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The study explores the role of dialogic public communication and information quality (IQ) in evaluating the operational performance of donation-based crowdfunding (DBC) tasks. These tasks are primarily used to support disaster relief operations. The authors also test the influence of cognitive trust and swift trust as moderating variables in explaining the relationship between both IQ and dialogic communication with operational performance.

The authors used a primary survey to test the hypotheses. A total of 203 responses were collected from multiple crowdfunding platforms. The authors used archival data from task creators on donation-based crowdfunding platforms, and a structured questionnaire is also used to collect responses. Data are analyzed using Warp PLS 6.0. Warp PLS 6.0 works on the principle of partial least square (PLS) structured equation modeling (SEM) and has been used widely to test path analytical models.

The authors found out that the operational performance is explained significantly by the quality of information and its association with dialogic public communication. The results support the arguments offered by dialogic public communication theory and trust transfer theory in assessing the operational success of DBC. The study also confirms that cognitive trust positively moderates the relationship between IQ and organizational public dialogic communication and operational performance. It is also revealed that the duration of the DBC task has no significant control over dialogic public communication.

The study lays practical foundations for task creators on DBC platforms and website designers as it sets the importance of both IQ and dialogic communication channels. The communication made by the task creator and/or the DBC platforms with the donors and potential donors in the form of timely and appropriate information forms the key to the success of any DBC task. The study also helps task creators choose a suitable platform to improve performance.

The authors propose a unique framework by integrating two theoretical perspectives: dialogic public relation theory and trust transfer theory in understanding the operational performance of donation-based crowdfunding tasks. The authors address DBC tasks catering to disaster relief operations by collecting responses from task creators on DBC platforms. The study uniquely positions itself in the area of information and communication.

The supply chain (SC) of the fast-moving consumer goods (FMCG) sector in India witnessed a significant change soon after introducing the Goods and Services Tax (GST). With the initiation of this tax, companies started moving from individual state-wise warehouses to consolidation warehouses model to save costs. This paper proposes a model that frames a mathematical formulation to optimize the distribution network in the downstream SC by considering the complexities of multi-product lines, multi-transport modes and consolidated warehouses.

The model is designed as mixed-integer linear programming (MILP), and an algorithm is developed that works on the feedback loop mechanism. It optimizes the transportation and warehouses rental costs simultaneously with impact analysis.

Total cost is primarily influenced by the critical factor transportation price rather than the warehouse rent. The choice of warehouses at prime locations was a trade-off between a lower distribution cost and higher rent tariffs.

The study enables FMCG firms to plan their downstream SC efficiently and to be in line with the recent trend of consolidation of warehouses. The study will help SC managers solve complexities such as multi-product categories, truck selection and consolidation warehouse selection problems and find the optimum value for each.

The issues addressed in the proposed work are transporting products with different sizes and weights, selecting consolidated warehouses, selecting suitable vehicles for transportation and optimizing distance in the distribution network by considering consolidated warehouses.

The emergence of risk in today's business environment is affecting every managerial decision, majorly due to globalization, disruptions, poor infrastructure, forecasting errors and different uncertainties. The impact of such disruptive events is significantly high for perishable items due to their susceptibility toward economic loss. This paper aims to design and address an operational planning problem of a perishable food supply chain (SC).

The proposed model considers the simultaneous effect of disruption, random demand and deterioration of food items on business objectives under constrained conditions. The study describes this situation using a mixed-integer nonlinear program with a piecewise approximation algorithm. The proposed algorithm is easy to implement and competitive to handle stationary as well as nonstationary random variables in place of scenario techniques. The mathematical model includes a real-life case study from a kiwi fruit distribution industry.

The study quantifies the performance of SC in terms of SC cost and fill rate. Additionally, it investigates the effects of disruption due to suppliers, transport losses, product perishability and demand stochasticity. The model incorporates an incentive-based strategy to provide cost-cutting in the existing business plan considering the effect of deterioration. The study performs sensitivity analysis to show various “what-if” situations and derives implications for managerial insights.

The study contributes to the scant literature of quantitative modeling of food SC. The research work is original as it integrates a stochastic (uncertain) nature of SC simultaneously coupled with the effect of disruption, transport losses and product perishability. It incorporates proactive planning strategies to minimize the disruption impact and the concept of incremental quantity discounts on lot sizes at a destination node.

This study investigates the performance of Indian states based on infrastructural investment in social and economic sectors using data envelopment analysis (DEA). Most of the studies in the literature are based on how different elements of infrastructure such as transport, energy, education, healthcare system affect the economy of different countries/regions. In this study, we consider these elements under two different sub-systems, namely, social and economic infrastructure and measure the cooperative efficiency for competitive growth.

A four-stage DEA approach is proposed for the analysis of a sample of 28 Indian states for the years 2011, 2013 and 2015 under consideration. First stage calculates the per capita GDP contribution, while stage-2 evaluates the efficiency of investments in social infrastructure followed by the efficiency analysis in economic infrastructure in stage-3. Finally, fourth stage evaluates the co-operative efficiency for the overall performance.

The findings of three different cases based on population sizes, viz., highly populated, moderately populated and less populated regions suggest that the government can identify the top and poor performers. It also studies the variations in efficiency tally of states using Malmquist indices.

This kind of study will vigilant government and local authorities on the investments made in all the states for social and economic infrastructure and establish a competitive environment among state governments to compete for improved infrastructural growth.

This study is the first of its kind in developing countries like India, which focuses on efficiency analysis using DEA based on two sub-sectors of social–economic infrastructural investments.

The purpose of this paper is to estimate the relative efficiencies of banks of the Indian domestic banking sector by employing various models of data envelopment analysis (DEA) using the panel data of the recent decade (2008–2017). The paper provides a comparative analysis of these models based on the efficiency outputs. It compares the performance of banks based on their ownership and sizes and studies the decade-long trend of productivity using Malmquist indices.

This paper estimates overall technical, pure technical and scale efficiencies of 21 public sector banks and 17 private banks. It compares the descriptive statistics of efficiency estimates found out through 18 different DEA models and compares them using two non-parametric statistical tests. It studies the difference in efficiencies based on ownership and size by applying the same statistical tests. It employs the Malmquist index method to study the technological and technical progress in the banks’ productivity over the decade of FY 2008–FY 2017.

During FY 2016–2017, only 9 out of 38 banks were overall technically efficient with the whole sample having a mean overall technical inefficiency of 5 percent with scale inefficiency contributing more than pure technical inefficiency. The comparative study ascertains that private sector and public sector banks (PSBs) possess efficiencies that are similar based on super-efficiency slack-based model – variable returns to scale and non-oriented, a model that the authors argue to be the most suitable for the real-life business banking scenarios whereas the private sector banks possess better efficiency than the PSBs. The Malmquist indices prove that private sector banks have a higher increase in productivity based on both technological progress and efficiency improvements whereas PSBs had a loss of efficiency and comparatively less improvement in technology.

This study has a limitation of choosing a single model of inputs and outputs. Improved insights can be drawn by employing more models based on different inputs and outputs. Further, relevance of each input and output can be examined using a regression-based feedback mechanism (Ouenniche and Carrales, 2018). The influence of environmental factors on the efficiencies can be studied using second-stage regression models and the relationship between efficiency scores and financial ratios can be examined.

This study is based on the panel data of the recent decade (2008–2017) and provides insights into the efficiency scenario of the Indian banking industry and how it changed over the past decade, to the leadership of banks, the banking regulators and the policy makers. The comparative analysis of DEA models based on a sample of Indian banks is first of its kind in the Indian context and helps the researchers to select an appropriate model and delve into further research on the same.

This study identifies barriers faced by online e-waste collection platforms in India and proposes a novel approach to manage strategies that contribute towards making such online platforms economically, socially, and environmentally sustainable by mitigating their interacting barriers.

The approach consists of a barrier assessment process and a strategy evaluation and planning process. The barrier assessment process uses multiple criteria decision making (MCDM) based method to evaluate the interactive effects of the barriers to derive their direct and indirect impacts on sustainability. The strategy evaluation and planning process evaluates the overall contribution value of strategies for mitigating interacting barriers and then categorizes them into planning zones based on their contribution and feasibility.

The interaction among the barriers is a significant component of the overall impact of the barriers on sustainability. The most impactful barriers are the lack of dissemination of information, lack of government support, insufficient infrastructure, and awareness and attitude of consumers. Lack of government support is the most influential causal barrier. Lack of information dissemination is a significant causal barrier with the highest overall impact on sustainability. Priority strategies that must be implemented to ensure sustainability include government support, offering higher prices or discounts for giving up e-waste, and increasing information dissemination.

Different from existing approaches for evaluating sustainability strategies, the strategies in this study are identified and evaluated based on their overall contribution towards mitigating interacting barriers to sustainability. The approach is applied in the Indian context to reveal the most influential barriers to the sustainability of online e-waste collection platforms and the most effective strategies for mitigating these barriers. The outcomes of this study contribute to strategy planning for sustainable online e-waste collection platforms in India.

This paper presents a systematic literature review of the various aspects of reverse logistics (RL) and closed-loop supply chains (CLSC) in implementing and achieving circular economy (CE) motives. CE is identified as a method of embracing imperishability into the economic structure, helping shift from a linear to a condition leading to ecological and social benefits.

Systematic literature was used to review a total of 80 peer-reviewed articles are included in the study and covers different concepts related to the implementation of CE, such as cost-saving, network design, sustainable RL, waste management and extended producer responsibility.

The findings reveal that the research in the domain is in a growing phase, and in recent years, a lot of attention has been given by researchers across the globe. However, further research is required in crucial areas for the adoption of CE, such as retail reverse logistics, pharmaceutical industries and resource recycling industry.

The study discusses the business needs and solutions for industries. Key enablers and barriers are listed along with the main activities involved in each sector in CLSC. Managers can design a pathway to decide which lever to use to overcome a particular challenge.

The work contributes theoretically by developing research themes in RL and CLSC practices applied to CE. It also provides theoretical and practical implications of the study, which can be used as a signboard for further research.

This paper aims to design an optimal supply chain network and to develop a suitable distribution planning under uncertainty for perishable product's supply chain. The ultimate goal is to help in making decisions under uncertain environments.

In this paper, stochastic programming is used under conditions of demand, supply and process uncertainties, and a non-linear mathematical model is developed for perishable product’s supply chain. Authors’ study considers disruptions in transportation routes and also within the facilities and investigates optimal facility location and shipment decisions while minimising the total supply chain cost. A scenario-based approach is used to model these disruptions. The retailer level uncertainty due to demand-supply mismatch is handled by incorporating the newsvendor model into the last echelon of supply chain network. In this paper, two policies are proposed for making decisions under uncertain environments. In the first one, the expected cost of the supply chain is minimised. To also consider the risk behaviour of the decision maker, authors propose the second policy through a conditional value-at-risk approach.

Authors discuss the model output through various examples that are provided via a case study from the milk industry. The supply chain design and planning of the disruption-free model are different from those of the resilient model.

Authors’ research benefits the perishable products industries which encounter the disruption problems in their transportation routes as well as in the facilities. Authors have demonstrated the research through a real-life case in a milk industry.

The major contribution of authors’ work is the design of the supply chain network under disruption risks by incorporating aspects of product perishability. This work provides insight into areas such as the simultaneous consideration of demand, supply and process uncertainties. The amalgamation of newsvendor model and the approximation of the non-linearity of retailer level cost function especially in the context of supply chain under uncertainty is the first of its kind. We provide a comprehensive statistical study of uncertainties that are present in the supply chain in a unique manner.

The purpose of this paper is to identify and analyze the interactions among different readiness factors for implementing agility in healthcare organization. Total interpretive structural modeling (TISM) based readiness framework for agility has been developed to understand the mutual interactions among the factors and to identify the driving and dependence power of these factors.

The identification of factors is done by TISM approach used for analyzing the mutual interactions between factors. Cross-impact matrix multiplication applied to classification analysis is utilized to find the driving and dependent factors of agile readiness in healthcare.

This paper identifies 12 factors of readiness for change in literature review, which is followed by an expert interview to understand the interconnection of factors and to study interrelationships of factors. The study suggests that factors like environmental scanning, resource availability, innovativeness, cost effectiveness, organizational leadership, training and development are important for implementing/improving the readiness of agility in healthcare organizations.

This research focuses mainly on readiness factors for agility in healthcare sector.

Top management must stress on readiness factors that have a strong driving power for efficient implementation of agility in healthcare. This study helps the managers to take quick decisions, and continuous monitoring of readiness factors would be more beneficial to improve the quality of service, which makes the organization more agile.

In this research, TISM-based readiness for agile framework structural model has been proposed for healthcare organizations, which is a new effort for implementation of agility in healthcare.

The purpose of this paper is to identify, analyze and develop a model to measure the interactions among different factors of organizational readiness for change in service sector specific to healthcare organization. The total interpretive structural modeling (TISM)-based readiness for change is to build a theoretical framework to understand the mutual interactions among the factors and to identify the driving and dependence power of these factors.

TISM is used to identify factors that contribute to analyze the readiness state before starting a change implementation process in healthcare. Matrice d’Impacts croises-multiplication applique´ a classement analysis is used to find the driving and dependent factors of change in healthcare.

This paper identified 12 factors of readiness for change from literature review followed by expert interview to understand the inner connection of factors and study inner relationships. The result says that state of affairs, recent trends in healthcare sector, technology advancement and interdependence among departments are key factors for readiness of change.

This research mainly focused on readiness factors for change in the healthcare sector.

This study will be useful for researcher and practitioners to understand the readiness factors for change.

In this research work, TISM-based readiness for the change framework structural model has been proposed for healthcare organizations which is a new effort in the area of organizational change management in this sector.