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Recently, a shift regarding the negative consequences of organizational citizenship behaviors (OCBs) has put them forward as employees’ negative aspects carrying dangerous consequences for organizations. Considering this issue’s seriousness, the purpose of this paper is to examine the process through which compulsory citizenship behavior fosters citizenship fatigue.

A total of 370 nurses working in Pakistani public-sector hospitals were this study’s final data set sample using SmartPLS4.

Partial least squares structural equation modeling (PLS-SEM) results revealed that when employees are compelled to engage in extra-role actions, they frequently experience work-life conflict, which results in citizenship fatigue. The higher the employee’s age and the lower the education level, the lower his/her citizenship fatigue. On the other hand, findings revealed that workaholic personality aspects tend to reduce the strength of the relationship between work-life conflict and citizenship fatigue.

To the best of the authors’ knowledge, this paper is the first attempt to examine the recently emerged concept of citizenship fatigue among health-care professionals through conservation of resources theory. Besides, this research will highlight how the demand for voluntary actions in routine or forced citizenship behavior can become the reason for work–family conflict and ultimately create citizenship fatigue. Additionally, this paper presents the novel concept of workaholic personality and how it can play a positive role in the linkage between work–family conflict and citizenship fatigue.

Big data analytics capabilities are the driving force and deemed as an operational excellence approach to improving the green supply chain performance in the post COVID-19 situation. Motivated by the COVID-19 epidemic and the problems it poses to the supply chain's long-term viability, this study used dynamic capabilities theory as a foundation to assess the imperative role of big data analytics capabilities (management, talent and technological) toward green supply chain performance.

This study was quantitative and cross-sectional. Data were collected from 374 executives through a survey questionnaire method by applying an appropriate random sampling technique. The authors employed PLS-SEM to analyze the data.

The findings revealed that big data analytics capabilities play a significant role in boosting up sustainable supply chain performance. It was found that big data analytics capabilities significantly contributed to supply chain risk management and innovative green product development that ultimately enhanced innovation and learning performance. Moreover, innovation and green learning performance has a significant and positive relationship with sustainable supply chain performance. In the post COVID-19 situation, organizations can enhance their sustainable supply chain performance by giving extra attention to big data analytics capabilities and supply chain risk and innovativeness.

The paper specifically emphasizes on the factors that result in the sustainability in supply chain integrated with the big data analytics. Additionally, it offers the boundary condition for gaining the sustainable supply chain management.

The purpose of this paper is to analyze the effect of economic growth, international trade and energy consumption on the global carbon dioxide (CO₂) emissions, in the case of top CO₂ emitters, namely, USA, Japan, Canada, Iran, Saudi Arabia, UK, Australia, Italy, France and Spain using the annual data from 1971 to 2013.

For this purpose, the time series, data technique is applied. Unit root test with structural break and the bounds testing approach for cointegration in the presence of structural break is tested. Finally, a vector error correction model for the Granger causality test is applied to detect the direction of causality. The authors have used the techniques that will help in examining the structural break in the time series data.

The results reveal that their exists a long-run relationship between CO₂ emissions and its determinants in the USA, Canada, Iran, Saudi Arabia, the UK, Australia, Italy, France and Spain, energy consumption is the main determinant of carbon dioxide (CO₂) emissions in the long run and for direction of causality, the authors found bidirectional causality in the long run between energy consumption and CO₂ emissions in the USA, Canada, Iran, Saudi Arabia and the UK, and Granger causality running in opposite direction in the case of Australia from CO₂ emissions to energy consumption was analyzed. In terms of growth-trade-pollution nexus (USA, Canada, Iran and France) hold one-way causality running from economic growth and trade openness to CO₂ emissions (IV) the environmental Kuznets curve hypothesis is validated only for the USA. Robust policy implications can be derived from this study. First, without harming the economy, these countries can reduce the use of energy consumption for lower pollution. Second, the amount of trade should be decreased to lower the emissions because the authors find that an increase in trade does Granger cause to CO₂ emissions in the long run.

There has been no study that investigated the relationship between CO₂ emissions, real income, consumption of energy and international trade in the environmental Kuznets relation for the top CO₂ emitter’s countries over the period of 1971–2013. The authors did a comparative study of the empirical finding among these nations.

The purpose of this paper is investigate the dynamic linkages among industrial energy use, industrial value added, financial development (FD) and trade openness, in case of India. The study covers the annual frequency data on both aggregate and disaggregate variables for the period 1971–2016.

The autoregressive distributed lag bounds testing approach is applied to examine the long-run relation among variables under consideration. Also, Johansen and Juselius (1990) and vector error-correction mechanism results confirm the result of cointegration. Furthermore, non-linear relationship in the model is also tested.

It has been found that there exists long-run relationship among variables. Long-run estimates show that increasing FD leads to more energy uses. Hence, FD should be directed in such a way that it incentivises firms to invest in energy-efficient technology. Furthermore, it is also found that study supports the evidence of conservative hypothesis, which supports that the energy conservation policy should be adopted in the industrial sector. Energy efficiency programme needs to be designed very carefully to achieve a higher level of energy efficiency. This leads to a sustainable growth and low carbon emission.

This paper examines the recent trend in Indian industrial energy consumption and does a comprehensive analysis using a robust econometric method. We have developed a lucid model to examine the deriving factors of industrial energy consumption.

Covid-19 is a serious threat to the dietary quality of vulnerable communities in developing countries with limited economic resources. This study explored the implications of COVID-19 on daily dietary behavior and food consumption patterns of perishable and nonperishable food commodities based on the gender of household headship in Pakistan.

The data was collected through an online survey using the snowball sampling technique. A mixed design ANOVA, an independent t-test and a multivariate probit model were used to look at the data from 417 female-headed households and 1,131 male-headed households that were surveyed in the study.

Female-headed households experienced a greater decrease in their daily intake of perishable and nonperishable food commodities compared to male-headed households during COVID-19. The decrease in consumption of perishable food commodities was greater than the decrease in nonperishable food commodities for both female-headed and male-headed households. Female-headed households witnessed the largest decrease in daily intake of animal-derived foods, while male-headed households observed the highest diminution in consumption of fruits. Female-headed households and male-headed households reduced their consumption of perishable food commodities by more than one-third and one-fifth, respectively. Households with lower socioeconomic status reported higher reductions in their daily intakes of food commodities compared to households with higher socioeconomic status.

The cross-sectional nature of the collected data does not allow the development of a causal relationship between COVID-19 implications and food consumption changes in daily dietary patterns.

Dietary and consumption patterns of populations are changing worldwide due to COVID-19. There is no study to assist policymakers in determining how COVID-19 is affecting the daily food consumption patterns of perishable and non-perishable food commodities of households based on gender in the developing world.

This study aims to investigate in a country-specific comparative and panel form, the impact of energy use on financial development in Organisation of Petroleum Exporting Countries (OPEC)-African countries of Algeria, Gabon, Libya and Nigeria.

With data sets covering the period 1980 to 2020, this study used a combination of country-specific autoregressive distributed lag model (ARDL) and panel-ARDL as well geo-maps to show the spatiotemporal nuances of the investigated countries.

It was discovered across the investigated countries and in the panel framework that energy consumption significantly impacts both bank development and institutional development, which are subsets of financial development. In addition, evidence in favor of adjustment of financial development to the shocks and dynamics of energy consumption was found.

Integrative developmental drive for the two sectors can enhance growth and value-chain interactions for the imperatives of the overall growth and development of the OPEC-African countries.

This study adds to the literature on finance and energy development by the introduction of the spatiotemporal analysis.

This study aims to examine the asymmetric effects of energy efficiency on employment in India. Instead of relying on partial factor energy efficiency measures, this study uses a total factor energy efficiency (TFE) measure to estimate sector-specific energy efficiency for empirical investigation.

Multi-sectoral panel data for India from 2000 to 2014 are considered for empirical estimation. The sector-specific energy efficiency estimates (using the TFE measure) are estimated in the initial stage using the stochastic frontier approach (SFA). Then the asymmetric effect of energy efficiency on employment is investigated by using a non-linear panel autoregressive distributed lag model.

The estimates of energy efficiency display that there is not much significant change in the trend of average energy efficiency over the period. The negative and statistically significant value of the error-correction term confirms the existence of asymmetric cointegrating relationship between energy efficiency and employment in India. Moreover, in the empirical findings, the positive and negative shocks in energy efficiency provide a long-run asymmetric and short-run symmetric effect on employment in India.

Rather than depending on the absolute measure of energy efficiency (energy to output ratio), this study estimates the sector-specific energy efficiency for India using panel SFA, which provides a relative measure of energy efficiency. Moreover, to the best of the authors’ knowledge, it is the first empirical study investigating the asymmetric impact of energy efficiency on employment at an aggregate level in developing countries like India. By contrast, previous studies have either concentrated on the symmetric effect of energy efficiency on employment or primarily restricted to developed countries.

The aim of this study is to investigate how green supply chain management (GSCM) practices mediate the effect of Industry 4.0 technologies on operational and green innovation performances.

To explore the study, data were collected from 225 different manufacturing industries in Pakistan. Gathered data were used to test the hypotheses using SmartPLS 3 software by using structural equation modeling.

The findings reveal that operational and green innovation performances are directly affected by the adoption of Industry 4.0 technologies and GSCM practices. Furthermore, the GSCM practices positively affect operational and green innovation performances. The study also investigated that the GSCM practices partially mediate the effect of Industry 4.0 on operational and green innovation performances.

This study has some limitations, the data of this study were majorly collected from large enterprises of Pakistani firms and related to the manufacturing sector only. So, there is a huge need for attention toward small and medium-sized enterprises (SMEs). Very few researchers are focusing on SMEs, so future research can be on SMEs. It can be suggested that the relationship between digital technologies and green innovation performance can be tested through a quantitative procedure. Moreover, the effect of GSCM's aspects can be estimated on manageable execution.

Through the mediating relationship of GSCM practices, this research has made a unique contribution by investigating the influence of Industry 4.0 on operational and green innovation performances. To the author's knowledge, no research has been undertaken in this area.

Big Data can be utilised for efficient use of resources and to provide better services to the resident in order to enhance the delivery of urban services and create sustainable build environment. However, the adoption of Big Data faces many challenges at the implementation level. Therefore, the purpose of this paper is to identify the challenges towards the efficient application of Big Data in smart cities development and analyse the inter-relationships.

The 14 Big Data challenges are identified through the literature review and validated with the expert’s feedback. After that the inter-relationships among the identified challenges are developed using an integrated approach of fuzzy Interpretive Structural Modelling (fuzzy-ISM) and fuzzy Decision-Making Trial and Evaluation Laboratory (fuzzy-DEMATEL).

Evaluation of interrelationships among the challenges suggests that diverse population in smart cities and lack of infrastructure are the significant challenges that impede the integration of Big Data in the development of smart cities.

This study will enable practitioners, policy planners involved in smart city projects in tackling the challenges in an optimised manner for the hindrance free and accelerated development of smart cities.

This research is an initial effort to develop an interpretive structural model of Big Data challenges for smart cities development which gives a clearer picture of how the identified challenges interact with each other.