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This study aims to establish a robust evaluation framework for suppliers within the automotive supply chain, specifically in the stamping sector. The primary objectives are to elucidate the performance criteria of suppliers, identify indicators and scales for measuring these criteria and find the importance of the criteria.

The evaluation framework comprises a criteria hierarchy and indicators developed based on the evaluation criteria of major automotive manufacturers. Specific indicators and measurement scales are recommended for assessing suppliers. Importance weights for the criteria are assigned based on the input of nine experts using the Analytic Hierarchy Process (AHP). Finally, four sheet metal stamping tooling (SMST) suppliers are evaluated by four specialists using the proposed evaluation framework.

The study introduces a novel classification of criteria, encompassing financial and commercial perspectives, delivery capability, supplier facility and cultural approaches and business process necessities. The findings underscore the significance of financial and commercial stability in the selection of SMST suppliers, emphasizing their role in mitigating risks associated with disruptions, bankruptcies and unforeseen events. Additionally, several SMST evaluation factors identified in this study contribute to the development of resilience capabilities, highlighting the crucial importance of their inclusion and assessment in the proposed evaluation framework.

This research presents a comprehensive model for evaluating SMST suppliers, which tackles the multidisciplinary challenges within the automotive supply chain. Given the inadequacy or nonexistence of current SMTS selection models, this study bridges the gap by exploring potential and necessary criteria, alongside 116 specific indicators and measurement scales.

In recent years, there has been significant interest in adopting XR (extended reality) technologies such as VR (virtual reality) and AR (augmented reality), particularly in retail. However, extending activities through reality-mediation is still mostly believed to offer an inferior experience due to their shortcomings in usability, wearability, graphical fidelity, etc. This study aims to address the research gap by experimentally examining the acceptance of metaverse shopping.

This study conducts a 2 (VR: with vs. without) × 2 (AR: with vs. without) between-subjects laboratory experiment involving 157 participants in simulated daily shopping environments. This study builds a physical brick-and-mortar store at the campus and stocked it with approximately 600 products with accompanying product information and pricing. The XR devices and a 3D laser scanner were used in constructing the three XR shopping conditions.

Results indicate that XR can offer an experience comparable to, or even surpassing, traditional shopping in terms of its instrumental and hedonic aspects, regardless of a slightly reduced perception of usability. AR negatively affected perceived ease of use, while VR significantly increased perceived enjoyment. It is surprising that the lower perceived ease of use appeared to be disconnected from the attitude toward metaverse shopping.

This study provides important experimental evidence on the acceptance of XR shopping, and the finding that low perceived ease of use may not always be detrimental adds to the theory of technology adoption as a whole. Additionally, it provides an important reference point for future randomized controlled studies exploring the effects of technology on adoption.

As the novel coronavirus 2019 (COVID-19) impacts the world, software practitioners are collaboratively working remotely from home. The pandemic has disrupted software practitioners’ productivity forcing changes to agile methodology adopted by software practitioners in software organizations. Therefore, this study aims to provide implication on the issues and recommendations for improving software practitioners’ productivity and also examine the impact of the COVID-19 pandemic on agile software development.

This paper adopts a narrative literature review to provide early assessment based on secondary data from the literature and available document reports from studies published from 2019 to 2022 to explore software practitioners’ productivity and agile software development during the working from home directive amidst the COVID-19 pandemic. A total of 60 sources which met the inclusion criteria were used to provide preliminary evidence grounded on secondary data from the literature. Descriptive analysis was used to provide qualitative findings from the literature.

Findings from this study present the significance of working from home directive on agile software development and software practitioners’ productivity. More importantly, findings from the secondary data shed light on software practitioners’ productivity adopting agile software development amidst the COVID-19 pandemic. Additionally, the findings present virtual collaborative platforms used by software practitioners, technical and social barriers of agile software development during the pandemic and recommendations for remote agile software development.

This study explores the significance of working from home directive on software practitioners’ productivity during COVID-19 pandemic and further investigates how are software practitioners’ productivity adopting agile software development practices amidst the COVID-19 pandemic. Besides, this study discusses the challenges software practitioners currently face and offers some strategies to bridge the gaps in agile software development to help software practitioners, system developers, software managers and software organizations adapt to the changes caused by the pandemic.

Social media platforms that disseminate scientific information to the public during the COVID-19 pandemic highlighted the importance of the topic of scientific communication. Content creators in the field, as well as researchers who study the impact of scientific information online, are interested in how people react to these information resources and how they judge them. This study aims to devise a framework for extracting large social media datasets and find specific feedback to content delivery, enabling scientific content creators to gain insights into how the public perceives scientific information.

To collect public reactions to scientific information, the study focused on Twitter users who are doctors, researchers, science communicators or representatives of research institutes, and processed their replies for two years from the start of the pandemic. The study aimed in developing a solution powered by topic modeling enhanced by manual validation and other machine learning techniques, such as word embeddings, that is capable of filtering massive social media datasets in search of documents related to reactions to scientific communication. The architecture developed in this paper can be replicated for finding any documents related to niche topics in social media data. As a final step of our framework, we also fine-tuned a large language model to be able to perform the classification task with even more accuracy, forgoing the need of more human validation after the first step.

We provided a framework capable of receiving a large document dataset, and, with the help of with a small degree of human validation at different stages, is able to filter out documents within the corpus that are relevant to a very underrepresented niche theme inside the database, with much higher precision than traditional state-of-the-art machine learning algorithms. Performance was improved even further by the fine-tuning of a large language model based on BERT, which would allow for the use of such model to classify even larger unseen datasets in search of reactions to scientific communication without the need for further manual validation or topic modeling.

The challenges of scientific communication are even higher with the rampant increase of misinformation in social media, and the difficulty of competing in a saturated attention economy of the social media landscape. Our study aimed at creating a solution that could be used by scientific content creators to better locate and understand constructive feedback toward their content and how it is received, which can be hidden as a minor subject between hundreds of thousands of comments. By leveraging an ensemble of techniques ranging from heuristics to state-of-the-art machine learning algorithms, we created a framework that is able to detect texts related to very niche subjects in very large datasets, with just a small amount of examples of texts related to the subject being given as input.

With this tool, scientific content creators can sift through their social media following and quickly understand how to adapt their content to their current user’s needs and standards of content consumption.

This study aimed to find reactions to scientific communication in social media. We applied three methods with human intervention and compared their performance. This study shows for the first time, the topics of interest which were discussed in Brazil during the COVID-19 pandemic.

This study aims to identify the status of virtual library system development in university libraries. It also attempted to examine the availability of virtual library services and reveal the challenges being faced by university librarians of Punjab province, Pakistan in providing virtual library services.

The study used a quantitative research method by using a questionnaire to address the study’s objectives. In total, 62 university libraries in Punjab province, Pakistan were the population of the study. A purposive sampling technique was applied to gather the required data from the respondents.

Results showed that most of the study’s respondents are well aware of the potential benefits of virtual library systems in universities for the delivery of innovative smart services to library patrons. The libraries delivered different virtual library services to meet the diverse needs of the library patrons. The respondents faced the challenges of funding, expertise, technical support, leadership, strategic planning and training to adopt virtual library systems and services in university libraries.

The study has provided a framework based on empirical findings of the study to efficiently adopt virtual library systems and services in university libraries for the delivery of smart services to end users. It has provided pertinent theoretical and practical implications.

Aside from grappling with technological advancements, enterprises in the industrial internet era are embracing business model innovation to align with the evolution of the industrial internet. However, a gap persists in the existing research regarding the strategies and methods available to small and medium-sized enterprises (SMEs) for executing business model innovation. Therefore, this paper aims to explore the connotation, characteristics and logic of business model innovation for SMEs in the industrial internet era.

To explore the business model innovation logic of small and medium-sized enterprises in the era of industrial internet, the paper adopts a longitudinal single-case study approach, with PAYA, a medium-sized enterprise in the electromechanical industry, serving as the subject of research. It systematically analyzes PAYA’s business model innovation, centering on four key elements of the business model: value proposition, value creation, value delivery and value capture.

The study proposes two types of business model innovation, namely, “Migration” and “Expansion”, and explains the logic of business model innovation for SMEs in the industrial internet era: faced with a rapidly changing market environment, entrepreneurs put forward the value proposition through the insight of the market environment, then enterprises conduct technological innovation to support the value creation by their own unique experience and knowledge, and then improve the legitimacy of the market by expanding the influence of market acceptance of the new business model to promote the value delivery, and finally capture the economic value and ecological value.

The types and logic of business model innovation proposed in this paper contribute to supplementing and developing the theory of business model innovation and meanwhile have important reference value for SMEs in the industrial internet era.

Many organizations currently transition towards digitalized process design, execution, control, assurance and improvement, and the purpose of this research is to empirically demonstrate how data-based operational excellence techniques are useful in digitalized environments by means of the optimization of a robotic process automation deployment.

An interpretive mixed-method case study approach comprising both secondary Lean Six Sigma (LSS) project data together with participant-as-observer archival observations is applied. A case report, comprising per DMAIC phase (1) the objectives, (2) the main deliverables, (3) the results and (4) the key actions leading to achieving the presented results is presented.

Key findings comprise (1) the importance of understanding how to acquire and prepare large system generated data and (2) the need for better large system-generated database validation mechanisms. Finally (3) the importance of process contextual understanding of the LSS project lead is emphasized, together with (4) the need for LSS foundational curriculum developments in order to be effective in digitalized environments.

This study provides a rich prescriptive demonstration of LSS methodology implementation for RPA deployment improvement, and is one of the few empirical demonstrations of LSS based problem solving methodology in industry 4.0 contexts.

The purpose of this paper is to investigate how different manufacturing technologies are bundled together and how these bundles influence operations performance and, indirectly, business performance. With the emergence of Industry 4.0 (I4.0) technologies, manufacturing companies can use a wide variety of advanced manufacturing technologies (AMT) to build an efficient and effective production system. Nevertheless, the literature offers little guidance on how these technologies, including novel I4.0 technologies, should be combined in practice and how these combinations might have a different impact on performance.

Using a survey study of 165 manufacturing plants from 11 different countries, we use factor analysis to empirically derive three distinct manufacturing technology bundles and structural equation modeling to quantify their relationship with operations and business performance.

Our findings support an evolutionary rather than a revolutionary perspective. I4.0 technologies build on traditional manufacturing technologies and do not constitute a separate direction that would point towards a fundamental digital transformation of companies within our sample. Performance effects are rather weak: out of the three technology bundles identified, only “automation and robotization” have a positive influence on cost efficiency, while “base technologies” and “data-enabled technologies” do not offer a competitive advantage, neither in terms of cost nor in terms of differentiation. Furthermore, while the business performance impact is positive, it is quite weak, suggesting that financial returns on technology investments might require longer time periods.

Relying on a complementarity approach, our research offers a novel perspective on technology implementation in the I4.0 era by investigating novel and traditional manufacturing technologies together.

In the context of modern urbanization, optimizing resources such as energy, materials, water and labour is no longer solely an environmental concern but a strategic economic necessity. This chapter underscores the vital connection between smart cities and resource efficiency, highlighting sustainable practices as crucial amidst the ever-expanding urban landscape. This chapter commences by demystifying key terms like ‘smart city,’ ‘data analytics,’ ‘artificial intelligence’ and ‘resource efficiency.’ It illuminates how these concepts intertwine and emphasizes their pivotal roles in shaping urban sustainability. Furthermore, this chapter unravels the multifaceted components of smart cities, showcasing their real-world use cases and the techniques of data analytics and artificial intelligence (AI) driving transformative changes. It draws from an extensive body of research, exemplifying how various data analytics techniques have been leveraged in the realm of smart cities. Towards its conclusion, this chapter provides a comprehensive overview of these techniques and their applications, shedding light on their potential to revolutionize resource management in urban environments. In essence, this chapter serves as a valuable compendium of knowledge, offering insights into the critical synergy between smart cities, data analytics, AI and resource efficiency. It underscores the imperative for cities to harness data-driven insights and technological advancements to achieve sustainable and prosperous urban futures.

This paper examines the factors influencing the adoption of Quality 4.0 technologies by quality professionals. The study evaluates perceived usefulness, perceived ease of use, attitude towards use, and intention to use new technologies.

The research involves a literature review, identification of latent variables derived from the Technology Acceptance Model (TAM), and a survey conducted among 200 quality professionals in the high-tech sector using computer-assisted web interviews.

The study elucidates the attitudes and intentions of high-tech industry employees towards adopting Quality 4.0 technologies. The primary conclusion drawn is that the predominant factor shaping the attitude of quality professionals towards new technologies is their confidence in their ability to effectively engage with these technologies rather than solely the perceived usefulness of such technologies to themselves or their organization.

This study is subject to certain limitations. Firstly, it focuses on five variables identified in the TAM model, potentially overlooking other pertinent factors that could provide a more comprehensive understanding. Secondly, the analysis of Quality 4.0 technologies is presented in a generalized manner, possibly resulting in nuanced differences if each specific technology were examined individually.

This article fills a gap in the literature by identifying the factors influencing quality professionals' adoption of Quality 4.0 technologies and delineating the relationships between these factors.