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Turnover intention is a critical predictor of an employee’s turnover behaviour. A high level of turnover rate significantly affects the productivity and morale of an enterprise. Previous research has indicated that job satisfaction plays a critical role in influencing an employee's turnover intention, but the underlying factors related to job satisfaction remain under-explored, which impedes the development of effective strategies for reducing turnover intention. In addition, little research examined job satisfaction and turnover intention in the context of the COVID-19 pandemic, specifically in the Chinese construction industry. This study aims to investigate the impact of job satisfaction on turnover intention among professionals in the construction industry.

A questionnaire survey was employed to collect viewpoints from 449 professionals in the Chinese construction industry, followed by descriptive analysis, correlation analysis and structural equation modelling analysis to derive results.

The findings indicate that professionals in the industry generally have a slightly high level of job satisfaction while a slightly low level of turnover intention in the special period of the pandemic outbreak. Leadership and management, training and career development and interpersonal relationships are critical underlying factors leading to their turnover intention. Although demographic factors have no moderating effect between job satisfaction and turnover intention, among them, age, marital status and years of working experience have strongly positive relationships with job satisfaction while significantly negative relationships with turnover intention.

The findings provide valuable insights to fully understand the critical factors leading to turnover intention from the perspective of job satisfaction, which is helpful in developing effective measures to address the turnover problems for enterprises in the Chinese construction industry and those industries with similar characteristics in other regions.

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.

To examine objective measures of economic job quality for a broad sample of workers in the US spectator sports industry and compare job quality in spectator sports to other industries.

Logistic and linear regressions are performed on American Community Survey (ACS) data collected from 2015 to 2019. Earnings and employer provision of health insurance are the outcomes.

Earnings and employer-provided health insurance are lower in the spectator sports industry than in other industries after controlling for relevant factors. Differences are partly explained by the occupational composition of the industry and the higher incidence of part-time work. Many but not all occupational groups have lower earnings and less employer-provided health insurance in sports.

ACS data only reports one job, so the results likely underestimate the prevalence of part-time work in the US spectator sports industry. The study finds support for a micro-class occupational composition effect and a pulsating organization effect. Some support is also found for a sports industry compensating wage differential, but the effect is not industry wide, counter to some depictions.

This is the first study to examine objective, economic measures of job quality across all occupational sub-groups in the sports industry. This is the first study to propose theoretical explanations for poor economic job quality in sport.

The purpose of this study is to present a methodical procedure on how to prepare event logs and analyse them through process mining, statistics and visualisations. The aim is to derive roots and patterns of quality deviations and non-conforming finished products as well as best practice facilitating employee training in the food processing industry. Thereby, a key focus is on recognising tacit knowledge hidden in event logs to improve quality processes.

This study applied process mining to detect root causes of quality deviations in operational process of food production. In addition, a data-ecosystem was developed which illustrates a continuous improvement feedback loop and serves as a role model for other applications in the food processing industry. The approach was applied to a real-case study in the processed cheese industry.

The findings revealed practical and conceptional contributions which can be used to continuously improve quality management (QM) in food processing. Thereby, the developed data-ecosystem supports production and QM in the decision-making processes. The findings of the analysis are a valuable basis to enhance operational processes, aiming to prevent quality deviations and non-conforming finished products.

Process mining is still rarely used in the food industry. Thereby, the proposed method helps to identify tacit knowledge in the food processing industry, which was shown by the framework for the preparation of event logs and the data ecosystem.

This study examines the barriers impeding the implementation of circular economy (CE) practices in the Nigerian architecture, engineering, construction and operation (AECO) industry across diverse regions of Nigeria.

The study adopts a quantitative approach using a five-Likert scale questionnaire (Google Forms), utilizing various analytical tools, encompassing descriptive and inferential statistics. This methodological approach allows for a multifaceted exploration of the intricacies surrounding the barriers to circular CE.

The study identifies and examines 24 CE barriers which are determined to be significant to the implementation of CE. These barriers were categorized into four categories: “Economic”, “Market and Information”, “Policy and Operational” and “ product development”.

The adopted sampling technique may restrict the generalizability of the findings within and beyond the selected regions. Furthermore, while the analytical methods offer a comprehensive analysis, they may not capture the nuanced intricacies that qualitative methodology could provide. However, the findings can be applied to other developing countries with characteristics similar to those of the Nigerian construction industry.

The study’s findings are significant for stakeholders such as policymakers, industry and academia. By elucidating CE-specific barriers, the research facilitates the formulation of targeted and tailored strategies, fostering the integration of CE principles in the Nigerian construction industry.

This study presents novel insights into the barriers hindering the implementation of CE in the Nigerian construction industry. Offering tailored strategies and categorizing barriers into clusters adds a unique perspective to CE literature, particularly in developing countries.

This empirical study aims to examine the COVID impact on the rate of return to schooling in 20 US industries.

An extended Mincer earnings equation with the COVID dummy variable and dummy interactive terms is used to examine the COVID effect on the rate of return to schooling for different industries. We use Heckman selection model to account for sample selection bias.

During COVID years, the change in the wage differential between college-and-above and below-college workers is different for industries, which leads to different changes in the rate or return to schooling among the 20 industries. During COVID, the rate of return to schooling increased for seven industries, decreased for seven industries and remained the same for six industries.

There is a lack of empirical tests of recession effects on the rate of return to schooling focusing on industry differentials. This study fills the research gap in this field. Our results will contribute to the ongoing discussion of the COVID impact on wages and returns from human capital investment.

Quality 4.0 is essential to the Industry 4.0 framework, notably in the electronics sector. It evaluates product quality in real-time using automatic process controls, quality tools and procedures. The implementation of Quality 4.0 criteria in the electronics industry is the subject of this study’s investigation and analysis. In this study, nine Customer Requirements (CRs) and 18 Design Requirements (DRs) have been defined to adopt Quality 4.0, aiming to increase yield while reducing defects. This study has developed a Quality 4.0 framework for effective implementation, incorporating the People, Process and Technology categories.

Many CRs and DRs of Quality 4.0 exhibit interdependencies. The Analytic Network Process (ANP) considers interdependencies among the criteria at various levels. Quality Function Deployment (QFD) can capture the customer’s voice, which is particularly important in Quality 4.0. Therefore, in this research, we use an integrated ANP-QFD methodology for prioritizing DRs based on the customers' needs and preferences, ultimately leading to better product and service development.

According to the research findings, the most critical consumer criteria for Quality 4.0 in the electronics sector are automatic systems, connectivity, compliance and leadership. The Intelligent Internet of Things (IIOTs) has emerged as the most significant design requirement that enables effective control in production. It is observed that robotics process automation and a workforce aligned with Quality 4.0 also play crucial roles.

Existing literature does not include studies on identifying CRs and DRs for implementing Quality 4.0 in the electronics industry. To address this gap, we propose a framework to integrate real-time quality measures into the Industry 4.0 context, thereby facilitating the implementation of Quality 4.0 in the electronics industry. This study can provide valuable insights for industry practitioners to implement Quality 4.0 effectively in their organizations.

The purpose of our research on blockchain technology is to unveil its immense potential, understand its applications and implications and identify opportunities to revolutionize existing systems and processes. This research aims to inspire the creation of new innovative solutions for industries. By harnessing blockchain technology, organizations can pinpoint key areas that could significantly benefit from its use, such as streamlining operations, providing secure and transparent digital solutions and fortifying data security.

This study presents a robust multi-criteria decision-making framework for assessing blockchain drivers in selected Indian industries. We initiated with an extensive literature review to identify potential drivers. We then sought the opinions of experts in the field to validate and refine our list. This meticulous process led us to identify 26 drivers, which we categorized into five main categories. Finally, we employed the Best-Worst Method to determine the relative importance of each criterion, ensuring a comprehensive and reliable assessment.

The authors have ranked the blockchain drivers based on their degree of importance using the Best-Worst Method. This study reveals the priority of BC implementation, with the retail industry identified as the most in need, followed by the Banking and Healthcare industries. Various critical factors are identified where blockchain technology could help reduce costs, increase efficiency and enable new innovative business models.

While this study acknowledges potential bias in driver assessment relying on literature and expert opinions, its findings carry significant practical implications. We have identified key areas where blockchain technology could be transformative by focusing on select industries. Future research should encompass other industries and real-world case studies for practical insights that could delve into the adoption challenges and benefits of blockchain technology in many other industries, thereby amplifying the relevance of our findings.

Blockchain is a groundbreaking, innovative technology with immense potential to revolutionize industries. Past research has explored the benefits and challenges of blockchain implementation in specific industries or sectors. This creates a gap in research regarding systematically classifying and ranking the importance of blockchain across different Indian industries. Our research seeks to address this gap by using advanced multi-criteria decision-making techniques. We aim to provide a comprehensive understanding of the significance of blockchain technology in critical Indian industries, offering valuable insights that can inform strategic decision-making and drive innovation in the country’s business landscape.

This paper aims to provide a reference for the development of digital transformation from the perspective of manufacturing process links.

This paper applies canonical correlation analysis based on digital technology patents in the key links of manufacturing industries (product design, procurement, product manufacturing, warehousing and transportation, and wholesale and retail) and the related indicators of economic benefits of regions in China.

(1) The degree of digitalization of manufacturing process links is significantly correlated with economic benefits. (2) The improvement of the degree of digitalization in the “product design” link, the “warehousing and transportation” link, the “product manufacturing” link and the “wholesale and retail” link has significant impacts on the economic benefits of manufacturing industry. (3) The digital degree of the “procurement” link has no obvious influence on the economic benefits of manufacturing industry.

The research results can provide reference for the formulation and implementation of micro policies. The strategy of improving the level of digital transformation of key links of manufacturing industry is put forward to better promote both the digital transformation of manufacturing industry and economic development.

This paper innovatively studies the relationship between digitalization of manufacturing process links and economic benefits. The findings can provide theoretical and empirical support for the digital transformation of China's manufacturing industry and high-quality development of economy.

This study aims to examine the methods of converting the threats caused by sanctions on Iran’s petrochemical industry to economic opportunities and investigating the growth rate of Iran’s petrochemical industry during sanctions.

The present research was descriptive-exploratory and the research method was based on a mixed research strategy. In the qualitative part, two methods of content analysis and semistructured interview were used and in the quantitative part, the survey method was used. The participants of the qualitative part were the content of all related published researches that have been published in the past 20 years and to conduct a semistructured interview with experts that were selected by nonprobability sampling method. In the quantitative part, the statistical population, there were including managers, supervisors and experts active in the Iran’s petrochemical industry (especially in Asaluyeh area of Iran), the sampling method was nonprobability of the type available.

The findings of the research showed that the potential risks resulting from the sanctions on Iran’s petrochemical industry from the review of the selected researches include 5 main risks (marketing, financial, supply and procurement, technological and production) and 26 related subrisks. Also, the ranking of the mentioned risks showed that among the main risks, the highest rank is related to the financial risk and among the subrisks, the increase in transaction costs and disruption in the implementation of contractual obligations have the highest rank.

The investigation of the growth rate of Iran’s petrochemical industry during the sanctions in terms of performance (production, export and foreign exchange) showed that the amount of production has gone through a growing trend during the period under review (it has grown by more than 68%). Also, the amount of exports in the investigated time period has been growing (it has grown by more than 70%) and finally, the amount of foreign exchange in this time period has experienced growth (more than 58% growth).

Introducing and examining methods of turning threats into economic opportunities in Iran’s petrochemical industry under the shadow of sanctions, which can be used in other active industries under sanctions in the energy field.