The present study extends prior research on stockouts by examining how product-related attributes interact with OOS justifications to influence consumer responses to stockouts. Drawing from Out-of-stock (OOS) literature, this study investigates how perceived popularity, perceived uniqueness, negative affect, and behavioral outcomes are influenced by out-of-stock justifications, product type, and sales level. Two scenario-based experiments and two field studies are conducted in which the cause of the stockout, product type, and sales level are manipulated at different levels. The results of the studies provide the following primary insights- 1) Consumers infer popularity of the product when the product is OOS due to high demand or when the sales level is high 2) Consumers infer uniqueness of the product when the product is OOS due to short supply but not when sales level is low 3) As the perceived popularity of utilitarian products or the perceived uniqueness of hedonic products increases, consumers' propensity to switch stores to purchase the OOS products increases 4) Consumers’ intention to spread negative word of mouth decreases when the cause of OOS or a high sales level is specified. Retailers can influence behavioral outcomes by specifying the cause of stockout tailored according to the type of product, or by providing sales level information. © 2021 Elsevier Ltd

Increased similarity of features of technology products has led to a waning of unique differentiating factors. In an absence of any tangible unique selling proposition, coolness has emerged as one of the compelling differentiators. This study investigates the coolness of technology products through in-depth interviews and an application of the critical incident technique (CIT). Thereafter, the findings of the qualitative study are empirically validated by collecting data through survey methodology and analyzing it by using structural equation modeling technique. Six dimensions of perceived coolness, viz., rebelliousness, desirability, innovativeness of technology, reliability, attractiveness, and usability are identified and empirically validated. The impact of coolness on brand love (which is a brand-related outcome of coolness), an under-researched construct, is studied and the relationship is found to be positive. © 2020 Elsevier Ltd

Generation of healthcare waste from different patient care activities in hospitals, pathology labs and research centres has been a matter of great concern for environmental and social bodies across the world. This concern comes from its infectious and hazardous nature which brings life taking disease such as human immunodeficiency virus and Hepatitis-B. Moreover, with the outbreak of corona virus disease 2019 (COVID-19) pandemic across the world, healthcare waste has become even more infectious like never before and showing its potential for claiming lives if not disposed properly. Additionally, the COVID-19 has put up another challenge in terms of exponentially increasing demand for personal protective equipments for healthcare workers such as doctors, nurses, ward boys, and sanitation workers. In this paper, seven criteria related to smart healthcare waste disposal system infused by circular economy aspects to recover value from disposables are identified and analysed using a decision making trial and evaluation laboratory (DEMATEL) method. The criteria have been prioritized by its importance and net cause and effect relationship through a causal diagram. Two criteria, (i) digitally connected healthcare centres, waste disposal firms and pollution control board, and (ii) providing a pollution control board's feedback app to public and other stakeholders, feature as strong reasons for a smart healthcare waste disposal system. Conclusively, this study provides a causal relationship model among the intertwined drivers of industry 4.0 and circular economy for developing a smart healthcare waste disposal system enriched with the benefits of circular economy. © 2020 Elsevier Ltd

Awareness of antecedents and consequences of trust in m-commerce can enable m-commerce service providers to design suitable marketing strategies. Present study conducted a meta-analysis of 118 related empirical studies. The results indicate that antecedents namely perceived usefulness, perceived ease of use, system quality, information quality, service quality, user interface, perceived risk, perceived security, structural assurance, ubiquity, and disposition to trust, while consequences namely attitude, user satisfaction, behavioral intention, and loyalty have significant relationship with trust in m-commerce. Further, all the relationships were found to be moderated by culture except perceived ease of use, disposition to trust, and attitude. © 2019 Elsevier Ltd

Every year, natural disasters such as earthquakes, hurricanes, landslides, etc. kill thousands of people and destroy habitats and assets worth millions-of-dollars. Choice of temporary shelter areas and subsequent relocation of homeless people play a crucial role in post-earthquake relief operations. This paper proposes a multi-objective location-relocation model based on goal programming approach considering uncertainties of damage to infrastructure due to earthquakes. The model considers multiple objectives of risk, number of sites, unmet demand and qualitative suitability of locations and generates solutions under different scenarios of damage. A numerical illustration is also presented to demonstrate the applicability of proposed approach in solving the decision problem. Copyright © 2020 Inderscience Enterprises Ltd.

The rising electronics waste (e-waste), increased pollution, disposal issues and exploitation of natural resources have augmented ecological and resource scarcity problems. These problems can be abate by adopting reverse logistics (RL) implementation so, the environmental protection and resource recovery options such as reuse, refurbish, recycle, remanufacturing and proper disposal can be executed to deal and manage with the e-waste. The goal of this research work is to identifies, evaluate and analyze the factors for successful implementation of RL practices. Fifteen critical RL factors are recognized from pertinent literature and endorsed by industry professionals. This paper utilized Grey based Decision Making Trial and Evaluation Laboratory (DEMATEL) approach to sort the identified factors into cause and effect clusters. Grey DEMATEL resolves the problem of data uncertainty and impreciseness involves in decision making in analyzing the factors. The results of the study found that 11 RL factors came into causal cluster as vital factors and 4 RL factors into the effect cluster as fundamental problems factors. Among causal cluster, the highest ranked four influential factors are Top management commitment (RLF1) with weightage value 0.7604, RLF2 i.e. enforced government regulations with weightage value 0.6549, RLF6 i.e. environmental/green concerns factor with weightage value 0.5678 and RLF8 i.e. Waste minimization (especially e-waste) factor with weightage value 0.5113 are respectively. Similarly, among effect cluster, the most easily influenced two factors are “Market and competition factors” (RLF15) with weightage value (? 1.1207) and “Increased e-commerce” (RLF13) with weightage value (? 1.2855) respectively. A case example of an Indian electronics firm is taken to showcase the application of the proposed model. This paper can help supply chain managers and analysts in superior understanding of critical factors and increase the chances of practicing more effective and successful RL implementation in electronics industry. At the end, few key country and industry-specific recommendations are offered to support major decision for management and policy makers to embrace and successfully accomplish the reverse supply chain practices in India. © 2020, Operational Research Society of India.

This work reports on a study to measure tractor owners' (mostly farmers') repurchase behavior (RPB). While earlier studies have focused on the technical aspects of the tractors, none as yet have considered farmer intention and behavior for predicting purchase decision. A conceptual model was built considering all possible antecedents of farmers' RPB based on in-depth interviews and discussions with marketing managers. Interviews with customers were used to understand these antecedents or interactions with salespersons, either directly or indirectly during conversations or visits to stores. The authors have attempted to develop a scale on farmers' consumption experience from the perspective of farmers. Design/methodology/approach: The model was validated using Smart-PLS, and the best tractor brand was identified using data envelopment analysis (DEA). At the village level, snowball sampling was adopted to identify potential tractor owners who had repurchased the same brand or a higher model as respondents. Findings: Findings reveal that all the paths were found to be significant. Farmers' consumption experience (FE) seems to be the biggest predictor of RPB, followed by image, satisfaction and trust. The newly introduced construct FE has a significant effect on farmers' RPB. DEA results further indicate that most tractor companies function with 100% efficiency. Research limitations/implications: The study was carried out in India; it can be extended to other countries. Also, the sample was collected from one state in India and is cross-sectional in nature, so it cannot be generalized. Originality/value: First, the authors developed a conceptual model considering all possible antecedents of RPB. No studies had yet developed a scale on FE. Second, the authors created a benchmark for the various preferred tractor brands from the farmers' perspective using DEA. © 2020, Emerald Publishing Limited.

This study examines the influence of factors related to consumer resistance on the intention to continue using the Unified Payment Interface (UPI) for electronic payments. UPI facilitates advanced, peer-to-peer, immediate payment with seamless interoperability among banks in India. The study extends the innovation resistance theory by including two behavioral measures - privacy concerns and visibility - and two moderators - security concerns and word of mouth (WOM). It used cross-sectional data collected from 714 UPI users aged between 16 and 55 years to test the proposed research model. The findings suggest that privacy concerns and usage barrier are the two crucial factors to be addressed for breaking down consumer resistance towards continuing usage of UPI. The other significant factors are image barrier and visibility. In addition, security concerns and WOM are found to partially moderate the influences on the associations between the key variables and continuing usage of UPI. © 2020, IFIP International Federation for Information Processing.

Customers prefer apps over websites for travel search and booking. However, websites offer a competitive advantage over apps in terms of branding and CRM opportunities. Therefore, to retain customers on travel websites, this research investigated the role of monetary incentives resulting from network benefits, cultural adaptation of websites by offering pilgrimage packages, website interactivity, ease of information access, trust, and customer reviews on website continuance intention. Data was collected from 356 travel website customers. The results indicate that network benefits, interactivity, pilgrimage packages act as antecedents to trust, and customer reviews moderate trust and website continuance intention relationship. © 2020, © 2020 Taylor & Francis Group, LLC.

This study investigates the degree to which movements in stock liquidity is determined by common underlying factors in a large emerging market, India. This degree is called commonality. Commonality has been measured for NIFTY50 stocks using high frequency data across a variety of liquidity measures. This study empirically verifies the relative strength of market- and industry-wide liquidity in explaining commonality. Furthermore, the study analyses the impact of industry-wide liquidity on the liquidity of individual stocks belonging to the key industries of Indian economy, viz. consumer goods and pharma, energy, financial services, infrastructure, information technology (IT) and telecom, manufacturing and natural resources. Among all the sectors studied infrastructure, IT and telecom, manufacturing and natural resources sectors possess higher degree of Industry-wide commonality. This means fund managers find it difficult in altering a portfolio having greater exposure to these sectors. Studying the behaviour of commonality will also assist regulators in monitoring abnormal market fluctuations. The study contributes to the understanding of commonality on an emerging order driven market like India. © 2020 International Management Institute, New Delhi.

The literature has proposed several barriers to Industry 4.0 adoption or digitalisation. The present study assesses how the intrinsic and extrinsic barriers to digitalisation affect Industry 4.0 adoption by the firms. The paper also evaluates how these barriers influence the linkage between digitalisation and the firm's performance with regards to its supply chain competency as well as operational performance. Sketching the arguments from the resource-based view as well as the contingency approach, a model for Industry 4.0 adoption is developed. Eight hypotheses encompassing the intrinsic and extrinsic barriers to digitalisation, digitalisation practices, and two constructs of firm performance constitute the model, which is tested on 143 manufacturing companies using Analysis of Moment Structure (AMOS). The findings suggest that intrinsic and extrinsic barriers are negatively linked with the digitalisation. The analysis also reveals that Industry 4.0 adoption improves operational performance as well as supply chain competency. The paper sheds light on the contingency effects of extrinsic barriers on digitalisation. This research would assist the practitioners and policymakers to comprehend the impact of intrinsic and extrinsic barriers. © 2020 Elsevier Ltd

Understanding the developmental trajectories of big data analytics in the corporate context is highly relevant for information systems research and practice. This study presents a comprehensive bibliometric analysis of applications of big data analytics in enterprises. The sample for this study contained a total of 1727 articles from the Scopus database. The sample was analyzed with techniques such as bibliographic coupling, citation analysis, co-word analysis, and co-authorship analysis. Findings from the co-citation analysis identified four major thematic areas in the extant literature. The evolution of these thematic areas was documented with dynamic co-citation analysis. © 2020, © 2020 Informa UK Limited, trading as Taylor & Francis Group.

Purpose: This study aims to examine the role of leader-member exchange social comparison (LMXSC) perceptions in triggering the instigation of uncivil behavior in the workplace. This study also explores the intervening role of envy and the buffering role of aggression-preventive supervisor behavior within the proposed relationship. Design/methodology/approach: Data were collected in two phases separated by an interval of four weeks. The final sample consisted of 224 full-time white-collar employees working in five large pharmaceutical manufacturing organizations in India. Findings: The findings indicate that employees’ subjective perceptions of being involved in poorer-quality LMX relationships than their workgroup members generate envy, which, in turn, evokes them to instigate uncivil behaviors onto those higher-LMX counterparts. The indirect effect of LMX social comparison on instigated workplace incivility through envy gets attenuated when supervisors engage in aggression-preventive behavior. Practical implications: To protect organizations from the financial and productivity losses associated with incivility, supervisors are encouraged to exhibit aggression-preventive behavior if they form differentiated exchange relationships with the subordinates in their teams. Supervisors are further advised to avoid the altogether neglect of lower-LMX subordinates as doing so may give rise to negative emotions (envy) and behavior (incivility) among them. Originality/value: This study expands the limited body of knowledge on the antecedents of uncivil employee behavior in the workplace. Specifically, it unveils that incivility toward coworkers may be stemming from unfavorable LMX social comparisons and ensuing negative emotions such as envy. It also offers insights on reducing uncivil behavior by highlighting that the impact of LMXSC and envy on incivility instigation gets buffered in the presence of aggression-preventive supervisor behavior. © 2020, Emerald Publishing Limited.

Purpose: Public service motivation (PSM) is a universal construct, but indigenous traditions and culture of a country are known to influence its measurement. Currently, no research on PSM in India is available. To facilitate PSM research in India, this article compares the two most used measures of PSM (PSM-14 and PSM-16) to identify the most suitable scale for further exploration. Design/methodology/approach: The study uses a cross-sectional survey research design. Data were collected from 387 employees working in the public sector. ADANCO, a PLS-SEM package, was used to analyze the data. Findings: Contrary to expectations, it was found that the older PSM-14 exhibited better psychometric properties than the newer PSM-16. The PSM-14 also exhibited greater predictive validity than PSM-16. Practical implications: The study demonstrates that PSM is a valid construct in India and can be measured adequately by existing instruments. However, certain sub-dimensions of the scale (such as compassion) may be reworded/changed to reflect Indian cultural ethos better. Originality/value: The findings will be tremendously helpful to researchers interested in examining the correlates of PSM in the Indian context by making it easier to select the appropriate measurement instrument. The study also provides a careful examination of each of the sub-dimensions of the construct to enable the development of more robust PSM measures in the future. © 2020, Emerald Publishing Limited.

With increasing varieties and products, management of limited shelf space becomes quite difficult for retailers. Hence, an efficient product assortment, which in turn helps to plan the organization of various products across limited shelf space, is extremely important for retailers. Products can be distinguished based on quality, price, brand, and other attributes, and decision needs to be made about an assortment of the products based on these attributes. An efficient assortment planning improves the financial performance of the retailer by increasing profits and reducing operational costs. Clustering techniques can be very effective in grouping products, stores, etc. and help managers solve the problem of assortment planning. This paper proposes data mining approaches for assortment planning for profit maximization with space, and cost constraints by mapping it into well-known knapsack problem. © 2020 Antai College of Economics and Management, Shanghai Jiao Tong University.

Benefits of recycling will be mitigated if the recycling plant location is not aligned with the sustainability principles. The present study aims to build a guiding framework for managers and policymakers in selecting a sustainable location for Waste Electrical and Electronic Equipment (WEEE) recycling plant. It uses a case study approach and a three-phase novel methodology which includes Best-Worst Method (BWM) to rank the criteria for a sustainable plant location and VIsekriterijumska optimisacija i KOmpromisno Resenje (VIKOR) to rank the recycling candidate locations present in the case. The results indicate that environmental and natural criteria like, distance from the residential areas and water bodies, and biodiversity conservation are the most significant considerations for a sustainable plant location. Policy and legal criteria like, support from local authorities and financial support, ranked as the second while, economical aspects like, proximity to the waste collection point, and slope ranked as the third best criteria for decision on a sustainable recycling plant location. The study established the consistency and robustness of the results by performing two sets of sensitivity analysis. One by changing the weight specified for the main criteria and the other, by modifying the weight specified for a specific expert. The study will facilitate WEEE recycling companies in their decision making for facility location and also, the policy makers in increasing the effectiveness of the national WEEE management policy, by providing insights on the sustainability factors for recycling plant locations around which incentives can be built and disclosure regulations be designed. © 2020 Elsevier Ltd

Building on resource-based view theory, this article investigates the impact of human resource practice flexibility (HRPF) on innovation performance with (a) the mediating role of intellectual capital (IC) and (b) moderating role of the industry type (service or manufacturing) of an organisation. We empirically examined the relations using a survey dataset of managers of 257 Indian organisations. We have used the structural equation modelling method for data analysis. Findings of the moderated mediation analysis revealed that IC mediates the relationship between HRPF and innovation performance (a) partially when the organisation operates in the service industry and (b) fully when an organisation operates in the manufacturing industry. Additionally, our study explains the underlying mechanism governing the same relationship. © 2020 Sage Publications India Pvt. Ltd.

This study investigates the impact of Information and Communications Technology (ICT) on economic growth (gross domestic product) for member countries of South Asian Association for Regional Cooperation (SAARC) using annual data for the period 1990–2014. The study has employed augmented Cobb–Douglas production function by incorporating ICT along with capital and labor. We have taken teledensity (number of fixed and mobile phones per 10,000 people) as the proxy of ICT. This study has included only four SAARC countries (Bangladesh, India, Sri Lanka, and Pakistan) due to data constraints. Our findings reveal a positive and statistically significant effect of ICT on economic growth using panel data techniques. However, the impact of ICT on economic growth is highest for India followed by Sri Lanka, Bangladesh, and Pakistan respectively. This study has crucial policy implications for SAARC countries as they have started giving due significance to the issues related to ICT these years. © 2020, © 2020 Commonwealth Secretariat.

In the standard literature, empirical assessments of the productivity effects of infrastructure are marked by contradictory results with little robustness. In this context, this paper tests the effects of public infrastructure on the total factor productivity (TFP) of Indian manufacturing industries. We utilize a new productivity database on Indian manufacturing for the period 1980–2012, as well as a range of infrastructure measures, for empirical analysis. Based on a bootstrap based test, preliminary evidence is found for cointegration between infrastructure and TFP of various manufacturing sectors. Next, the productivity effects of infrastructure are estimated using a cross-sectionally augmented pooled mean group estimator, where we control for sectoral dependence, endogeneity and heterogenous effects of infrastructure across various sectors. Overall, the results of the analysis confirm the presence of a positive and sizeable effect of infrastructure on manufacturing productivity. We find wide disparities in the productivity effects of different types of infrastructure such as road, rail, energy, port and telecommunication. Specifically, the results show that 1 percent increase in the aggregate infrastructure stock results in productivity growth by 0.16 percent. Policy recommendations are made on the basis of these findings. © 2020 Board of Trustees of the University of Illinois

This study examines the road accident rates from 29 Indian states and 6 Union Territories during 2006–2015. In this paper, we have employed several empirical techniques such as regression, generalized method of moments (GMM), and threshold regression models to examine how education and attitude of the drivers impact the road accident rates. In our empirical analysis, we demonstrate that lawless driving is found to be positive and statistically significant while incorporating both state and year effects. We also find some interesting result in the context of education and road accidents. We notice positive relationship between higher education and road accident rates across Indian states, which tells us highly educated people are more prone to road accidents as compared with less educated people. In the threshold analysis, the study discovers the positive relationship between road injury and accidents caused by people with higher education, in cases of considering road injury rate from drunken driving as the threshold variable. There appears to be a large number of behavioral issues emanating from higher education, which contributes significantly to the road accident rates in the Indian states. Hence, there is a need for policy intervention. © 2020 John Wiley & Sons, Ltd