Gopalakrishnan Narayanamurthy

Whereas extant literature treats social voice, that is, complaining via brand‐managed social media pages, as a mere channel for existing consumer complaining behaviours (CCBs), like voice and negative word of mouth, this research aims to assess it as a distinct CCB. We draw from CCB theory and its associated taxonomies towards this goal. To identify the unique palette of motives and antecedents of social voice, we adopt a mixed‐method research design by extracting archival social voice posts and interviewing social voicers across four sub‐studies. Our analysis reveals the motives, CCB process stage, antecedents and other peculiarities associated with social voice, thereby demarcating it from other CCBs. Further, social voice is identified as a predominantly secondary‐stage CCB, triggered by a blend of constructive and vindictive motivations. This work is among the few examining social voice from a consumer viewpoint and attempts to place it within the overall CCB framework as divergent from its standalone assessments. We thus pave a foundation for examining behavioural changes implied by the Internet and social media within the CCB landscape and provide managerial guidelines towards reducing primary social voice while optimizing its secondary‐stage incidence.

Purpose: Smart farming (SF) holds immense potential in making farming viable and improving farmers' livelihoods. However, its adoption is still in the early stages, and the resulting impacts are underexplored. This study investigates the information systems/policy and implementation barriers to the adoption of SF and its implications for various socioeconomic aspects.Design/methodology/approach: This survey research used a structured questionnaire to collect data on farm details, farmer characteristics and usage of SF technologies from a sample of 197 farmers based in the State of Karnataka, India. Exploratory factor analysis with principal component extraction is used to validate the proposed questionnaire constructs. Results are analyzed using one-way and two-way ANOVAs and interaction plots.Findings: Our study throws light on the benefits and barriers of SF technologies. Advanced SF technologies displayed a significant positive effect on socioeconomic variables compared to startup SF technologies, which displayed no significant effect. Overcoming information systems/policy barriers for adopting startup and advanced SF technologies displayed significant positive effects on farmers' health and net income, whereas overcoming implementation barriers for adopting advanced SF technologies led to improvement in the farmers' gross income.Originality/value: Although the technical feasibility of SF has been explored in the literature, its adoption barriers and socioeconomic impacts have been underexplored. To the best of our knowledge, the interaction between different types of barriers and the level of SF adoption has not yet been investigated in the literature. Our study addresses those research gaps using data from a previously underexplored context, viz. developing nations.

Purpose: Anchored in the Natural Resource-Based View (NRBV) and Dynamic Capabilities theory, this study examines blockchain’s role in facilitating firms' transition to a circular economy (CE), aiming to provide a robust framework for understanding the interplay between blockchain, CE and sustainability.Design/methodology/approach: The study uses an interpretivistic approach and semi-structured interviews to explore how blockchain can drive the transition to a CE.Findings: The study shows blockchain can expedite the shift to a CE through pollution prevention, product stewardship and sustainable development, by leveraging dynamic capabilities (DC). It emphasizes blockchain as a micro foundation of DCs, with these capabilities enabling NRBV strategic capabilities.Originality/value: This study investigates the intersection of blockchain and CE, offering empirical validation for a robust conceptual framework and revealing the societal impact of the CE transition.

Purpose: The purpose of this research is to investigate the contingent adoption of Additive Manufacturing (AM) and propose a typology to evaluate its adoption viability within a firm's supply chain.Design/methodology/approach: By conducting semi-structured interviews of practitioners with deep knowledge of AM and supply chains from diverse industries, this research explores the contingent factors influencing AM adoption and their interaction.Findings: While the AM literature is growing, there is a lack of research investigating how contingent factors influence AM adoption. By reviewing the extant literature on the benefits and barriers of AM, we explain the underlying contingencies that enact them. Further, we use an exploratory approach to validate and uncover underexplored contingent factors that influence AM adoption and group them into technological, organizational and strategic factors. By anchoring to a selected set of contingent factors, a typological framework is developed to explain when and how AM is a viable option.Research limitations/implications: This study focuses on specific industries such as automotive, machine manufacturing, aerospace and defense. Scholars are encouraged to explore the contextual factors affecting AM adoption in particular industries to expand our findings. The authors also acknowledge that the robustness of their framework can be enhanced by integrating the remaining contingent factors.Practical implications: The developed typological framework provides a pathway for practitioners to see how and when AM can be useful in their supply chains.Originality/value: This is the first paper in the supply chain management literature to synthesize contingent factors and identify some overlooked factors for AM adoption. The research is also unique in explaining the interaction among selected factors to provide a typological framework for AM adoption. This research provides novel insights for managers to understand when and where to adopt AM and the key contingent factors involved in AM adoption.

At the start of this review, 168 million individuals required humanitarian assistance, at the conclusion of the research, the number had risen to 235 million. Humanitarian aid is critical not just for dealing with a pandemic that occurs once every century, but more for assisting amid civil conflicts, surging natural disasters, as well as other kinds of emergencies. Technology's dependability to support humanitarian and disaster operations has never been more pertinent and significant than it is right now. The ever-increasing volume of data, as well as innovations in the field of data analytics, present an incentive for the humanitarian sector. Given that the interaction between big data and humanitarian and disaster operations is crucial in the coming days, this systematic literature review offers a comprehensive overview of big data analytics in a humanitarian and disaster setting. In addition to presenting the descriptive aspects of the literature reviewed, the results explain review of existent reviews, the current state of research by disaster categories, disaster phases, disaster locations, and the big data sources used. A framework is also created to understand why researchers employ various big data sources in different crisis situations. The study, in particular, uncovered a considerable research disparity in the disaster group, disaster phase, and disaster regions, emphasising how the focus is on reactionary interventions rather than preventative approaches. These measures will merely compound the crisis, and so is the reality in many COVID-19-affected countries. Implications for practice and policy-making are also discussed.

Over half of India's employment is attached to the agriculture sector and their survival is dependent on the performance of farms. The uncertainty in the performance of farms due to weather fluctuations and other risks is tackled by providing insurance cover. However, policymakers’ choice of administrative measures for estimating crop loss has resulted in inaccurate data collection, opened vulnerability to the politicization of the process, and created bottlenecks to operate at scale. These problems have led to skewed timelines for data collation, lack of confidence in the data produced by the agri-insurance providers, and caused long-drawn delays in settling claims made by farmers. In this article, we present a case study on the assessment of using satellite big data as a technology deployed in Northern India to solve the aforementioned problems between the stakeholders in the agri-insurance claim settlement process. Satellite big data based analytics provides an independent data source and decision-making platform for the agri-insurers to conduct an assessment for calculating the indemnity payments. The results showcase how transparency brought in by the satellite big data analytics curbs the plausible exploitation of the claim settlement process and leads to increased efficiency and efficacy in settling farmer claims.

In supply chains where stakeholders belong to the economically disadvantaged segment and form an important part of the supply chain distribution, the complexities grow manifold. Fisheries in developing nations are one such sector where the complexity is not only due to the produce being perishable but also due to the livelihood dependence of others in the coastal regions that belong to the section of economically disadvantaged. This paper explains the contextual challenges of fish supply chain in a developing country and describes how integrating disruptive technologies can address those challenges. Through a positive deviance approach, we show how firms can help unorganized supply chains with economically disadvantaged suppliers by carefully redesigning the supply chain through the integration of satellite imagery and blockchain technology. With COVID-19 in the backdrop, we highlight how such technologies significantly improves the supply chain resilience and at the same time contributes to the income generating opportunities of poor fisherfolks in developing nations. Our study has important implications to both developing markets and food supply chain practitioners as this paper tackles issues such as perishability, demand-supply mismatch, unfair prices, and quality related data transparency in the entire value chain.