large number of variables that could be considered, dimensionality reduction method, such as the principal component analysis (PCA) is followed. This method helps in determining the most individual stocks contributing to the main EGX index (EGX 30). The PCA, also, addresses the multicollinearity between the variables under investigation. Additionally, a principal component regression (PCR) model is developed to predict the future behavior of the EGX 30.

The results demonstrate that the first three principal components (PCs) could be considered to explain 89%, 85%, and 88% of data variability at (1) before COVID-19, (2) during COVID-19 and (3) the whole period, respectively. Furthermore, sectors of food and beverage, basic resources and real estate have not been affected by the COVID-19. The resulted Principal Component Regression (PCR) model performs very well. This could be concluded by comparing the observed values of EGX 30 with the predicted ones (R-squared estimated as 0.99).

To the best of our knowledge, no research has been conducted to investigate the effect of the COVID-19 on the EGX following an unsupervised machine learning method.

This research aims at arriving at a broad scope of the lessons learnt after two years of the coronavirus disease 2019 (COVID-19) pandemic outbreak by analysing the catalyst and inhibiting factors within three aspects of the tourism sector: destination crisis management, tourist behaviour and tourism industry trends.

The methodology of this paper involves semi-structured interviews with high-ranking European travel agents as the agents represent the intermediates between the tourism offer and demand.

Data obtained from travel agents disclosed the factors that catalysed and inhibited the destination, the behaviour of tourists and the tourism industry trends. By contrasting data with previous literature, constructing an overview of the positive and negative outcomes of the pandemic in the tourism sector is possible.

Governments, destination marketing and management organisations and tourism and hospitality organisations could learn from the lessons of COVID-19 outbreak to cope better with future disruptive events affecting the tourism industry.

The paper is novel as it is the first overview that attempts to synthesise the lessons from the COVID-19 pandemic in the tourism sector by analysing tourism sector's three dimensions: the destination, the tourists and the industry.

This study aims to explain why online reviews in Airbnb are skewed toward positive ratings. The authors examine customer perceptions of the service quality of an Airbnb stay as a relevant antecedent of whether customers leave an online review of that Airbnb stay. To this end, the authors test the hypothesis that the relationship between service quality and leaving an online review is linear and positive.

To test the hypothesis, the authors rely on primary survey data from 177 Airbnb customers combined with secondary data coming from their personal online Airbnb accounts. The authors conducted a binary logistic regression analysis to test the hypothesis.

The results show that customers’ service quality perceptions are positively and linearly related to leaving an online review of an Airbnb stay. In other words, satisfied customers are more likely to leave a review after an Airbnb stay than those who are dissatisfied.

The study is original in two respects. First, it reconsiders the role of customer experiences in explaining online customer reviews. In doing so, it empirically shows that the conventional wisdom of a U-shaped relationship between customer experiences and online reviewing does not hold in the context of the sharing economy. Second, by relying on primary survey data, the authors reveal the risk of dissatisfied customers creating an underreporting bias in online reviews, which ultimately make online reviews of Airbnb skewed toward positive ratings.

The purpose of this study is to examine whether there is a unidirectional or a bidirectional relationship between women and the environment, and to further study the effect of women on environmental quality.

To achieve this purpose, a Granger causality test and a random effects panel data model are used to study women–environment relationship in developing countries. Error correction model (ECM) is the chosen estimation technique. A Granger causality test is used because of its frequent use in examining the existence of a unidirectional or a bidirectional relationship between two or more variables. A random effects panel data model is used as it has proven to be more efficient than the fixed-effects panel data model.

Women Granger-cause environmental quality while the opposite is not true in developing countries in the long run. This indicates the existence of a unidirectional relationship between women and the environment when the long-run relationship is considered. However, when considering the long- and short-run relationship together, the results indicate the presence of a bidirectional relationship. The empirical results of the random effects panel data model through ECM estimation indicate the positive effect of women on improving environmental quality as illustrated by the coefficient of the current change of women. This shows that women are concerned about environmental degradation. In addition, the empirical results highlight the persistence of CO₂ emissions. Results also confirm that foreign direct investment inflows lead to further environmental degradation. However, education and trade openness coefficients are found insignificant at the current period.

The research results have great implications on women empowerment, the reduction of gender bias and the increase in government expenditure on women’s education and health because of women’s positive effect in improving environmental quality.

To the best of the author’s knowledge, this is the first paper that examines the two-way relationship between women and the environment and, hence, it fills the gap present in the literature.

Solar cells could make textile-based wearable systems energy independent without the need for battery replacement or recharging; however, their laundry resistance, which is prerequisite for the product acceptance of e-textiles, has been rarely examined. This paper aims to report a systematic study of the laundry durability of solar cells embedded in textiles.

This research included small commercial monocrystalline silicon solar cells which were encapsulated with functional synthetic textile materials using an industrially relevant textile lamination process and found them to reliably endure laundry washing (ISO 6330:2012). The energy harvesting capability of eight textile laminated solar cells was measured after 10–50 cycles of laundry at 40 °C and compared with light transmittance spectroscopy and visual inspection.

Five of the eight textile solar cell samples fully maintained their efficiency over the 50 laundry cycles, whereas the other three showed a 20%–27% decrease. The cells did not cause any visual damage to the fabric. The result indicates that the textile encapsulated solar cell module provides sufficient protection for the solar cells against water, washing agents and mechanical stress to endure repetitive domestic laundry.

This study used rigid monocrystalline silicon solar cells. Flexible amorphous silicon cells were excluded because of low durability in preliminary tests. Other types of solar cells were not tested.

A review of literature reveals the tendency of researchers to avoid standardized textile washing resistance testing. This study removes the most critical obstacle of textile integrated solar energy harvesting, the washing resistance.