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Total quality management is a valuable approach to continuously improve the quality of organizations; however, scholars debate its applicability to services, which require specific best practices that are different from those related to manufacturing. Moreover, digitization is pervading all kinds of services, but little has been written about total quality service practices in digital-based companies. For this purpose, the authors provide a holistic model of total quality service that reflects the peculiarities of such companies, guided by the question: how do total quality service practices change in digital-based service organizations?

The authors conduct an illustrative case study on Healthware Group, a global integrated digital health organization, to evaluate theoretical assumptions about total quality service practices in the digital environment.

The findings allow to validate the model provided. In addition, the study enables them to observe the changes the authors are witnessing in service provision in the digital era and the consequent transformation of best practices. To be accurate, the authors cannot refer to a full transformation in digital-based companies but rather to the enrichment and extension of TQS practices. The best illustration of these conclusions has been summarized in a set of propositions corresponding to seven of the key levers of a TQS model.

The paper represents the first attempt to discuss the relationship between total quality service and digitalization, offering a set of propositions for academics and insights for practitioners. The model can be used as a tool to visualize the different levers that successful implementation of TQS in digital-based services companies can rely on.

This study aims to investigate the adoption of Quality 4.0 (Q4.0) and assess the critical failure factors (CFFs) for its implementation and how its failure is measured.

A qualitative study based on in-depth interviews with quality managers and executives was conducted to establish the CFFs for Q4.0.

The significant CFFs highlighted were resistance to change and a lack of understanding of the concept of Q4.0. There was also a complete lack of access to or availability of training around Q4.0.

The study enhances the body of literature on Q4.0 and is one of the first research studies to provide insight into the CFFs of Q4.0.

Based on the discussions with experts in the area of quality in various large and small organizations, one can understand the types of Q4.0 initiatives and the CFFs of Q4.0. By identifying the CFFs, one can establish the steps for improvements for organizations worldwide if they want to implement Q4.0 in the future on the competitive global stage.

The concept of Q4.0 is at the very nascent stage, and thus, the CFFs have not been found in the extant literature. As a result, the article aids businesses in understanding possible problems that might derail their Q4.0 activities.

Collaborative consumption, such as car sharing, specifically implicates customer-to-customer interaction, which must be regulated by service providers (companies, peers and self-regulating communities), comprising different challenges for business organizations. While in conventional business relations, consumers are protected from undesirable customer behavior by laws, regulations (power) in the context of collaborative consumption are rare, so that trust becomes more relevant. It is the purpose of the study to investigate possible mechanisms to prevent undesirable customers in collaborative consumption.

In between subject designs, samples of 186 and 328 consumers filled in experimental online questionnaires with vignettes. Analyses were made of differences among car sharing companies, private persons and car sharing communities in terms of the power of providers, trust in providers and trust in other users of the shared goods, undesirable customer behavior and consumer–provider relations.

Companies, private persons and self-regulating communities differ in terms of perceived power and trust. Participants specifically perceive mainly coercive power with the car sharing company, but with the private person and the community, reason-based trust in other users is perceived as prevalent. Nevertheless, undesirable customer behavior varies only marginally over the models.

The present study is the first to investigate measures to prevent undesirable customer behavior over different collaborative consumption models. This enables appropriate identification of market segments and tailoring of services. The study identifies opportunities for companies in contrast to private persons and self-regulating communities and, in doing so, provides important stimulation for marketing strategy and theory development.

The purpose of this paper is to analyze the role of bank branch managers’ perceptions of corporate social responsibility (CSR) in CSR marketing outcomes.

The paper proposes a causal model establishing that managers’ perceptions of CSR influence the perception of CSR held by the branch’s customers, which in turn directly affects customer satisfaction, customer trust, customer engagement and customer loyalty. The unit of analysis in this quantitative study is the bank branch. Two questionnaires were administered: one to branch managers and another to five customers in each branch.

Branch managers’ perceptions of CSR have a marked influence on customers’ perceptions of CSR, which again have a notable impact on the relationship variables studied: customer satisfaction, customer trust, customer engagement and customer loyalty.

The sample was taken from two banks in the same country (Spain) and only five customers were interviewed in each branch. The type of customers analyzed should be taken into account since a growing number of customers now carry out all of their banking online and are less likely to visit their branch.

The results highlight the importance of adopting socially responsible actions not only in the bank as a whole, but also in individual branches. It would, therefore, seem crucial for high level bank executives not only to involve branch managers in the bank’s CSR strategy, but also to empower them to undertake CSR actions that involve the customers and local community with which they interact.

First, the paper reveals the differences within the same organization in the way its CSR strategy is implemented. Second, intermediary figures or supervisors are shown to have a key role in ensuring the organization’s CSR strategy is effective. Third, the study emphasizes the importance of customers’ perception of CSR in achieving the main outcomes of relationship marketing (satisfaction, trust, engagement and loyalty). Fourth, the methodology applied in the study is innovative in its construction of dyads in which the branch is the unit of analysis, enabling a comparison between the manager’s perceptions of CSR with that of five customers from the same branch. Fifth, the findings add to the knowledge of a particularly relevant sector in the recent economic crisis, namely, the retail banking industry.

This study proposes a machine learning framework to predict customer complaints from production line tests in an automotive company's lot-release process, enhancing Quality 4.0. It aims to design and implement the framework, compare different machine learning (ML) models and evaluate a non-sampling threshold-moving approach for adjusting prediction capabilities based on product requirements.

This study applies the Cross-Industry Standard Process for Data Mining (CRISP-DM) and four ML models to predict customer complaints from automotive production tests. It employs cost-sensitive and threshold-moving techniques to address data imbalance, with the F1-Score and Matthews correlation coefficient assessing model performance.

The framework effectively predicts customer complaint-related tests. XGBoost outperformed the other models with an F1-Score of 72.4% and a Matthews correlation coefficient of 75%. It improves the lot-release process and cost efficiency over heuristic methods.

The framework has been tested on real-world data and shows promising results in improving lot-release decisions and reducing complaints and costs. It enables companies to adjust predictive models by changing only the threshold, eliminating the need for retraining.

To the best of our knowledge, there is limited literature on using ML to predict customer complaints for the lot-release process in an automotive company. Our proposed framework integrates ML with a non-sampling approach, demonstrating its effectiveness in predicting complaints and reducing costs, fostering Quality 4.0.

The purpose of this paper is to investigate and report students’ perception of service quality in a university by examining the perceptual context of service quality with respect to students’ loyalty behavior, image of the university and culture/values.

A research framework is developed for quality assessment with three hypotheses. A questionnaire with 65 instruments was used for gathering the required data for the analysis. The questionnaire was sent through email to all engineering students. The analysis included descriptive statistics, reliability analysis, gap analysis and hypotheses tests. Seven dimensions of service quality were identified: the original dimensions of the SERVQUAL, namely, reliability, responsiveness, assurance, empathy and tangibles. Two additional dimensions image and culture/value were added for the research to understand perceived service quality and loyalty.

The results provide a significant positive correlation between service quality and student's loyalty. It also shows that there is statistically significant relation between the image of the institution and the perceived service quality, and culture/values of the students in the institution and perceived service quality.

This study used data collected from a survey in the university in a given period.

The findings indicate that to provide quality education, meeting students’ needs, wants and expectations of services quality should be carefully understood and addressed. Management also needs to consider factors such as corporate image and culture/value, as they have the ability to heavily impact the type of services provided by the institution.

The findings presented in this paper fill the gap in the current literature by providing empirical knowledge on the quality of service assessment and customer satisfaction in the higher education context. The study is the first of its kind in Qatar’s context and provides opportunities for higher institutions to focus more on current students’ services. This can lead to an increased brand value representing one of the premier institutes of higher education in the Middle East Gulf Region.

This study aims to analyze the importance of disruptive technological innovations on qualitative service delivery and their impact on the investment banks’ employee performance.

The cluster sampling method has been used to collect the primary data from the 250 respondents from foreign investment banks. Variables used are employee performance, service delivery, technology, security, operations, strategy and quality through chi-square, linear stepwise multiple regression analysis and correlation.

Storage network, operating cost, client reporting, cloud system and money laundering are the highest and most significant predictors of employee performance. Employee performance multiplies every unit with a strategic solution owing to positive and robust correlation (0.944). Fusion technology-based banks offer quality service to their clients.

A combination of artificial intelligence and blockchain ensures increasing automation to improve efficiency and reduce the operating cost creating a seamless integration in fraud detection, customer support, risk management, security, digitization and automation process, algorithmic trading, wealth management, etc.