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Drawing upon the needs–supplies fit theory, this study aims to examine how the expected and perceived algorithmic autonomy support (AAS) influences platform workers’ work stress and incivility, with a focus on the moderating role of self-direction.

Using data collected from 422 platform workers in China through multiple waves, the model is tested through polynomial regression and response surface analysis.

The results indicate that a mismatch between expected and perceived AAS is associated with higher levels of incivility among platform workers, and this relationship is mediated by work stress. These findings are particularly salient when self-direction is emphasized.

This study suggests that a universally high level of AAS may not necessarily reduce workers’ stress and incivility. Instead, it is important for platforms to ensure that their workers’ expectations of autonomy support are met and for workers to be given the space to exercise self-direction.

Previous studies have highlighted the need to pay attention to workers in mobile and ambiguous environments, and this study adds to this literature by focusing specifically on platform organizations and workplaces. This study provides valuable insights into the relationship between AAS, work stress and platform workers’ incivility.

The energy efficiency policy is a priority component of the overall economic policy of different countries striving to ensure the competitiveness and sustainability of national economic development. The improvement of energy efficiency represents an important economic task for the post-Soviet countries, characterized by excessive energy intensity of the economy, and the solution of this task requires proper information and analytical support: a system for accounting and analyzing energy consumption indicators. The present research is aimed at developing the tools to support decision-making in the sphere of evaluation and estimation of performance of the State energy efficiency policy of territories and testing these tools on the example of Russian regions. The study has been carried out using the methods of statistics, economic, mathematical and econometric modeling, structural, dynamic and comparative analyses. The following tools have been proposed: the method for differentiated accounting of various factors’ influence on the dynamics of energy consumption in the regions and for estimating the index of technological efficiency of electricity consumption; the method for the empirical classification of territories by types of their energy and economic development. We’ve revealed the general trend and typological features in the dynamics of electricity consumption efficiency indicators in the constituent entities of the Russian Federation and carried out the decomposition factor and comparative analysis of energy consumption patterns of the Volgograd region over 2005–2014 on the basis of the proposed tools.

The purpose of this paper is to benchmark the energy use of Indian iron and steel industry. For this purpose, the authors have estimated a production frontier to know the best performing states. Further, the energy-saving targets are estimated to lie below the benchmark level for those states. Panel data for this purpose are extracted from the Annual Survey of Industry (an official database from the government of India) for 19 major steel-producing states over the period from 2004–2005 to 2013–2014.

The authors employed a radial and non-radial (slack-based measure) variant of the data envelopment analysis (DEA) to estimate the production frontier. Particularly, slack-based measures (SBMs) developed by Tone (2001) are used to get a more comprehensive measure of energy efficiency along with technical efficiency. Variable returns to scale technology is specified to accommodate market imperfection and heterogeneity across states. Four inputs (capital, labour, energy and material) and a single output are conceptualised for the production process to accommodate input substitution. The relative position of each state in terms of the level of energy efficiency is then identified.

The authors started by examining energy-output ratio. The average level of energy intensity shows declining trends over the period of time. States like Bihar, Jharkhand, Gujarat and Uttarakhand remain stagnant in the energy intensity level. SBM of energy efficiency shows an overall average energy saving potential of 8 per cent without reducing average output level. Considerable heterogeneity exists among states in terms of the energy efficiency scores. Further, the authors calculated scale efficiency (SE) which shows the overall average level of SE is 0.91; hence, the scale of operation is not optimal and needs to adjusted to enhance energy efficiency.

The authors demonstrate the empirical application of DEA with SBM to energy use performance. This is the first study that benchmarks Indian states in terms of the consumption of energy input to produce iron and steel by applying DEA.

The purpose of this paper is to improve the accuracy of evaluation efficiency by constructing parallel structures considering the main components of industrial pollutants, and then to consider some external influence factors to eliminate random errors.

In this paper, data transformation has been used to deal with undesirable output, and a model with a parallel structure based on the three-stage data envelopment analysis model to calculate the efficiency scores of different division in pollution treatment has been composed.

The analysis shows that the external environmental factors and random factors of the economy and society greatly affect the efficiency of industrial pollutant treatment; moreover, there is an imbalance between regions in China in the treatment of industrial pollutants.

Optimal improvement requires each province to take targeted measures to improve its efficiency of pollutant treatment measures, which are tailored to specific situations and determined by efficiency analysis in this paper.

This chapter empirically investigates the main drivers of the circular economy (CE) and sustainable development (SD) of European countries. The European Union (EU) legislation imposes equal rules for the members who should be followed to achieve CE and SD. This chapter gives a critical overview of the related literature on this topic. The second part focuses on measuring the efficiency of EU countries in achieving CE and SD via a nonparametric approach. Furthermore, the results from the efficiency evaluation are used as a dependent variable in determining which economic, social, institutional, and other factors have the greatest influence on CE and SD achievements. The nonparametric approach consists of selected models of data envelopment analysis (DEA), as this is a methodology useful in constructing a ranking system based on selected criteria. The results indicate that on average, the most efficient countries were (besides Malta and Luxembourg) the Netherlands, Poland, Germany, Sweden, Denmark, France, and the United Kingdom. The worst performing ones were Cyprus, Spain, Greece, Belgium, Portugal, and Croatia. The second part of the research indicates that the resource production and corruption perception index has the greatest effect on the efficiency scores, followed by education attainment. The research and development (R&D) variable is not significant in the observed sample. Based on these results, specific policy recommendations are given at the end of this chapter.

The aim of this paper is to show current position of domestic airports and provide an improvement scheme through the comparative analysis of efficiency and social efficiency. We used SBM (Slack Based Measure) for efficiency and undesirable output model that is extended from SBM for social efficiency. In addition, window analysis is used for analyzing the trend of the values. For the scope of this study, we analyzed fourteen airports in Korea from 2004 to 2009. In the models, we considered the length of runway, the number of employees and terminal area as input factors, and the number of passengers, the amount of cargo and the number of flights as desirable outputs and directly controllable CO2 emissions from airports as undesirable output. The results show that all of the efficiencies are higher than the social efficiencies and both of them are decreasing by years. To improve social efficiency in 2009, the average amounts of reduced CO2 emissions which account for 48.3% of the total emissions are required.

The purpose of this paper is to investigate the individual effects of boundary-spanning search from suppliers (supplier-side search (SS)). It is proposed that SS contributes to innovation ambidexterity (IA) and then business performance (BP). Further, this paper includes buyer–supplier relationships (BSRs) and competitive intensity (CI) as moderators to clarify boundary conditions.

An ordinary least squares regression was employed to test hypotheses, based on 184 sets of data from Hong Kong manufacturing firms. The SPSS version of PROCESS was utilized.

The results show that IA partially mediates the relationship between SS and BP. Contingently, the direct effect is negatively moderated by BSRs and CI.

This paper confirms the partial mediating effect of IA on the relationship between SS and BP. Additional mediators, such as organizational innovation and marketing innovation, can be examined in the future.

This paper contributes to practice by suggesting that suppliers are a rewarding single source for firms to undertake boundary-spanning search. IA plays a significant role in reinforcing the effects of SS on BP and should be implemented with sustaining efforts. BSRs and CI can be detrimental and should be responded to cautiously.

This paper highlights the individual effects of SS on BP. Furthermore, the underlying process and boundary conditions are analyzed. The exploitation-exploration framework is applied throughout the entire study.

The chapter provides a comprehensive analysis of the interplay between organizational socioecology, green technological innovation, and environmental regulations. It emphasizes the significance of organizational strategies in enhancing performance, particularly in contexts where environmental sustainability is a priority. The research delves into the theory of organizational socioecology, suggesting a convergence with sociological perspectives in organizational research. This approach underscores the interdependence between organizations and society, especially in the realm of environmental responsibility and climate change. A key aspect of the study is the exploration of green technological innovation in product and service development, aiming to reduce environmental impact. The dynamics of adopting green innovation are influenced by numerous factors, including government policies, market conditions, and organizational characteristics. The chapter examines the impact of environmental regulations on organizational behavior and innovation, discussing how these regulations can drive organizations towards green innovation, thus balancing the need for economic growth with environmental sustainability. Furthermore, the chapter addresses the role of government subsidies and incentives in encouraging organizations to adopt green technologies and practices. The effectiveness of these mechanisms in fostering a more sustainable and innovative organizational landscape is analyzed. Additionally, the article provides a comparative analysis of various theories and models related to organizational innovation and sustainability, integrating insights from different disciplinary perspectives. By combining empirical data with theoretical frameworks, the article assesses the effectiveness of organizational strategies in enhancing green innovation and meeting environmental regulations. It offers practical implications for organizations striving to align their practices with sustainability goals, contributing valuable insights for researchers, policymakers, and practitioners in the field of sustainability and organizational change.

Using the most recent dataset from 2013–2014 to 2017–2018, the study examines the efficiency of 75 coal-fired power plants in the Indian thermal power sector. The authors obtained robust estimates of efficiency scores by employing Seiford and Zhu’s (2002) DEA-based classification invariance technique to account for CO₂ emissions as an undesirable output. Meta-frontier analysis and the Tobit regression are used to compute technology heterogeneity across power plants belonging to public and private groups and investigate the factors driving carbon-adjusted efficiency, respectively. The results reveal that, on average, the efficiency of power plants during the study period is 78.26%, showing significant room for reduction in CO₂ emissions alongside augmentation in electricity generation. Private plants are more efficient than public ones, and relative performance inefficiency is the primary source of inefficiency in the thermal power sector. Regression analysis indicates that domestic-equipped plants perform with lesser levels of efficiency, and plants with more units are more inefficient than plants with fewer units. Carbon productivity significantly improves efficiency since fewer fossil fuels with high carbon will generate more electricity.

The purpose of this paper is to generate quantitative managerial insights for the improvement of the energy-saving potential and the coordinated development between economic growth and environmental protection.

A novel data envelopment analysis (DEA) model, based on the classical DEA theory, is developed from the perspective of emission reduction.

The empirical results indicate that China’s overall environmental efficiency is low and that there is huge improvement space for energy saving. Under the concerns of emission reduction, the energy-saving potential of the central region exceeds that of both the eastern and western regions. With regard to water, electricity and gas consumption, the electricity-saving potential exceeds the potential for both water saving and gas saving.

Previous studies rarely focused on the energy-saving potential, while considering environmental pollution. This paper applies a novel DEA method to evaluate the energy-saving potential of 30 Chinese provinces for 2015 with a focus on emission reduction concerns. Furthermore, both regional differences and energy type differences of the saving potential were analyzed.