The preference structure of consumers remains a perplexing issue for sharing platforms. This study aims to broaden the understanding of shared consumption (SC) adoption. It employed the theory of interpersonal behaviour (TIB) and the theory of planned behaviour (TPB) to propose a conceptual model integrating cognitive, affective and normative beliefs to explain sharing intention. A cross-sectional survey was conducted using a self-administered questionnaire to gather responses. Using partial least square structural equation modelling (PLS-SEM), the assessment of 298 valid responses revealed that economic incentives, normative beliefs, environmental concerns and perceived moral norms are significant predictors of sharing intention. The results of this study offer insights into the fact that consumers are cognizant of the sustainability concern and are ready to amend their consumption patterns driven by moral norms. Additionally, non-cognitive factors like freedom from ownership hassles and availability uncertainty influence sharing intention. The study's theoretical contributions include extending the application of TIB to shared consumption behaviour and integrating non-cognitive factors with TPB factors to understand shared consumption intention better. Accordingly, the study offers guidance to marketers for promoting shared consumption. © 2023 ERP Environment and John Wiley & Sons Ltd.

This case details the evolution of the Hyperlocal Grocery Delivery Industry in India and a comparative analysis between Dunzo and Blinkit. The case describes the macro and industry level imperatives, growth drivers and competitive dynamics, and the resultant evolution of alternate business models. In an emerging economy context traditionally characterized by institutional voids, technological and infrastructural challenges, and a lack of entrepreneurial resources, the evolution, proliferation, and growth of the Hyperlocal Delivery business model makes for an intriguing story. We intend to make readers aware of the industry dynamics and allow them to use strategy, technological innovation, and the fundamentals of platform businesses frameworks from strategy, innovation, and platform businesses domains to evaluate the value proposition and offering that these firms have for consumers and the operational model to craft winning strategies in this emerging industry. © Association for Information Technology Trust 2023.

This study considers the fresh food city logistics that involves the last-mile distribution of commodities to the customer locations from the local distribution centres (LDCs) established by the e-commerce firms. In this scenario, the last-mile logistics is crucial for its speed of response and the effectiveness in distribution of packages to the target destinations. We propose a clustering-based routing heuristic (CRH) to manage the vehicle routing for the last-mile logistic operations of fresh food in e-commerce. CRH is a clustering algorithm that performs repetitive clustering of demand nodes until the nodes within each cluster become serviceable by a single vehicle. The computational complexity of the algorithm is reduced due to the downsizing of the network through clustering and, hence, produces an optimum feasible solution in less computational time. The algorithm performance was analysed using various operating scenarios and satisfactory results were obtained. © 2022 IMI.

This examines the six drivers and twelve detailed practices of sustainable human resource development (S-HRD) before and during the COVID-19 pandemic across different organizations in Poland. The empirical strategy is based on explorative research conducted using surveys in Poland between 2020 and 2021. The results confirm that the surveyed organizations implemented S-HRD practices driven mainly by the expectations of external stakeholders. They neglected the areas of caring for employees’ well-being and developing environmental awareness before the COVID-19 pandemic. During the pandemic, most companies maintained their approach to S-HRD. This research is unique because it adds to the body of literature advocating the significance of S-HRD for organizational resilience before, during, and after extreme events. Generalizing the results is challenging because the snowball sample has significant restrictions. However, future research may overcome these shortcomings by using larger samples based on probability or random sampling techniques. © 2023, The Author(s), under exclusive licence to Springer Nature B.V.

Purpose: In this research, collaboration attributes related to the firm's intrinsic and extrinsic facets at pertinent levels (i.e. enterprise, strategic, operational, and tactical levels) for construction equipment OEMs (original equipment manufacturers) operating in India have been quantified and modeled. Design/methodology/approach: For modeling the intra-firm collaboration at respective organizational levels, relevant attributes have been populated employing literature review followed by subsequent validation from pertinent focus groups. The focus groups comprising professionals working in the construction and mining equipment industry in India aided us in estimating the extent of interdependencies and influences within/amongst collaboration attributes. The collaboration attributes and respective interdependencies/influences are modeled employing the concept of graph theory wherein the individual attributes are represented using vertices and influences/interdependencies are represented using edges. The collaboration indices resulting from the variable permanent matrix have been derived as well. Findings: Scenario and subsequent sensitivity analysis are performed. This research discusses the significance and aspects related to various collaborative attributes and the interrelations amongst them. Further, the research also evolves quantitative measures of collaboration indices at enterprise, strategic, tactical and operational levels by employing a graph-theoretic approach (GTA). The authors have also extricated and discussed a number of meaningful implications from both the perspectives of interorganizational relationships (IORs) and the normative theory of organizations using a cross-case analysis of five firms having operations in India. Originality/value: The research would aid organizations (particularly those belonging to the construction equipment sector) measure the efficacy of collaboration in respective value-chains at strategic, tactical and operational levels. From the theoretical perspective, the integration of the IORs and normative theory of organizations enables looking at the intra-firm collaboration problem from a multi-dimensional standpoint involving activities, performance measures, action initiation, communication, shades of top management, level of activity, etc. © 2022, Emerald Publishing Limited.

Organizations and their supply chains generate vast amounts of structured and unstructured data, popularly known as Big Data. This paper proposes a framework in the prescriptive analytics category of big data analytics (BDA) and is especially relevant for organizations on a permissioned blockchain network. The framework integrates the concepts of Artificial intelligence (Case-based reasoning, Interval-valued rough fuzzy set theory, and Value closeness relation algorithm) and blockchain to help decision-makers make accountable and conformable decisions. The framework comprises five stages - the first three stages generate the decisions, while the last two stages enable validation and communication of decisions. Its application is demonstrated on an organizational dataset with 20 supply chain KPIs. The numerical example shows how the organization's business strategy can translate to framework parameters resulting in varying outputs. The proposed framework is compared with the rough set approach through sensitivity analysis, highlighting the differences. The concept of L-Graphs is proposed to facilitate the visual display of prescribing decisions, which can help decision-makers make optimal decision choices. The framework has several unique features: (i) it is one of the first decision-making frameworks proposed for a blockchain-based supply chain that can be a part of the BDA toolbox of an organization; (ii) it uses concepts of artificial intelligence; (iii) the optimal decisions are proposed based on analysis of multicriteria; (iv) it uses multiple approaches to validate the decision rules on blockchain. © 2023 Elsevier Ltd

This paper investigates the antecedents and consequences of electronic word of mouth (eWOM) credibility using a meta-analysis technique. The extant literature provides inconsistent findings related to eWOM credibility. These inconsistencies are primarily because of methodological differences or heterogeneity among previous studies, which leads to confusion amongst researchers and managers. We resolve this inconsistency and provide a conclusive takeaway on the determinants and consequences of eWOM credibility by conducting a meta-analysis of the 51 primary studies comprising 124 effect sizes. Results show that both review and reviewer-related determinants have a significant positive influence on eWOM credibility while consequences, namely purchase intention, eWOM adoption, and attitude towards the product, have a significant relationship with eWOM credibility. Furthermore, results indicate that culture, product type, and platform type are the moderating factors. Results of the study can be used by researchers to address the issue of omitted variable bias, while managers can use the findings to enhance the credibility of eWOM messages on various eWOM platforms. © 2022 Elsevier Inc.

Investment decision making is usually a multi-objective optimization problem in an uncertain environment. In a real-life scenario, an investor aims to choose a portfolio based on his/her preferences for each objective. Credibility theory has been attributed as one of the most effective ways to model uncertain portfolio attributes. LR fuzzy numbers based credibilistic moments have been used successfully in the recent past to model portfolio selection problems in uncertain environments. Traditionally, the preferences on portfolio attributes are reflected as user defined threshold values. This work proposes a novel way to integrate the investor's preferences in the portfolio selection model. Investor's preferences are specified as LR fuzzy numbers corresponding to the portfolio's expected return and illiquidity distributions. The portfolio models formulated in this study attempt to attain them to the best possible extent. Four new models are presented considering uncertain portfolio attributes, namely, fuzzy return and fuzzy illiquidity. The models are solved using the MIBEX-SM Genetic Algorithm (GA) and ECE methodology. In order to demonstrate the efficient working of the proposed models, practical applications of portfolio selection problem using historical time series data of National Stock Exchange (NSE) is presented. Credibilistic Sharpe Ratio (CSR) has been used to compare the efficiency of models among themselves as well as with the Nifty−50 index. © 2022 Elsevier Ltd

We study a small farmer's optimal market choice in the presence of multiple markets in an emerging economy. A small farmer sells her product through either of the three markets available: government-regulated market, corporate-owned market (a private market having high-quality requirements), and fixed-price market (a government procurement center). Using a dynamic game of incomplete information, we examine the impact of technology choice, quality effort, and yield quantity on the farmer's market choice. We discuss the implications of the full information and asymmetric information case analysis with the help of real-life examples. We find out that the farmer does not put her optimal quality effort due to misalignment of stakeholder payoffs. We analytically prove that market inefficiency exists due to this misalignment; we subsequently propose that contract farming can eliminate this problem. We demonstrate that the corporate-owned and government-regulated markets have individual incentives to keep their markets segregated by maintaining different threshold quality requirements. We conduct a logistic regression analysis to explain the impact of various parameters on the small farmer's market choice. It shows the impact of change in critical parameters on the small farmer's optimal market choice. Our analysis allows policymakers to understand the behavior of small farmers as strategic players. Our study emphasizes the importance of the threshold quality requirement of markets and prices offered in these markets on a small farmer's market choice rather than traditional factors such as transportation cost and minimum support price. We conduct field interviews of small farmers to augment our findings. © 2023 Elsevier B.V.

Surviving in a digitally disrupted world required organizations to continuously innovate and digitally transform their work practices, especially HR processes. Scholarly failure to define and conceptualize the construct of human resource digital transformation (HRDT) suggests the lack of an integrated approach to understanding HRDT as a discipline. This study presents an overarching and integrated conceptual framework of HRDT, grounded in robust qualitative research, to describe factors in successfully implementing HRDT. Drawing insights from 20 senior HR professionals in multinational organizations, this study defines HRDT as a multidimensional construct resulting from successful integration of digital and individual factors into the innovation capability of organizations. Expanding the dynamic-capabilities perspective, this study proposes that in uncertain and turbulent times, such as COVID-19, businesses must constantly upgrade organizational capabilities, manifested in the innovation capability with enablers such as- digital infrastructure, architecture, and individual capability and creativity. This research further elaborates on HRDT indicators. © 2022 Elsevier Inc.

Drawing on social learning perspective, we examine the ‘trickle-down’ effect of the unethical tendencies of Machiavellian (Mach) leaders on followers' behaviour. In particular, we examine follower moral disengagement as a mediator to leader Machiavellianism-follower unethical behaviour link. Further, following personality theory, we investigated whether follower agreeableness acts as a boundary condition for Mach leaders to predict follower moral disengagement, which, in turn, accounts for follower unethical behaviours. We adopted a multilevel design and performed three-phase data collection from leader–follower–follower's proximal coworker triads (triad sets = 316 with a total of 948 data points), where leaders and followers represented sales function, and followers' coworkers represented warehouse supervisory roles whom followers closely associated for work needs. The results of this study provide support for our predictions. This is possibly one study of its kind that examines the trickle-down effect of a leader's negative behaviour manifested through their Machiavellianism trait on followers' unethical behaviour. © 2023

The study investigates if the mean-downside risk optimization technique for asset allocation can shed new light on the equity home bias puzzle. We propose a combined EGARCH-EVT-C-vine copula approach to properly capture the stylized properties of asset return series and estimate the downside risk appropriately by CVaR. Using weekly stock price data from 12 countries including seven G7 countries and five BRICS countries, we estimate international portfolio allocations based on mean-CVaR optimization model and compare the results to the mean–variance allocations. The study has been done from the perspective of US investors. The results of the study suggest that the US equity home bias is overestimated by the mean–variance approach. The mean-CVaR model helps to factor in the additional risk that investors face in international portfolio diversification and provides a plausible empirical explanation for the equity home bias phenomenon. The findings of the study have direct implications for portfolio managers and investors for taking better international investment decisions based on the knowledge of optimum portfolio. © 2022 Elsevier Inc.

Being an emergent concept, sharing economy (SE) lacks granularity in its conceptualization. This study offers a typology to delineate the boundaries of this phenomenon and thereby helps in achieving conceptual clarity. Using the lens of social exchange theory, a typology of SE is developed to trace the boundaries and crystalize its contour. The logical delineation attempts to resolve the extant ideological tensions within SE. A two-level analysis employing nine defining characteristics of exchange derived from the theoretical framework of the anatomy of exchange is used for this purpose. Two distinct styles of sharing – collaborative consumption (CC) and access-based consumption (ABC) are identified. It is further sub-classified into eight discrete modes of sharing types: six of CC and two of ABC. The proposed framework identifies a neglected but relevant SE practice - hybrid access. © 2022 Elsevier Inc.

With the enormous growth in the public and private vehicles fleet, traffic congestion is increasing at a very high rate. To deal with this, an intelligent mechanism is required.Therefore, this work proposes a novel Neuro-fuzzy based intelligent traffic light control system, which accounts for vehicle heterogeneity by dynamically generating traffic light phase duration considering the real-time heterogeneous traffic load. For this purpose, the proposed model establishes peer-to-peer connections among neighboring traffic light junctions to fetch the respective real-time traffic conditions and congestion. A fuzzy membership function is utilized to generate an intelligent traffic light phase duration. Further, to obtain an effective fuzzy membership function input value considering real-time heterogeneous traffic scenarios, an adaptive neural network is utilized. The proposed system adopts three execution modes: Congestion Mode (CM), Priority Mode (PM), and Fair Mode (FM). It automatically activates and switches to the best mode based on the live traffic conditions. The performance of the proposed model is evaluated via a realistic simulation on the Gwalior city map of India using an open-source simulator known as Simulation of Urban Mobility (SUMO). The results evident the effectiveness of the proposed model over the existing state-of-the-art approaches. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Research and practice emphasize the criticality of supply chain agility in responding to external disruptions. However, many organizations struggled to respond at enhanced speed to the global supply chain shocks caused by the COVID-19 pandemic. Organizations need hyperagile supply chains to survive and remain competitive in an environment characterized by unexpected and sudden disruptions such as the COVID-19 pandemic. We propose that supply chain hyperagility (SCH) is a distinctive organization-specific capability. It enables organizations to effectively manage demand shocks at extreme speeds and under extreme time pressures. We advance the concept of supply chain hyperagility and establish its antecedents, taking the dynamic capability perspective. This study operationalizes the SCH construct for the first time and investigates its antecedents using structural equation modeling. The results highlight the significance of data analytical capabilities, market orientation, entrepreneurial orientation, and supply chain integration in shaping supply chain hyperagility. The study offers practical insights for managers regarding designing supply chains that can navigate hyperagile environments and benefit from the opportunities presented by such environments. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

We propose a game-theoretic model and a mechanism design solution to allocate slots fairly at congested airports. This mechanism: (a) ensures that the slots are allocated according to the true valuations of airlines, (b) provides fair opportunities for flights connecting remote cities to large airports, and (c) controls the number of flights in each slot to minimize congestion. Drawing inspiration from economic theory, this mechanism allocates the slots based on an affine maximizer allocation rule and charges payments to the airlines to incentivize them to participate in the allocation process and reveal their actual valuations. The allocation also optimizes the occupancy of each slot to keep them as uncongested as possible. The formulation solves an optimal integral solution in strongly polynomial time. We conduct experiments on the data collected from two primary airports in India. We also compare our results with existing allocations and an allocation based on the International Air Transport Association (IATA) guidelines. The computational results show that our mechanism is more egalitarian and generates 20%−30% higher social utility than the IATA based state-of-the-art approach and current allocations. © 2022 Elsevier Ltd

We employ event study methodology to analyze the impact of unprecedented unconventional monetary policy (UMP) measures employed by the Reserve Bank of India to fortify monetary transmission mechanism and to restore financial stability. We find that the UMP announcements result in a decline in bond yields and yield spread as well as increase in market capitalization and sectoral portfolio of stock returns. Evaluating the relative efficacy of UMP measures, we find that targeted long-term repo operation announcements are more effective in easing bond yields than mere long-term repo operations. Our findings provide beneficial inference for day-traders and investors as asset prices increase significantly and durable goods producing stock returns found to be higher than those of non-durable goods. The lessons that can be drawn for the emerging market economy central banks, who do not have enough space to conduct conventional monetary policy and even when they do not face zero lower bound interest rate, they still can employ UMP tools to directly influence banks cost of funds, and long-term bond yields and interest rates, and in turn, portfolio of stock returns and investments to stimulate aggregate demand. JEL Classification: C13, C54, E52, E65. © 2023 Institute of Financial Management and Research.

Purpose: The study examines the effect of increased Economic Policy uncertainty on analyst optimism in the Indian market. The study also explores whether the SEBI Research Analyst Regulation, 2014, has effectively contained the optimistic nature of analysts. Design/methodology/approach: The study is based on firms in the Indian market. The sample period is 2003–2020. It runs a linear panel regression to measure the impact of Economic Policy uncertainty on the optimism level of analysts' forecasts and recommendations, controlling for firm fixed effects. Further, the impact of the SEBI Research Analyst Regulation, 2014, has been assessed with the help of the difference-in-difference approach. Findings: The Economic Policy uncertainty is significantly and positively related to the analyst optimism, reflected in the forecast bias and recommendation in the Indian context. The experience of analysts and the age of the firm positively drive optimism. However, introducing the Research Analyst Regulation by SEBI led to a decline in analyst optimism. The regulation decoupled the analysts' compensation from brokerage service transactions. Thus, the results suggest that the regulation has effectively curbed the incentive to produce optimistic output. Originality/value: This is the first study in the Indian market to assess the impact of uncertainty on analyst output. It also investigates the effectiveness of the first analyst-specific regulation in India, i.e. The Research Analyst Regulation, 2014. © 2023, Emerald Publishing Limited.

This study investigates whether gold, USD, and Bitcoin are hedge and safe haven assets against stock and if they are useful in diversifying downside risk for international stock markets. We propose a combined GO-GARCH-EVT-copula approach to examine the hedge and safe haven properties of gold, USD, and Bitcoin. We then examine the attractiveness of these assets in reducing stock portfolio risk by using downside risk measures estimated by the proposed approach and other competing models. We also evaluate the relative performance of the proposed model in reducing downside risk with the competing models. The findings of the study indicate that the USD is the most valuable hedge and safe haven asset closely followed by gold, while Bitcoin is the least valuable. It is also observed that the proposed combined approach performs best in reducing the portfolio downside risk. The findings of this study are of significance for portfolio managers and individual investors who wish to protect the portfolio value during market turmoil. © 2022 University of New Orleans.

Purpose: This study aims to investigate the determinant factors as barriers and facilitators of the B2B degree of digital use and customer–brand engagement in travel services by applying technology and behavioral theories. Design/methodology/approach: A face-to-face survey was administered to retail travel agencies offering offline and online services (N = 301). Structural equation modeling using the partial least square method was conducted using Smart PLS 3.0 software to examine the proposed hypotheses in the research model. Findings: The results revealed that the integrated composite model significantly predicts the B2B degree of digital use and brand engagement. The study established that facilitators had positive effects, and barriers negatively impacted the degree of digital use, which positively impacted brand engagement. However, the facilitator perceived cost and barrier lack of critical mass showed the opposite influence. The effects of facilitator customer pressure and barriers, information and communication technology infrastructure problems and security risks were insignificant. The results also established that the buyer firm size moderated the relationships between barriers and facilitators with the degree of digital use. Originality/value: This study combined technology and behavioral theories to explain the buyer–seller relationship. The expanded framework contributed to understanding B2B digital usages and brand engagement in the seller–intermediary relationship. This study conceptualized firm size as a contingency variable and established its moderating effect. The study defined cost as a formative construct and an organizational factor. The study suggested practical implications for travel agencies and online travel service sellers. © 2023, Emerald Publishing Limited.