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This study aims to develop and test an integrative model that examines the effects of customization and perceived employee authenticity on customer delight, which in turn influences customers’ willingness to recommend (WTR) and willingness to pay a premium (WTPP) as outcomes in a hotel context. The moderating role of online review valence in this process is also examined.

This study adopts a 2 (customization: low vs high) × 2 (perceived employee authenticity: low vs high) × 2 (online review valence: negative vs positive) experimental design. A total of 409 US consumers were recruited and randomly assigned to a hotel check-in scenario. Partial least squares structural equation modeling was used to test the hypothesized relationships.

Findings confirmed the role of customer delight in mediating customization and employee authenticity on WTR and WTPP. In addition, perceived employee authenticity was a stronger driver of customer delight for consumers exposed to negative online reviews than for those exposed to positive reviews.

The findings provide useful guidance in designing efficient service strategies for generating a delightful customer experience. Hotel practitioners should provide customized services and manage employees in a way that helps them deliver authentic services that achieve customer delight. Understanding that customer expectations formed through online reviews play a significant role in service evaluations, hotel managers make an extra effort to monitor online reviews and manage customer expectations.

Although existing research suggests that customer delight plays an important role in positive consumer outcomes, there is still potential space to explore the theoretical mediational mechanisms underlying this effect and the moderating effect on this relationship between customer delight and consumer responses. This study contributes by testing the moderating impact of online review valence and the mediating impact of customer delight.

This paper aims to identify the antecedents and postcedents of customer satisfaction, including utilitarian, social and emotional factors, in a fair trade (FT) coffee consumption context.

This paper is based on a broad range of 177 consumers of FT coffee in Spain, the data analysis used structural equation modeling (SEM) with SPSS/AMOS 26.0 software.

This paper supports that both customer social value and quality affect perceived value (PV). PV in turn has effects on customer satisfaction and the latter influences loyalty. Conversely, both customer emotional value and customer expectations were not confirmed as antecedents of PV.

The consumer satisfaction analysis conducted differs substantially from those of conventionally traded coffee, as social and emotional factors were considered along with utilitarian factors.

Practitioners, retailers and relevant institutions should design strategies to manage efficiently channel efforts to improve the consumer satisfaction and its loyalty.

This paper contributes to a substantial improvement in the understanding of consumer satisfaction and its consequences, in FT coffee consumption contexts. A new integrated theoretical model on customer satisfaction has been provided, which includes social and emotional perception factors, along with cognitive perception (quality and expectations) factors.

研究旨在確認在公平貿易咖啡消費的課題上，顧客滿意的誘因及其後因，這包括實用的因素，社會的因素和情感的因素。

研究之數據廣泛來自在西班牙177名公平貿易咖啡消費者; 分析則以結構方程模型，並以SPSS Amos 26軟件來進行。

研究結果證實，顧客社會價值和質量是會影響認知價值的;認知價值繼而影響顧客滿意度，而顧客滿意度又進而影響他們的忠誠。相反的，顧客情緒價值或他們的期望、均未能證實是認知價值的先決條件。

本研究所進行的消費者滿意度分析，與其它以傳統方法銷售的咖啡之相關研究有很大的分別，這是因為本研究除了考慮實用的因素外，還納入了社會因素和情感因素。

從業人員、零售商和有關的機構應制訂適切的策略，以能有效地管理各個管道，來提升消費者的滿意度和忠誠。

本研究的貢獻在於它幫助我們在公平貿易咖啡消費的課題上，對消費者滿意及其效果有更深入的認識。研究亦提供了一個探討顧客滿意度的嶄新、綜合的理論模型，而這個理論模型，除了涵蓋知覺認知 (質量和期望) 的因素外，還納入了社會的和情感的知覺因素。

The study investigates the consumer’s attitude to using artificial intelligence (AI) devices in hospitality service settings considering social influence, hedonic motivation, anthropomorphism, effort expectancy, performance expectancy and emotions.

This study employed a quantitative methodology to collect data from Bangladeshi consumers who utilized AI-enabled technologies in the hospitality sector. A total of 343 data were collected using a purposive sampling method. The SmartPLS 4.0 software was used to determine the constructs' internal consistency, reliability and validity. This study also applied the partial least squares structural equation modeling (PLS-SEM) to test the research model and hypotheses.

The finding shows that consumer attitude toward AI is influenced by social influence, hedonic motivation, anthropomorphism, performance and effort expectancy and emotions. Specifically, hedonic motivation, social influence and anthropomorphism affect performance and effort expectations, affecting consumer emotion. Moreover, emotions ultimately influenced the perceptions of hotel customers' willingness to use AI devices.

This study provides a practical understanding of issues when adopting more stringent AI-enabled devices in the hospitality sector. Managers, practitioners and decision-makers will get helpful information discussed in this article.

This study investigates the perceptions of guests' attitudes toward the use of AI devices in hospitality services. This study emphasizes the cultural context of the hospitality industry in Bangladesh, but its findings may be reflected in other areas and regions.

The use of multi-level labels can enhance product visibility by enabling labeling of various items. Moreover, it can better accommodate the diversity on both the producer and consumer sides. However, studies on the willingness to pay (WTP) for premium levels of those animal welfare labels are scarce.

We investigate consumers’ WTP for a four-level animal husbandry label introduced to the market by German retailers in 2019 by conducting an online survey with 1,223 German meat consumers using Van Westendorp’s price sensitivity meter (PSM).

There is a significant increase in WTP for level 3 of the husbandry label, but only a slight increase for level 4. One explanation is that consumers may have the mistaken belief that level 3 already includes outdoor access for animals. As a result of this expectation, consumers may not perceive much added value in level 4, which is reflected in their reluctance to pay a higher price. This is reinforced by the finding that once informed of the criteria, 18% of the participants reduced their WTP for level 3, whereas only 6% considered a discount for level 4. Furthermore, 40% were prepared to pay more for level 4 after being informed of the respective criteria than they had previously stated.

To the best of our knowledge, this study is the first to analyze and emphasize the importance of clear label communication, particularly for multi-level animal husbandry labels.

The paper addresses the privacy–personalisation paradox in the post-GDPR-2018 era. As the regulation came in a bid to regulate the collection and use of personal data, its implications remain underexplored. The research question is: How do consumers perceive the matter of personal data collection for the use of highly targeted and personalised ads post-GDPR-2018? The invasion of privacy vs the benefits of highly personalised digital marketing.

To address the research question, this qualitative study conducts semi-structured interviews with 14 individuals, consisting of average users and digital experts.

This paper reports on increasing consumer vulnerability post-GDPR-2018 due to increased awareness of personal data collection yet incessant lack of control, particularly regarding the repercussions of the digital footprint. The privacy paradox remains an issue except among experts, and personalisation remains necessary, yet critical challenges arise (e.g. filter bubbles and intrusion).

Policy implications include education, regulating consent platforms and encouraging consensual sharing of personal data.

While the privacy–personalisation paradox has been widely studied, the impact of GDPR-2018 has rarely been addressed in the literature. GDPR-2018 has seemingly had little impact on instilling a sense of security for consumers; if anything, this paper highlights greater concerns for privacy as users sign away their rights on consent forms to access websites, thus contributing novel insights to this area of research.

Although the effects of both news sentiment and expectations on price in financial markets have now been extensively demonstrated, the jointness that these predictors can have in their effects on price has not been well-defined. Investigating causal ordering in their effects on price can further our understanding of both direct and indirect effects in their relationship to market price.

We use autoregressive distributed lag (ARDL) methodology to examine the relationship between agent expectations and news sentiment in predicting price in a financial market. The ARDL estimation is supplemented by Grainger causality testing.

In the ARDL models we implement, measures of expectations and news sentiment and their lags were confirmed to be significantly related to market price in separate estimates. Our results further indicate that in models of relationships between these predictors, news sentiment is a significant predictor of agent expectations, but agent expectations are not significant predictors of news sentiment. Granger-causality estimates confirmed the causal inferences from ARDL results.

Taken together, the results extend our understanding of the dynamics of expectations and sentiment as exogenous information sources that relate to price in financial markets. They suggest that the extensively cited predictor of news sentiment can have both a direct effect on market price and an indirect effect on price through agent expectations.

Even traditional financial management firms now commonly track behavioral measures of expectations and market sentiment. More complete understanding of the relationship between these predictors of market price can further their representation in predictive models.

This article extends the frequently reported bivariate relationship of expectations and sentiment to market price to examine jointness in the relationship between these variables in predicting price. Inference from ARDL estimates is supported by Grainger-causality estimates.

The Internet has changed consumer decision-making and influenced business behaviour. User-generated product information is abundant and readily available. This paper argues that user-generated content can be efficiently utilised for business intelligence using data science and develops an approach to demonstrate the methods and benefits of the different techniques.

Using Python Selenium, Beautiful Soup and various text mining approaches in R to access, retrieve and analyse user-generated content, we argue that (1) companies can extract information about the product attributes that matter most to consumers and (2) user-generated reviews enable the use of text mining results in combination with other demographic and statistical information (e.g. ratings) as an efficient input for competitive analysis.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The paper shows that combining different types of data (textual and numerical data) and applying and combining different methods can provide organisations with important business information and improve business performance.

The study makes several contributions to the marketing and management literature, mainly by illustrating the methodological advantages of text mining and accompanying statistical analysis, the different types of distilled information and their use in decision-making.

This study aims to investigate the ecological awareness of Chinese consumers towards fast fashion and examine the effect of social sustainability claims on green brand image and purchase intentions in China, considering China’s unique environmental policy landscape and its significant role in the global fast fashion industry. The study explores the role of altruistic values in promoting sustainability within the well-known fast fashion brand “H” and how they shape brand image, consumer satisfaction and brand equity.

The study collected data from 257 Chinese participants and used a serial mediation model through the PROCESS macro in SPSS to analyse the correlation between green brand image, created through sustainability claims and consumer purchase intentions. The model also assessed the intermediary effects of brand image, satisfaction and equity.

The findings of the research indicate a direct and positive relationship between green brand image and consumer purchase intentions, emphasising the need for clothing and textile industry marketers to strategically promote altruistic values in their sustainability efforts and highlighting the importance of ecological awareness in shaping consumer behaviour in the Chinese context. This approach enhances green satisfaction and green brand equity and ultimately leads to higher green purchase intentions.

This study provides significant insights into the effectiveness of incorporating social sustainability claims in advertising to improve a brand’s green image and influence consumer behaviour. It emphasises the importance of altruistic values in sustainability strategies, offering valuable guidelines for marketers in enhancing green satisfaction and brand equity, thereby boosting consumer purchase intentions in the context of green branding and sustainability advertising. Focussing specifically on the Chinese market, this research sheds light on the impact of ecological awareness among Chinese consumers within the fast-fashion industry. Given China’s substantial role in shaping global fast-fashion production and its evolving environmental policies, this focus adds significant depth to our understanding of sustainability claims’ influence within this crucial consumer base.

This study aimed to determine whether the brand image of fast-food restaurants moderates the relationship between satisfaction and loyalty among Chilean customers.

The study was conducted using a structural equation modeling approach and involved a sample of 1,000 fast-food customers who were surveyed through nonprobabilistic sampling with a questionnaire consisting of 21 items. The instrument underwent validation by a panel of experts, and the results obtained were processed using the statistical software Smart PLS4. Convergent validity, discriminant validity and the structural model were tested to assess the acceptance or rejection of the proposed hypotheses.

The results of the research indicate that product quality, service quality and physical environment are crucial factors influencing customer satisfaction and shaping customer loyalty. However, it was found that brand image does not moderate the relationship between satisfaction and loyalty in fast-food consumption.

The present study contributes to the field of knowledge on fast-food consumption behavior and unveils findings that help fill knowledge gaps regarding the influence of restaurant brand image on the relationship between satisfaction and consumption loyalty. Thus, it demonstrates that, for certain customers, the brand becomes an irrelevant factor for their consumption loyalty as long as the restaurant offers product quality, excellent service and a pleasant physical environment.

This study aims to clarify why and when digital business strategy (DBS) helps manufacturing firms generate value co-creation (VC) with different stakeholders in the digital context of China. This study considers external network capability (ENC) and internal network capability (INC) as mediation mechanism, and strategic flexibility (SF) as theoretical boundary.

Questionnaires were used and filled out by executives from manufacturing firms. The manufacturing samples from 289 different fields in China were used for hypothesis testing, and the structural equation model was the main analytical method.

This study found that DBS of manufacturing enterprises has a positive impact on VC. Specifically, DBS affects firm-partner VC and firm-consumer VC through the indirect positive effect of ENC, and affects firm-employee VC through INV. The positive effects of ENC on firm-partner VC and firm-consumer VC, as well as INC on firm-employee VC, are weak at high (or low) SF, and are strongest at moderate SF.

This study provides manufacturing firms with practical insights into why and when they can implement DBS to generate VC, with a particular emphasis on the weighted role of SF.

This study spotlights gaps in the literature on why and when manufacturing firms can reap the benefits of DBS, focusing on one important business outcome – VC. The authors clarify the mediating role of differences in ENC and INC, as well as the inverted U-shaped moderating role of SF.