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The purpose of this research is to investigate the effects of service recovery experiences on customer engagement in negative word-of-mouth (WOM) in the hotel industry and explore the psychological motives and mediating mechanisms driving consumer behavior.

A scenario-based experimental design on Qualtrics was used, with a pre-test (N = 200). The main study data were collected using Amazon's Mechanical Turk platform.

Findings reveal that negative service experiences lead to higher engagement in negative WOM compared to positive and satisfactory recovery service experiences. Even well-executed recovery efforts may not completely eliminate negative WOM. The mediating role of emotional responses is substantiated, as heightened negative service experiences result in more intense negative emotional responses, leading to increased engagement in negative WOM.

The study emphasizes the importance of service recovery strategies and the need for businesses to consistently strive for exceptional service quality. It also highlights the complexity of customer reactions to service experiences, suggesting that further research is needed to explore the factors that minimize negative WOM across various service contexts.

This study is based on the heuristic-systematic model (HSM) to dynamically examine the effect of review variance on sales and the boundary conditions that mitigate this effect.

Based on the theoretical domain of HSM, a conceptual model is proposed that analyzes the nonlinear relationship between review variance and sales and the interaction and motivation factors that moderate these relationships. Review data from websites targeting the film industry in the USA and South Korea (Korea) were collected to empirically analyze the authors' hypothesis, and panel regression analysis was used for confirmation.

Moderated by interactive and motivational factors, review variance exhibits an inverse-U-shaped relationship with review variance. Specifically, as an interaction factor, review valence and owned social media (OSM) resulted in positive interaction effects, and as a motivation factor, the number of alternatives exhibited a positive interaction effect with review variance. The effect of review variance was less pronounced in the USA than in Korea.

The study outcomes reveal a nonlinear relationship between review variance and sales, thus supporting the contradictory findings of previous studies. This study contributes to the literature by using the HSM as a theoretical framework to verify various HSM mechanisms using online review data. This exploratory study also contributes to the international marketing literature by showing that the effects of review variance vary across cultures.

This study aims to use emotions-as-social information theory to investigate how physical (customer perceived store atmosphere) and social servicescapes (customer information searching) influence the effects of employees’ positive affective displays on customer outcomes via customer positive moods and positive expectation disconfirmation.

This research included two studies, each using a distinct research design, to empirically test the proposed model. Study 1 involved 200 observational data points on objective purchase amounts from designer watch shops. In Study 2, data were collected from 230 customers in designer jewelry stores.

The results of path analyses revealed that: employee positive affective displays are positively associated with customer purchase outcomes; employee positive affective displays had positive indirect effects on customer purchase outcomes by enhancing customer positive moods and positive expectation disconfirmation; these positive indirect effects were strengthened when customers engaged in information search behaviors; and these positive indirect effects were attenuated when customers perceive store atmosphere as favorable, indicating a substitution effect of customer perceived store atmosphere.

Previous research has not thoroughly examined the role of the servicescape in moderating the effects of employees’ positive affective displays on customer purchase outcomes. This present study not only clarified the affective and cognitive mechanisms that link employees’ positive affective displays on purchase outcomes but also identified servicescape as a critical boundary condition of these effects.

The concept of online shopping has been in vogue for the past two decades and is on the rise. Even developing countries like Pakistan are using electronic platforms to buy and sell goods and services, and the trend has been increasing ever since the COVID-19 pandemic. Drawing on the expectancy-disconfirmation theory, this study aims to test the e-service quality (E-SQ) and e-customer satisfaction (ECS) linkage with the mediating roles of functional values (FVs) and hedonic values (HVs).

The data have been collected from 298 customers of AliExpress and Daraz e-commerce platforms and analyzed using covariance-based structural equation modeling (CB-SEM).

The results of the study showed a significant positive relationship between E-SQ and ECS and indirect linkage through FVs and HVs have also been established.

E-commerce platforms, particularly in Pakistan, should place a strong emphasis on FVs by providing accurate product details, user-friendly navigation, transparent pricing and streamlined transactions. Customers' trust and confidence will increase if they have a smooth and effective online purchasing experience. Customer satisfaction may be influenced by regular platform functionality and usability changes.

The use of functional and HVs is considered to be a novel factor in testing the relationship between E-SQ and ECS.

Social participation and user-generated contents have engendered an era when “any receiver can be a provider.” Although studies have emphasized on increasing the number of subscribers conducive to platform success, the issue regarding why viewers drop their registered YouTuber channels has been less addressed. This article aims to present and empirically test a causal model to account for the pattern of discontinuous intention of attention to YouTuber channels.

Inspired by the expectation–disconfirmation theory, the study proposes and examines a research model by using an empirical quantitative approach. The proposed model considers channel and YouTuber facets, as well as potential moderation effects of viewing seniority.

Analysis of a sample of 406 questionnaires revealed that ads interference is not a significant reason for discontinuation of viewing a channel, in contrast to insufficient playfulness and inconsistency. Regarding YouTubers’ features, both interaction inadequacy and negative self-disclosure are significant determinants of perceived dissatisfaction and discontinuation of viewing. The effects of determinants of discontinuity depend on viewers' seniorities, except for negative self-disclosure.

While existing studies have focused mainly on revealing the determinants associated with the behavioral intention of viewing YouTuber channels to increase self-media performance, this research contributes insights into the literature investigating why existing viewers stop following a certain YouTuber channel. From a psychological perspective, this study demonstrates the value of modeling discontinuous behaviors toward YouTuber channels in the domain of social marketing.

Enterprise information systems (EISs) are intricate technological artifacts with wide user base within organizations. While much is known about the adoption and implementation of EISs, little is known about what subsequently follows them, i.e. the assimilation of EISs. This article aims to examine the assimilation of the EISs which is consequential to realizing any benefits from such enterprise technology.

The author conceptually draws on the insights from the expectation confirmation theory, theory of reasoned action, equity theory, and prospect theory to examine the assimilation of the EISs. In doing so, the author generates competing testable hypotheses regarding the relationship between individual users' psychological and social influences through expectation (dis)confirmation and the users' intention to assimilate the EISs.

By conceptually articulating the individual users' psychological and social influences through expectation (dis)confirmation, the author offers a more complete account of the assimilation of EISs, and provide several avenues for future empirical and theoretical research on enterprise technology assimilation.

The extant research that there is on the assimilation of the EISs focuses more on the organizational – as opposed to individual – level determinants of EISs assimilation and largely considers the functional – rather than psychological and social – drivers. This article addresses these important, yet understudied, factors to offer a more nuanced account of EISs assimilation.

The aggressive competition in the real estate market forces real estate developers to tackle the challenge of selecting the best project construction phasing alternative. The real estate industry is characterized by high costs, high profit and high risks. The schedules of real estate projects are also characterized by having large number of repetitive activities that are executed over a long duration. The repetitiveness, long duration of execution, the high amounts of money involved and the high risk made it desirable to leverage the impact of changes in phasing plans on net present value of amounts incurred and received over the long execution and selling duration. This also changes the project progress, and delivery time as well as their respective impact on customer degree of satisfaction. This research addresses the problem of selecting the best phasing alternative for real estate development projects while maximizing customer satisfaction and project profit.

The research proposes a model that generates all construction phasing alternatives and performs decision-making to rank all possible phasing alternatives. The proposed model consists of five modules: (1) Phasing Sequencing module, (2) Customer Satisfaction module, (3) Cash-In calculation module, (4) Cost Estimation module and (5) Decision-making module. A case study was presented to demonstrate the practicality of the model.

The proposed model satisfies the real estate market's need for proper construction phasing plans evaluation and selection against the project's main success criteria, customer satisfaction and project profit. The proposed model generates all construction phasing alternatives and performs multi-criteria decision making to rank all possible phasing alternatives. It quantifies the score of the two previously mentioned criteria and ranks all solutions according to their overall score.

The research proposes a model that assist real estate market's need for proper construction phasing plans evaluation and selection against the project's main success criteria, customer satisfaction and project profit. The proposed model can be used to conclude general guidelines and common successful practices to be used by real estate developers when deciding the construction phasing plan. In this study the model is based on business models where all the project units are sold, rental cases are not considered. Also, the budget limitations that might exist when phasing is not considered in the model computations.

The model can be used as a complete platform that can hold all real estate project data, process revenues and cost information for estimating profit, plotting cash flow profiles, quantifying the degree of customer satisfaction attributable to each phasing alternative and providing recommendation showing the best one. The model can be used to conclude general guidelines and common successful practices to be used by real estate developers when tackling the challenge of selecting construction phasing plans.

This study aims to focus on assessing the psychometric properties necessary to validate the internal structure of the TOURQUAL scale.

A quantitative research study was conducted in collaboration with the Brazilian Network of Tourism Observatories, comprising 927 respondents surveyed between October 2021 and May 2022. The data analysis involved the application of descriptive statistics and exploratory factor analysis, in alignment with the principles outlined in the Standards for Educational and Psychological Testing 2014 to validate the scale.

The findings of this study validate the TOURQUAL scale as a robust tool for assessing the perceived quality of tourist services, with results demonstrating one-dimensionality and replicability.

To the best of the authors’ knowledge, this study is the first to assess the psychometric properties for validating the internal structure of the TOURQUAL scale.

This study has aimed to investigate the impact of service innovation capabilities (SIC), supply chain digitalization capabilities (SDC) and customer risk protection (CRP) capabilities of GrabFood on customer satisfaction. GrabFood has been chosen as a case study because GrabPay is one of the Malaysian government's digital wallet partners, and GrabFood is one of the region's leading Online Food Delivery Service (OFDS) providers.

A total of 410 valid responses have been gathered from the GrabFood users for data analysis using the partial least square technique.

The findings reveal that SIC, SDC and CRP of GrabFood have a highly significant influence on customer satisfaction. CRP has been found to partially mediate the relationship between SIC and customer satisfaction and the relationship between SDC and customer satisfaction.

OFDS providers need to consider how to minimize the potential risk to be encountered by customers in delivering services that satisfy the customers. OFDS providers must identify the gap between their capabilities and customer perception and continuously improve their service quality to mitigate the gap.

This research proposes to organise and distil this massive amount of data, making it easier to understand. Using data mining, machine learning techniques and visual approaches, researchers and managers can extract valuable insights (on guests' preferences) and convert them into strategic thinking based on exploration and predictive analysis. Consequently, this research aims to assist hotel managers in making informed decisions, thus improving the overall guest experience and increasing competitiveness.

This research employs natural language processing techniques, data visualisation proposals and machine learning methodologies to analyse unstructured guest service experience content. In particular, this research (1) applies data mining to evaluate the role and significance of critical terms and semantic structures in hotel assessments; (2) identifies salient tokens to depict guests' narratives based on term frequency and the information quantity they convey; and (3) tackles the challenge of managing extensive document repositories through automated identification of latent topics in reviews by using machine learning methods for semantic grouping and pattern visualisation.

This study’s findings (1) aim to identify critical features and topics that guests highlight during their hotel stays, (2) visually explore the relationships between these features and differences among diverse types of travellers through online hotel reviews and (3) determine predictive power. Their implications are crucial for the hospitality domain, as they provide real-time insights into guests' perceptions and business performance and are essential for making informed decisions and staying competitive.

This research seeks to minimise the cognitive processing costs of the enormous amount of content published by the user through a better organisation of hotel service reviews and their visualisation. Likewise, this research aims to propose a methodology and method available to tourism organisations to obtain truly useable knowledge in the design of the hotel offer and its value propositions.