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Food insecurity is a major concern for developing economies. Operational inadequacies get introduced with unorganized interactions among stakeholders in the food supply chain, affecting social, economic, environmental and operational (SEEO) aspects of a nation. This study analyzes India's largest food safety net program, Public Distribution System (PDS) and develops a perception-based model, mapping the root causes (of food insecurity) with possible solutions. The novelty lies in leveraging blockchain in the implementation of food traceability system.

Soft system methodology (SSM) is used to identify and analyze problems in PDS, leveraging the learning and inquiry process. It relies on system thinking and action research to create a defendable and rational model, which helps in proposing recommendations for addressing the problem.

Blockchain-enabled food traceability system increases transparency, thus enabling the fulfillment of basic food necessities for beneficiaries.

The proposed model enables policymakers to build a profound understanding of existing operational issues and provides insightful recommendations for making informed decisions to deal with the grave issue of food insecurity.

Unlike previous studies, this research attempts to understand operational inefficiencies during interactions among stakeholders. It proposes a perception-based conceptual model for the final implementation at the ground level. It also reveals significance of three systems: a delivery system, an enabling system empowering delivery system and a criteria system to control and monitor processes. This study thus bridges an important gap in the literature by proposing a blockchain-driven traceability system, under the control of criteria system, through the integration of system-thinking and action-research approach.

The aim of this paper is to develop a model for the prioritization of collusive behaviours within Indian food grain supply chain (FGSC) to enable government authorities, entrusted with the task of public distribution, to address those frauds based on their priority for making an existing supply chain more sustainable.

An interval 2-tuple linguistic Technique for Order Preference by Similarity to Ideal Solution (ITL-TOPSIS) method has been used to deal with the problem of prioritization of frauds under incomplete and uncertain information. Unlike traditional methods, this methodology offers an ability to make informed decisions, without loss of information, while factoring in various ambiguities.

The outcome indicates that the most severe fraud is adulteration, which adversely impacts the health of a person. Bogus Ration Card comes next, as it results into the distribution of grains to non-poor, ineligible population rather than the deserving beneficiaries. Next is diversion, where diverted food grains end up being sold at much higher rates than specified subsidized rates. Theft is least severe, as this would not affect FGSC much until done on large scale.

More decision-makers can be consulted to entertain more uncertainty and ambiguity. Also, a comparative study can be performed using different methodologies.

The proposed modelling could empower various governmental and non-governmental regulatory bodies in formulation of food policies to effectively tackle the problem of inappropriate delivery of food to the unintended population and to take necessary informed decisions for ensuring food security and safety to the society at large.

There is a dearth of studies related to the prioritization of frauds within FGSC. This research bridges the gap in literature by providing a decision-making framework for prioritizing collusive behaviour under ambiguous and uncertain information.

Over last few years, a major innovation known as blockchain technology has emerged as potentially one of the most disruptive technology of recent times. The purpose of this paper is to identify and analyze various critical success factors (CSFs) that can facilitate success of blockchain-based cloud services. Further, this paper aims to analyze and understand mutual interactions among these CSFs.

In this paper, 19 CSFs have been identified through literature review and expert opinions. The hierarchical framework developed using total interpretive structural modeling has revealed the inter-dependencies among these CSFs. The methodology employed in this study provides a mechanism to conduct an exploratory study by identifying the factors and analyzing their interactions through the development of a hierarchical framework. This research further categorizes CSFs into multiple clusters based on their driving power and dependence power.

This paper has identified 19 CSFs, namely, user engagement, industry collaboration, rich ecosystem, blockchain technology standardization, regulatory clarity, cost efficiency, energy efficiency (wasted resources), handling blockchain bloat, miner incentives, business case alignment to blockchain capability, sidechains development, blockchain talent pool availability, leadership readiness for a decentralized consensus based technology, technology investment and maturity, trust on blockchain networks, integration with other cloud services, robust and mature smart contracts platform, blockchain security and user control on data (privacy). Further, driver and dependent variables have been identified.

Future research can discover and detail the sub-factors behind the 19 CSFs identified in this paper. Additionally, more work can be done to extend the current structural model for blockchain-based services to a more functional form.

It provides a comprehensive list of CSFs that are relevant for development of blockchain-based cloud services. This will help industry leaders to strategically focus on the main drivers that will ensure that businesses get maximum benefit of this disruptive technology.

This study makes a significant contribution in the literature of blockchain-based cloud services, which captures the perspective of different stakeholders. This study is one of the first (if not the first) systematic research on adoption of blockchain-based services. It creates the foundation to carry out further research in this area.

The purpose of this paper is to identify and analyze various critical success factors (CSFs) that can facilitate smartphone manufacturing in India. This paper further aims to understand the mutual interactions among these CSFs through identification of the hierarchical relationships among them.

A framework for a smartphone manufacturing system has been developed, wherein the hierarchical inter-relationships between identified CSFs have been presented and interpreted using total interpretive structural modeling (TISM). Cross-impact matrix multiplication applied to classification analysis has been further employed to identify the driving power as well as dependence power of these CSFs.

In the present research, 15 CSFs have been identified through literature review and expert opinions. The hierarchical framework developed using TISM has revealed the inter-dependencies among these CSFs. This research further categorizes CSFs into three clusters. The first cluster comprises of CSFs having high dependence power, the second cluster identifies CSFs having high driving power and the third cluster identifies CSFs which act as linkages between the driver CSFs and dependent CSFs.

This study has implications for both practitioners and academia. It provides a comprehensive list of CSFs that are relevant to develop a smartphone ecosystem in India. In addition, this study will help decision makers to strategically focus on the main drivers of the ecosystem that requires the immediate attention of decision makers. The methodology employed in this study provides a mechanism to conduct an exploratory study by identifying the factors and analyzing their interactions through the development of a hierarchical framework.

The proposed framework developed through qualitative modeling is an effort to understand relevant factors that can promote the smartphone manufacturing ecosystem. This study makes a significant contribution in the literature of smartphone manufacturing, which captures the perspective of different stakeholders.

The study evaluates the accident-adjusted dynamic efficiency of public bus operators providing bus transportation services in eight major metropolitan cities of India.

The slack-based measure (SBM)–undesirable window analysis approach is used to gauge the dynamic efficiency levels and identify the sources of inefficiency in bus transportation services. This innovative approach integrates the SBM model developed by Tone (2001, 2004) and the window analysis approach of Charnes et al. (1985). The main advantage of this approach is that one can explicitly incorporate the number of accidents in the production technology specification as an undesirable (bad) output and potently handle the issue of the “curse of dimensionality” in a small sample like ours.

The key empirical findings suggest wide variations in average efficiency levels across sample bus operators in metropolitan cities. The Chennai Transport Corporation is observed as the most efficient and consistent bus operator due to its most stable efficiency performance. The results additionally unveil that the role of managerial inefficiency was diminutive, and the scale-related issues were the real cause of sub-optimal or supra-optimal behaviour of sample bus operators in the resource-utilisation process.

There is an urgent requirement for effective policy intercessions to mitigate the sizeable observed inefficiency in the production process and resolve scale-related issues of public bus operators offering transit services in major metropolitan cities of India.

This paper is maybe the first to assess the dynamic efficiency of public bus transit systems in India's major metropolitan cities after treating accidents.

The coronavirus disease 2019 (COVID-19) pandemic altered business and personal activities globally especially stimulating contactless financial transactions. However, despite the similar national lockdowns in cash-based economies, the adoption of contactless transactions through the widely available mechanism, mobile wallets, remained low. This research aimed to identify the factors surrounding this peculiarity.

The study was investigated using a composite model based on the diffusion of innovation theory (DIT), technology acceptance model (TAM) and information systems success model (ISSM). Data were collected from 621 Cameroonian mobile wallet users and analyzed using partial least squares structural equation (PLS-SEM) modeling.

The key findings revealed that the usage of mobile wallets, in the current form, were not affected by the perceived ease of use and did not match the existing lifestyle of users in Cameroon (no compatibility). The branding of mobile wallets (image) which was based on global messaging did not appeal to Cameroonians; in fact, the branding gave mobile wallets a negative image.

These key findings reveal the dangers of assuming that global strategies which have been effective in dealing with the pandemic will be effective in low-income or cash-based economies. The findings suggest that considering essential contextual dispositions is critical.