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This paper aims to explore the negative effect of power distance belief (PDB) on do-it-yourself (DIY) preference. It extends previous studies by delving into the underlying mechanism and identifying three theoretically driven moderators that could mitigate this negative effect.

The paper uses secondary data at the country level and conducts three experiments involving participants from the USA and Germany.

The results suggest that the adverse impact of PDB on DIY preference exists through the underlying mechanism of attitude toward customer power. This negative effect can be mitigated when individuals with high PDB focus on status, find themselves in a position of low power or engage in activities within a private consumption setting.

For DIY companies, this study offers crucial insights into the impact of cultural values on consumers’ DIY preferences. By customizing their marketing communications, companies can resonate with high PDB customers more effectively.

This research enhances DIY literature by introducing novel moderators within a theoretical framework, explaining why DIY preference might be low among individuals with high PDB and suggests ways to attenuate this effect.

This study aims to progress scholarly understanding of the staycation phenomenon by examining customer segments and documenting local customers’ attribute preferences.

A stated choice experiment is used to examine customer preferences for staycation package attributes. Latent class discrete choice modeling is deployed to classify customers into market segments based on their preferences. The profile of each segment is enhanced by documenting customer characteristics and consumption styles.

Six prominent market segments are identified using a combination of sociodemographics, consumption styles and staycation attribute preferences. The findings draw on consumer experiences during the COVID-19 pandemic to generate theoretical insights into preferred staycation packages. Empirically, the estimation results from the research framework and choice experimental method demonstrate that staycation market segments exhibit distinct preference structures.

Practitioners and policymakers can incorporate the findings of this study in designing and/or assessing staycation packages. This can ensure differentiated products for defined segments that resonate within local communities through positive word of mouth, thus offering prospective spillovers to visiting friends and relatives.

This is a pioneering study on preference heterogeneity from the customer perspective, with a focus on staycation markets. The findings can encourage and assist hotel sector leaders to capitalize on local market developments to achieve a more resilient hospitality business model.

In this paper, the relationship of brand logo and purchase intention is investigated along with the mediating role of customer satisfaction, brand preference and brand attitude. The research is conducted on an online passenger transport company called Tapsi.

In this study, we examine the positive effect of brand logo on brand attitude, consumer satisfaction and strengthening the intention to buy and brand preference by customer satisfaction. The statistical population was randomly selected. We design a conceptual model and then prepare a standard online questionnaire and send it to the target groups. Among this, the participants, 59% are women and 41% are men. After collecting the data through the software Smart-PLS3, we start the analysis. According to Cronbach's alpha and AVE, the validity and reliability of the model are confirmed.

The study shows that the brand logo has a positive and direct influence on customer attitude and satisfaction, and customer satisfaction mediates the purchase intention and brand preference. Given that the brand logo describes the company, managers must be very sensitive to design of a proper logo and spend enough time and money on it.

Few studies have examined the effectiveness of the brand logo the present study and the results show that the brand logo and its structures are directly related to brand attitude as a result of consumer satisfaction in all services even transportation services. The first thing consumers see when they first use a company’s services is the company logo. The brand and its logo can change the attitude and decision of the customer. Past studies have also shown that the brand logo can have a direct impact on customer satisfaction and customer preference for the brand. Therefore a model was prepared and the mentioned variables were selected. Brand preference as a mediating variable has a positive role on buying intention. However all relationships and their predictive power have been confirmed.

Artificial intelligence (AI) is shaping the future of the marketing world. This study is the first to examine the effect of AI marketing efforts, brand experience (BE) and brand preference (BP) in light of the stimulus-organism-response (SOR) model.

The data collected from 398 participants by the questionnaire method were analyzed by SEM (structural equation modeling) using Smart PLS 4.0 and IBM SPSS 26 programs.

We find that four SOR elements of AI marketing efforts (information, interactivity, accessibility and personalization) positively impact bank customer BE, BP and repurchase intention (RPI). Further, we find that BE plays a mediator role in the relationship between AI marketing efforts, RPI and BP.

The findings of the study have significant implications for the bank marketing literature and the banking industry, given the limited evidence to date regarding AI marketing efforts and bank–customer relationships. Moreover, the study makes important contributions to the AI marketing and brand literature and helps banks increase customer experience with artificial intelligence activities and create long-term relationships with customers.

This paper aims to propose a bi-level mixed integer linear location-allocation problem. The upper-level objective function is dedicated to minimizing the total distances covered by customers to meet the p-selected facilities and the fixed cost values for establishing these facilities. While in the lower level, a customer preference function evaluates the priority of customers in selecting facilities.

The solution approach to the proposed model uses the Karush–Kuhn–Tucker (KKT) optimality conditions to the lower-level problem where a set of p-selected facilities are introduced as the selection of the upper-level decision maker. The bi-level model reduces to a single-level model with some added binary variables.

Sensitivity analysis of the proposed bi-level model concerning variations of such different parameters as customers’ preferences and the number of selected facilities have been provided, using some numerical examples. Also, locating a recreational facility in Mazandaran province, Iran, has been provided to evaluate the reliability of the proposed model and efficiency of the solution approach, as well.

To the best of the authors’ knowledge, this paper is original and its findings are not available elsewhere.

The hospitality industry values segmentation and loyalty programs (LPs), but there is limited research on new methods for segmenting loyalty program members, so managers often rely on conventional techniques. This study aims to use big data-driven segmentation methods to cluster customers and provide a new solution for customer segmentation in hotel LPs.

Using the k-means algorithm, this study examined 498,655 profiles of guests enrolled in a multinational hotel chain’s loyalty program. The objective was to cluster guests according to their consumption behavior and monetary value and compare data-driven segments based on brand preferences, demographic data and monetary value with loyalty program tiers.

This study shows that current tier-based LPs lack features to improve customer segmentation, and some high-tier members generate less revenue than low-tier members. Therefore, more attention should be given to truly valuable customers.

Hotels can segment LP members to develop targeted campaigns and uncover new insights. This will help to transform LPs to make them more valuable and profitable and use differentiated rewards and strategies.

As not all guests or hotel brands benefit equally from LPs, additional segmentation is required to suit varying guest behaviors. Hotel managers can use data mining techniques to develop more efficient and valuable LPs with personalized strategies and rewards.

This study investigates the brand selfies that have the capability to help brands thrive through crises. The brand selfies spark a self-inferential process that makes customers feel connected to the brand and makes them biased toward a specific brand during an uncertain situation.

A total of 166 questionnaires were analyzed through structural equation modelling (Smart PLS) and a niche group of young millennials from Thailand was selected based on their luxury items usage, frequency of visits to leisure spas and hotels, expensive car showrooms, branded jewelry stores and luxury watch shops.

The study highlights the emergence of brand selfies during the crisis and the priority given by customers as compared to brand-generated content or promotional campaigns. The results indicated a positive influence of brand selfies on brand preferences directly and through the mediation of brand signature.

It is fascinating for brands that customers voluntarily include their products in their carefully crafted and staged selfies that deliver their image and massages as social signifiers during a chaotic situation.

The research classifies the impacts of brand selfies in the luxury, leisure and tourism market of Thailand and its assistance in thriving through crises. The study is one of the rare studies that present brand selfies as a hassle-free promotional tool for brand signature and a game-changing strategy to deal with crises.

As an alternative to hiring financial specialists or investment consultants, robo-advisors offer financially automated investment services. This study aims to investigate how robo-advisors' service attributes, risk attitude and financial self-efficacy influence customers' choice preferences of adopting robo-advisors.

Two hundred fifty-one online surveys were used to collect data, and choice-based conjoint analysis was conducted.

Results show that increasing annual fees negatively impact customers' choice preferences. Promotion, general investment education and additional human assistance have a positive impact. Furthermore, risk-seeking and risk-averse customers require more human assistance than risk-neutral customer and customers with high levels of financial self-efficacy prefer more general investment education and additional human assistance than those with lower levels. In addition, customers in the older age group prefer promotion, general investment education and additional human assistance, while wealthy customers prefer lower annual fees, higher general investment education and more additional human assistance compared to middle-class and low-income groups.

This study contributes to robo-advisor providers to provide appropriate service attributes for each customer group.

Although user stickiness has been studied for several years in the field of live e-commerce, little attention has been paid to the effects of streamer attributes on user stickiness in this field. Rooted in the stimulus-organism-response (S-O-R) theory, this study investigated how streamer attributes influence user stickiness.

The authors obtained 496 valid samples from Chinese live e-commerce users and explored the formation of user stickiness using partial least squares-structural equation modeling (PLS-SEM). Artificial neural network (ANN) was used to capture linear and non-linear relationships and analyze the normalized importance ranking of significant variables, supplementing the PLS-SEM results.

The authors found that attractiveness and similarity positively impacted parasocial interaction (PSI). Expertise and trustworthiness positively impacted perceived information quality. Moreover, streamer-brand preference mediated the relationship between PSI and user stickiness, as well as the relationship between perceived information quality and user stickiness. Compared to PLS-SEM, the predictive ability of ANN was more robust. Further, the results of PLS-SEM and ANN both showed that attractiveness was the strongest predictor of user stickiness.

This study explained how streamer attributes affect user stickiness and provided a reference value for future research on user behavior in live e-commerce. The exploration of the linear and non-linear relationships between variables based on ANN supplements existing research. Moreover, the results of this study have implications for practitioners on how to improve user stickiness and contribute to the development of the livestreaming industry.

This study examined the reciprocal influence of demand learning and preference matching in the context of store brand customization. The demand-learning effect refers to the collection of market demand information through production, based on pre-order demands, enabling retailers to accurately predict and allocate product quantities, thus improving inventory management. The preference-matching effect involves engaging consumers in the production and design processes of store brands to align fully with their preferences, thereby increasing the purchase impact of store brand products and promoting consumption.

We employ game-theoretic models to analyze a two-echelon supply chain consisting of a manufacturer and a retailer. The retailer offers both national brands, manufactured by the supplier and in-house store brands. To enhance their competitive edge, the retailer can adopt a customized strategy targeting the store brand to attract a wider consumer base.

The analysis reveals that, under low commission fees, the manufacturer consistently opts for high production quantities, irrespective of the level of demand uncertainty. However, when the perceived value of a store brand is low and demand uncertainty is either low or high, the retailer should choose a minimal or zero production quantity. The decision-making process is influenced by the customization process, wherein the effects of demand learning and preference matching occasionally mutually reinforce each other. Specifically, when the perceived value of a store brand is low, or the product cost is high, along with high customization costs, the interplay between demand learning and preference matching becomes mutually inhibiting. Consequently, the significance of store brand customization diminishes.

This study enhances the current body of knowledge by providing a deeper understanding of the theoretical value of store brand customization. In addition, it offers valuable decision-making support to enterprises by assisting them in selecting appropriate inventory and customization strategies.