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This research paper aims to analyze the scientific and grey literature on Quality 4.0 and zero-defect manufacturing (ZDM) frameworks to develop an integrated quality 4.0 framework (IQ4.0F) for quality improvement (QI) based on Six Sigma and machine learning (ML) techniques towards ZDM. The IQ4.0F aims to contribute to the advancement of defect prediction approaches in diverse manufacturing processes. Furthermore, the work enables a comprehensive analysis of process variables influencing product quality with emphasis on the use of supervised and unsupervised ML techniques in Six Sigma’s DMAIC (Define, Measure, Analyze, Improve and Control) cycle stage of “Analyze.”

The research methodology employed a systematic literature review (SLR) based on PRISMA guidelines to develop the integrated framework, followed by a real industrial case study set in the automotive industry to fulfill the objectives of verifying and validating the proposed IQ4.0F with primary data.

This research work demonstrates the value of a “stepwise framework” to facilitate a shift from conventional quality management systems (QMSs) to QMSs 4.0. It uses the IDEF0 modeling methodology and Six Sigma’s DMAIC cycle to structure the steps to be followed to adopt the Quality 4.0 paradigm for QI. It also proves the worth of integrating Six Sigma and ML techniques into the “Analyze” stage of the DMAIC cycle for improving defect prediction in manufacturing processes and supporting problem-solving activities for quality managers.

This research paper introduces a first-of-its-kind Quality 4.0 framework – the IQ4.0F. Each step of the IQ4.0F was verified and validated in an original industrial case study set in the automotive industry. It is the first Quality 4.0 framework, according to the SLR conducted, to utilize the principal component analysis technique as a substitute for “Screening Design” in the Design of Experiments phase and K-means clustering technique for multivariable analysis, identifying process parameters that significantly impact product quality. The proposed IQ4.0F not only empowers decision-makers with the knowledge to launch a Quality 4.0 initiative but also provides quality managers with a systematic problem-solving methodology for quality improvement.

This research examines how responsible deployment of ChatGPT in the UAE’s government sector, guided by New Public Management principles, can enhance customer journeys by integrating services across government bodies. Through semi-structured interviews with UAE government officers, the study investigates this approach’s benefits, challenges, and applications for achieving efficient and integrated public service delivery.

This research adopts a qualitative approach, purposive sampling strategy, and semi-structured interviews to explore the subjective viewpoints of 20 high-level UAE government authorities. The thematic analysis uncovers ChatGPT’s benefits, challenges, and applications, aligning with New Public Management principles.

Thematic analysis reveals four themes: Benefits and Applications of ChatGPT, Challenges, Strategies to Overcome Challenges, and Steps for Customer Journey Enhancement through ChatGPT.

The analysis is based on participant responses provided during the interviews, which may be subject to biases or incomplete information. Secondly, the study focuses solely on the provided applications and participant responses, limiting the generalizability of the conclusions to other contexts.

The implementation of ChatGPT in the government sector has practical implications for transforming its operations and enhancing communication, efficiency, decision-making, and service offerings: citizen engagement, streamlined processes, and informed governance.

This study uniquely examines ChatGPT’s role in government, offering insights into communication, efficiency, decision-making, and service offerings. Identifying hurdles enriches understanding of ChatGPT’s practical integration in government.

This study comprehensively reviews the Fourth Industrial Revolution, which refers to Industry 4.0 (IR 4.0) applications in small and medium enterprises (SMEs). Multinational companies and big corporations have the capacity and resources to implement IR 4.0, but SMEs are limited due to financial constraints, expertise and lack of resources. Even so, IR 4.0 is required as technologies evolve and market demand has changed how firms do business.

To uncover the potential of IR 4.0 and critical determinants of SMEs’ adoption of IR 4.0, this study presents a bibliometric analysis to evaluate the current research streams in IR 4.0 adoption among SMEs through bibliographic coupling. Furthermore, this review provides a glimpse of the future by analyzing prospective trends on IR 4.0 in SMEs.

Bibliographic coupling produces five clusters: (1) challenges and barriers in IR 4.0 implementation among SMEs, (2) technological adoption of IR 4.0, (3) opportunities and benefits of IR 4.0, (4) business model innovation and (5) implication of IR 4.0 on SMEs technologies. On the contrary, co-word analysis produces three clusters: (1) technologies in IR 4.0, (2) strategy and management of IR 4.0 among SMEs and (3) IR 4.0 model for SMEs.

Implications are directly related to business owners, policymakers and technology developers meeting the needs of the industry and SMEs, which are the focus of this review.

The findings contribute significantly to the body of knowledge by presenting a state-of-the-art science mapping approach to uncover the knowledge structure and intellectual linkage of IR 4.0 adoption within SMEs.

This chapter seeks to explore different aspects of Modernism in the works of Ernest Hemingway, in particular The Garden of Eden that is set in the 1920s and The Sun Also Rises that was created in 1926. Therefore, the aim is to demonstrate what makes The Garden of Eden Modernist even though it was first published in 1986. In other words, the main point is to show how Hemingway constructs Modernism and whether there are parallels between the depictions of Modernism in both works. The first part of this chapter particularly emphasises aspects of American Modernism as the basis for the second part that focuses on the analyses of the works.

This study aims to understand and explain factors that influence how, when and under which conditions local governments adopt digital technologies for citizen collaboration. It discusses what these findings mean for city digital twin adoption.

This research uses the systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) process to collect and evaluate evidence needed to answer the research questions. It uses the technology–organisation–environment (TOE) framework and proposes an additional dimension: “stakeholders” as the analytical framework.

Critical influential factors identified include the technology dimension: security and privacy; organisation dimension: top management support; environment dimension: political influence; and stakeholders’ dimension: technological experience.

This research extends the TOE framework and comprehensively analyses those factors which relate to citizens but significantly impact local government’s decision to adopt digital tools for collaboration purposes. This research posits that in the context of local government technology adoption for collaboration, both the organisation and stakeholders’ dimensions are critical.

This research contributes to the government-citizen discourse and provides a constructive understanding of technological transformation in collaborative planning. The findings are helpful for local governments, researchers and geospatial industries as they offer a critical understanding of digital technology adoption, particularly city digital twins, for collaborative planning.

This study extends the TOE framework to include aspects relating to citizens. It provides a nuanced understanding of the influential factors and intricacies of technology adoption by local governments for citizen collaboration. It also discusses relevant issues of city digital twins’ adoption by local governments for citizen participation.

Large supermarket chains are adopting customer-service robots to improve service delivery in physical stores. Successful deployment of these robots depends on shoppers' willingness to interact with them, requiring an understanding of influencing factors. This study, grounded in the Cognitive-Affective-Normative (CAN) theory, seeks to systematically explore the factors influencing Gen Z shoppers' willingness to interact with customer-service robots.

A hybrid approach combining Structural Equation Modeling (SEM) and Necessary Condition Analysis (NCA) was employed to analyze survey data collected from 945 Gen Zs in the Czech Republic.

The results from SEM highlight significant cognitive, normative, and affective factors that influence the intention of Gen Z shoppers to interact with a customer-service robot. Specifically, cognitive factors such as effort and performance expectancy, along with normative factors like subjective norms, emerged as critical determinants. Furthermore, affective factors such as technology anxiety and positive emotions significantly influence users' readiness to use customer-service robots for service requests. The study also underscores that positive emotions, effort expectancy, performance expectancy, and subjective norms are vital prerequisites for interacting with customer-service robots.

The originality of this work lies in its two significant contributions to the burgeoning field of SRs in retail literature. First, it extends the CAN theory to the context of SRs among Gen Z shoppers in Czechia, thereby enriching the existing literature on SRs in retail. Second, by employing a hybrid analytical approach, our research offers both empirical and methodological advancements, providing rigorous insights crucial for enhancing the understanding of the pivotal factors influencing shoppers' interactions with SRs in physical store environments.

This chapter provides a literature review on the topic of emotional intelligence (EI) in the workplace. Quantitative methods were used, with surveys sent to a predetermined sample and processed with the SPSS statistical package. The overall aim of the study was to investigate the effect of EI based on self-awareness, self-management, empathy, and relationship management on work engagement in a public university. One hundred eight professors at the public university. The data for this study were collected by means of a questionnaire. In total, there are 23 questions on a Likert scale. Cronbach's alpha showed that the reliability of the instrument was higher than 0.763. In light of the data, it has been shown that there is correlation between self-awareness, self-management, relationship management, empathy with both work engagement and job satisfaction. Furthermore, the results show that EI is significantly related to both university loyalty and job happiness. Only a conditional link was created between professors' achievements and the success of the public university.

In this chapter, we seek to bring greater attention to language-based stigma at work and how it affects employees speaking English as their second language. We integrate research findings across multiple disciplines, including management, psychology, linguistics, education, and business ethics, identifying three major themes and knowledge gaps in this research domain. Based on the multidisciplinary knowledge integration, we suggest six promising future directions for organizational and business ethics scholars to pursue. We call for more empirical research focusing on how language-based stigma may harm nonnative English speakers (NNESs) with and without actual mistreatment or discrimination at work and how organizations may intervene to support NNESs. We hope our chapter will spark conducive conversations about mitigating language-based stigma in the workplace.