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This paper aims to systematically review the managerial literature on Industry 4.0 (I4.0) in emerging markets (EMs) through bibliometric analyses to identify incipient research streams and literature gaps and recommend avenues for future research.

This research uses bibliographic coupling analysis (BCA) to obtain a comprehensive view of the intellectual contours within the addressed literature. The primary source utilised is the Web of Science database. A total of 345 peer-reviewed journal articles were retrieved. Complementing BCA, we use social network analysis and the content analysis of articles to study the resulting literature clusters.

The results reveal four thematic clusters: (1) Adoption of I4.0 in EMs; (2) impact of I4.0 on organisational aspects and financial performance of companies and supply chains in EMs; (3) I4.0, lean management and operational performance in EMs and (4) I4.0 and the development of sustainable practices in EMs. We supplement these results with the proposal of different future avenues of research, both general and specific, for each identified cluster.

The current study has certain limitations arising from using the bibliometric method and techniques employed in the analyses.

To the best of the authors’ knowledge, there is no comprehensive literature review article on this subject. This research is deemed valuable for future scholars as it facilitates the identification of research fronts that define the forefront of knowledge, reveals current trends and sets the stage for further exploration of key issues in the field. This, in turn, can offer valuable insights to academics, policymakers and practitioners.

This study examined the role of artificial intelligence (AI) tools in facilitating the accessibility and usability of electronic resources (e-resources) in academic libraries.

This study employed a quantitative descriptive survey to collect data from library users. The population targeted was sampled using a purposive sampling technique. A total of 427 (58%) participated in this study.

Most respondents preferred electronic journals (e-journals) among the e-resources stored in academic libraries. Chatbots were identified as preferred AI tools for accessing and enhancing the usability of these resources. Strategies mentioned included the potential for integrating AI tools across various e-resources. However, among the challenges reported was the inability to integrate AI tools with the existing library management systems. Improving e-resource discovery and access can significantly enhance the effectiveness of AI tools in academic libraries.

Originality in the context of AI applications in academic libraries refers to the unique approaches, innovative tools and creative solutions that enhance the accessibility and usability of electronic resources. By focusing on unique solutions that enhance the accessibility and usability of e-resources, these libraries can better serve their diverse user populations and adapt to the evolving landscape of information needs.

A vaccination strategy to cover the susceptible population is key to containing the spread of any virus during a healthcare emergency. This study quantifies the susceptibility of a region based on initial infection rates to prioritize optimal vaccine distribution strategies. The authors propose a metric, the regional vulnerability index (RVI), that identifies the degree of susceptibility/vulnerability of a region to virus infections for strategically locating hubs for vaccine storage and distribution.

A two-phase methodology is used to address this problem. Phase 1 uses a modified Susceptible-Infected-Recovered (SIR) model, ModSIR, to estimate the RVI. Phase 2 leverages this index to model a P-Center problem, prioritizing vulnerable regions through a Mixed Integer Quadratically Constrained Programming model, along with three variations that incorporate the RVI.

Results indicate a weighting scheme based on the population-to-RVI ratio fosters fair distribution and equitable coverage of vulnerable regions. Comparisons with the public distribution strategy outlined by the Government of India reveal similar zonal segregations. Additionally, the network generated by our model outperforms the actual distribution network, corroborated by network metrics such as degree centrality, weighted degree centrality and closeness centrality.

This research presents a novel approach to prioritizing vaccine distribution during pandemics by applying epidemiological predictions to an integer-programming framework, optimizing COVID-19 vaccine allocation based on historical infection data. The study highlights the importance of strategic planning in public health response to effectively manage resources in emergencies.

Asset maintenance in manufacturing industries is a critical issue as organizations are highly sensitive towards maximizing asset uptime. In the advent of Industry 4.0, maintenance is increasingly becoming technology driven and is being termed as Maintenance 4.0. Several barriers impede the implementation of Maintenance 4.0. This article aims at - exploring the barriers to implementation of Maintenance 4.0 in manufacturing industries, categorizing them, analysing them to prioritize and suggesting the digital technologies to overcome them.

Twenty barriers to the implementation of Maintenance 4.0 were identified through literature survey and discussion with the industry experts. The identified barriers were divided into five categories based on their source of occurrence and prioritized using fuzzy-technique for order preference by similarity to ideal solution (TOPSIS), sensitivity analysis was carried out to check the robustness of the solution.

“Data security issues” has been ranked as the most influencing barrier towards the implementation of Maintenance 4.0, whereas “lack of skilled engineers and data scientists” is the least influencing barrier that impacts the implementation of Maintenance 4.0 in the manufacwturing industries.

The outcomes of this research will help manufacturing industries, maintenance engineers/managers, policymakers, and industry professionals for detailed understanding of barriers and identify easy pickings while implementing Maintenance 4.0.

Manufacturing industries are witnessing a paradigm shift due to digitization and maintenance 4.0 forms the cornerstone. Little research has been carried in Maintenance 4.0 and its implementation; this article will help in bridging the gap. The prioritization of the barriers and digital course of actions to overcome those is a unique contribution of this article.

In this research, experimental and numerical methods were used to study the effect of pore geometry on residual stress and mechanical behavior of 3D-printed parts. In this regard, samples with circular, rhombic and hexagonal pore geometries were printed using fused deposition modeling (FDM), and their residual stress was measured through the mechanical strain release method. The finite-element method (FEM) was utilized to study the strength and natural frequency of the samples.

As a modern method of part manufacturing and repair, 3D printing has been highly regarded in industrial arenas for its ability to offer high precision without the need for different dies. Porosity has been studied as a solution for reducing weight in structures, and its effect on the mechanical behavior of a structure depends on the loading conditions and applications.

The results of the investigation showed that the rhombic pore geometry had the highest residual stress, while the sample with circular pores exhibited the lowest residual stress. Stress distribution and modal analyses indicated that the sample with rhombic pore geometry had the lowest displacement coupled with the highest strength and natural frequency. However, considering the total of external load-induced stress and residual stresses, the sample with hexagonal pore geometry outperformed the other samples and showed the longest fatigue life.

According to the literature review, residual stress is one of the key factors influencing the performance of 3D-printed parts. However, the effects of pore geometry on residual stress and structural strength in 3D-printed components remain underexplored. Therefore, this study investigates the impact of hexagonal, rhombic and circular pore geometries on residual stress and structural strength through both experimental and numerical analyses.

Total productive maintenance consists of strategies and procedures that aim to guarantee the entire functioning of machines in a production process so that production is not interrupted and no loss of quality in the final product occurs. Planned maintenance is one of the eight pillars of total productive maintenance, a set of tools considered essential to ensure equipment reliability and availability, reduce unplanned stoppage and increase productivity. This study aims to analyze the influence of statistical reliability on the performance of such a pillar.

In this study, we utilized a multi-method approach to rigorously examine the impact of statistical reliability on the planned maintenance pillar within total productive maintenance. Our methodology combined a detailed statistical analysis of maintenance data with advanced reliability modeling, specifically employing Weibull distribution to analyze failure patterns. Additionally, we integrated qualitative insights gathered through semi-structured interviews with the maintenance team, enhancing the depth of our analysis. The case study, conducted in a fertilizer granulation plant, focused on a critical failure in the granulator pillow block bearing, providing a comprehensive perspective on the practical application of statistical reliability within total productive maintenance; and not presupposing statistical reliability is the solution over more effective methods for the case.

Our findings reveal that the integration of statistical reliability within the planned maintenance pillar significantly enhances predictive maintenance capabilities, leading to more accurate forecasts of equipment failure modes. The Weibull analysis of the granulator pillow block bearing indicated a mean time between failures of 191.3 days, providing support for optimizing maintenance schedules. Moreover, the qualitative insights from the maintenance team highlighted the operational benefits of our approach, such as improved resource allocation and the need for specialized training. These results demonstrate the practical impact of statistical reliability in preventing unplanned downtimes and informing strategic decisions in maintenance planning, thereby emphasizing the importance of your work in the field.

In terms of the originality and practicality of this study, we emphasize the significant findings that underscore the positive influence of using statistical reliability in conjunction with the planned maintenance pillar. This approach can be instrumental in designing and enhancing component preventive maintenance plans. Furthermore, it can effectively manage equipment failure modes and monitor their useful life, providing valuable insights for professionals in total productive maintenance.

This study aims to explore a methodology based on the Kano model. And use this method to determine user requirement attributes in the field of school uniform customization. To construct a set of processes that can be used as a reference for constructing a clothing customization platform.

An optimized quantitative Kano model was applied. Initially, a survey was conducted to assess the current market for customized school uniforms in China. Subsequently, a Kano attribute questionnaire was developed, and experts from both supply and procurement sectors were invited to evaluate it. This was followed by categorizing user demands based on the Kano model’s evaluation criteria and conducting a validity analysis using Cronbach’s alpha coefficient. The priority ranking of user demands was determined through a sensitivity analysis of better-worse coefficients. Ultimately, a platform was established, and a fuzzy comprehensive evaluation was conducted.

Regarding user demands, procurement-side demand elements prioritize modular design, fabric libraries and online reviews. In contrast, supply-side demand elements focus on style product libraries, layout adaptation to user habits and the preview effects of 3D models. Elements such as qualification verification, personal information uploading, design draft archiving and main tag categorization have a lesser impact on user satisfaction.

The results of the study provide a complete methodological reference for the construction of a garment customization platform. By applying the Kano model, this study categorizes and filters the demands of users for school uniform customization design platforms in China and establishes a 3D virtual display platform aimed at improving user satisfaction.

The purpose of this article is to propose measurements for assessing various service quality dimensions in the field of automobile maintenance and repair businesses. The study also examines the impact of service quality dimensions on continuance and recommendation intentions of electric vehicle (EV) users.

Samples comprised 397 EV users who experienced the services provided by automobile maintenance and repair centers. Convenience and snowball sampling methods were conducted to obtain suitable participants.

For continuance intention, the strongest dimension of service is reliability, followed by responsiveness, empathy, price fairness, queue and waiting management and physical environment. For recommendation intention, the strongest dimension of service quality is also reliability, followed by empathy, responsiveness, queue and waiting management, physical environment and price fairness.

First, the samples of this study consisted of EV users only in Thailand. The findings may not be universally applicable to other nations due to varying levels of EV adoption. Second, the degree to which the six dimensions of service quality vary can be influenced by the demographic characteristics of customers.

Prioritize staff training in responsiveness, empathy and reliability to effectively meet customer needs. Enhance the physical environment for a comfortable experience. Promote transparent pricing aligned with service quality and explain the pricing rationale. Implement effective queueing and waiting management strategies for improved customer comfort during waits. These measures aim to enhance intention to reuse the service and positive recommendations.

This research undertakes an original survey comprising EV users who utilized services from automobile maintenance and repair centers in Thailand. The results sheds light on the influence of service quality on individual’s intentions.

This paper analyzed the effect of voluntary corporate disclosure on firm value and how audit quality and cross-border stock market listing moderate this relationship.

The paper analyzed S&P BSE index constituents’ 90 Indian enterprises for 2017–2019. The India Disclosure Index Report was used to fetch the voluntary disclosure scores. Further, the study was conducted in two parts using six different panel-data regression models in the framework of legitimacy, agency, signaling and market segmentation theory. First, the study investigated the direct impact of voluntary disclosures on return on assets (ROA) and Tobin’s Q. Second, the moderating effect of the “Big 4” was tested. Third, the paper also examined the moderating role of “cross-border stock market listing” in the direction of voluntary disclosure-firm value relationships.

Primarily, the results postulate a significant positive impact of voluntary disclosures on ROA and Tobin’s Q. A higher voluntary disclosure leads to a higher ROA and Tobin’s Q for firms. Moreover, the improvement effect of such disclosures on ROA and Tobin’s Q is more pronounced for companies “listed abroad” and audited by “Big 4.”

The findings will enhance managers’ learning about the financial impact of voluntary disclosures. The choice of a “Big 4” and “Cross border stock market listing” indicates firms’ future positive perspectives, strengthening investor trust in the market.

The results suggest that companies’ potential auditing, agency and litigation issues could be addressed through fairness in the information content of voluntary disclosures.

This examination presents a firm valuation model in which voluntary disclosure tackles an ethical issue, the resolution of which depends on the “audit quality” and “cross-border stock market listing.”

The purpose of this paper is to examine whether increased labour productivity could reduce the impact of output growth on the unemployment rate in India over the period 1991–2019 through Okun’s law and its expanded form.

The study uses Okun’s law and its expanded form, with the inclusion of labour productivity in the actual model. Further, the relationship between output growth, unemployment rate, and labour productivity is analysed by using the gap model, the difference model, the dynamic model, the error correction model (ECM), and the vector autoregressive (VAR) approach.

The empirical results from the applied models do not confirm an inverse relationship between output growth and the unemployment rate with an unexpected positive sign of Okun’s coefficient. The evidence of preference for more capital-intensive techniques in the Indian economy is also strongly supported by the results of the expanded form of Okun’s law with a statistically significant positive coefficient of GDP and labour productivity.

The study examined the proposed relationship using Okun’s law and its expanded form, which had not been employed in earlier studies in the context of India. The authors also show that a high economic growth rate is a necessary but not sufficient condition to solve the chronic and structural unemployment problem in India.