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In any quality improvement programme, measurement plays a vital role as it provides information for decision making. Finding the characteristics of quality is a prerequisite for the measurement process. Despite recent research on general service’s quality dimensions, little work has been concentrated on public services and in particular higher education. Examines conceptual models proposed for different environments for consistency with higher education. Reviews quality factors found in the relevant literature and presents a new framework for the dimensions of quality in higher education.

Uses survey results from a national sample of quality managers to examine the relationship between how a firm defines quality and what product quality dimensions it considers important to its competitive strategy. Garvin proposed a well‐known framework for thinking about product quality based on eight dimensions: performance, features, reliability, conformance, durability, serviceability, aesthetics, and perceived quality. Alternative definitions of quality have evolved from five different approaches: transcendent, product‐based, user‐based, manufacturing‐based, and value‐based. Of the five approaches to defining quality, the manufacturing firms in our sample subscribed most often to the user‐based definition. Using regression analysis within a factor analytic framework, some empirical support was found for hypothesized linkages between the product quality dimensions and the alternative definitions of quality. Specifically, the user‐based definition was related significantly to aesthetics and perceived quality, the manufacturing‐based definition to conformance, and the product‐based definition to performance and features.

The purpose of this paper is to investigate which dimension of salespersons' service quality is of most importance for customer loyalty in a fashion chain stores setting. It also aims to assess the effects of two retail environmental factors (price level and customers' demographic variables) on the customer loyalty of salespersons service quality.

The study uses the SERVQUAL service quality instrument with modification in measuring the salespersons' service quality in the Hong Kong fashion retail environment. In order to measure customer loyalty in fashion chain stores, multi‐item measures were used to collect data on repatronage intentions, word‐of‐mouth intentions, and satisfaction. A total of 232 surveys were administrated to shoppers who were leaving a fashion chain store in Hong Kong.

The results showed that the empathy dimension of salesperson service quality is the most important for customer loyalty in Hong Kong's fashion chain stores but the empathy dimension of salespersons' service quality in fashion retail stores could not be affected by these two retail environmental factors. Furthermore, the salespersons' service quality in the reliability dimension is significantly impacted by the customers' demographic characteristics, but not by price level set by fashion chain stores.

The study was carried out in four popular retail districts of Hong Kong and the results obtained may not be generalized to the country as a whole. The findings that are relevant in a fashion retail setting may not applicable in other retail environments.

The findings can direct fashion retailers to improve the specific service dimensions and work to provide customers with more value through services which will consequently improve internal and external standards of quality and performances in fashion retail settings, thus bringing about repeat customers and increased profitability.

The objective of this work is twofold: firstly, to develop a model for assessing healthcare service quality (HSQ), and secondly, to evaluate the effectiveness of machine learning algorithms in predicting the quality of healthcare services.

In this study, a comprehensive literature review has been performed to identify key quality dimensions in the healthcare services domain. Delphi’s method has been used to confirm the criticality of these dimensions based on experts’ opinions and proposed a novel CIRMQUAL model. Factor analysis techniques have been used to further validate the CIRMQUAL model. Using the data collected through a questionnaire survey, a number of machine learning models have been developed to predict the customer satisfaction level based on the service quality (SQ) performance of a healthcare unit on different dimensions of the CIRMQUAL model.

The study developed a CIRMQUAL model with 14 dimensions (quality of care, safety and security, skill and conduct, staff attitude, tangibles, quality of the atmosphere, patient rights, follow-up, communication, cost of treatment, availability of resources, accessibility, waiting time and services), and these dimensions have been clubbed into four major dimensions, i.e. clinical quality, infrastructural quality, relationship and managerial quality. Furthermore, the application of machine learning algorithms has demonstrated significant accuracy in predicting SQ, highlighting its ability to improve healthcare services and the satisfaction level of patients.

Managers of healthcare units work hard to identify and address the pain points of the patients and improve the working of the healthcare units being managed by them. The availability of many scales with numerous dimensions adds to their confusion in selecting a suitable scale. The current work addresses this confusion and provides four clear areas for assessing the quality of healthcare units. By using this scale, managers can assess the quality of services provided by them, identify the dimensions of low performance, plan and take suitable corrective actions to improve the performance of their healthcare units.

A comprehensive SQ model, i.e. CIRMQUAL has been proposed as a new scale to assess SQ in healthcare units. The model has been developed after analyzing the dimensions used by many researchers available in the literature. This model can be used by future researchers to assess the SQ in healthcare units. Moreover, an attempt has been made to use artificial intelligence-based techniques for predicting customer satisfaction. Such attempts are in the initial stage for healthcare sector. Future researchers can take this concept forward and test the applicability of different machine learning techniques in different functional areas of healthcare.

Good health is of utmost importance for all human beings. In spite of the expenditure of substantial time and efforts by various stakeholders, the service delivery doesn’t match the expectations of patients. Many times, the service providers are not aware of this dissatisfaction and specific aspects of service delivery that need to be improved to reduce dissatisfaction. The model proposed will help the service providers in this regard and the service providers will be able to take focused steps. Such initiatives will definitely improve patient’s satisfaction and their social well-being.

This work is unique because it uses a novel technique to redefine the quality of services in healthcare by using a dual methodology. The research presents a model that includes various factors and it is specially developed to evaluate the quality of services in healthcare settings. This study advances the area’s progress by implementing computational tools for accurate evaluation of HSQ. The healthcare decision-makers may use this novel perspective to evaluate and enhance the quality of service.

The study aims to explore the dimensions of Quality 4.0 adoption, prioritization of these dimensions and the influential dimensions and their causal relationships that can guide the smooth adoption of Quality 4.0 to boost organizational performance.

The Quality 4.0 dimensions are explored from the extant literature. The qualitative data were captured from 12 highly experienced experts from diverse industries and academia through structured interview questions and group discussions in multiple phases. The inputs obtained from the experts were analyzed using Fuzzy-Technique for Order of Preference by Similarity to Ideal Solution for dimension priority, and Fuzzy-Decision-Making Trial and Evaluation Laboratory was employed to reveal the influential relationship between them.

The analysis reveals that quality scalability, quality culture and quality conformance are investigated as primary drivers of Quality 4.0 adoption. Data-driven analytical thinking and customer centricity emerge as dynamic dimensions that act as quality deliverable ends. Integrating these methodologies provides a robust framework for understanding and managing Quality 4.0 complexities, offering actionable insights for prioritizing initiatives and addressing interdependencies to ensure successful adoption and implementation.

The practical implications guide industries in creating strategic action plans tailored to their needs and fostering a quality-focused culture. The study also offers valuable insights into government policies, promoting sustainability, efficiency and a circular economy.

The study’s novelty lies in its prioritization and examination of the most influential causes and effects within the Quality 4.0 dimensions. This approach highlights core drivers and critical factors, providing a comprehensive framework for successful implementation.

Purpose: We examined whether the five-service quality dimensions described by SERVQUAL (SQ) and SERVPERF (SP) are consistent with perceived dimensions of management accounting (MA) service quality and we compared responses from users and providers.

Design/methodology/approach: We surveyed experienced providers and users of MA services to learn their perceptions and expectations of accounting service quality using SQ/SP adapted to an MA context. We used principal components analysis (PCA) to investigate service quality dimensions.

Findings: Participant responses identified three dimensions of MA service quality. There was a high degree of correspondence in dimensions of service quality between users and providers, but with notable differences in service priorities. A performance-only (SP) approach seems to provide a better measure of overall service quality than performance minus expectations (SQ).

Research limitations/implications: Participants self-selected to participate. Respondents were not matched by organization. The SQ/SP instrument may not capture important organization specific attributes. Our approach may serve as a guide for future studies of accounting service quality.

Practical implications: SP may be more useful to managers who wish to evaluate overall service quality. SQ may be more useful to identify specific gaps between user perceptions and expectations. SQ/SP assessments may help to improve the quality of MA service delivery and provider-user communications.

Originality/value: This is the first empirical study to our knowledge that reports on MA service quality dimensions using both the SQ and SP instruments. This study investigated perceptions and expectations of MA service users and providers. Our sample is a cross-section of experienced professionals.

This study investigates the service quality dimensions of hotel resorts in renowned beach destinations of Thailand. It also explores the relationship between review text sentiment expressed in online platforms and the satisfaction ratings provided for those reviews.

The study employs a two-step analysis approach: first, supervised and unsupervised machine learning via support vector machine (SVM) and latent Dirichlet allocation (LDA) are used to identify service quality dimensions, and second, SmartPLS with PROCESS macro is applied to analyze the moderating roles of quality signals and reviewer’s experience on the relationship between sentiment and satisfaction rating. The dataset comprises 102,179 online reviews from TripAdvisor, focusing on 187 selected hotels rated from 3 to 5 stars.

Eight service quality dimensions were identified, including leisure activities, tangibles and surroundings, reliability, responsiveness, service process, food, empathy and ambience. The study underscores that the service process stands as the sole dimension exhibiting negative sentiment. Furthermore, the analysis revealed a robust positive association between sentiment of review texts and satisfaction, and reviewers’ experience and brand affiliation influenced the relationship between customer sentiment and satisfaction.

Hotel managers should focus efforts on maintaining tangible aspects while enhancing existing service quality level of other dimensions, particularly those related to intangible elements. Independent hotels might implement quality audit to ensure that service quality gaps are monitored.

This study contributes an examination of the moderating roles of quality signals and reviewer’s experience on the relationship between review sentiment and satisfaction rating in online reviews.