Rama Charan Tripathi, Vaibhav Dwivedi and Rashmi Kumar
This study aims to understand factors that explain the use of revenge and forgiveness by Hindu and Muslim group members in reaction to the rival group’s negative reciprocal…
Abstract
Purpose
This study aims to understand factors that explain the use of revenge and forgiveness by Hindu and Muslim group members in reaction to the rival group’s negative reciprocal behaviour based on norms of negative reciprocity.
Design/methodology/approach
Participants from Hindu (n = 175) and Muslim (n = 134) groups in India were presented with two norm-violating situations. Situation 1 involved an intergroup episode and Situation 2 involved an inter-community episode. Their own group members had engaged in the violation of the norms of the other group to which the rival group members had responded negatively. Participants anticipated the likelihood of their group members using revenge or forgiveness in response to the other group’s negative reaction. These reactions were predicted by religious, political and cultural identities, fraternalistic relative deprivation (FRD), relative power, anger and hate, and perception of the appropriateness of their reaction.
Findings
Social identities predicted intergroup revenge and forgiveness differently for the two groups in the two situations. The stronger religious identity of Muslims, not of Hindus, reduced the likelihood of their using revenge but increased it for forgiveness in both situations. Political identity associated positively with forgiveness in Situation 2 for both groups. Cultural identity predicted the likelihood of Muslims opting for forgiveness in both situations. FRD was not a significant predictor of revenge or forgiveness for Muslims. In the case of Hindus, it reduced the likelihood of their engaging in forgiveness in Situation 2. Relative power associated positively with the likelihood of Muslims, not Hindus, using revenge in both situations. Anger increased the possibility of Hindus reacting in revenge, as well as, forgiveness in the two situations. Anger did not predict revenge for Muslims but it related negatively with forgiveness in the two situations. Stronger hate was associated with revenge for Muslims. The choice of using revenge or forgiveness by own group members was positively predicted by the norms of negative reciprocity for both Hindus and Muslims.
Research limitations/implications
The study used a convenience sample of young people which reduces the generalizability of the findings.
Social implications
The findings of this study have implications for designing interventions for resolving intergroup conflicts in various social settings.
Originality/value
The paper adds to the norm violation theory of intergroup relations by focusing on counter-reactions and the understanding of the dynamics of intergroup conflicts.
Details
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Kirti Nayal, Rakesh Raut, Pragati Priyadarshinee, Balkrishna Eknath Narkhede, Yigit Kazancoglu and Vaibhav Narwane
In India, artificial intelligence (AI) application in supply chain management (SCM) is still in a stage of infancy. Therefore, this article aims to study the factors affecting…
Abstract
Purpose
In India, artificial intelligence (AI) application in supply chain management (SCM) is still in a stage of infancy. Therefore, this article aims to study the factors affecting artificial intelligence adoption and validate AI’s influence on supply chain risk mitigation (SCRM).
Design/methodology/approach
This study explores the effect of factors based on the technology, organization and environment (TOE) framework and three other factors, including supply chain integration (SCI), information sharing (IS) and process factors (PF) on AI adoption. Data for the survey were collected from 297 respondents from Indian agro-industries, and structural equation modeling (SEM) was used for testing the proposed hypotheses.
Findings
This study’s findings show that process factors, information sharing, and supply chain integration (SCI) play an essential role in influencing AI adoption, and AI positively influences SCRM. The technological, organizational and environmental factors have a nonsignificant negative relation with artificial intelligence.
Originality/value
This study provides an insight to researchers, academicians, policymakers, innovative project handlers, technology service providers, and managers to better understand the role of AI adoption and the importance of AI in mitigating supply chain risks caused by disruptions like the COVID-19 pandemic.
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Shashank Kumar, Rakesh D. Raut, Vaibhav S. Narwane, Balkrishna E. Narkhede and Kamalakanta Muduli
In the digitalization era, supply chain processes and activities have changed entirely, and smart technology impacts each sustainable supply chain movement. The warehouse and…
Abstract
Purpose
In the digitalization era, supply chain processes and activities have changed entirely, and smart technology impacts each sustainable supply chain movement. The warehouse and distribution of various organizations have started adopting smart technologies globally. However, the adoption of smart technologies in the Indian warehousing industry is minimal. The study aims to identify the implementation barriers of smart technology in the Indian warehouse to achieve sustainability.
Design/methodology/approach
This study employs an integrated Delphi-ISM-ANP research approach. The study uses the Delphi approach to finalize the barriers identified from the detailed literature review and expert opinion. The finalized 17 barriers are modeled using interpretive structural modeling (ISM) to get the contextual relationship. The ISM method's output and analysis using the analytical network process (ANP) illustrate priorities.
Findings
The study's findings showed that the lack of government support, lack of vision and mission and the lack of skilled manpower are the most significant barriers restricting the organization from implementing smart and sustainable supply chain practices in the warehouse.
Practical implications
This study would help the practitioners enable the sustainable warehousing system or convert the existing warehouse into a smart and sustainable warehouse by developing an appropriate strategy. This study would also help reduce the impact of different barriers that would strengthen the chance of technology adoption in the warehouses.
Originality/value
The literature related to adopting smart and sustainable practices in the warehouse is scarce. Modeling of adoption barrier for smart and sustainable warehouse using an integrated research approach is the uniqueness of this study that have added value in the existing scientific knowledge.
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Parveen Kumar, Pankaj Kumar and Vaibhav Aggarwal
This study aims to examine the determinants of adoption intention toward the rooftop solar photovoltaic (RSPV) systems among residents of peri-urban villages of Gurugram, Haryana…
Abstract
Purpose
This study aims to examine the determinants of adoption intention toward the rooftop solar photovoltaic (RSPV) systems among residents of peri-urban villages of Gurugram, Haryana, India. This study also analyzes the impact of the adoption of RSPV systems on carbon neutrality from a behavioral perspective.
Design/methodology/approach
Data was collected using a self-administrated structured questionnaire from 208 male villagers (195 usable) of 22 villages using the purposive sampling technique.
Findings
Results revealed that relative advantage, followed by simplicity, trialability, observability and compatibility, positively and significantly impact villagers’ attitude toward adopting RSPV systems in their homes. Perceived severity and perceived vulnerability significantly influence the perceived behavioral control of villagers toward adopting the RSPV systems. The results show villagers’ attitudes, subjective norms and perceived behavioral control are the essential predictors of their adoption intention of the RSPV systems. Most notably, carbon neutrality was significantly affected by villagers’ adoption intention of RSPV systems as the renewable energy source in their homes.
Originality/value
The findings of this study provide that innovation attributes are important factors in shaping the adoption intentions of customers toward RSPV systems. This study is also the extent of previous studies measuring customers’ perception of adopting renewable energy in developed and emerging countries worldwide.
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Vaibhav Sidraya Ganachari, Uday Chate, Laxman Waghmode, Prashant Jadhav and Satish Mullya
Many engineering applications in this era require new age materials; however, some classic alloys like spring steel are still used in critical applications such as aerospace…
Abstract
Purpose
Many engineering applications in this era require new age materials; however, some classic alloys like spring steel are still used in critical applications such as aerospace, defense and automobile. To machine spring steel material, there exist various difficulties such as rapid tool wear rate, the rough surface formation of a workpiece and higher power consumption. The purpose of this paper is to address these issues, various approaches in addition to electrical discharge machines (EDM) are used such as dry EDM (DEDM) and near dry EDM (NDEDM).
Design/methodology/approach
This study focuses on these two approaches and their comparative analysis with respect to tool wear during machining of spring steel material. For this study, current, gap voltage, cycle time and dielectric medium pressure are considered input variables. This study shows that the near dry EDM approach yields better results. Hence, the thermo-electrical model for this approach is developed using ANSYS workbench, which is further validated by comparing with experimental results. This thermo-electrical model covers spark radius variation and formation of temperature profile due to electric discharge. Transient thermal analysis is used to simulate the electric discharge machining.
Findings
It is observed from this study that discharge environment parameters such as debris concentration and fluid viscosity largely influences the dielectric fluid pressure value. Experimental results revealed that NDEDM yields better results in comparison with DEDM as it shows a 25% lesser tool wear rate in NDEDM.
Originality/value
The range of predicted results and the experimental results are in close agreement, authenticating the model.
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Shailendra Singh Chauhan, Vaibhav Singh, Gauranshu Saini, Nitin Kaushik, Vishal Pandey and Anuj Chaudhary
The growing environmental awareness all through the world has motivated a standard change toward planning and designing better materials having good performance, which are very…
Abstract
Purpose
The growing environmental awareness all through the world has motivated a standard change toward planning and designing better materials having good performance, which are very much suited to the environmental factors. The purpose of this study is to investigate the impact on mechanical, thermal and water absorption properties of sawdust-based composites reinforced by epoxy, and the amount of sawdust in each form.
Design/methodology/approach
Manufacturing of the sawdust reinforced epoxy composites is the main area of the research for promoting the green composite by having good mechanical properties, biodegradability or many applications. Throughout this research work, the authors emphasize the importance of explaining the methodology for the evaluation of the mechanical and water absorption properties of the sawdust reinforced epoxy composites used by researchers.
Findings
In this paper, a comprehensive review of the mechanical properties of sawdust reinforced epoxy composite is presented. This study is reported about the use of different Wt.% of sawdust composites prepared by different processes and their mechanical, thermal and water absorption properties. It is studied that after optimum filler percentage, mechanical, thermal properties gradually decrease, but water absorption property increases with Wt.% of sawdust. The changes in the microstructure are studied by using scanning electron microscopy.
Originality/value
The novelty of this study lies in its use of a systematic approach that offers a perspective on choosing suitable processing parameters for the fabrication of composite materials for persons from both industry and academia. A study of sawdust reinforced epoxy composites guides new researchers in the fabrication and characterization of the materials.
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Dhanraj P. Tambuskar, Prashant Jain and Vaibhav S. Narwane
With big data (BD), traditional supply chain is shifting to digital supply chain. This study aims to address the issues and challenges in the way toward the implementation of big…
Abstract
Purpose
With big data (BD), traditional supply chain is shifting to digital supply chain. This study aims to address the issues and challenges in the way toward the implementation of big data analytics (BDA) in sustainable supply chain management (SSCM).
Design/methodology/approach
The factors that affect the implementation of BDA in SSCM are identified through a widespread literature review. The PESTEL framework is used for this purpose as it covers all the political, economic, social, technological, environmental and legal factors. These factors are then finalized by means of experts' opinion and analyzed using structural equation modeling (SEM).
Findings
A total of 10 factors are finalized with 31 sub-factors, of which sustainable performance, competitive advantage, stakeholders' involvement and capabilities, lean and green practices and improvement in environmental performance are found to be the critical factors for the implementation of BDA in SSCM.
Research limitations/implications
This research has taken up the case of Indian manufacturing industry. It can be diversified to other geographical areas and industry sectors. Further, the quantitative analysis may be undertaken with structured or semi-structured interviews for validation of the proposed model.
Practical implications
This research provides an insight to managers regarding the implementation of BDA in SSCM by identifying and examining the influencing factors. The results may be useful for managers for the implementation of BDA and budget allocation for BDA project.
Social implications
The result includes green practices and environmental performance as critical factors for the implementation of BDA in SSCM. Thus the research establishes a positive relationship between BDA and sustainable manufacturing that ultimately benefits the environment and society.
Originality/value
This research addresses the challenges in the implementation of BDA in SSCM in Indian manufacturing sector, where such application is at its nascent stage. The use of PESTEL framework for identifying and categorizing the factors makes the study more worthwhile, as it covers full spectrum of the various factors that affect the strategic business decisions.
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Prashant Jain, Dhanraj P. Tambuskar and Vaibhav Narwane
The advancements in internet technologies and the use of sophisticated digital devices in supply chain operations incessantly generate enormous amounts of data, which is termed as…
Abstract
Purpose
The advancements in internet technologies and the use of sophisticated digital devices in supply chain operations incessantly generate enormous amounts of data, which is termed as big data (BD). The BD technologies have brought about a paradigm shift in the supply chain decision-making towards profitability and sustainability. The aim of this work is to address the issue of implementation of the big data analytics (BDA) in sustainable supply chain management (SSCM) by identifying the relevant factors and developing a structural model for this purpose.
Design/methodology/approach
Through a comprehensive literature review and experts’ opinion, the crucial factors are found using the PESTEL framework, which covers political, economic, social, technological, environmental and legal factors. The structural model is developed based on the results of the total interpretive structural modelling (TISM) procedure and MICMAC analysis.
Findings
The policy support regarding IT, culture of data-based decision-making, inappropriate selection of BDA technologies and the laws related to data security and privacy are found to affect most of the other factors. Also, the company’s vision towards environmental performance and willingness for material and energy optimization are found to be crucial for the environmental and social sustainability of the supply chain.
Research limitations/implications
The study is focused on the manufacturing supply chain in emerging economies. It may be extended to other industry sectors and geographical areas. Also, additional factors may be included to make the model more robust.
Practical implications
The proposed model imparts an understanding of the relative importance and interrelationship of factors. This may be useful to managers to assess their strengths and weaknesses and ascertain their priorities in the context of their organization for developing a suitable investment plan.
Social implications
The study establishes the importance of BDA for conservation and management of energy and material. This is crucial to develop strategies for enhancing eco-efficiency of the supply chain, which in turn enhances the economic returns for the society.
Originality/value
This study addresses the implementation of BDA in SSCM in the context of emerging economies. It uses the PESTEL framework for identifying the factors, which is a comprehensive framework for strategic planning and decision-making. This study makes use of the TISM methodology for model development and deliberates on the social and environmental implications too, apart from theoretical and managerial implications.
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Chhavi Luthra, Pankaj Deshwal, Shiksha Kushwah and Samir Gokarn
The demand for green personal care products (GPPs) has been growing globally due to increasing health-care concerns. However, the purchase rate of these products among consumers…
Abstract
Purpose
The demand for green personal care products (GPPs) has been growing globally due to increasing health-care concerns. However, the purchase rate of these products among consumers remains low. This study aims to identify and model the key barriers to the purchase of GPPs.
Design/methodology/approach
For this purpose, the study used innovation resistance theory (IRT) as a framework to identify key barriers to the purchasing of GPPs. The barriers were identified through a systematic literature review and validated by industry and academia experts. Furthermore, using interpretive structural modelling and Matrice d’Impacts Croisés Multiplication Appliquée a un Classement, the study identifies the interrelationships among the barriers and categorizes them based on their driving and dependence power.
Findings
The findings reveal that limited availability, improper labelling standard and certification, poor performance of products and lack of government regulations are key barriers to the purchase of GPPs.
Research limitations/implications
The study contributes to the existing literature on green purchase behaviour. Furthermore, it informs marketing strategies to overcome the identified barriers and increase the purchase of GPPs.
Originality/value
This study is the foremost empirical study that identifies and analyses the industry specific barriers to GPPs based on experts’ input and under the purview of IRT.