Search Results (503)

In today’s competitive environment of online service industries, particularly e-commerce, meeting consumer expectations is essential for service providers to ensure service quality. However, service failures are unavoidable, leading to unfavorable consequences for businesses. Understanding the mechanisms for customer recovery after negative service experiences is crucial. Using cognitive–emotional personality systems theory and benign violation theory, this study constructed a theoretical model. A total of 351 samples were collected through a situational simulation experiment for a linear regression analysis. A self-mocking response strategy positively influenced brand trust through perceived brand authenticity regarding the dimensions of credibility, integrity, and symbolism. Simultaneously, brand trust was identified as a key driver of post-recovery satisfaction. This study proposes a chain mediation model, which incorporates perceived authenticity and brand trust, to fully comprehend the mechanisms underlying consumers’ satisfaction after service recovery. Our findings provide empirical evidence for the effects of self-mockery on post-recovery satisfaction, as well as suggestions for marketers seeking efficient means to meet consumers’ emotional and cognitive demands during service recovery situations. Full article

The article aims to identify consumer types based on their attitudes and behaviors toward phygital tools and solutions. The analysis was based on the authors’ empirical research. The research was conducted on a sample of 2160 Polish consumers. The study employed an online survey technique. To determine the types of consumers, a 20-item scale was used, allowing the respondents to express their attitudes toward solutions and tools that improve shopping in the phygital space. The extraction of types was carried out in two steps. The first was cluster analysis, conducted using the hierarchical Ward method with the square of the Euclidean distance, and the second was non-hierarchical cluster analysis using the k-means method. As a result of the analyses, three relatively homogeneous types of consumers were distinguished: phygital integrators, digital frequenters, and physical reality anchors. The behaviours of consumers from each type were examined in the context of their impact on sustainable consumption and the sustainable development of the planet. The proposed typology contributes to developing consumer behavior theory in sustainable consumption environments. It provides practical implications for designing customer experiences that are more inclusive, resource-efficient, and aligned with responsible consumption patterns. Understanding how different consumer groups engage with phygital tools allows businesses and policymakers to tailor strategies that support equitable access to digital services and foster more sustainable, adaptive consumption journeys in an increasingly digitized marketplace. Full article

This study examines a single-server Markovian queueing system featuring continuous service and an infinite number of customers at both ends—namely, offline and online clients. Offline customers are conventional clients who arrive at the system following a Poisson process, while online customers are assumed to be endlessly present in the system. All service times are exponentially and identically distributed and independent. Utilizing generating functions and Laplace transform techniques, this study derives exact analytical expressions for the system size probabilities in both transient and steady states. Furthermore, it evaluates key performance measures for each state and provides graphical representations to illustrate the system’s dynamics, thereby enriching the understanding of its operational behavior. This work contributes to the advancement of priority-based queueing models and proposes a novel framework applicable to hybrid service architectures in contemporary digital ecosystems. Full article

Online travel agencies (OTAs) function as e-commerce platforms that facilitate transactions between accommodation providers and consumers, enabling users to efficiently search for, compare, and book travel and lodging services. As the number of OTAs continues to grow, delivering superior service quality has become essential for increasing customer repurchase intentions. Despite its significance, existing research has primarily focused on factors such as website quality, pricing strategies, brand image, and perceived value as determinants of repurchase intention. However, relatively little attention has been paid to the alignment between online information disclosure and customers’ actual offline experiences. To address this gap, the present study introduces the concept of the information disclosure gap and examines its effects on search cost, customer satisfaction, and trust, as well as the subsequent influence of these variables on repurchase intention. A questionnaire-based survey method was conducted with individuals in Taiwan who had prior experience using OTAs, yielding 365 valid responses. This study offers practical insights and recommendations for both OTAs and accommodation providers aimed at reducing the information disclosure gap and strengthening customer repurchase intention. Full article

The development of artificial intelligence has enabled the automation of an increasing number of processes and actions in the online environment, from creating unique and engaging content to simulating user behaviors (likes, comments, reviews). This automation has brought several positives to the online environment, including reduced working time and better results, among others. However, at the other end of the spectrum, consumer trust is starting to decline. Before the advent of artificial intelligence, reviews were often the opinions of other customers who had tried the product or service in question. With the evolution of these reviews, providers can now automatically post them to create a favorable image. Given the increasing concern among young people about environmental issues, this study investigates how AI-generated content affects their trust in sustainability-related online reviews and how this trust influences their pro-environmental purchasing decisions. Quantitative research was conducted in the article, based on which a conceptual model of the degree of trust users have in online reviews and reactions in the context of artificial intelligence was developed. The research methodology involved conducting quantitative research and constructing variables based on the data collected. The results revealed significant links between the evolution of artificial intelligence and the degree of trust users place in general feedback found in online environments. Full article

This study aimed to identify the impact of the e-marketing mix on brand equity in the Jordanian banking sector. This study used a descriptive analysis approach. The study population consisted of all (13) commercial banks in Jordan listed on the Amman Stock Exchange. A random sample of administrative personnel at commercial banks was selected, including administrators and department heads. It was distributed electronically via Google Forms, and 293 questionnaires (79.1%) from the total study sample were retrieved. The SPSS program version 26 was used to examine the collected data. The results showed that there is a statistically significant effect at the level (α ≥ 0.05) of e-marketing on brand equity in the Jordanian banking sector. It was found that there is a positive correlation between e-marketing and the three dimensions of brand equity (brand loyalty, perceived quality, and brand image). Moreover, a positive brand image should be maintained through the consistent delivery of promises, excellent customer service, and a strong online reputation. Cultivating brand loyalty among customers can be achieved through personalized services, exclusive rewards, and tailored promotions. By integrating these strategies into their e-marketing efforts, banks can effectively engage with customers, differentiate themselves from competitors, and build long-term relationships. Full article

Cosmetics companies are increasingly integrating augmented reality and artificial intelligence technologies into products and services referred to as beauty tech; consumer perceptions of these solutions, however, remain understudied. Data generated via an online survey are implemented in a stimulus–organism–response framework, deduced from the beauty tech literature. Thereupon, the study identifies how interactivity, informativeness, personalization, and service quality of digital and physical beauty tech solutions for home use affect utilitarian and hedonistic values and the perceived risk factors among consumers. Via customer satisfaction, the effect of the value perception on the purchase intention and loyalty is considered. Results hint at strong effects of characteristics of the services and applications on the utilitarian and the hedonistic dimension of customer experience, which in turn strongly influence customer satisfaction. Perceived risk factors play only a marginal role. Only regarding the tested physical product does higher service quality add to the customer experience. Customer satisfaction in turn results in positive brand perception across different stages of the customer journey and leads to a higher purchase intention, positive brand advocacy, and a higher re-purchase intention. Consequently, well-designed solutions can generate higher customer satisfaction and loyalty on multiple stages along the customer journey. Full article

Service quality research has traditionally focused either on identifying Kano two-dimensional quality categories or detecting service quality deficiencies. However, integrating these perspectives remains a challenge due to the Kano model’s nonlinear characteristics and the importance-performance and gap analysis (IPGA) model’s linear approach. This study proposes the Kano-IPGA (KIPGA) model, incorporating mutual information (MI) to bridge the gap between these two models. The KIPGA model first employs moderated regression analysis to classify service attributes into Kano’s quality categories. MI is then used to calculate the relative importance (RI), while relative performance (RP) is determined using the original IPGA approach. The results are mapped into the KIPGA strategic matrix, categorizing service attributes into eight management strategies. An empirical analysis of Taiwan’s online insurance systems demonstrates the model’s effectiveness in simultaneously identifying Kano categories and prioritizing service quality improvements. The findings reveal that critical improvement and enhanced improvement regions require immediate attention. The proposed KIPGA model offers a systematic approach for service quality management, providing decision-makers with a structured framework to allocate resources effectively and enhance customer satisfaction. This study contributes to service quality research by offering an integrated model that accounts for both linear and nonlinear quality assessment perspectives. Full article

The adoption of Intelligent Virtual Assistants (IVAs) in the banking sector presents new opportunities to enhance customer service efficiency, reduce operational costs, and modernize service delivery channels. However, the factors driving IVA adoption and usage, particularly in specific regional contexts such as Portugal, remain underexplored. This study examined the determinants of IVA adoption intention and actual usage in the Portuguese banking sector, drawing on the Technology Acceptance Model (TAM) as its theoretical foundation. Data were collected through an online questionnaire distributed to 154 banking customers after they interacted with a commercial bank’s IVA. The analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM). The findings revealed that perceived usefulness significantly influences the intention to adopt, which in turn significantly impacts actual usage. In contrast, other variables—including trust, ease of use, anthropomorphism, awareness, service quality, and gendered voice—did not show a significant effect. These results suggest that Portuguese users adopt IVAs based primarily on functional utility, highlighting the importance of outcome-oriented design and communication strategies. This study contributes to the understanding of technology adoption in mature digital markets and offers practical guidance for banks seeking to enhance the perceived value of their virtual assistants. Full article

This study aims to analyze online job postings using machine learning-based, semantic approaches and to identify the expertise roles and competencies required for big data professions. The methodology of this study employs latent Dirichlet allocation (LDA), a probabilistic topic modeling technique, to reveal hidden semantic structures within a corpus of big data job postings. As a result of our analysis, we have identified seven expertise roles, six proficiency areas, and 32 competencies (knowledge, skills, and abilities) necessary for big data professions. These positions include “developer”, “engineer”, “architect”, “analyst”, “manager”, “administrator”, and “consultant”. The six essential proficiency areas for big data are “big data knowledge”, “developer skills”, “big data analytics”, “cloud services”, “soft skills”, and “technical background”. Furthermore, the top five skills emerged as “big data processing”, “big data tools”, “communication skills”, “remote development”, and “big data architecture”. The findings of our study indicated that the competencies required for big data careers cover a broad spectrum, including technical, analytical, developer, and soft skills. Our findings provide a competency map for big data professions, detailing the roles and skills required. It is anticipated that the findings will assist big data professionals in assessing and enhancing their competencies, businesses in meeting their big data labor force needs, and academies in customizing their big data training programs to meet industry requirements. Full article

The online shopping consulting service (online customer service) is an important component of online shopping, and the rapid advancement of artificial intelligence technology has allowed online retailers to apply this technology to customer service. This study explores an online retailer’s customer service innovation decisions. We develop a stylized model to investigate an online retailer’s choices of two different online shopping consulting service strategies, as well as the impact of the different strategies on both online retailer and consumers. This study demonstrates that providing AI customer service with higher performance and higher probability of consumer shopping problem resolution may not always benefit the online retailer. Existing studies have already shown that consumers prefer human customer service. However, this study shows that consumers do not always prefer human customer service, and they prefer AI customer service in some situations. Moreover, this study reveals that the probability of consumers’ shopping problems being resolved and the consulting cost can affect the online retailer’s choice of online customer service strategies. Full article

The fintech sector is entering the maturity phase of the life cycle, in which customer brand engagement (CBE) is connecting companies and customers. The aim of this paper is to investigate the driving forces behind CBE in the fintech sector and its contribution to loyalty intentions. In the framework of service-dominant logic, a quantitative approach on 239 fintech users was undertaken to propose and validate a conceptual model of customer brand engagement in fintech, using Partial Least Square–Structural Equation Modeling. The results showed that the perception of personalization, the perception of interaction between customers and brands, and the perception of benefits contribute to CBE in fintech, and the latter is positively associated with loyalty intentions. This research contributes to the literature of brand engagement by offering an integrative framework to analyze CBE in the fintech sector. Full article

Although integrating smart technologies into service encounters can provide hoteliers with a competitive advantage, managing customer satisfaction in smart hotels remains challenging due to limited knowledge of how to prioritize improvements across smart service and traditional service. Therefore, the study aims to evaluate customer satisfaction with both smart and non-smart technology attributes in smart hotels, identify attributes with high improvement priorities, and uncover factors contributing to customer dissatisfaction. This study proposes a prioritization method for service improvement in smart hotels by analyzing online reviews from 42 smart hotels. The findings reveal that customers’ technological needs are well met in smart hotels, but smart hotels need to promptly address three key issues: long check-in wait times, staff attitude and competence, and breakfast quality. To maximize customer satisfaction, managers should adopt a hybrid service model that strikes the right balance between technology and human interaction. Full article

Wine tourism is a key segment of the global tourism industry, offering visitors opportunities to explore vineyards, taste wines, and experience cultural heritage. This study examines online reviews to identify positive and negative themes of wine tourism experiences and explores how perceptions vary among traveler types. Data were collected from 2850 TripAdvisor reviews of wineries in Bordeaux, Champagne, and Burgundy, France, and analyzed using Leximancer software. The findings reveal seven key themes, including Wine Quality, Tasting Experiences, Tour Guides, Vineyard Views, and Cultural Charm, alongside Dissatisfaction with Time Management and Value for Money. Variations among traveler types highlight distinct preferences, with solo travelers valuing education, couples prioritizing romance, groups focusing on culture, and families concerned with organization. This study contributes to research on experiential tourism by addressing diverse visitor expectations and enhancing service quality. Full article

This study investigates tandem queues with two service nodes. The consumer needs to obtain services at the following two nodes in this system: the IBM online sphere for artificial intelligence (AI) and the complex RDX mechanism. Before the second essential service (SES), by utilizing AI to validate the data in IBM WebSphere and insight, the first essential service (FES) begins with the RDX mechanism. If there are fewer customers than “a” after a service at node 1 is finished, the server departs for a subsequent assignment. As soon as the vacation value reached the threshold, the service began. After the service concludes at node 1, it moves on to node 2. In this study, the supplemental variable technique is used to determine the probability-generating function (PGF) at any given time. A numerical solution also yields certain features of the queueing system. Full article