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This study aims to assess the efficacy of nano-alumina (nano-Al₂O₃) in improving the performance of epoxy adhesives used to assemble archaeological glass. The conservators face a significant problem in assembling this type of artifact. Therefore, the assembling process is considered one of the important stages that must be taken care of to preserve these artifacts from damage and loss.

To evaluate the stability of adhesives, the samples were subjected to artificial aging under varying environmental conditions. Some investigative techniques and mechanical testing were used in this study to evaluate the selected materials. It includes a transmission electron microscope, X-ray diffraction, visual assessment, digital microscope, scanning electron microscopy (SEM), color change and tensile strength test.

The visual evaluation and the digital microscope results showed that the epoxy/nano-Al₂O₃ greatly resisted artificial aging. Although slight yellowing was present, it did not significantly affect the general appearance of the samples. On the other hand, the pure epoxy sample showed cracks of different sizes on its surface due to aging, as evidenced by SEM examination. Furthermore, epoxy/nano-Al₂O₃ has a better tensile strength (11.27 MPa) and slight color change (ΔE = 2.06).

The main objective of the experimental study was to identify appropriate adhesive materials that possess key properties such as non-yellowing and improved tensile strength by conducting various tests and evaluations. Ultimately, the goal was to identify materials that could serve as effective adhesives for assembling the archaeological glass.

This study investigates the challenges and opportunities that organisations face in implementing Quality 4.0 as an approach to quality management and investigate the current state of Quality 4.0 implementation.

This study uses a qualitative research methodology to interview senior managers from globally based manufacturing and service industries.

The study explicates that most organisations implemented Quality 4.0 to improve their flexibility, efficiency, transparency and productivity while focusing on improving service quality, customer satisfaction and reducing cost. In terms of sustainability of Quality 4.0 the key factors found were a consistent effort from the top management, continuous training to employees, building leadership quality and creating a habit of using Quality 4.0.

The findings of this study offer useful guidance to organisations desirous of implementing Quality 4.0. In addition, the findings have identified key sustainability factors, helping organisations ensure a successful implementation and long-term returns from Quality 4.0.

The findings of this study contribute to the body of knowledge related to Quality 4.0 and help organisations in their digital transformation journey. In addition, it is one of the first studies to investigate the key factors for Quality 4.0 sustainability.

The purpose of this study is to explore how Industry 4.0 (I40) technologies support workers' engagement in soft total quality management (TQM) practices for organisational performance.

The authors conducted a multiple case study of six Italian manufacturing organisations that operate with I40 production and implement TQM practices. The authors concentrated on the relationship between I40 technologies and soft TQM aspects.

I40 technologies provide two forms of engagement with workers. Workers can act as machine supervisors and expert assembly operators. Organisations use five soft TQM practices to involve and develop workers for TQM that vary according to automation levels. The five soft TQM practices are top management design around workers, incremental trials with I40 technologies, worker empowerment, I40 sociotechnical collaboration and individual feedback systems.

In the literature that focusses primarily on how I40 technologies support the hard side of TQM by creating a data-driven and automated quality management system, the authors illustrate how the workforce can be engaged in I40 with five soft TQM practices to improve organisational performance. Thus, the authors complement the theory of hard and soft TQM aspects for I40 production systems.

This study aims to conceptualize the characteristics of artificial intelligence (AI) dogs while exploring their applications in tourism and hospitality settings.

The total of 30 in-depth interviews were conducted, and data were analyzed through thematic analysis.

This study proposed differences between AI dogs and real dogs and human-like robots, core characteristics of AI dogs’ functions, a matrix of appearance and expectation regarding intelligence for AI dogs and human-like robots, the relationship between ethical barriers and task complexity, adoptions of AI dogs in different user segments and practical applications in hospitality and tourism settings, such as restaurants, city tour guides, extended-stay resorts and event organizations.

This research advances the field of tourism and hospitality studies by introducing the new concept of AI dogs and their practical applications. This present study adds new insights into the opportunities and contexts of human–robot interaction in the field of tourism and hospitality.

To the best of the authors’ knowledge, this research is one of the first studies of AI dogs in tourism and hospitality.

There is a growing interest in the use of HR-based Industry 4.0 technologies for equality, diversity, and inclusion (EDI) issues yet the emerging trends of Industry 4.0 in EDI implementations and interventions are not fully covered. This chapter investigates the emerging themes regarding EDI and Industry 4.0 interaction through Google-based big data that show the actual interest in Industry 4.0 and EDI. Drawing on a web analytics method that tracks the real click behaviours of web users through querying combined sets of keywords, the study explores the trends and interactions between Industry 4.0 technologies and EDI-related HR practices. Our search engine results page (SERP) analyses find a high volume of queries and a significant interest between EDI elements and artificial intelligence (AI) only. In contrast to the suggestions of the extant literature, no significant user interest in other Industry 4.0 applications for EDI implementations was observed. The authors suggest that other Industry 4.0 technologies such as machine learning (ML) and natural language processing (NLP) for EDI implementations are in their early stages.

Research at the interface of marketing and entrepreneurship has emphasized digital entrepreneurship and how entrepreneurs pursue business opportunities centered on new technologies. However, a different type of entrepreneurship focused on opportunities involving consumers’ (re)adoption of displaced analog technologies when digital alternatives are dominant – analog entrepreneurship – is a trend and counter phenomenon to digital entrepreneurship that is receiving intense practitioner interest but limited scholarly attention. The purpose of this article is to present a theoretical framework that explains the role of analog entrepreneurship in technology revitalization.

In this conceptual paper, the authors use the microfoundations perspective to develop a multilevel theory of analog entrepreneurship. The authors define and delineate the “analog entrepreneurship” concept and formulate a midrange theory explaining how entrepreneurs influence the reemergence of analog technologies.

The theory’s main insight is that the renewal of analog technologies is not confined to consumers. Entrepreneurs are creating businesses that stimulate demand for analog technologies. As a result of entrepreneurs’ activities, legacy analog technologies do not fade into nonexistence in the face of rival digital technologies.

The theory of analog entrepreneurship contributes to research at the intersection of entrepreneurship and marketing by expanding its focus to consider the entrepreneurs who revitalize displaced analog technologies when digital alternatives are dominant. The authors provide insight into the potential trajectories of technologies after their initial displacement and the role entrepreneurs play in shaping the late stages of technology lifecycles. The theory draws attention to an underexplored phenomenon made increasingly prevalent by recent technological disruptions and suggests an agenda for studying how entrepreneurs renew analog technologies.

Smart manufacturing (SM) lies at the core of Industry 4.0. Uniform adoption of SM across business partners is crucial to exploit its value creation potential. However, firms' willingness to invest in SM is limited by insufficient or inconclusive evidence on its performance-related benefits. To close this gap, this paper develops and tests a model linking SM adoption to firms' financial performance. Improvements along the four dimensions of operational performance (i.e. cost quality, delivery and flexibility) mediate this relation.

This study follows an empirical research approach. In particular, survey data from 234 automotive component suppliers are analyzed via covariance-based structural equation modeling to explore the link between SM adoption and operational performance. Survey data are then matched with secondary data from balance sheets of 81 firms to investigate the impact of SM on financial performance via partial least square structural equation modeling.

Findings highlight that adoption of SM results in improvements in cost, quality, delivery performance, thus suggesting that SM is a mean to overcome performance trade-offs. Improvements in operational performance enabled by SM do not give rise to superior financial performance, thus implying that SM might support firms in maintaining the competitive position in the market, but could be insufficient to generate higher margin.

Results have implications for SM research and for manufacturing executives engaged in the adoption of SM, as they provide a detailed analysis of the impact of SM on operational performance and clarify the effect that SM adoption has on financial performance.

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.

The learning outcomes of this case study are as follows: to understand the concept of social commerce and how it is different from e-commerce business, to discuss the unique features of Meesho’s social commerce model, to understand concepts of entrepreneurship (e.g. addressing the gap through business, pivoting), to understand the dynamics of online grocery market and e-commerce market and to apply business strategy concepts to make recommendations.

This case study presents Meesho, an organization in social commerce in India. Meesho was founded by Indian Institute of Technology graduates Vidit Aatrey and Sanjeev Barnwal in the year 2015 to help the small business owners with online selling. It was initially launched as an app that connected local retailers to the customers. Owing to low customer interest and low profit margins, they pivoted the business to a reseller app that facilitated the individuals and small retailers to resell the wholesalers’ products (unbranded and long-tail products) to the customers on social media channels. However, the tough competition from other start-ups in social commerce and retail giants such as Amazon and Flipkart who targeted the same customers impacted their growth. After receiving a funding of US$300m, the founders were considering if they should enter the e-commerce market and directly compete with giants such as Amazon and Flipkart or extend the product line to the online groceries market and compete with dominant players such as BigBasket and Blinkit. Through this case study, the students could be provided an opportunity to evaluate a situation, apply the strategic management concepts and make a recommendation on the strategic plan.

The case study can be taught in the business and strategy courses at the graduate and postgraduate levels in business schools. It is also suitable for the entrepreneurship course with focus on e-commerce start-up and sustainability, which is also taught at the MBA level. This case study can also be used in executive development programs for abovementioned courses.

Teaching notes are available for educators only.

CSS 11: Strategy.

Industry 4.0-driven digitalisation is said to offer a way to redesign traditional compliance-oriented quality management (QM) models. However, despite a rising academic and practitioner interest, it is still unclear how companies transform their current QM models to meet the real-time needs of the new manufacturing paradigm. The purpose of this study is to explore practices for the digitalisation of QM and to uncover the digitalisation journey.

An exploratory research approach of an embedded case study of a multinational auto-component manufacturer was adopted to achieve the research aim.

A guiding framework called the “Quality 4.0 transition framework” was developed based on literature and expert knowledge. The framework is made up of three building blocks, i.e. the foundation of “as-is” digitalisation maturity assessment; pillars representing horizontally and vertically integrated QM processes, and roof signifying reinforcement of total quality management (TQM) principles at all levels.

The study provides empirical evidence of the case company's digitalisation journey to avert product recall due to field failure issues. The study contributes to theory and practice in many ways. First, the study uses empirical data from a real-world case to understand how digitalisation affects QM processes. Next, the guiding framework for the Quality 4.0 transition adds to the existing literature on the digitalisation of business processes.