Varsha Yadav and Himani Sharma
The purpose of this paper is to investigate the effect of perceived support from family-friendly policies and supervisors on job satisfaction of employees by incorporating…
Abstract
Purpose
The purpose of this paper is to investigate the effect of perceived support from family-friendly policies and supervisors on job satisfaction of employees by incorporating work-family conflict as a mediator.
Design/methodology/approach
Primary data were collected from 369 employees working in different organizations from the service sector in India. Smart PLS software was used to perform partial least square structural equational modeling.
Findings
The result confirms that both family-friendly policies and supervisor support negatively influences the work-family conflict. Also, work-family conflict partially mediates between family-friendly policies and job satisfaction as well as between supervisor support and job satisfaction. Also, supervisor support directly influences the job satisfaction of the employees.
Research limitations/implications
Management needs to know the relevance of work-life policies and supervisor support to increase job satisfaction and reduce employees’ work-family conflict. Results will be useful for implementing family-friendly policies and designing training courses for the supervisors. This will make the workplace more family-friendly.
Originality/value
This study creates value for the employees in meeting their family obligations by reducing their work-family conflict. Organizations are benefited by attracting positive outcomes like satisfied employees, which will, in turn, lead to a more productive and happier workforce. Studies examining the influence of these policies and supervisory support on job satisfaction with work-family conflict as the mediating variable are difficult to find in the Indian context.
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Varsha Shukla, Rahul Arora and Sahil Gupta
The present study examines the fluctuations in Socioeconomic and demographic (SED) factors and the prevalence of Non-Communicable Diseases (NCDs) across clusters of states in…
Abstract
Purpose
The present study examines the fluctuations in Socioeconomic and demographic (SED) factors and the prevalence of Non-Communicable Diseases (NCDs) across clusters of states in India. Further, it attempts to analyze the extent to which the SED determinants can serve as predictive indicators for the prevalence of NCDs.
Design/methodology/approach
The study uses three rounds of unit-level National Sample Survey self-reported morbidity data for the analysis. A machine learning model was constructed to predict the prevalence of NCDs based on SED characteristics. In addition, probit regression was adopted to identify the relevant SED variables across the cluster of states that significantly impact disease prevalence.
Findings
Overall, the study finds that the disease prevalence can be reasonably predicted with a given set of SED characteristics. Also, it highlights age as the most important factor across a cluster of states in understanding the distribution of disease prevalence, followed by income, education, and marital status. Understanding these variations is essential for policymakers and public health officials to develop targeted strategies that address each state’s unique challenges and opportunities.
Originality/value
The study complements the existing literature on the interplay of SEDs with the prevalence of NCDs across diverse state-level dynamics. Its predictive analysis of NCD distribution through SED factors adds valuable depth to our understanding, making a notable contribution to the field.
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Jiayuan Zhao, Hong Huo, Sheng Wei, Chunjia Han, Mu Yang, Brij B. Gupta and Varsha Arya
The study employs two independent experimental studies to collect data. It focuses on the matching effect between advertising appeals and product types. The Elaboration Likelihood…
Abstract
Purpose
The study employs two independent experimental studies to collect data. It focuses on the matching effect between advertising appeals and product types. The Elaboration Likelihood Model serves as the theoretical framework for understanding the cognitive processing involved in consumers' responses to these advertising appeals and product combinations.
Design/methodology/approach
This paper aims to investigate the impact of advertising appeals on consumers' intentions to purchase organic food. We explored the interaction between advertising appeals (egoistic vs altruistic) and product types (virtue vs vice) and purchase intention. The goal is to provide insights that can enhance the advertising effectiveness of organic food manufacturers and retailers.
Findings
The analysis reveals significant effects on consumers' purchase intentions based on the matching of advertising appeals with product types. Specifically, when egoistic appeals align with virtuous products, there is an improvement in consumers' purchase intentions. When altruistic appeals match vice products, a positive impact on purchase intention is observed. The results suggest that the matching of advertising appeals with product types enhances processing fluency, contributing to increased purchase intention.
Originality/value
This research contributes to the field by providing nuanced insights into the interplay between advertising appeals and product types within the context of organic food. The findings highlight the importance of considering the synergy between egoistic appeals and virtuous products, as well as altruistic appeals and vice products. This understanding can be strategically employed by organic food manufacturers and retailers to optimize their advertising strategies, thereby improving their overall effectiveness in influencing consumers' purchase intentions.
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Varsha Singh Dadia and Rachita Gulati
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…
Abstract
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.
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Santosh Nandi, Madhavi Latha Nandi and Varsha Khandker
The purpose of this paper is to ascertain the determinants of mobile app stickiness (MASS) in emerging economies.
Abstract
Purpose
The purpose of this paper is to ascertain the determinants of mobile app stickiness (MASS) in emerging economies.
Design/methodology/approach
The study proposes a research model about how perceived interactivity (PI), perceived value, flow and self-efficacy influence MASS. The proposed model is then assessed in partial least square structural equation modeling using a survey sample of 587 mobile app users in India. Follow-up in-depth interviews are conducted to corroborate with statistical findings.
Findings
PI does not exert a significant direct influence on MASS. Rather, it is through perceived hedonic and utilitarian values and flow, which magnifies MASS. Also, mobile app users in emerging economies perceive an app to be interactive based on the app’s higher degrees of connectedness, non-verbal information and responsiveness, and not so much as reciprocity and control.
Research limitations/implications
Besides the demographic and geographic limitations of the sample, the study emphasizes only the positive cursors of MASS, such as value and loyalty benefits. It presents a future scope to empirically examine stickiness using negative cursors, such as identity theft, stress and health issues.
Practical implications
The study serves as a potential landscape for mobile app developers, consultants and service providers to identify unique daily-life requirements for mobile apps in emerging economies.
Social implications
The study creates a case for the mobile-commerce industry to consider socio-economic and socio-environmental factors while developing mobile apps for emerging economies.
Originality/value
Given the recent growth of mobile devices, services and broadband connectivity in emerging economies, this study provides a new perspective about different factors leading to MASS.
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Varsha Vihan, V.P. Singh, Pramila Umaraw, Akhilesh Kumar Verma, Shardanand Verma and Chirag Singh
The purpose of this study is to investigate the impact of integrating “Licorice powder” into curd balls on their storage stability under refrigeration conditions. Through this…
Abstract
Purpose
The purpose of this study is to investigate the impact of integrating “Licorice powder” into curd balls on their storage stability under refrigeration conditions. Through this examination, this study aims to evaluate the potential effects of licorice powder on extending the shelf life, maintaining quality attributes and preserving the overall stability of curd balls when stored at refrigeration temperatures.
Design/methodology/approach
Licorice powder, in varying quantities (1%, 2% and 3%), was incorporated into curd balls alongside a control group lacking licorice (0%). These batches were subsequently stored for 25 days under refrigeration at a temperature of 4 ± 1ºC, using aerobic packaging conditions. During this storage period, the samples were regularly monitored and analyzed for various parameters to assess changes in their properties and qualities.
Findings
The findings indicated that in the treatment groups, pH and titratable acidity were notably lower than those in the control group (p = 0.05). Curd balls enriched with licorice powder exhibited significantly higher levels of 2, 2-diphenyl-1-picrylhydrazyl, 2-2-azinobis-3ethylbenthiazoline-6-sulphonic acid and total phenolic contents compared to the control (p = 0.05). Furthermore, curd balls containing licorice powder displayed notably lower levels of peroxide, thiobarbituric acid reactive substances and free fatty acids in comparison to the control (p = 0.05). Among all samples, T3 (3%) demonstrated significantly less microbial growth (p = 0.05) than the other groups. Conversely, the sensory panel rated T2 significantly higher than T3 (p = 0.05).
Originality/value
The investigation highlights that curd balls enriched with 2.0% licorice powder demonstrated significant efficacy in preventing the deterioration of physicochemical attributes, enhancing antioxidant capacity, restraining lipid oxidation, curbing microbial growth and ultimately exhibiting the most favorable organoleptic properties among the tested variations. This finding underscores the potential of incorporating 2.0% licorice powder as an effective agent for bolstering the storage stability and overall quality of curd balls during refrigerated storage.
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Xinyue Hao, Emrah Demir and Daniel Eyers
The purpose of this study is to provide a holistic understanding of the factors that either promote or hinder the adoption of artificial intelligence (AI) in supply chain…
Abstract
Purpose
The purpose of this study is to provide a holistic understanding of the factors that either promote or hinder the adoption of artificial intelligence (AI) in supply chain management (SCM) and operations management (OM). By segmenting the AI lifecycle and examining the interactions between critical success factors and critical failure factors, this study aims to offer predictive insights that can help in proactively managing these factors, ultimately reducing the risk of failure, and facilitating a smoother transition into AI-enabled SCM and OM.
Design/methodology/approach
This study develops a knowledge graph model of the AI lifecycle, divided into pre-development, deployment and post-development stages. The methodology combines a comprehensive literature review for ontology extraction and expert surveys to establish relationships among ontologies. Using exploratory factor analysis, composite reliability and average variance extracted ensures the validity of constructed dimensions. Pearson correlation analysis is applied to quantify the strength and significance of relationships between entities, providing metrics for labeling the edges in the resource description framework.
Findings
This study identifies 11 dimensions critical for AI integration in SCM and OM: (1) setting clear goals and standards; (2) ensuring accountable AI with leadership-driven strategies; (3) activating leadership to bridge expertise gaps; (4) gaining a competitive edge through expert partnerships and advanced IT infrastructure; (5) improving data quality through customer demand; (6) overcoming AI resistance via awareness of benefits; (7) linking domain knowledge to infrastructure robustness; (8) enhancing stakeholder engagement through effective communication; (9) strengthening AI robustness and change management via training and governance; (10) using key performance indicators-driven reviews for AI performance management; (11) ensuring AI accountability and copyright integrity through governance.
Originality/value
This study enhances decision-making by developing a knowledge graph model that segments the AI lifecycle into pre-development, deployment and post-development stages, introducing a novel approach in SCM and OM research. By incorporating a predictive element that uses knowledge graphs to anticipate outcomes from interactions between ontologies. These insights assist practitioners in making informed decisions about AI use, improving the overall quality of decisions in managing AI integration and ensuring a smoother transition into AI-enabled SCM and OM.
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Pankaj Singh and Gaurav Agrawal
Agriculture insurance is the panacea for the farming community. Many policy interventions were implemented for stimulating agriculture insurance access to farmers in India…
Abstract
Purpose
Agriculture insurance is the panacea for the farming community. Many policy interventions were implemented for stimulating agriculture insurance access to farmers in India. However, access to agriculture insurance constantly remained one of the major challenges to Indian policy planners. The goal of the present paper is to explore current policy interventions in the area of agriculture insurance in India.
Design/methodology/approach
The present paper reviews and analyzes the evidence literature through a content analysis method on development and performance analysis perspective of existing agriculture insurance schemes in India.
Findings
Agriculture insurance is a significant risk management policy, but this is not easily reachable to the majority of farmers in India. The government of India introduces a novel agriculture scheme every decade, but every crop insurance scheme was inconsistent and ineffective owing to operational defects. Agriculture insurance in India is still developing in terms of coverage, scope, and exposure, but farmers' dissatisfaction about agriculture insurance turned out to be a negative word of mouth. Insurance illiteracy and farmers' preference for agriculture relief payments are the main reasons for limited access to agriculture insurance. The current crop insurance schemes are improperly operated because of implementation issues at the state level.
Research limitations/implications
This paper will be useful for researchers and academicians to analyze the past and present status of crop insurance in India.
Originality/value
The paper is the unique work of the authors as it has attempted to present India's journey with agriculture insurance. An effort is made in the present study to provide a comprehensive and holistic developmental and performance analysis perspective of agriculture insurance in India.
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Mengxi Yang, Walton Wider, Shuoran Xiao, Leilei Jiang, Muhammad Ashraf Fauzi and Alex Lee
This research is the first to use bibliometric analysis to provide insight into the landscape and forecast the future of customer experience research in the banking sector.
Abstract
Purpose
This research is the first to use bibliometric analysis to provide insight into the landscape and forecast the future of customer experience research in the banking sector.
Design/methodology/approach
We used bibliographic coupling and co-word analysis to delineate the existing knowledge structure after reviewing 338 articles from the Web of Science database.
Findings
The bibliographic coupling analysis revealed five key clusters: customer engagement and experience in digital banking; customer experience and service management; customer experience and market resilience; digital transformation and customer experience; and digital technology and customer experience—each representing a significant strand of current research. In addition, the co-word analysis revealed four emerging themes: customer experience through AI and blockchain, digital evolution in banking, experience-driven ecosystems for customer satisfaction, and trust-based holistic banking experience.
Practical implications
These findings not only sketch an overview of the current research domain but also hint at emerging areas ideal for scholarly investigation. While highlighting the industry’s rapid adaptation to technological advances, this study calls for more integrative research to unravel the complexities of customer experience in the evolving digital banking ecosystem.
Originality/value
This review presents a novel state-of-the-art analysis of customer banking experience research by employing a science mapping via bibliometric analysis to unveil the knowledge and temporal structure.
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Aman Kumar, Amit Shankar, Ankit Mehrotra, Muhammad Zafar Yaqub and Ebtesam Abdullah A. Alzeiby
Metaverse is one of the decade’s most exciting and transformative technological innovations. While the metaverse holds immense promise, it has potential risks and dark sides. This…
Abstract
Purpose
Metaverse is one of the decade’s most exciting and transformative technological innovations. While the metaverse holds immense promise, it has potential risks and dark sides. This research aims to investigate and identify the crucial dark dimensions associated with the metaverse platforms.
Design/methodology/approach
Employing a qualitative phenomenological methodology, the authors interviewed 45 metaverse users to unravel dark dimensions related to the metaverse. Analyzing the themes extracted from the participants' insights revealed an alignment with the underpinnings of the Technology Threat Avoidance (TTA) theory.
Findings
The findings of this study revealed seven major dark dimensions: addiction and dependency, isolation and loneliness, mental health issues, privacy and security, cyberbullying and harassment, digital identity theft and financial exploitation.
Practical implications
The study helps organizations and metaverse platforms understand the crucial dark dimensions of the metaverse. This study concludes by synthesizing prevalent themes and proposing propositions, offering insights for practical application and policy considerations.
Originality/value
This study provides a deeper understanding of the dark side of the metaverse environment from a user perspective using the underpinnings of TTA theory.