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Climate change-related weather events significantly affect rice production. In this paper, we investigate the impact of and interrelationships between agriculture inputs, climate change factors and financial variables on rice production in India from 1970–2021.

This study is based on the time series analysis; the unit root test has been employed to unveil the integration order. Further, the study used various econometric techniques, including vector autoregression estimates (VAR), cointegration test, autoregressive distributed lag (ARDL) model and diagnostic test for ARDL, fully modified least squares (FMOLS), canonical cointegrating regression (CCR), impulse response functions (IRF) and the variance decomposition method (VDM) to validate the long- and short-term impacts of climate change on rice production in India of the scrutinized variables.

The study's findings revealed that the rice area, precipitation and maximum temperature have a significant and positive impact on rice production in the short run. In the long run, rice area (ß = 1.162), pesticide consumption (ß = 0.089) and domestic credit to private sector (ß = 0.068) have a positive and significant impact on rice production. The results show that minimum temperature and direct institutional credit for agriculture have a significant but negative impact on rice production in the short run. Minimum temperature, pesticide consumption, domestic credit to the private sector and direct institutional credit for agriculture have a negative and significant impact on rice production in the long run.

The present study makes valuable and original contributions to the literature by examining the short- and long-term impacts of climate change on rice production in India over 1970–2021. To the best of the authors’ knowledge, The majority of the studies examined the impact of climate change on rice production with the consideration of only “mean temperature” as one of the climatic variables, while in the present study, the authors have considered both minimum as well as maximum temperature. Furthermore, the authors also considered the financial variables in the model.

While extant literature explores the influence of institutions on the national innovation system (NIS), most research has either focused on specific institutional aspects or treated institutions as a unified entity. This study aims to examine the effect of various institutional factors on a country’s NIS.

The conceptual model was empirically validated using regression analysis. The study sample comprised a total of 84 countries.

This study identifies and empirically validates a comprehensive set of institutional factors. It also highlights the significant institutional factors (including political stability, government effectiveness, ease of resolving insolvency and the rule of law) that can help improve a country’s NIS.

The research provides practical implications for organizations and policymakers seeking to understand and foster an innovative culture within the NIS. Policymakers are encouraged to develop a nurturing environment within the NIS by focusing on significant institutional factors. Organizations are encouraged to closely monitor developments in the NIS of a country to make informed strategic decisions at the business, corporate and international levels.

This study aims to highlight the impact of introducing women directors to board committees, thereby empowering them to contribute to decision-making, and as a result, influence firms’ financial performance in an emerging economy.

This study uses a fixed-effects panel data regression model to test the impact of gender diversity on corporate boards as well as board committees on firms’ financial performance. Two widely used diversity measures, the Blau index and the Shannon index, have been used to enhance the robustness of the results.

The findings suggest that gender diversity on prominent board committees (remuneration committee and nomination committee) positively affects firms’ financial performance when measured by the market-based performance measure, but it is insignificant when measured through accounting-based performance indicator. Furthermore, the benefits of gender diversity accrue to the firms only when women are part of prominent committees and are engaged in governance mechanisms, rather than just being appointed on corporate boards as a means of tokenism.

This study is among the first to investigate the relationship between gender diversity and financial performance through the lens of committee assignments. Moreover, the unique cultural and institutional setting offered by India, which is an emerging economy, provides a fertile ground for understanding the role of women leaders in the workforce.

The study aims to analyze the impact of market participation on small paddy farmers' income and consumption expenditure. The study also estimates various determinants affecting the market participation of smallholders. Further, the study computes the efficiency of different paddy marketing channels and identifies the determinants that impact the marketing channel selection of paddy growers in Eastern India.

The study used the propensity score matching (PSM) approach to measure the impact of market participation on farm income and per capita consumption. Further, the study employed Acharya and Aggarwal's composite index approach to estimate the marketing efficiency of various paddy marketing channels. Further, a multinomial logit model was used to determine the marketing channel selection constraints.

The outcomes indicate that market participation positively impacts farm income and consumption expenditure. Education, membership in farmers' organizations, price information and distance to the marketplace significantly affect farmers' market participation. The results show that the producer–retailer marketing channel is the most efficient compared to others. However, most paddy farmers sell paddy to farmgate collectors due to a lack of market information, vehicle ownership, storage system, and inability to take the risk of venturing out of the farmgate into markets.

The study uses primary data and captures only farmers' perspectives to measure the impact of market participation, marketing channel efficiency and determinants for market channel selection. The other stakeholder's perceptions can be included in future studies.

Rarely does any study identifies the efficiency of different marketing channels for paddy farmers in India and includes cognitive factors like risk perception and trust in buyers as constraints for market channel selection.

This study aims to explore the different effects of inter-community group networks and intra-community group networks on group innovation.

The authors used a pooled panel dataset of 12,111 self-organizing innovation groups in 463 game product creative workshop communities from Steam support to test the hypothesis. The pooled ordinary least squares (OLS) model is used for analyzing the data.

The results show that network constraint is negatively associated with the innovation performance of online groups. The average path length of the inter-community group network negatively moderates the relationship between network constraint and group innovation, while the average path length of the intra-community group network positively moderates the relationship between network constraint and group innovation. In addition, both the network density of inter-community group networks and intra-community group networks can negatively moderate the negative relationship between network constraint and group innovation.

The findings of this study suggest that network structural characteristics of inter-community networks and intra-community networks have different effects on online groups’ product innovation, and therefore, group members should consider their inter- and intra-community connections when choosing other groups to form a collaborative innovation relationship.

This study aims to identify and prioritize the key practices and strategies for effective global sourcing and supply chain management (SCM).

The study uses a combination of Pareto analysis and multi-objective optimization based on ratio analysis research methodology to analyze and establish the relationships among the identified key practices and strategies. Pareto analysis enables organization to prioritize organizational efforts and resources by focusing on the most critical factors.

The study shows that the “eco-friendly sourcing strategy”, “lean manufacturing” and “tool cost analysis” are the top critical practices and strategy variables for global sourcing and SCM, whereas the “risk management”, “procurement strategy” and “leverage digital solutions” are the critical practices and strategy variables.

The findings of this research can also assist organizations in making informed decisions to optimize their global sourcing and supply chain operations.

By using these methods, this research paper gives valuable insights into the critical practices and strategies that can enhance efficiency, mitigate risks and drive success in global sourcing and SCM. The subjects and elements this study identified will serve as a framework and suggestions for further theoretical investigation and real-world implementations.

This paper aims to introduce a novel structure for the physical internet (PI)–enabled sustainable supplier selection and inventory management problem under uncertain environments.

To address hybrid uncertainty both in the objective function and constraints, a novel interactive hybrid multi-objective optimization solution approach combining Me-based fuzzy possibilistic programming and interval programming approaches is tailored.

Various numerical experiments are introduced to validate the feasibility of the established model and the proposed solution method.

Due to its interconnectedness, the PI has the opportunity to support firms in addressing sustainability challenges and reducing initial impact. The sustainable supplier selection and inventory management have become critical operational challenges in PI-enabled supply chain problems. This is the first attempt on this issue, which uses the presented novel interactive possibilistic programming method.

Social media has played a pivotal role in polarizing views on Russia–Ukraine conflict. The effects of polarization in online interactions have been extensively studied in many contexts. This research aims to examine how multiple social media sources may act as an integrator of information and act as a platform for depolarizing behaviors.

This study analyzes the communications of 6,662 tweets related to the sanctions imposed on Russia by using textual analytics and predictive modeling.

The research findings reveal that the tweeting behavior of netizens was depolarized because of information from multiple social media sources. However, the influx of information from non-organizational sources such as trending topics and discussions has a depolarizing impact on the user’s pre-established attitude.

For policymakers, conflict mediators and observers, and members of society in general, there is a need for (1) continuous and consistent communication throughout the crisis, (2) transparency in the information being communicated and (3) public awareness of the polarized and conflicting information being provided from multiple actors that may be biased in the claims being made about the conflict crisis.

While previous research has examined Russia–Ukraine conflict from a variety of perspectives, this is the first study to examine how social media might be used to reduce attitude polarization during times of conflict.

The purpose of this study is to present the systematic literature review (SLR) on Lean Six Sigma (LSS) by exploring the state of the art on growth of literature on LSS within the manufacturing sector, critical factors to implement LSS, the role of LSS in the manufacturing sector from an implementation and sustainability viewpoint and Industry 4.0 viewpoints while highlighting the research gaps.

An SLR of 2,876 published articles extracted from Scopus, WoS, Emerald Insight, IEEE Xplore, Taylor & Francis, Springer and Inderscience databases was carried out following the protocol of systematic review. In total, 154 articles published in different journals over the past 10 years were selected for quantitative and qualitative analysis which revealed a number of research gaps.

The findings of the SLR revealed the growth of literature on LSS within the manufacturing sector. The review also highlighted the most cited critical success factors, critical failure factors, performance indicators and associated tools and techniques applied during LSS implementation. The review also focused on studies related to LSS and sustainability viewpoint and LSS and Industry 4.0 viewpoints.

The findings of this SLR can help senior managers, practitioners and researchers to understand the current developments and future requirements to adopt LSS in manufacturing sectors from sustainability and Industry 4.0 viewpoints.

Academic publications in the context of the role of LSS in various research streams are sparse, and to the best of the authors’ knowledge, this paper is one of the first SLRs which explore current developments and future requirements to implement LSS from sustainability and Industry 4.0 perspective.

The objective of this research is to put forward a novel closed-loop circular economy (CE) approach to forecast the sustainability of supply chains (SCs). We provide a practical and real-world CE framework to improve and fill the current knowledge gap in evaluating sustainability of SCs. Besides, we aim to propose a real-life managerial forecasting approach to alert the decision-makers on the future unsustainability of SCs.

It is needed to develop an integrated mathematical model to deal with the complexity of sustainability and CE criteria. To address this necessity, for the first time, network data envelopment analysis (NDEA) is incorporated into the dynamic data envelopment analysis (DEA) and artificial neural network (ANN). In general, methodologically, the paper uses a novel hybrid decision-making approach based on a combination of dynamic and network DEA and ANN models to evaluate sustainability of supply chains using environmental, social, and economic criteria based on real life data and experiences of knowledge-based companies so that the study has a good adaptation with the scope of the journal.

A practical CE evaluation framework is proposed by incorporating recyclable undesirable outputs into the models and developing a new hybrid “dynamic NDEA” and “ANN” model. Using ANN, the sustainability trend of supply chains for future periods is forecasted, and the benchmarks are proposed. We deal with the undesirable recycling outputs, inputs, desirable outputs and carry-overs simultaneously.

We propose a novel hybrid dynamic NDEA and ANN approach for forecasting the sustainability of SCs. To do so, for the first time, we incorporate a practical CE concept into the NDEA. Applying the hybrid framework provides us a new ranking approach based on the sustainability trend of SCs, so that we can forecast unsustainable supply chains and recommend preventive solutions (benchmarks) to avoid future losses. A practicable case study is given to demonstrate the real-life applications of the proposed method.