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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.

Recently there has been a surge in interest about the use of artificial intelligence in organisations with art galleries introducing new technological innovations that coincide with the digitalisation revolution. Virtual and immersive environments that are supported by social media and digital platforms are significantly changing customer experiences at art galleries. This is internationalising and making art gallery experiences more accessible thereby fostering the competitive advantage of art galleries.

Art gallery customers, stakeholders and managers are appreciating the use of artificial intelligence with resulting higher satisfaction rates. Building on competency and transformational entrepreneurship theory international art gallery managers were interviewed to understand the role of artificial intelligence in their organisations and the impact of internationalisation.

The data analysis revealed that the internationalisation of art galleries enabled artificial intelligence to transform in person and online visitor experience, work and marketing, and future art gallery development ideas. Results show that artificial intelligence is opening up new transformations derived from entrepreneurial behaviours.

Key managerial implications are that art gallery managers need to utilise their international networks in order to learn about artificial intelligence and other new technological innovation. Theoretical implications are that existing theory can be adapted to an art gallery and artificial intelligence context. Limitations and future research suggestions focus on the need to focus more on art galleries as cultural entities that are more likely to utilise new technology innovation such as artificial intelligence.

This study aims to conduct a quantitative meta-analysis of previous research on lifelong vocational education to generate generalized conclusions about its effects, set directions for future lifelong vocational education and identify implementation measures.

To conduct a meta-analysis on research results that have a heterogeneous distribution, it is important to specify the analysis category for examining the effects of research variables.

First, lifelong vocational education has an effect on dependent variables. And action appears to have the highest effect size on dependent variables. Next, when calculating the size of variables that had an effect on lifelong vocational education by educational type, the effect size of informal education was found to be larger than that of formal education. Finally, regarding the effect on the participants, office workers were influenced most, followed by university students, North Korean defectors, job seekers and foreigners.

Although this study attempted to conduct an in-depth analysis of subcomponents, it was not possible to analyze variables at a more detailed level. Therefore, future studies should aim to conduct a more comprehensive analysis of different variables based on a wider composition. Because lifelong vocational education is relevant to people’s daily lives, it should be investigated in the context of their personal characteristics and social backgrounds.

This research was designed to uncover general effects of lifelong vocational education and discover relevant variables affecting lifelong vocational education in South Korea. A meta-analysis of 15 studies with 67 subgroups examining lifelong vocational education was conducted.

In the current era of VUCA (Volatility, Uncertainty, Complexity and Ambiguity), lifelong vocational education needs to be organized systematically, unlike in the past. With the rapid advancements in technology influenced by artificial intelligence and the fourth industrial revolution, there is a surge in social demands for continued reeducation and redevelopment of employees to prepare for talent development paradigm innovation, increasing unemployment among unskilled workers and competence enhancement needs among job seekers and employed individuals.

This study aims to conduct a quantitative meta-analysis of previous research on lifelong vocational education to draw generalized conclusions on its effectiveness and discuss its implications for implementation measures. Specifically, this study will analyze the general effect size; differences in the effect size among different dependent variable groups; and the effect size based on lifelong vocational education participants.

Quality has always been a competitive advantage for every organisation that strives for customer satisfaction when offering services or products. Technological advancements during the industrial revolutions have enabled organisations to grub improvement and transition opportunities into a new paradigm in operating business processes. In light of the fourth industrial revolution (FIR), in which Quality 4.0 (Q4.0) leveraged its technologies, this study establishes the need for service organisations to transition to Q4.0. It unveils the awareness level within the organisation, the existing challenges and the benefits of transitioning to Q4.0.

The data for this study was acquired through a survey methodology; it involved qualitative and quantitative methodologies and a mix of primary and secondary sources. The challenges and benefits regarding Q4.0 adoption were obtained from the relevant literature and used as a base of assessment in the selected service organisations. Minitab version 20 and SPSS 21.0 software packages analysed the gathered data.

The study found a high level of awareness regarding Q4.0 among the selected service organisations. Despite the high level of awareness, it was revealed that neither of the Q4.0-related technologies have been implemented nor in the process within the selected service industries. This is due to numerous challenges, including inadequate high-speed internet, a high cost of investment, inadequate skilled personnel and inadequate scepticalness to the implementation outcomes. Despite these challenges, leveraged technologies, potential benefits and Q4.0 awareness all demonstrate the need for Q4.0.

The study introduces the advent of the FIR and the disruptive nature of the associated technologies. It also unveils the potential of the contemporary technologies of the FIR that could elevate quality service provision to increase their competitiveness. Moreover, it also assists service organisations in planning and properly allocating their resources to ensure all the challenges are addressed in the Q4.0 adoption process. The study is limited in that it merely considers service organisations in Tanzania without categorising what type of service organisations were considered, i.e. banking, telecommunications, health, etc. thus generalising the findings.

Much has been discussed in the literature regarding Q4.0 in manufacturing organisations, focusing less on service organisations. This study uniquely assessed the need to transition to Q4.0 for service organisations, which has yet to be covered in the literature.

The primary aim of this study is to identify and analyze the key digital transformation areas and determine their impact on technological unemployment in the textile sector. In addition, this study explores whether digital transformation contributes to neo-Luddism or Robot Breaking.

The advent of digital transformation has raised significant concerns, particularly concerning technological unemployment. This study focuses on conducting an analytical hierarchical process (AHP) analysis to determine the impact of different disciplines within digital transformation on technological unemployment. The investigation specifically delves into the ongoing transition to Industry 4.0 within the textile industry. This study uses a mixed-method approach, consisting of a literature review, bibliometric analysis, eight expert phenomenological interviews, and AHP.

This study revealed that artificial intelligence, machine learning and deep learning are the most crucial disciplines that will affect the concept of neo-Luddism. The fact that technological unemployment in the textile sector is examined with AHP Analysis makes this study unique.

This study contributes to Industry 4.0 literature by examining the nexus of technological unemployment in textile manufacturing and the emergence of neo-Luddism.

This study aims to comprehensively analyze the intersection of technology management and innovation management amidst the fourth industrial revolution, uncovering evolving trends and influential contributors.

Using the Bibliometrix R-package, this pioneering research conducts a bibliometric analysis to delve into innovation and technology management literature, quantifying scholarly output and identifying thematic breakthroughs.

The study reveals quantitative insights into the progression of innovation and technology management research, offering guidance on evolving trends, thematic breakthroughs and influential contributors.

The findings offer valuable insights for practitioners and managers, guiding them through emerging trends and recommending a dual focus on fundamental principles and emerging areas for strategic decision-making.

By fostering active engagement with evolving trends, this research contributes to the ongoing technology and innovation management discourse, potentially leading to societal benefits and advancements.

This study pioneers an in-depth bibliometric analysis at the intersection of innovation and technology management, offering unique insights and quantitative assessments of scholarly output and thematic trends, thus adding significant value to the existing literature.

This study aims to investigate whether the increasing robot adoption will affect employment rate and wages to contribute to the economic cycle and sustainable development in the world.

The authors introduce a two-way fixed effect model and ordinary least-squares (OLS) model to evaluate the influence based on relevant data of the eighteen countries with the largest robot stocks and robot densities in the world from 2006 to 2019 to test the influences and do the robustness test and endogeneity test by using empirical models.

The authors’ research findings suggest that increasing robot adoption can cause strong negative impacts on employment for both males and females in these economies. Second, the effect of robots on reducing job opportunities has penetrated different industries. It means that this negative impact of robots is comprehensive for the industry. Third, robot adoption can have a strong positive influence on wages and increase workers' incomes.

The limitations of the study are that the influence of industrial intelligence technologies on the circular economy is diversities in different countries. Thus, this study should consider the development levels of different economies to do additional confirmatory studies.

This study makes out the correlations between industrial robots and the employment market from the circular economy perspective. The result proves the existence of this influence relationship, and the authors propose some suggestions to promote sustainable economic development.

This paper addresses the activity of industrial intelligence technologies in the labor market. The employment market is an important part of the circular economy, and it will benefit social development if the government provides appropriate guidance for social investment and industrial layout.

This study is one of the few studies which considered the impact of industrial robots on employment and wages from the perspective of different industries, and this is very important for the circular economy in the world. The results of this paper provide an instructive reference for government policymakers and other countries to stabilize the labor market and optimize human resources for sustainable economic development.

This paper analyzes the ethics of integrating Artificial Intelligence (AI), particularly regarding AI-generated educational content in academia. It attempts to explore how AI customization mimics human interaction and behavior in education, investigate ethical concerns in educational AI adoption, and assess ChatGPT’s ethical use for nurturing curiosity and maintaining academic integrity in education.

Fictional tales may help us think critically and creatively to uncover hidden truths. The narratives are analyzed to determine the affordances and drawbacks of Artificial Intelligence in Education (AIEd).

The study highlights the imperative for innovative, ethically grounded strategies in harnessing AI/GPT technology for education. AI can enhance learning, and human educators’ irreplaceable role is even more prominent, emphasizing the need to harmonize technology with pedagogical principles. However, ensuring the ethical integration of AI/GPT technology demands a delicate balance where the potential benefits of technology should not eclipse the essential role of human educators in the learning process.

This paper presents futuristic academic scenarios to explore critical dimensions and their impact on 21^st century learning. As AI assumes tasks once exclusive to human educators, it is essential to redefine the roles of both technology and human teachers, focusing on the future.

The implementation of the digital economy has had a considerable influence on the manufacturing industry, and this paper aims to address the important issues of how to capture the opportunities presented by digital innovation and promote the transformation and upgrading of the manufacturing industry, as well as the improvement of quality and efficiency.

Using panel data from 30 Chinese provinces and cities between 2010 and 2021, this study establishes the panel vector autoregression (PVAR) model and uses impulse response function analysis to evaluate the influence of the digital economy on the high-quality transformation and upgrading of China's small home appliance industry across five dimensions under the digital economy.

The development of digital infrastructure has not demonstrated a noteworthy capacity for advancing the transformation and upgrading of the small home appliance industry. Furthermore, digital industrialization has exerted a minimal restraining influence on this process. Nevertheless, digital governance has consistently exhibited a substantial impact on facilitating the transformation and upgrading of the small home appliance industry. While both industrial digitization and digital innovation hold significant potential for promoting the transformation and upgrading of the small home appliance industry, their sustainability remains limited.

The organization should logically join independent innovation and open innovation, construct an industrial ecosystem for the profound convergence of the digital economy and compact household appliances, use digital-wise science and technology to empower the establishment of brand effects, strengthen the portrayal of the digital standard framework for the intelligent compact household appliance industry, advance the development of a public stage for computerized administrations in the compact household appliance industry and develop a strategy ecosystem for computerized assets in the compact household appliance industry.

This study offers systematic evidence of the relationship between the digital economy and the development of the small home appliance industry. The results of this research contribute to the literature on the impact of the digital economy on the manufacturing sector and provide a logical explanation for the transformation and upgrading of the small home appliance industry within the context of the digital economy.

This paper tries to retrospect the mounting application of machine learning (ML) and artificial intelligence (AI) in the human resource management area. The document applies bibliometric analysis and uses relational techniques to explore dimensions of documents in the field. The results highlight publication trends, most impactful authors, countries and institutes in the research area. The science mapping along with co-citation and bibliometric coupling analysis revealed major developments in the field. The thematic mapping and trend analysis highlighted the past and emerging trends towards significant and impactful research in the areas of robotics, big data, AI and data analytics. This paper sets the base for future researchers by coordinating and combining various past researches to help in understanding the evolution of ML and AI in human resource management and expansion of knowledgebase.