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Coastal cities offer great ecological, cultural and economic benefits due to their tourism potential. The objective of this research is to (1) identify tourists' post-pandemic motivations, (2) establish a post-pandemic demand segmentation and (3) determine the relationship between post-pandemic segments and loyalty.

This study was carried out in Lima, Peru, a tourist destination on the Pacific Ocean coast. The sample was collected between June and July 2020, during the coronavirus disease 2019 (COVID-19) pandemic. In total, 354 valid questionnaires represented the sample size of this quantitative study. For data analysis, factor analysis and K-means non-hierarchical clustering were used.

The results show four post-pandemic motivational dimensions in coastal cities: “novelty and escape,” “learning and culture,” “destination safety” and “service safety.” Likewise, there are two post-pandemic segments in coastal cities: “safety seekers” who want to feel safe at the destination and with its services, and “multiple motives,” motivated by several reasons simultaneously, such as safety, novelty and escape, and learning and culture. The multiple motives group shows higher return intentions, making it a crucial post-pandemic segment in coastal cities.

The limitations of the present study were the online sampling and the timing when collecting the data since the demand can vary due to seasonal reasons.

Since coastal cities have natural and cultural attractions appealing to many travelers, they should adopt the necessary biosecurity measures to attract the safety seekers’ segment, who wants to feel safe at the destination and with its services. Similarly, the multiple motives’ segment favors safety over other recreational activities in the coastal area, so it is necessary that activities such as sports on the beach, walks, observation of flora and fauna, navigation and interaction with the community, meet the required biosecurity standards.

The results will be used to plan the following actions in coastal destinations and meet the tourists’ demands when this health crisis ends.

In this context, up to date, demand segmentation by motivations in coastal cities during the COVID-19 pandemic has not been investigated. Such a study will help to obtain post-pandemic results regarding the tourism demand for these destinations. To date, there are no studies in coastal cities that analyze demand segmentation and its motivations for the post-COVID-2019 pandemic.

The present study, drawing on boundary theory, attempts to investigate entrepreneurs’ work–life balance (WLB) in terms of how they manage and construct the borders between their non-work and work lives.

The current qualitative study is grounded on in-depth interviews with 30 entrepreneurs currently living and working in the United Arab Emirates (UAE). When selecting the interviewees, the authors used purposeful sampling to ensure a diverse sample of interviewees with respect to nationality, age, gender and the nature of their business.

The findings suggest that most interviewees believe an entrepreneurial career can contribute positively to their lives. However, some individuals question whether it is possible to have some level of WLB while managing entrepreneurial businesses in the UAE’s competitive environment. Further, some entrepreneurs prioritize “work” over “family life,” indicating a love for their work and a lack of desire for any boundaries. Working long hours and managing a successful business over a longer period of time is also found to be negatively related to WLB.

Entrepreneurship has become a desirable career option, thus stimulating much research attention. However, little is known about entrepreneurs’ WLB challenges, especially regarding whether entrepreneurship improves individuals’ WLB. The present study uses boundary theory to understand how subjective insights regarding WLB and effective boundary management might address this gap.

With rapidly changing marketing landscape, nowadays, the formulation of various marketing strategies is increasingly focused on how consumers tend to make decisions. To meet the highly demanding consumer expectations, market segmentation can be used as an important marketing strategy. Due to gender marketing concept familiarity in the contemporary world, gender difference is one of the reference features in the process of market segmentation for marketers. This research is aimed to examine various determining factors that foster consumer purchase decision-making and the differences between consumers of different genders while making shopping and purchase decisions with special reference to an emerging economy, i.e. Pakistan.

Based on a cross-sectional sample of 367 consumers, the study adapted Sproles and Kendall's (1986) Consumer Style Inventory (CSI) to scrutinize the decision-making of both genders in Pakistan. For data analysis, the exploratory and confirmatory factor analysis in addition to the structural equation modeling has been used.

The study emphasized that, with the exception of quality awareness, brand consciousness, fashion consciousness, option overload and price consciousness greatly affect buying decisions. In addition, when it comes to consumer purchase decision-making, significant gender variations were discovered for both fashion consciousness and price consciousness.

Drawing upon the distinctive cultural characteristics of Pakistan and its people, in-depth research was conducted on purchasing behaviors of Pakistani consumers and the decision-making characteristics of customers of different genders were summarized. The outcomes are expected to make a significant contribution to the field of gender marketing by organizations.

Utilizing boundary theory as a guiding framework, this study aims to explore facets of work–life balance (WLB) that women entrepreneurs experience in the context of the United Arab Emirates (UAE). It sheds light on strategies women entrepreneurs use to manage and shape boundaries between their personal and professional lives.

In this qualitative study, we conducted in-depth interviews with 50 women entrepreneurs to gain a deeper understanding of their WLB challenges.

Integration is a boundary management approach used by most women in our sample, facilitated by the thin work–life boundary inferable from their entrepreneurial careers. Integration has all the hallmarks of being imposed on women entrepreneurs because of family role challenges and societal expectations, on top of their entrepreneurial obligations. Women are reactors; they shoulder societal, family and entrepreneurial roles while having little control over events and circumstances.

Boundary theory suggests two roles must be interconnected to coexist successfully. Women entrepreneurs can benefit from the synergy between their personal and professional lives. As their roles tend to be more complex, it is essential to consider the consolidation of both spheres as an ongoing process to maximize their benefits.

Today’s independent forms of working are contingent on flexible work arrangements, work intensification and wireless communication. Understanding how women entrepreneurs find balance amid boundarylessness adds to our limited knowledge of people in comparable environments.

This paper aims to classify tourist accommodation using data from Booking.com and TripAdvisor and analyse the extent to which the different segments identified differ in terms of being adults-only.

In total, 1,535 properties located in nine Spanish sun and beach destinations were examined using a latent class cluster analysis (LCCA). The bias-adjusted three-step approach was used to investigate the differences between belonging to adults-only accommodation or not among the identified clusters.

Results show that adults-only accommodation tends to belong to the cluster with higher online ratings. In small Spanish islands, adults-only hotels account for a large share (more than 25%) of hotels.

It was not possible to analyse whether the higher rating was due to the accommodation being better or due to the tourists being more satisfied with their stay.

In urban destinations, the model is not widely used. However, in coastal destinations, it is becoming more than a novelty or a new trend.

In small Spanish islands, people traveling with children are becoming a minority. Families may feel discriminated against and express dissatisfaction with this situation in the future.

This study covers the gap in the academic literature on this growing hotel segment.

With the growth of organizations and businesses, customer acquisition and retention processes have become more complex in the long run. That is why customer lifetime value (CLV) has become crucial to sales managers. Predicting the CLV is a strategic weapon and competitive advantage in increasing profitability and identifying customers with more splendid profitability and is one of the essential key performance indicators (KPI) used in customer segmentation. Thus, this paper proposes a stacked ensemble learning method, a combination of multiple machine learning methods, for CLV prediction.

In order to utilize customers’ behavioral features for predicting the value of each customer’s CLV, the data of a textile sales company was used as a case study. The proposed stacked ensemble learning method is compared with several popular predictive methods named deep neural networks, bagging support vector regression, light gradient boosting machine, random forest and extreme gradient boosting.

Empirical results indicate that the regression performance of the stacked ensemble learning method outperformed other methods in terms of normalized rooted mean squared error, normalized mean absolute error and coefficient of determination, at 0.248, 0.364 and 0.848, respectively. In addition, the prediction capability of the proposed method improved significantly after optimizing its hyperparameters.

This paper proposes a stacked ensemble learning method as a new method for accurate CLV prediction. The results and comparisons support the robustness and efficiency of the proposed method for CLV prediction.

To generate the measurement instrument seven steps were implemented. A total of 819 questionnaires were collected in Yunnan Province, China, where it has long tradition of eating insects.

This study aims to explore the influences of the multidimensional benefits of consuming insect-based food on its consequences. Food neophilia and hedonic motivation are used as moderating variables.

Health, nutritional value, taste and cultural domains effectively explained consumers’ attitudes toward insect-based food, food consumption value, satisfaction, subjective well-being, loyalty to the restaurant and community attachment. Food neophilia and hedonic motivation partially moderated the relationships between the proposed constructs.

This study provides a conceptual model for exploring insect-based food consumption experiences and offers a useful guideline for developing and designing marketing strategies for stakeholders in the food and restaurant industry.

This is one of the few studies examining the benefits of consuming insect-based food from the perspective of consumers.

An increasing number of images are generated daily, and images are gradually becoming a search target. Content-based image retrieval (CBIR) is helpful for users to express their requirements using an image query. Nevertheless, determining whether the retrieval system can provide convenient operation and relevant retrieval results is challenging. A CBIR system based on deep learning features was proposed in this study to effectively search and navigate images in digital articles.

Convolutional neural networks (CNNs) were used as the feature extractors in the author's experiments. Using pretrained parameters, the training time and retrieval time were reduced. Different CNN features were extracted from the constructed image databases consisting of images taken from the National Palace Museum Journals Archive and were compared in the CBIR system.

DenseNet201 achieved the best performance, with a top-10 mAP of 89% and a query time of 0.14 s.

The CBIR homepage displayed image categories showing the content of the database and provided the default query images. After retrieval, the result showed the metadata of the retrieved images and links back to the original pages.

With the interface and retrieval demonstration, a novel image-based reading mode can be established via the CBIR and links to the original images and contextual descriptions.

This study aims to explore the use of time series analyses to examine changes in travelers’ preferences in accommodation features by disentangling seasonal, trend and the COVID-19 pandemic’s once-off disruptive effects.

Longitudinal data are retrieved by online traveler reviews (n = 519,200) from the Canary Islands, Spain, over a period of seven years (2015 to 2022). A time series analysis decomposes the seasonal, trend and disruptive effects of six prominent accommodation features (view, terrace, pool, shop, location and room).

Single accommodation features reveal different seasonal patterns. Trend analyses indicate long-term trend effects and short-term disruption effects caused by Covid-19. In contrast, no long-term effect of the pandemic was found.

The findings stress the need to address seasonality at the single accommodation feature level. Beyond targeting specific features at different guest groups, new approaches could allow dynamic price optimization. Real-time insight can be used for the targeted marketing of platform providers and accommodation owners.

A novel application of a time series perspective reveals trends and seasonal changes in travelers’ accommodation feature preferences. The findings help better address travelers’ needs in P2P offerings.

Information technology as a source of information and decision-making has wider acceptance in contemporary times. Studies have identified the importance of electronic word of mouth (eWOM) and its impact on decision-making. The primary objective of this research is to investigate the relationship between eWOM (pre-travel), destination image (post-visit), tourist satisfaction and eWOM intentions post the COVID-19 crisis. This study is important as it is anticipated that in the post-pandemic world, tourists would seek well-being-enhancing experiences more often than any other form of tourism.

Data were collected through an online questionnaire circulated over a period of six months from November 2020 to April 2021. Non-probability purposive sampling technique was used.

The results depicted that wellness destination’s image has a significant influence on wellness tourists’ satisfaction level and their eWOM intentions. Furthermore, it also came to light that the satisfaction level of wellness tourist satisfaction was found to be significantly influencing their eWOM intentions. The mediating role of wellness tourists’ satisfaction was found to be significant from destination image (post-visit) to eWOM intentions. COVID-19 pandemic perceived health risk was also found to be significantly moderating the relationship between eWOM (pre-travel) and eWOM intentions.

Pre-travel online information about a wellness destination is an important determinant of travel decisions, especially during the COVID-19 crisis. This empirical study proves that effective use of this information can advance a destination’s marketing efforts and ensure future demand.