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Based on prior studies, the performance of customer relationships depends highly on the characteristics of the e‐service. However, the strength of this association can be impacted when businesses employ multichannel services (e.g. offering online and offline services). With multichannel services, any inconsistency in perceived quality across channels may result in customer distrust toward a service provider. The purpose of this study is to investigate the effect of inconsistent quality on the association between e‐service quality and customer relationships in a university context.

This study conducted a web survey and 318 respondents who have both physical and e‐service experiences were collected. The inconsistent quality across channels was divided into three groups by k‐means clustering approach. Next, the hypothesized associations were analyzed using regression analysis based on three groups.

The results show that inconsistent quality has different impacts on the association between e‐service quality and customer relationships across the three groups. Especially in the positive disconfirmation group, the investment in e‐services will be in vain because certain e‐service sub‐constructs lose their impact on customer relationships.

The findings of this study provide implications for improving customer relationships under different cross‐channel quality inconsistency conditions for managers.

This study extends the concept of expectancy disconfirmation theory to the multichannel service context and pioneers the exploration of the moderating effect of cross‐channel quality inconsistency in customer relationships, contributing to the understanding of the literature about the impacts of inconsistent quality on customer relationships.

Introduction Summary: Tourism is accepted as a multidisciplinary industry .Hoteliers and tour operators/travel agencies (TO/TA) count as two main stakeholders in the tourism sector, and they are interdependent with regard to their marketing and service objectives. Distribution channels, as TO and TA, are an important part of tourism growth. As TOs and TAs are the intermediaries between tourist and tourism service providers. Tourists travel to satisfy or fulfill their dreams because travel is a need for psychological relief that motivates people for temporary movement to different places and different countries. The research aims to examine the effects of perceived stakeholder conflict factors (PSCF) issues identified from the existing literature on guests’ outcomes in accommodation establishment located in Kyrenia, Northern Cyprus. PSCF is, namely, misinformation, unmanaged bookings and operational mistakes involving quality-price inconsistency and unsolved guest’s problems should be considered by both parties. Therefore, the objective of this study is to address the research gap regarding the effects of PSCFs on guests’ perceived value (GPV), guest satisfaction (GS) and behavioral intention (BI). Purpose: This study aims to investigate stakeholders conflict and its effects on guests’ outcomes, namely GPV, GS and BI associated with accommodation establishments, which has not been examining empirically before. Design/Methodology/Approach: This study conceptualized PSCFs and developed a scale for assessing this conflict and its outcomes. Through careful instrument development process, four sub-dimensions and 17 items of PSCFs were identified. Findings: The overall PSCFs’ effects obtained indicate that GPV, GS and BI are associated with accommodation establishments, and are negatively affected by the unsatisfactory relationship between these two key stakeholders. Originality/Value: The study empirically tested the conceptual model through conducting survey research to collect data from the guests whose trips were organized through a TO/TA and staying in five-star or four-star accommodation establishments located in Kyrenia, Northern Cyprus.

The purpose of the study is to propose a framework for fleet management and make suitable distribution solution choices in the food industry.

This study reviews the literature to examine food distribution criteria. These criteria are used in the analytic hierarchy process (AHP) assessment and combined with discrete events simulation in a structured framework, which is validated through an empirical study.

The empirical case results demonstrate that both the AHP and discrete events simulation converge toward the same solution in most cases.

This study contributes to the literature on distribution management and develops a framework that can both guide future research and aid logistics practitioners in analysing distribution decision-making systems in dynamic environments.

Consumers have pervasively relied on mobile reviews in digital economy. However, little knowledge exists regarding how customers adopt several mobile reviews to make purchasing decisions. With the assistance of reader-response theory, this study investigates how the consistency of product reviews, in terms of their adherence to both other reviews and the prior experience of the customer, affect perceived quality, confirmation of the customer's expectations, the customer's level of trust in the seller and the consequent purchase intention.

Based on a scenario simulation and an online experiment to collect data, the authors employed AMOS to test the proposed hypotheses using survey data collected from 314 customers in Study 1 and 420 consumers in Study 2.

The results indicate that global consistency positively and significantly contributes to confirmation, perceived quality and trust in sellers while sequential inconsistency positively and significantly influences perceived quality. Meanwhile, purchase intention is positively and significantly promoted by confirmation, perceived quality and trust in sellers, and initial valence has some moderating effects on these relationships.

This study contributes to the understanding of how customers apply product reviews to make purchasing decisions from a new angle. It also elucidates the way in which the perceived consistency of product reviews affects how reviewers are perceived and the consequent effect of these perceptions on a customer's purchase intentions.

Although the multi-criteria technique analytic hierarchy process (AHP) has successfully been applied in many areas, either selecting or ranking alternatives or to derive priority vector (weights) for a set of criteria, there is a significant drawback in using this technique if the pairwise comparison matrix (PCM) has inconsistent comparisons, in other words, a consistency ratio (CR) above the value of 0.1, the final solution cannot be validated. Many studies have been developed to treat the inconsistency problem, but few of them tried to satisfy different quality measures, which are minimum inconsistency (fMI), the total number of adjusted pairwise comparisons (fNC), original rank preservation (fKT), minimum average weights adjustment (fWA) and finally, minimum L1 matrix norm between the original PCM and the adjusted PCM (fLM).

The approach is defined in four steps: first, the decision-maker should choose which quality measures she/he wishes to use, ranging from one to all quality measures. In the second step, the authors encode the PCM to be used in a many-objective optimization algorithm (MOOA), and each pairwise comparison can be adjusted individually. The authors generate consistent solutions from the obtained Pareto optimal front that carry the desired quality measures in the third step. Lastly, the decision-maker selects the most suitable solution for her/his problem. Remarkably, as the decision-maker can choose one (mono-objective), two (multi-objective), three or more (many-objectives) quality measures, not all MOOAs can handle or perform well in mono- or multi-objective problems. The unified non-sorting algorithm III (U-NSGA III) is the most appropriate MOOA for this type of scenario because it was specially designed to handle mono-, multi- and many-objective problems.

The use of two quality measures should not guarantee that the adjusted PCM is similar to the original PCM; hence, the decision-maker should consider using more quality measures if the objective is to preserve the original PCM characteristics.

For the first time, a many-objective approach reduces the CR to consistent levels with the ability to consider one or more quality measures and allows the decision-maker to adjust each pairwise comparison individually.

This study aims to investigate the effects of inconsistent word‐of‐mouth on service quality perception and purchase intention during the service encounter.

A pilot study and a subsequent formal experiment with six scenarios were designed to test the inconsistent word‐of‐mouth effect. Participants were recruited from a major university located in Southern China.

The results revealed that service quality perception and purchase intention were influenced more by the final word‐of‐mouth event than by the initial one and were more favorable with more positive word‐of‐mouth events.

Further research should study more factors such as source effect of word‐of‐mouth and knowledge about the service in investigating the inconsistent word‐of‐mouth effect on service quality perception and purchase intention.

Consumers' service quality judgment and purchase intention seem to be highly driven by the most recent word‐of‐mouth activities. Thus, to stimulate consumption levels, companies can use creative and innovative promotion tools for consumers to talk about their service and elicit consumers' purchase interest. Other tools such as involving consumers in delivering the service and developing referral incentive schemes are also beneficial to establish positive word‐of‐mouth.

This paper adds value to the word‐of‐mouth literature by studying the inconsistent word‐of‐mouth effect on consumers' perceptions of service quality and purchase intention towards the service, which lacks strong conceptual and empirical evidence.

Supply chain risks (SCRs) do not work in isolation and have impact both on each member of a chain and the performance of the entire supply chain. The purpose of this paper is to quantitatively assess the impact of dynamic risk propagation within and between integrated firms in global fresh produce supply chains.

A risk propagation ontology-based Bayesian network (BN) model was developed to measure dynamic SCR propagation. The proposed model was applied to a two-tier Australia-China table grape supply chain (ACTGSC) featured with an upstream Australian integrated grower and exporter and a downstream Chinese integrated importer and online retailer.

An ontology-based BN can be generated to accurately represent the risk domain of interest using the knowledge and inference capabilities inherent in a risk propagation ontology. In addition, the analyses revealed that supply discontinuity, product inconsistency and/or delivery delay originating in the upstream firm can propagate to increase the downstream firm’s customer value risk and business performance risk.

The work was conducted in an Australian-China table grape supply chain, so results are only product chain-specific in nature. Additionally, only two state values were considered for all nodes in the model, and finally, while the proposed methodology does provide a large-scale risk network map, it may not be appropriate for a large supply chain network as it only follows the process flow of a single supply chain.

This study supports the backward-looking traceability of risk root causes through the ACTGSC and the forward-looking prediction of risk propagation to key risk performance measures.

The methodology used in this paper provides an evidence-based decision-making capability as part of a system-wide risk management approach and fosters collaborative SCR management, which can yield numerous societal benefits.

The proposed methodology addresses the challenges in using a knowledge-based approach to develop a BN model, particularly with a large-scale model and integrates risk and performance for a holistic risk propagation assessment. The combination of modelling approaches to address the issue is unique.

The purpose of this paper is to examine the relationship between remuneration for the achievement of objectives and sustainability, and – more specifically – the amount of attention that listed companies in Italy devote to defining, and consequently to communicating externally, sustainability as a criterion in establishing the wage levels of managers and directors.

It was decided to ascertain whether the quality of information regarding sustainability provided in connection with the remuneration policies of listed companies tallies with the general quality of information regarding sustainability provided through companies’ main (obligatory and voluntary) reporting procedures.

The results of this research show that the inconsistency between the information provided in voluntary and obligatory reports (between reports on sustainability and remuneration reports) extends to the levels of information provided in the two types of obligatory report (the reports on remuneration and on management); there is also a discrepancy between the levels of information provided in these reports and the evaluation of that information by an external assessor.

One of the limitations of this research is that as the data examined were gleaned from public documents, it is not necessarily an accurate reflection of all the information that firms have at their disposal on questions of sustainability and remuneration policies. The existence of internal documents containing other information, and therefore leading to different results, cannot be ruled out.

This study is the first in Italy to examine the question of how limited companies report issues relating to management by objectives-corporate social responsibility. It does this through the introduction of a mixed system for ESG information, which counteracts the subjective limitations of the internal evaluation provided by the research group by adding in the authoritative evaluations of an external assessor.

This study aims to analyze the relationships between Chinese students' behavioral intentions, satisfaction, service quality, perceived value, and university image. A multi-level modeling approach is used to examine the relationships between these higher order constructs. In addition, service quality consists of three primary dimensions and 13 sub-dimensions. Finally, this study compares students' perceptions of the dimensions of service quality, overall perceived service quality, university image, perceived value, satisfaction and favorable behavioral intentions based on the demographic characteristics of the sample (gender, age, year of study, and major).

The data used in this study were based on a sample of 350 students studying at a public university in China. Data was analyzed using factor analysis, regression analysis, and analysis of variance (ANOVA).

The findings support using a multi-level model consisting of three primary dimensions and 13 sub-dimensions to conceptualize and measure perceived service quality. Service quality is the main determinant of satisfaction and has a significant influence on university image and perceived value. Perceived value has a significant moderating effect on the relationship between service quality and satisfaction. Satisfaction significantly influences recommending the university and future attendance. The results of this study also indicate that students' demographic characteristics (gender, age, year of study, and major) influence their perceptions of several of the constructs.

This is the first study synthesizing behavioral intentions, satisfaction, service quality, perceived value and university image in a Chinese university setting.

The overall goal of the data mining process is to extract information from an extensive data set and make it understandable for further use. When working with large volumes of unstructured data in research information systems, it is necessary to divide the information into logical groupings after examining their quality before attempting to analyze it. On the other hand, data quality results are valuable resources for defining quality excellence programs of any information system. Hence, the purpose of this study is to discover and extract knowledge to evaluate and improve data quality in research information systems.

Clustering in data analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found. In this study, data extracted from an information system are used in the first stage. Then, the data quality results are classified into an organized structure based on data quality dimension standards. Next, clustering algorithms (K-Means), density-based clustering (density-based spatial clustering of applications with noise [DBSCAN]) and hierarchical clustering (balanced iterative reducing and clustering using hierarchies [BIRCH]) are applied to compare and find the most appropriate clustering algorithms in the research information system.

This paper showed that quality control results of an information system could be categorized through well-known data quality dimensions, including precision, accuracy, completeness, consistency, reputation and timeliness. Furthermore, among different well-known clustering approaches, the BIRCH algorithm of hierarchical clustering methods performs better in data clustering and gives the highest silhouette coefficient value. Next in line is the DBSCAN method, which performs better than the K-Means method.

In the data quality assessment process, the discrepancies identified and the lack of proper classification for inconsistent data have led to unstructured reports, making the statistical analysis of qualitative metadata problems difficult and thus impossible to root out the observed errors. Therefore, in this study, the evaluation results of data quality have been categorized into various data quality dimensions, based on which multiple analyses have been performed in the form of data mining methods.

Although several pieces of research have been conducted to assess data quality results of research information systems, knowledge extraction from obtained data quality scores is a crucial work that has rarely been studied in the literature. Besides, clustering in data quality analysis and exploiting the outputs allows practitioners to gain an in-depth and extensive look at their information to form some logical structures based on what they have found.