India's forests, particularly in coal and other mineral-bearing regions, have been at the centre of many social and environmental conflicts. Such conflicts will likely remain salient over the next decade as India appears to be moving towards greater use of coal instead of transitioning away from it. The current governance architecture in mineral-bearing forested areas is characterized by fragmentation of rights across different stakeholders and a pluralistic institutional regime - a situation often characterized as 'anticommons.' 'Anticommons' lead to the resource getting gridlocked and underused. The Indian government undertook a critical and ambitious policy design exercise - the 'Go-No-Go' or Inviolate Forest Policy (IFP) policy - to address the resource dilemmas by 'objectively' demarcating forests for mining and conservation. This paper traces the conflict around the policy formulation as it manifested in the corridors and ministries of the government. We use a novel data source of government records of formal policy deliberations - obtained through India's transparency law, Right to Information (RTI) - supported by conversations with senior bureaucrats, policymakers, and expert observers. We find that the quest for 'objective' demarcation of 'pristine' forests for conservation was marked by interministerial conflict, laden with asymmetric power balances and involving the careful deployment of discursive frames and tactical maneuvers. We argue that the multiplicity of use-values that get valued differently a) by different individuals/groups of individuals and b) at different scales inscribe the possibility of conflict at the heart of any attempt to resolve anticommons, as can be seen in the IFP. In the process, we expose the problems in prioritizing amongst competing resource-uses, using 'efficiency'-based approaches. Further, our analysis presents an implicit critique of the monolithic understanding of the State in the supply of property rights.

The rise of service robots is changing how products and services are delivered, significantly affecting customer outcomes. However, research has yet to specifically examine how the design of a service robot to specific service contexts influences customer engagement and value co-creation. Our study adopts a contingency perspective to investigate how the design of robot capabilities and personalities aligned with specific service contexts impacts customer engagement and value co-creation intentions. We tested this proposition through three experiments carried out in the banking and restaurant service contexts. Our findings revealed the effectiveness of carefully designed robotic capabilities and personalities within a service infrastructure depends on the specific service contexts. The study findings have important implications for service firms in designing service robots through managing robot capabilities and personalities to align with specific service contexts.

This paper introduces a parametric finite mixture model (FMM) approach to analyze the dependent competing risks data subjected to progressive first-failure censoring and multiple causes of failure. The cause-specific failure times are assumed to be flexibly modeled by the Lehmann family of distributions (also known as the exponentiated distributions) with variation in both distribution parameters. Application of the expectation maximization (EM) algorithm facilitates the maximum likelihood estimation of the model parameters and illuminates the contribution of the censored data. For interval estimation purposes, we resort to using the asymptotic confidence intervals based on the observed Fisher information matrix. Practitioners often prefer employing simpler lifetime distribution in order to facilitate the data modeling process while knowing the true distribution. In this context, the effects of model misspecification are studied based on the p-th quantile when the true distribution is misspecified. An extensive simulation study is performed to validate our proposed model. Finally, an automotive warranty claims data set is used as an illustration to study the effectiveness of our proposed model, assuming some important members of the Lehmann family, like generalized exponential and exponentiated Pareto distributions.

Hackers follow a standard cyber kill chain process and install ransomware payloads using phishing emails on firms belonging to critical industry, resulting in huge losses in revenue, reputation, and customer churn. This study uses a mixed-method explanatory approach to mitigate ransomware attacks. We present the quantitative Ransomware Risk Management Model (R2M2) based on protection motivation theory (PMT). The Ransomware Risk Assessment module based on the threat appraisal component of PMT and the National Institute of Standards and Technology (NIST) guidelines can help the chief information security officer (CISO) to assess the risk using predictive analytics techniques. The Ransomware Risk Quantification module uses collective risk modeling to compute the severity of a ransomware attack on an organization. The Ransomware Risk Mitigation module helps the CISO to minimize the probability and impact of a ransomware attack on their organization by (i) investing in perimeter security technologies and training to ensure prepare, deter, detect, restrain, recover, and review mitigation strategies, (ii) adopting the National Institue of Standards and Technology (NIST) Cybersecurity Framework to ensure business resilience, and (iii) mitigating the residual risk by investing in cyber-risk insurance. We validated the proposed method using a qualitative study by interviewing participants form firms affected by ransomware, managed security service providers, security consultants, and cyber-risk insurers.

Phishing and social engineering contribute to various cyber incidents such as data breaches and ransomware attacks, financial frauds, and denial of service attacks. Often, phishers discuss these attack vectors in dark forums. Further, the probability of phishing attacks and the subsequent loss suffered by the firm are highly correlated. In this context, we propose a hybrid framework using explainable AI techniques to assess cyber-risks generated from correlated phishing attacks. The first phase computes the probability of expert phishers within a community of similar attackers with varying expertise. The second phase calculates the probability of phishing attacks upon a firm even after it has invested in IT security and adopted regulatory steps. The third phase categorises phishing and genuine URLs using various machine-learning-based classifiers. Next, it estimates the joint distribution of phishing attacks using an exponential-beta distribution and quantifies the expected loss using Archimedean Copula. Finally, we offer recommendations for firms through the computation of optimal investments in cyberinsurance versus IT security. First, based on the risk attitude of a firm, it can use this explainable-AI (XAI) framework to optimally invest in building security into its enterprise architecture and plan for cyber-risk mitigation strategies. Second, we identify a long-tail phenomenon demonstrated by the losses suffered during most cyber-attacks, which are not one-off incidents and are correlated. Third, contrary to the belief that cyberinsurance markets are ineffective, it can guide financial firms to design realistic cyber-insurance products.

The purpose of this paper is to propose an approach to meet the need for a robust, longitudinal, and an objective multi-criteria decision-making method. This paper presents a hybrid approach that uses time-series data to produce multi-criteria decision-making (MCDM) based future rankings of alternatives. The suggested approach leverages the strengths of existing methods such as grey forecasting for small sample prediction, Criteria Importance Through Inter-criteria Correlation (CRITIC) for objective criteria weighting, Multiplicative, Multi-Objective Optimization on the basis of Ratio Analysis (MULTIMOORA) for robust aggregation, and a combination of rank integration methods. The proposed approach is illustrated using the case of the Logistics Performance Index dataset published by the World Bank for The Organisation for Economic Co-operation and Development (OECD) countries. Further, results are compared with the aggregate ranks published by the World bank (2010-2018), and the differences are discussed. Practitioners would find the suggested approach useful because of its predictive ability, versatility, objectivity, and robustness of results. Further, the suggested approach is a useful contribution to existing research in terms of providing a MCDM method to generate future ranks.

PurposeThe primary objective of this article is to examine current procurement, inventory control and management practices in modern healthcare, with a particular focus on the procurement and management of surgical supplies in a prominent public, highly specialized healthcare sector.Design/methodology/approachThis study was conducted in three phases. In Phase 1, the study team interacted with various hospital management stakeholders, including the surgical hospital store, examined the current procurement process and identified challenges. Phase 2 focused on selecting items for a detailed study and collected the qualitative and quantitative details of the store department of the healthcare sector chosen. A detailed study analyzed revenue, output/demand, inventory levels, etc. In Phase 3, a decision-making framework is proposed, and inventory control systems are redesigned and demonstrated for the selected items.FindingsIt was observed that the demand for many surgical items had increased significantly over the years due to an increase in disposable/disposable items, while inventories fluctuated widely. Maximum inventory levels varied between 50 and 75%. Storage and availability were important issues for the hospital. It is assumed the hospital adopts the proposed inventory control system. In this case, the benefits can be a saving of 62% of the maximum inventory, 20% of the average stock in the system and optimal use of storage space, improving the performance and productivity of the hospital.Research limitations/implicationsThis study can help the healthcare sector administration to develop better systems for the procurement and delivery of common surgical items and efficient resource allocation. It can help provide adequate training to store staff. This study can help improve management/procurement policies, ordering and delivery systems, better service levels, and inventory control of items in the hospital business context. This study can serve as a pilot study to further investigate the overall hospital operations.Practical implicationsThis study can help the healthcare sector administration develop better systems for procuring and delivering common surgical items and efficient resource allocation. It can help provide adequate training to store staff. This study can help improve management/procurement policies, ordering and delivery systems, better service levels and inventory control of items in the hospital business context. This study can serve as a pilot study to further investigate the overall hospital operations.Originality/valueThis study is an early attempt to develop a decision framework and inventory control system from the perspective of healthcare inventory management. The gaps identified in real hospital scenarios are investigated, and theoretically based-inventory management strategies are applied and proposed.

PurposeA large number of organisations are moving towards adopting Industry 4.0 (I4.0), and simultaneously, the emphasis on attaining sustainability development goals is also increasing. Hence, it is imperative to understand the interplay between I4.0 and sustainability. However, the literature addressing the same is still in infancy. Accordingly, the purpose of this study is to fill this gap in the literature by exploring the potential sustainability impacts of I4.0 on the organisations and society in terms of sustainability risks.Design/methodology/approachTo gain an understanding of sustainability aspects in the I4.0 context, relevant literature is gathered using Scopus and Web-of-Science database. An in-depth review of 51 research papers is performed to determine the sustainability risks associated with I4.0.FindingsFrom the study, a total of 16 sustainability risks are identified, and I4.0 sustainability risk taxonomy is developed. The proposed taxonomy extends the sustainability implications of I4.0 beyond the triple bottom line umbrella and includes the organisational perspective as well. Furthermore, the study provides future research avenues to scholars by positing five potential research questions under different risk management stages.Research limitations/implicationsThe study provides an understanding of sustainability risks associated with the adoption of I4.0. The findings will help practitioners streamline their production and operation processes by finding out possible solution to the sustainability risks of their smart factories in advance. The present research will act as a stepping stone towards I4.0 sustainability. The proposed research questions will assist the future researchers in extending the field of I4.0.Originality/valueTo the best of the authors' knowledge, this is one of the first studies to address the topic of sustainability risks in the context of I4.0.

Robo-advisors are still in their nascent stages of use, and this technology has yet to find wider acceptance with investors. Our study extends the domain of technology acceptance to robo-advisors by identifying the drivers of investor adoption. Investment decisions carry high risks; thus, there is a greater need to understand investor behaviour to adopt robo-advisors. Trust, anxiety, performance expectancy, and preference for human advisors are used as variables to predict investors' intentions. PLS-SEM is employed to examine the data with a sample size of 445 investors. Results suggest that all these variables play a significant role in determining the behavioural intention of investors towards the adoption of robo-advisors. (c) 2024 Published by Elsevier Ltd on behalf of Indian Institute of Management Bangalore. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)

ObjectiveThe present study was carried out to describe contraceptive adoption following pregnancy terminations that resulted in outcomes other than live birth.MethodRetrospective calendar data on 31486 women who had experienced a pregnancy loss within the last 60 months prior to the survey date were drawn from a nationally representative dataset. Logistic regression was employed to model the associated factors with contraceptive uptake.ResultsOverall, 57.8% reported not adopting any method following the end of the recent pregnancy. There was a significant association between the choice of contraceptive method and timing of adoption. Women with living children were significantly more likely to adopt contraception as compared to women without any child.ConclusionContraceptive uptake following a non-live birth is considerably low in India. Interventions in reproductive health should focus on provision of different contraceptive methods and counseling emphasizing on effectiveness and correct use of the methods at the end of any pregnancy. Contraceptive adoption following a non-live birth is extremely low in India. The proportion of women opting for more effective methods was found to be considerably low.

PurposeOver the past two decades, the hub location domain has witnessed remarkable growth, yet no prior study reviewed and synthesised problem formulation and solution methodologies to address real-life challenges. Design/methodology/approachThe current study conducts a comprehensive bibliometric literature review to develop a thematic framework that describes and presents hub location problems. The work employs cluster, bibliometric, and social network analyses to delve into the essential themes. FindingsKey themes include cooperation, coopetition, sustainability, reshoring, and dynamic demand, contributing to the complex challenges in today's hub location problems. As the first work in this field, the study serves as a valuable single-source reference, providing scholars and industry practitioners with key insights into the evolution of hub location research, prominent research clusters, influential authors, leading countries, and crucial keywords. Research limitations/implicationsFindings have significant implications since they highlight the current state of hub location research and set the stage for future endeavours. Specifically, by identifying prominent research clusters, scholars can explore promising directions to push the boundaries of knowledge in this area. Originality/valueThis work is a valuable resource for scholars in this domain and offers practical insights for industry practitioners seeking to understand the hub location problems. Overall, the study's holistic approach provides a solid foundation for advancing future research work in the hub location field.

Although the scholarly debate on industrial relations (IR) transformation is inclined toward the conclusion that the IR transformation is bound to take place with changes in the surrounding business environment, we observe a few exceptions in each economy. The current study investigates one of such curious IR contexts, that is, the Indian coal sector. We rely on the 'logic of the action' framework and the IR transformation measures to assess the sector at an aggregate and micro level. The coal sector in India consists of a mix of both permanent and informal workforce. With respect to the permanent workforce, we analysed the collective bargaining agreements spanned over five decades (1975-2021). For the informal workforce, we analysed the recommendations of the HPC on wages and working conditions, the provisions of relevant legislation, internal circulars of coal companies and important judicial pronouncements. Our analyses revealed vast differences in wages and working conditions between the permanent and informal workforce. Despite a significant decline in the permanent workforce, they could negotiate better terms as the growing size of the informal workforce was yet to form a collective bargaining mechanism for better wages and working conditions. These results are indicative of a paradox which needs to be explored further. Our study advances the thesis of adaptive state capitalism in the coal sector through functional and numerical flexibility despite a politicised multi-union model in India.

Traditionally regarded as one of the least digitised industries, agriculture is now transforming due to the emergence of digital platforms and ecosystems. Extant literature underscores Agtech platforms as business solutions capable of remedying operational inefficiencies, diminishing transaction costs, enhancing information exchange, mitigating spatiotemporal disparities, facilitating market integration, and generating novel forms of value. Nevertheless, persistent ambiguity surrounds the identification and assessment of the functional roles of peripheral actors, referred to as complementors, who play a pivotal role in engendering network effects. Grounded in network externality theory and platform value proposition framework, this study seeks to comprehensively discern complementors, lead firms, and end-users associated with Agtech platforms and their overarching value propositions. Employing the case research method, we synthesise existing literature and review archival sources while focusing on four illustrative cases: Dehaat, Agrielevate, IFFCO Bazar, and KRIBHCO-CSC-eGov. The analysis was further strengthened through consultation with domain experts, facilitating triangulation and validation. This study offers insights into complementor-core, lead firm, and end-user groups within Agtech platforms, thus deepening our epistemic comprehension.

BackgroundAccurate and non-invasive estimation of MGMT promoter methylation status in glioblastoma (GBM) patients is of paramount clinical importance, as it is a predictive biomarker associated with improved overall survival (OS). In response to the clinical need, recent studies have focused on the development of non-invasive artificial intelligence (AI)-based methods for MGMT estimation. In this systematic review, we not only delve into the technical aspects of these AI-driven MGMT estimation methods but also emphasize their profound clinical implications. Specifically, we explore the potential impact of accurate non-invasive MGMT estimation on GBM patient care and treatment decisions.MethodsEmploying a PRISMA search strategy, we identified 33 relevant studies from reputable databases, including PubMed, ScienceDirect, Google Scholar, and IEEE Explore. These studies were comprehensively assessed using 21 diverse attributes, encompassing factors such as types of imaging modalities, machine learning (ML) methods, and cohort sizes, with clear rationales for attribute scoring. Subsequently, we ranked these studies and established a cutoff value to categorize them into low-bias and high-bias groups.ResultsBy analyzing the 'cumulative plot of mean score' and the 'frequency plot curve' of the studies, we determined a cutoff value of 6.00. A higher mean score indicated a lower risk of bias, with studies scoring above the cutoff mark categorized as low-bias (73%), while 27% fell into the high-bias category.ConclusionOur findings underscore the immense potential of AI-based machine learning (ML) and deep learning (DL) methods in non-invasively determining MGMT promoter methylation status. Importantly, the clinical significance of these AI-driven advancements lies in their capacity to transform GBM patient care by providing accurate and timely information for treatment decisions. However, the translation of these technical advancements into clinical practice presents challenges, including the need for large multi-institutional cohorts and the integration of diverse data types. Addressing these challenges will be critical in realizing the full potential of AI in improving the reliability and accessibility of MGMT estimation while lowering the risk of bias in clinical decision-making.

PurposeThis paper analyzes how air pollution and the public attention to it influence the returns of stocks in the Indian context.Design/methodology/approachThe study uses firm-level data for the stocks listed on National Stock Exchange in India. Air quality is measured using the Air Quality Index (AQI) values provided by US Embassy and Consulates' Air Quality Monitor in India. Google Search Volume Index (GSVI) of the relevant terms acts as the measure of public attention. Appropriate regression models are used to address how AQI and attention influence stock returns.FindingsIt is observed that degrading air quality alone is unable to explain the stock returns. It is the combined effect of increasing AQI and subsequent rise in associated public attention that negatively impacts these returns. Returns of firms with poor environment score component in their environmental, social, governance (ESG) scores are more negatively affected compared to firms with higher environment scores.Practical implicationsInvestors can make use of this knowledge to formulate effective trading strategies and ensure higher chances of profitability in the share market.Originality/valueTo the knowledge of the authors, no earlier study has investigated the effects of AQI and attention together to explain stock price movements. The study is conducted in the Indian context providing a unique opportunity to study the behavioral impact of these effects in one of the fastest growing global economies, which is also plagued by an alarming increase in ambient air pollution.

The paper discusses the multi-level portfolio selection problem, which combines hierarchical optimisation of credit portfolio, incorporating regulatory and capital constraints, in the context of emerging retail-banking loans. The proposed model allows for twin objectives of risk minimisation, simultaneously providing scope for maximising returns. The present paper analyses the portfolio optimisation problem, as a holistic 2-level optimisation problem: 1) at loan level, to reduce the default risk; 2) at bank level, to decide the right capital allocation between loan classes. The current study develops a model for multi-level optimisation of loans, and solves the model using multi objective algorithm for allocation of loan data across four retail asset classes, namely small business loans, credit card loans, home loans and auto loans, using a dataset of 229,000 loan records. The multi-level optimised portfolio is compared against the original portfolio for potential gains.

Immunization programs globally have played a pivotal role in reducing mortality rates. However, the effectiveness of these programs often falls short of the intended coverage targets, primarily due to challenges within the supply chain, storage facilities, and distribution processes. Notably, a substantial percentage of vaccine wastage has been observed in numerous countries, thereby imposing a significant financial burden on immunization initiatives, and presenting a formidable challenge to policymakers. This paper undertakes a comprehensive study of the financial dynamics inherent in an immunization program within the context of India. Employing a systems perspective, we scrutinize the intricate interactions among various cost factors. Through a five-year system dynamics simulation, we derive insights into the amplified financial constraints resulting from vaccine wastage and the inefficiencies associated with shared personnel. Furthermore, our analysis extends to evaluate the impact of an additional investment aimed at enhancing the quality of the cold chain infrastructure and health worker proficiency on the overall funds of the immunization program. This research contributes to a nuanced understanding of the financial intricacies of immunization programs, offering valuable insights for policymakers and stakeholders seeking to optimize program efficiency and resource allocation. (c) 2024 The Society for Policy Modeling. Published by Elsevier Inc. All rights reserved.

The present study examines the bioactive potential of sheep plasma protein hydrolysates (SPPH) produced by invitro gastrointestinal digestion as antioxidants, antimicrobials, anti-obesity agents, and inhibitors of lipid oxidation in sausage to address the oxidative stability and shelf-life issues of mutton. The antioxidant and antimicrobial activities, indicate a positive relationship between the degree of hydrolysis and digestion duration. The study finds that SPPH has a potent inhibitory effect on pancreatic lipase and cholesterol esterase. It has higher oil holding capacity than sheep plasma protein, observed at one hour of hydrolysis time. SPPH exhibit an improved behavior in foaming properties along alkaline pH and digestion time while display lower emulsifying activity and stability with hydrolysis advancement. The SPPH act as a natural preservative in developing functional mutton sausage by inhibiting lipid-oxidation. This study showed that the recovery of SPPH can be a cost-effective and sustainable strategy for generating available ingredients for enhanced shelf-life of meat products.

This paper explores the effects of innovational investments in information technology (IT) on the productivity performance of firms. We employ a micro-level dataset of 3582 firms from Indian manufacturing spanning 1998 to 2016 to identify some critical channels through which IT investments may impact productivity. We find a significant impact of inter-industry network spillovers, suggesting that the convenience drawn from IT assets transmits to firms in other sectors of the economy without the recipient firms paying for it. These spillovers are higher for firms in the top 50 percentile of the industry in terms of their IT assets. Our results also show strong complementarities between IT, R&D, and disembodied technology purchased by the firm. We find that these technological inputs offer a synergistic effect on productivity. Our results also highlighted that these synergies vary by ownership and export status. IT also seems to mitigate the diminishing gains to R&D as firms scale their R&D effort. Overall, we show that IT investments not only provide crucial productivity returns but also synergize other technological investments to produce superior overall gains.

Increasing complexity and the involvement of additional stakeholders make it impossible to predict the impact of each decision, which puts supply chain managers in uncertain situations. However, a supply chain that can adapt and react to the current scenario gives them some control over these ambiguous circumstances. These characteristics of sensing disturbances or threats and giving appropriate responses can be improved with the implementation of blockchain-enabled technologies and can prove critical to the success of supply chain resilience and sustainability. This study has identified 21 blockchain technology-enabled critical success factors for supply chain resilience and sustainability and grey theory is used to address the limitation of data availability. This study incorporates the combination of the Grey-DEMATEL (Decision Making Trial and Evaluation Laboratory) method to investigate the impact of critical success factors and to obtain the cause/effect relationship. Sensitivity analysis is performed to assess the robustness of obtained results. The findings indicate that internal integration is the most crucial causal factor, as it initiates the effects of many other critical success factors. Whereas Standardized Data Management, followed by Smart Ordering tops the effect group. As blockchain technology is still in its early stages of development, this study will encourage researchers and industry practitioners to strive for greater efficiency and effectiveness in their supply chain practices and to enhance the resilience and sustainability of their supply chains.