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In the process of industrial growth, when existing industries go for technology upgradation and new modernised industries emerge, both capital intensity and energy demand of overall industry tend to rise steadily. This poses a serious challenge for sustainable development objectives. Towards this end, enhancing energy efficiency of individual industries is the only remedy. Against this backdrop, the study aims to probe the trends in capital intensities and energy efficiencies of individual industries in India.

This study uses panel data regression analysis on data of two-digit industries from 1980/1981–2016/2017. The statistical analysis includes relevant macroeconomic variables derived from the literature to ascertain the drivers of energy efficiency in industries.

The results brought out that capital deepening due to technology upgradation and modernisation and capital productivity growth are the decisive determinants of energy efficiency growth. Furthermore, the ever-increasing fuel price motivated industries to conserve energy on a steady basis, supplemented by energy conservation-specific policy interventions.

This study recommends policy initiatives to ascertain and address technology gaps industry-wise, so that its subsequent efficient capital utilisation, and energy conservation measures of industries would result in energy efficiency growth in industry. The policy must focus on energy-efficient capital intensification in fabricated metals, leather, textile and wood industries that are found less-energy-efficient despite being less-capital-intensive.

This study empirically explores the capital efficiency of industries by investigating the interaction between capital intensity and energy efficiency at a two-digit industry level. It explores the determinants of energy efficiency and proposes industry-specific policies for energy-efficiency-enhancement of the overall industry.

The digital economy has become a key force supporting the high-quality development of tourism. This paper discusses the coupling coordination relationship and spatiotemporal evolution path of digital economy and tourism in China's provinces.

This paper uses the entropy method to measure the development level of digital economy and tourism, and establishes coupling coordination model and spatial autocorrelation model to study the interaction between the two industries.

Results show that the development levels of the two industries are rising, which spatially show a progressively decreasing pattern of east-middle-northeast-west. The coupling coordination degrees of the two industries have increased steadily, but the overall level is still near maladjusted. Spatially, the positive correlation is increasing, but the incongruity of spatial agglomeration is still significant. The coupling coordination evolution path in the provinces shows differentiated characteristics. The migration path is mainly concentrated in Zones I and II. The eastern region has an obvious trend of extending to Zone III, where the tourism industry was the most affected by the pandemic.

The study helps clarify the industrial coupling and coordination relationship in various regions and formulate regional tourism digital transformation strategies to promote the high-quality development of China's tourism industry.

This paper enriches the research on the relationship between digital economy and tourism from the perspective of industrial integration. The development commonality of China's tourism digital transformation summarized provides theoretical reference and demonstration for the coordinated development of China's tourism.

This paper aims to empirically investigate the impact of the carbon intensity constraint policy (CICP) on green innovation.

This study takes the implementation of the CICP as a quasi-natural experiment and uses a quasi–difference-in-difference method to investigate the impact of the CICP on firm green innovation from a microeconomic perspective.

The CICP significantly limits the quality of firms’ green innovation. Among the range of green patents, the CICP distorts only patents related to CO₂ emissions. The inhibitory effect is more pronounced in non-state-owned enterprises and heavily polluting firms. R&D investment and green investor are identified as the main mechanism.

These findings provide evidence for the influence of the CICP on firm green innovation, which can guide policymakers in China and other emerging economies that prioritize carbon intensity constraint targets and the improvement of relevant auxiliary measures.

Governments and firms should have a comprehensive understanding of environmental policies and corporate behavior and need to mitigate the negative impact through a combination of measures.

This study contributes to the literature by providing additional empirical evidence regarding the two opposing sides of the ongoing debate on the positive or negative effects of CICP. It also provides new evidence on the policy effect of the CICP on firm green innovation, together with its mechanisms and heterogeneous influences.

Driven by the development of the global digital economy, knowledge management in industrial enterprises offers more possibilities for green innovation. Based on the perspective of external knowledge sources, this study aims to construct a panel regression model to explore the relationship between digital economy and industrial green innovation efficiency.

Panel data from 30 regions in China from 2011 to 2020 were selected as research samples. All data are obtained from national and provincial statistical yearbooks. Coupling coordination degree analysis, entropy method, panel regression analysis, robustness test and threshold effect test by Stata 16.0 were used to test the hypotheses.

The empirical results demonstrate the hypotheses and reveal the following findings: the digital economy is positively related to industrial green innovation efficiency and external knowledge sources, and external knowledge sources mediate the relationship between them. Moreover, based on the threshold test results, the digital economy has a double-threshold effect on industrial green innovation efficiency.

Based on the perspective of external knowledge sources, the proposed mediating mechanism between the digital economy and industrial green innovation efficiency has not been established previously, further enriching the research on the antecedents and outcomes of external knowledge sources. Moreover, this study estimated the direct influence mechanism and double-threshold effect of the digital economy on industrial green innovation efficiency from theoretical and empirical analysis, thus responding to the call of scholars and adding to existing research on how the digital economy affects the green transformation of industrial enterprises.

(Digital) servitization, referring to service-driven strategies and their increasing implementation in manufacturing, is one of the most rapidly growing areas in industrial service research. However, the cultural change involved in successful servitization is a phenomenon that is widely observed but poorly understood. This research aims to clarify the processes of social construction as manufacturers change their organizational culture to transform into industrial service providers.

This research takes a systematic approach to integrate disparate literature on servitization into a cohesive framework for cultural change, which is purposefully augmented by rationale culled from organizational learning and sensemaking literature.

The organizational learning framework for cultural change in servitization introduces a dynamic perspective on servitizing organizations by explaining social processes between organizational and member-level cultural properties. It identifies three major cultural orientations toward service, digital and learning that govern successful servitization.

This research contributes to the servitization literature by presenting a new approach to reframe and explore cultural change processes across multiple levels, thus providing a concrete starting point for further research in this area.

Industrialization is a major contributor to pollution and the worsening of some social problems. A change in this context would help in a new industrial model aiming at a viable and sustainable manufacturing system. This research aims to verify the state of the art of sustainability within the industrial production process through a systematic literature review, verifying the main characteristics in relation to industrial sustainability that the literature demonstrates.

The development of the research took place in three stages: a survey of articles with Journal Citation Reports (JCR), the construction of the database and descriptive analysis and text mining analyses of social networks and content. The survey took place through academically endorsed research platforms, totaling a total of 352 scientific articles, which included 18 quality management tools and worked with at least one sustainability indicator (financial, social and environmental).

Lean manufacturing, integrated management system and Six Sigma were the most cited quality tools, and articles containing the three indicators were found more frequently. It was found that most authors treated sustainability only as an environmental contribution. Knowledge of the organization's structural and management issues is essential for implementing sustainability and production process improvement.

This work is the first to develop a systematic analysis regarding the use of sustainability implementation in the industrial production process, considering a wide scope of production process tools, guiding on the characteristics of sustainability relating to the main critical success factors (CSFs), motivations, difficulties and benefits that lead industries in different parts of the world to implement sustainability.

The current research has developed a novel method to update the decisions regarding real-time data, named the dynamic adjusted real-time decision-making (DARDEM), for updating the decisions of a grocery supply chain that avoids both frequent modifications of decisions and apathy. The DARDEM method is an integration of unsupervised machine learning and mathematical modeling. This study aims to propose a dynamic proposed a dynamic distribution structure and developed a bi-objective mixed-integer linear program to make distribution decisions along with supplier selection in the supply chain.

The constantly changing environment of the grocery supply chains shows the necessity for dynamic distribution systems. In addition, new disruptive technologies of Industry 4.0, such as the Internet of Things, provide real-time data availability. Under such conditions, updating decisions has a crucial impact on the continued success of the supply chains. Optimization models have traditionally relied on estimated average input parameters, making it challenging to incorporate real-time data into their framework.

The proposed dynamic distribution and DARDEM method are studied in an e-grocery supply chain to minimize the total cost and complexity of the supply chain simultaneously. The proposed dynamic structure outperforms traditional distribution structures in a grocery supply chain, particularly when there is higher demand dispersion. The study showed that the DARDEM solution, the online solution, achieved an average difference of 1.54% compared to the offline solution, the optimal solution obtained in the presence of complete information. Moreover, the proposed method reduced the number of changes in downstream and upstream decisions by 30.32% and 40%, respectively, compared to the shortsighted approach.

Introducing a dynamic distribution structure in the supply chain that can effectively manage the challenges posed by real-time demand data, providing a balance between distribution stability and flexibility. The research develops a bi-objective mixed-integer linear program to make distribution decisions and supplier selections in the supply chain simultaneously. This model helps minimize the total cost and complexity of the e-grocery supply chain, providing valuable insights into decision-making processes. Developing a novel method to determine the status of the supply chain and online decision-making in the supply chain based on real-time data, enhancing the adaptability of the system to changing conditions. Implementing and analyzing the proposed MILP model and the developed real-time decision-making method in a case study in a grocery supply chain.

In Morocco, as in many developing countries, environmental responsibility is not well integrated into corporate management at the operational, tactical, and strategic levels. While the management literature offers a rich body of knowledge on Corporate Social Responsibility (CSR) strategies and practices, less attention has been paid to exploring the complexity of environmental responsibility through the lens of corporate culture. This research aims to address this gap by examining the influence of cultural factors on the deployment of environmental responsibility using Johnson's (2000) model of corporate culture. This model identifies seven components of corporate culture: stories or myths, symbols, power structures, organizational structures, control systems, rituals and routines, and paradigms. Through a Moroccan industrial group case study, this chapter presents the successful deployment of environmental responsibility and describes how managing cultural factors facilitated this transition. This chapter also identifies the unique aspects of the group's culture that allowed redesigning the company's management systems. These insights offer valuable implications for managers and policymakers seeking to improve the environmental performance of large enterprises in developing countries.

This study investigates the influence of corporate income tax on two corporate financial decisions — dividend and capital structure policies, particularly for Shariah compliant companies in Malaysia.

The study considered data from a sample of 529 Malaysian listed companies from four industrial sectors from 2007–2021 (6,746 company-year observations, before eliminating outliers). Panel models such as Fixed Effect and Random effect models were used. The study specifically tested the effect of corporate income tax on dividend and capital structure policies for Shariah compliant companies (3,148 observations) and controlled for industrial sectors.

(1) Firms are mostly Shariah-compliant, less liquid, less profitable and smaller in size, (2) Broadly when analysed together, tax has no impact on debt-equity ratio while it has an impact on dividend per share, (3) However, when tested separately for Shariah compliant companies, the influence of effective tax on capital structure is very evident but not for dividend and (4) influence of industrial sector on the relationship between corporate tax and capital structure and dividend policy is significant. Results indicate that Shariah firms might be raising debt to gain tax advantage. Companies in general pay dividends to avoid reputational damage.

This study assumes that leverage and dividend policy decisions are the main outcomes of the changing tax policies, while it seems that there could be other important outcomes that can be tested in future research. The study also shows the changing tax regimes of different ASEAN countries but they have not been tested to see the differences between countries. It will be indeed interesting for future researchers to focus on this aspect.

The findings contribute to the literature on tax planning of the Shariah-compliant firms, a high growth business segment in the Asian context. The study discussed potential tax-based Islamic market product development.

This study aims to propose a refined dynamic network slacks-based measure (DNSBM) to evaluate the efficiency of China's regional green innovation system which consists of basic research, applied research and commercialization stages and explore the influencing factors of the stage efficiency.

A two-step procedure is employed. The first step proposes an improved DNSBM model with flexible settings of stages' input or output efficiency and uses second order cone programming (SOCP) to solve the non-linear problem. In the second step, least absolute shrinkage and selection operator (LASSO) and Tobit models are used to explore the influencing factors of the stage efficiency. Global Dynamic Malmquist Productivity Index (GDMPI) and Dagum Gini coefficient decomposition method are introduced for further discussion of the productivity change and regional differences.

On average, Chinese provincial green innovation efficiency should be improved by 24.11% to become efficient. The commercialization stage outperforms the stages of basic research and applied research. Comparisons between the proposed model and input-oriented, output-oriented and non-oriented DNSBM models show that the proposed model is more advanced because it allows some stages to have output-oriented model characteristics while the other stages have input-oriented model characteristics. The examination of the influencing factors reveals that the three stages of the green innovation system have quite diverse influencing factors. Further discussion reveals that Chinese green innovation productivity has increased by 39.85%, which is driven mainly by technology progress, and the increasing tendency of regional differences between northern and southern China should be paid attention to.

This study proposes an improved dynamic three-stage slacks-based measure (SBM) model that allows calculating output efficiency in some stages and input efficiency in the other stages with the application of SOCP approach. In order to capture productivity change, this study develops a GDMPI based on the DNSBM model. In practice, the efficiency of regional green innovation in China and the factors that influence each stage are examined.