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The purpose of this study is to examine how formal and informal institutions play a role in the Iranian context in shaping corporate social responsibility (CSR) policies and practices.

Using a multiple case-study approach combining comparative and cross-sectional methods with semi-structured interviews, primary data was collected from eight corporations that actively participated in CSR activities in Iran. A microanalysis approach was used to examine the meanings and dynamics in the data. Through thematic analysis and pattern-matching techniques, the authors separately examined the roles of formal and informal institutions. Cross-case analysis was used to highlight the cases’ similarities and differences.

This study demonstrates that both formal and informal institutional structures exist in Iran and that both types influence CSR. This study also shows that informal institutions (such as personal values, culture, religion, traditions, charity and philanthropy) play a more explicit role than formal institutions (such as legal regulations and laws) in shaping CSR adoption policies and practices. The results indicate that, among institutions linked to CSR, formal and informal institutions are complementary and potentiate each other in Iran. Nevertheless, compared to formal ones, informal institutions play a more prominent role in shaping CSR policies and practices.

The authors recognize that, although the eight corporations are large, and although they interviewed their key personnel, they do not claim that these findings are generalizable, owing to the qualitative nature of the study and the small number of selected corporations.

This study makes relevant theoretical and empirical contributions. First, it contributes to the growing body of CSR literature that highlights the necessity of linking informal and formal institutions. Although the CSR literature lacks research on informal institutions in developing economies, researchers have yet to push forward and explore how the CSR adoption process works in developing economies that have influential informal institutions.

This study examines the mediating roles of the three dimensions of business intelligence (sensing capability, transforming capability and driving capability) in the relationship between the three dimensions of big data analytics capability (big data analytics management, technology and talent capabilities), and radical innovation among Chinese manufacturing enterprises.

A theoretical framework was developed using the resource-based view. The hypothesis was tested using empirical survey data from 326 Chinese manufacturing enterprises.

Empirical results show that, in the Chinese manufacturing context, business intelligence sensing capability, business intelligence transforming capability and business intelligence driving capability positively mediate the impact of big data analytics capability on radical innovation.

The results offer managerial guidance for leaders to properly use big data analytics capability, business intelligence and radical innovation as well as offering theoretical insight for future research in the manufacturing industry’s radical innovation.

This is among the first studies to examine three dimensions of big data analytics capability on the manufacturing industry’s radical innovation by considering the mediating role of three dimensions of business intelligence.

Supply chain management (SCM)-embedded valuable resources, such as capital, raw-materials, products, partners, customers and finished inventories, where the evaluation of environmental texture and flexibilities are needed to perceive sustainability. The present study aims to identify and evaluate the directory of green and agile (G-A) attributes based on decision support framework (DSF) for identifying dominating measures in SCM.

DSF is developed by exploiting generalized interval valued trapezoidal fuzzy numbers (GIVTFNs). Two technical approaches, i.e. degree of similarity approach (DSA) and distance approach (DA) under the extent boundaries of GIVTFNs, are implicated for data analytics and for recognizing constructive G-A measures based on comparative study for robust decision. A fuzzy-based performance indicator, i.e. fuzzy performance important index (FPII), is presented to enumerate the weak and strong G-A characteristics to manage knowledge risks in allied business environment.

The modeling is illustrated from the insights of decision-makers for augmenting business value based on cognitive identification of measures, where the best performance score is identified by the “sustainable packaging” under the traits of green supply chain management (GSCM). “The use of Web-based applications” under the traits of agile supply chain management (ASCM) and “Outsourcing flexibility” under traits of ASCM is found as the second and third most significant performance characteristics for business sustainability. Additionally, the “Reutilization (recycling) and reprocessing” under GSCM in manufacturing and “Responsiveness and speed toward customers needs” under ASCM are found difficult in attainment.

The G-A evaluation will assist in attaining performance excellence in day-to-day operations and overall functioning. The outcomes will help executives to plan strategic objectives and attaining success.

To reinforce the capabilities of SCM, wide extent of G-A dimensions are presented, concept of FPII is reported to manage knowledge risks based on identification of strong attributes and two technical approaches, i.e. DSA and DA under GIVTFNs are presented for attaining robust decision and directing managerial decision-making process.

The purpose of this paper is twofold: first, a case study on applying lean principles in manufacturing operations to redesign and optimize an electronic device assembly process and its impact on performance and second, introducing cardboard prototyping as a Kaizen tool offering a novel approach to testing and simulating improvement scenarios.

The study employs value stream mapping, root cause analysis, and brainstorming tools to identify root causes of poor performance, followed by deploying a Kaizen event to redesign and optimize an electronic device assembly process. Using physical models, bottlenecks and opportunities for improvement were identified by the Kaizen approach at the workstations and assembly lines, enabling the testing of various scenarios and ideas. Changes in lead times, throughput, work in process inventory and assembly performance were analyzed and documented.

Pre- and post-improvement measures are provided to demonstrate the impact of the Kaizen event on the performance of the assembly cell. The study reveals that implementing lean tools and techniques reduced costs and increased throughput by reducing assembly cycle times, manufacturing lead time, space utilization, labor overtime and work-in-process inventory requirements.

This paper adds a new dimension to applying the Kaizen methodology in manufacturing processes by introducing cardboard prototyping, which offers a novel way of testing and simulating different scenarios for improvement. The paper describes the process implementation in detail, including the techniques and data utilized to improve the process.

The apparel industry of Bangladesh is rethinking lean manufacturing (LM) deployment because of the challenges imposed by the COVID-19 pandemic. Due to COVID-19, LM implementation in the apparel industry has become more difficult. Thus, the purpose of this study is to explore the barriers to implementing LM practices in the apparel industry of Bangladesh in the context of COVID-19 pandemic.

For evaluating the barriers, an integrated framework that combines the Delphi method and fuzzy total interpretive structural modeling (TISM) has been designed. The application of fuzzy TISM has resulted in a structured hierarchical relationship model of the barriers with driving and driven power.

The findings reveal that “lack of synchronization of lean planning with strategic planning”, “lack of proper understanding of lean concept” and “low priority from the top management” are the three top most important barriers of LM implementation in apparel industry.

These findings will help the apparel industry to formulate strategy for implementing the LM practices successfully. The proposed model is expected to contribute to the sustainable development goals (SDGs) such as Responsible Consumption and Production (SDG 12); Decent Work and Economic Growth (SDG 8); Industry, Innovation and Infrastructure (SDG 9) via resilient strategies.

This study is one of few initial efforts to investigate LM implementation barriers during the COVID-19 epidemic in a real-world setting.

A large number of organisations are moving towards adopting Industry 4.0 (I4.0), and simultaneously, the emphasis on attaining sustainability development goals is also increasing. Hence, it is imperative to understand the interplay between I4.0 and sustainability. However, the literature addressing the same is still in infancy. Accordingly, the purpose of this study is to fill this gap in the literature by exploring the potential sustainability impacts of I4.0 on the organisations and society in terms of sustainability risks.

To gain an understanding of sustainability aspects in the I4.0 context, relevant literature is gathered using Scopus and Web-of-Science database. An in-depth review of 51 research papers is performed to determine the sustainability risks associated with I4.0.

From the study, a total of 16 sustainability risks are identified, and I4.0 sustainability risk taxonomy is developed. The proposed taxonomy extends the sustainability implications of I4.0 beyond the triple bottom line umbrella and includes the organisational perspective as well. Furthermore, the study provides future research avenues to scholars by positing five potential research questions under different risk management stages.

The study provides an understanding of sustainability risks associated with the adoption of I4.0. The findings will help practitioners streamline their production and operation processes by finding out possible solution to the sustainability risks of their smart factories in advance. The present research will act as a stepping stone towards I4.0 sustainability. The proposed research questions will assist the future researchers in extending the field of I4.0.

To the best of the authors’ knowledge, this is one of the first studies to address the topic of sustainability risks in the context of I4.0.

Warranty-based big data analysis has attracted a great deal of attention because of its key capabilities and role in improving product quality while minimizing costs. Information and details about particular parts (components) repair and replacement during the warranty term, usually stored in the after-sales service database, can be used to solve problems in a variety of sectors. Due to the small number of studies related to the complete analysis of parts failure patterns in the automotive industry in the literature, this paper focuses on discovering and assessing the impact of lesser-studied factors on the failure of auto parts in the warranty period from the after-sales data of an automotive manufacturer.

The interconnected method used in this study for analyzing failure patterns is formed by combining association rules (AR) mining and Bayesian networks (BNs).

This research utilized AR analysis to extract valuable information from warranty data, exploring the relationship between component failure, time and location. Additionally, BNs were employed to investigate other potential factors influencing component failure, which could not be identified using Association Rules alone. This approach provided a more comprehensive evaluation of the data and valuable insights for decision-making in relevant industries.

This study's findings are believed to be practical in achieving a better dissection and providing a comprehensive package that can be utilized to increase component quality and overcome cross-sectional solutions. The integration of these methods allowed for a wider exploration of potential factors influencing component failure, enhancing the validity and depth of the research findings.

As Czech export is widely considered the key to the economic development of Czechia, this chapter explores the relationship between microeconomic profitability among companies in selected TOP10 export industries and the macroeconomic development of the export itself. An investigation was carried out to compare the differences caused by the COVID-19 pandemic. In addition, the comparison is developed according to the size and concentration of ownership among exporting companies. Annual data are obtained from the Bureau van Dijk Orbis database to analyse profitability among 4,283 companies in 10 NACE industries from 2012 to 2021. We have obtained encouraging results, demonstrating that not only those less profitable companies affected export development. However, in general, our results emphasise the importance of those less profitable medium-sized companies for Czech export, within the manufacture of machinery and equipment, and the manufacture of motor vehicles in particular.

The purpose of this paper is to define the local integration rate and how it is calculated to assess its relevance as a national performance indicator for the Moroccan automotive industry.

The research methodology first followed a systematic review approach through the analysis of published research articles and academic works. This study then followed a qualitative approach based on semi-structured interviews with various actors in the Moroccan automotive industry. Finally, the findings of this work were reinforced by a case study to analyze the supply chain of a locally produced vehicle.

The results indicate that the local integration rate as calculated today overestimates the performance of the automotive industry and does not systematically guarantee a significant creation of value added.

Due to the confidentiality of the data in terms of turnover, payroll and purchase prices as well as the large number of suppliers in the different supply chains of the car manufacturer, the case study focused on only one of the six existing ecosystems.

On the basis of research work on the Moroccan automotive industry as well as interviews with various actors, the local integration rate is unanimously considered as a viable performance indicator. This study has not only led us to the method of calculating this rate by the Ministry of Industry but also demonstrated its limitations while proposing a new method of calculation to increase the value added.

Purpose: This chapter aims to enlighten the stakeholders on the role and contribution and the issues and challenges of large-scale industries in the wake of the globally unified economies.

Need for the study: Large-scale industries are one of the pillars of any nation and can exercise an immense impact on the numerous facets of the economy of any country. Their role and contribution can benefit all the stakeholders, especially in today’s integrated and interdependent world economies. Hence, there is an absolute need to highlight the issues and challenges and suggest measures to overcome them to promote a resilient global economy.

Methodology: The study gathered data from secondary sources like textbooks, articles, and the internet.

Findings: The findings of the study state that large-scale industries are enormous contributors to employment creation, development of the economy, growth of revenue, research and development (R&D) and innovation, export promotion, and infrastructure. The significant challenges include regulatory compliance, workforce management, economic volatility, political instability, supply chain management, environmental compliance, and technology and infrastructure.

Protectionism, deregulation, public–private partnership, privatisation, and environmental regulation are significant government decisions that affect large-scale industries. The study identifies tax incentives, easy access to financing, and domestic and international trade policies to safeguard large-scale industries’ interests.

Practical implications: Large-scale industries contribute towards the growth of global economic resilience in terms of employment generation, technological advancements, and innovation, fostering international trade in today’s interconnected world.