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A continuous issue which plagues all service businesses is the process of handling complaints. Whilst the topic has been relatively well explored, extant literature has failed to fully explore how staff demographics influence the methods in which they manage complaints.

A qualitative approach was adopted with semi-structured interviews. A purposeful sample was selected, inviting managers from hotels in Dubai to share their views on factors affecting the complaint management process, including the impact of staff demographics.

Staff demographics were found to have an impact on staff's approach to handle complaints. However, participants generally felt that, with sufficient experience, the impact of many of these influences would be negated.

Literature on complaint management has considered numerous mitigating factors affecting the complaint management process. The impact of staff demographics on how they receive and respond to complaints has not been thoroughly explored.

Complaint service management, aimed at improving customer satisfaction, provides important content for incorporation into studying a closed-loop supply chain. An analysis of the relationship between two provides the basis for probing the role of complaint management (CM) in the closed-loop supply chain to help it perform more efficiently and effectively through the application of advanced technologies. This paper considers how CM can be computed combining computer communication and information technologies. This computing process involves collection, evaluation and disposal. Using computer telephone integration technology, an integrated multi-channel system is designed; complaint and production evaluated through an intelligent decision support system; and CM processing system established to implement corresponding disposal which reflects the utility of CM. This research on the process of incorporating CM into our studies has significance for computing business service in the future. Based on exergoeconomics theory, the closed-loop supply chain is discussed, and the metric about “system negative environment effect” is introduced to system performance in terms of energy expenditures; a case study illustrates the efficacy of the process

In the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribute to data-driven decision-making. This approach aimed not only to reduce the number of units to be analyzed and customer response time but also to underscore the pressing need for a paradigm shift in quality management. The application of AI techniques sought to enhance not only the efficiency of the complaint handling process and data accessibility but also to demonstrate how the integration of these innovative approaches could profoundly transform the way quality is conceived and managed within organizations.

To conduct this study, real customer complaint data from an automotive company was utilized. Our main objective was to highlight the importance of artificial intelligence (AI) techniques in the context of quality. To achieve this, we adopted a methodology consisting of 10 distinct phases: business analysis and understanding; project plan definition; sample definition; data exploration; data processing and pre-processing; feature selection; acquisition of predictive models; evaluation of the models; presentation of the results; and implementation. This methodology was adapted from data mining methodologies referenced in the literature, taking into account the specific reality of the company under study. This ensured that the obtained results were applicable and replicable across different fields, thereby strengthening the relevance and generalizability of our research findings.

The achieved results not only demonstrated the ability of ML models to predict complaint accountability with an accuracy of 64%, but also underscored the significance of the adopted approach within the context of Quality 4.0 (Q4.0). This study served as a proof of concept in complaint analysis, enabling process automation and the development of a guide applicable across various areas of the company. The successful integration of AI techniques and Q4.0 principles highlighted the pressing need to apply concepts of digitization and artificial intelligence in quality management. Furthermore, it emphasized the critical importance of data, its organization, analysis and availability in driving digital transformation and enhancing operational efficiency across all company domains. In summary, this work not only showcased the advancements achieved through ML application but also emphasized the pivotal role of data and digitization in the ongoing evolution of Quality 4.0.

This study presents a significant contribution by exploring complaint data within the organization, an area lacking investigation in real-world contexts, particularly focusing on practical applications. The development of standardized processes for data handling and the application of predictions for classification models not only demonstrated the viability of this approach but also provided a valuable proof of concept for the company. Most importantly, this work was designed to be replicable in other areas of the factory, serving as a fundamental basis for the company’s data scientists. Until then, limited data access and lack of automation in its treatment and analysis represented significant challenges. In the context of Quality 4.0, this study highlights not only the immediate advantages for decision-making and predicting complaint outcomes but also the long-term benefits, including clearer and standardized processes, data-driven decision-making and improved analysis time. Thus, this study not only underscores the importance of data and the application of AI techniques in the era of quality but also fills a knowledge gap by providing an innovative and replicable approach to complaint analysis within the organization. In terms of originality, this article stands out for addressing an underexplored area and providing a tangible and applicable solution for the company, highlighting the intrinsic value of aligning quality with AI and digitization.

This paper analyses how the purchase channel and customer complaint goals affect the sequential choice of post–purchase complaint channels when customers experience a service failure followed by a service recovery failure (double deviation).

An online survey involving a scenario manipulation was conducted with 577 apparel shoppers. The study employs multi-group latent class analysis to estimate latent customer segments within both online and offline groups of shoppers and compare latent classes between the two groups.

The results show that the purchase channel has a lock-in effect on the complaint channel, which is stronger for offline buyers. Moreover, there is evidence of channel synergy effects in the case of having to complain twice: shoppers who complain in store in the first attempt turn to online channels in the second complaint attempt, and vice versa. Complaint goals shape the choice of complaint channels and define different shopper segments.

The present study is the first to adopt a cross-stage approach that analyses the dependencies between the purchase channel and the complaint channel used on two subsequent occasions: the first complaint after a service failure and the second following a service recovery failure.

Since public sector organizations provide services to citizens but struggle with poor perceptions of their functioning, it is valuable to examine how their online responses to complaints on social media could impact their reputation. Yet, surprisingly little is known about effects of public organizations' webcare. Therefore, this study assesses the impact of the webcare's tone, response strategy and user's involvement on participants’ continuance intention and perceptions of reputation.

Two experimental studies (Study 1: N = 424; Study 2: N = 203) with an interval of one week were carried out to assess the effects of singular and repeated exposure to webcare by a Dutch public transport organization on the participants' continuance intention and perceived organizational reputation. Study 1 examined the effects of the webcare's tone (corporate vs conversational human voice (CHV)) and response strategy (accommodative vs defensive); Study 2 contained tone of voice and user's involvement (observer vs complainer). The effects of repeated exposure to the webcare's tone were also examined.

The results indicate that perceptions of CHV in webcare contribute to webcare as reputation management tool, since it leads to immediate higher reputation scores that also remain stable after repeated exposure. Furthermore, people's continuance intention increased after repeated exposure to webcare responses that were perceived as CHV, thus a natural and engaging communication style, indicating this is an effective strategy for customer care as well. No substantial impact was found for response strategy and user's involvement in the complaint handling.

The novelty of this study is that the authors assess the effects of the webcare's tone combined with response strategy and user's involvement in a public sector context with a sector-specific conceptualization of reputation and continuance intention measured after singular and repeated exposure to webcare.