Group testing has been widely used in epidemiology and related fields to estimate prevalence of rare diseases. Parametric binary regression models are used in group testing to estimate the covariate adjusted prevalence. Unlike the standard binary regression model (viz., logistic, complementary log–log, etc.), the regression model for group testing data connects the maximum of a group of independent binary responses to its covariate values. Recently, in group testing literature, it has been extensively used for estimating covariate adjusted prevalence of a disease making the tacit assumptions that (i) the regression model is identifiable, and (ii) the standard asymptotic theory is valid for the maximum likelihood estimators of the regression parameters. Verifying these assumptions is found to be non-trivial theoretical issues. In this paper, we give theoretical proofs of these assumptions under a set of simple sufficient conditions, thus, providing a theoretical justification for likelihood based inference on the regression parameters. We also provide an outline of the proof extending the asymptotic theory to the data obtained by Dorfman retesting, where all subjects in a positive group are retested. © 2019 Elsevier B.V.

A cutting-plane approach is developed for the problem of optimally scheduling jobs with arbitrary precedence constraints on unrelated parallel machines to minimize weighted completion time. While the single machine version of this problem has attracted much research efforts, enabling solving problems with up to 100 jobs, not much has been done on the multiple machines case. A novel mixed-integer programming model is presented for the problem with multiple machines. For this model, many classes of valid inequalities that cut off fractional linear programming solutions are developed. This leads to an increase of the linear programming lower bound from 89.3 to 94.6% of the corresponding optimal solution, and a substantial reduction in the computational time of an optimal branch-and-bound algorithm for this problem. This enables us to report optimal solutions for problem instances with up to 25 jobs and 5 machines, which is more than twice the size of problems for which optimal solutions have been reported in the literature thus far. For a special case of the problem—that of minimizing makespan—application of our model helps solve 18 of 27 previously unsolved problem instances to optimality. © 2019, Springer Science+Business Media, LLC, part of Springer Nature.

Planning and scheduling transportation operations is considered computationally hard and time demanding. We consider a complex network of interconnected links across a set of locations and a set of vehicle movements on these links to fulfil some traffic demands. In such a set-up, basically two types of computational solutions are sought: one is a decision making, if a link (and/or a vehicle) is to be included in a route and second, is computation of a specific time duration during which a link and a vehicle are allocated to fulfil a traffic demand. Such planning efforts are undertaken at a strategic level. At the operational level, the planned schedules are executed. For any unforeseen reasons, if a planned schedule gets disrupted, while en-route, the entire planning efforts turn futile. The situation turns an emergency, as the available time to restore the original schedule is very small and it also knocks down other transport schedules that follow. Using an available approach to reschedule disrupted transportation services, we developed a learning architecture that is capable of learning and creating a knowledge base from the solved instances of disruption resolution. When a new disruption happens, the learner deduces the applicability of any solved instances in the knowledge base to quickly resolve the disruption. If not, the disruption is resolved completely and the resolution is added to the existing knowledge base. The combined abilities of the agents based architecture include monitoring, identification, deduction, decision making, and resolution execution, by learning, storing solution seeds and facilitating quick resolutions. The architecture and the application were developed using JADE Giovanni et al. (2007) toolkit on Ubuntu OS; we verified the deployability of the system on a Windows machine. The agent architecture and empirical results are presented in this paper. Our proposed architecture is faster than traditional methods and novel as an evidence of agent capabilities in solving real-life complex problems and safety-critical domains. © 2018 IEEE.

Achieving fast and inclusive economic growth concurrently with greenhouse gases (GHG) emission control could have wide-ranging implications for the Indian economy, predominantly fuelled by fossil energies. India faces high income inequality with the bottom 50% of its population owning only 2% of total national wealth. Other developmental challenges include 304 million people living in poverty, 269 million without access to electricity, 92 million without access to safe drinking water, and around 2 million homeless. Despite such challenges, India has committed to reduce the GHG emission intensity of its GDP 33–35% below its 2005 level by 2030, including via turning 40% of its power-generation capacity away from fossil sources. To explore the macroeconomic consequences of achieving development along low-carbon pathways, we use a hybrid modelling architecture that combines the strengths of the AIM/Enduse bottom-up model of Indian energy systems and the IMACLIM top-down economy-wide model of India. This hybrid architecture stands upon an original dataset that reconciles national accounting, energy balance and energy price statistics. With this tool, we demonstrate that low-carbon scenarios can accommodate yearly economic growth of 5.8% from 2013 to 2050 i.e. perform close to if not slightly higher than our business-as-usual scenario, despite high investment costs. This result partly stems from improvement of the Indian trade balance via substantial reduction of large fossil fuel imports. Additionally, it is the consequence of significant shifts of sectoral activity and household consumption towards low-carbon products and services of higher value-added. These transitions would require policies to reconcile the conflicting interests of entrenched businesses in retreating sectors like coal and oil, and the emerging low-carbon sectors and technologies such as renewables, smart grids, electric vehicles, modern biomass energy, solar cooking, carbon capture and storage, etc. © 2019 Elsevier Ltd

This paper examines the determinants and impacts of the adoption of five mutually exclusive practices System of Rice Intensification (SRI) on yields and household incomes using a multinomial endogenous treatment effects model. Farm household survey data is collected from selected districts of three States of India. Results suggest that the decision to adopt SRI is a function of experience in terms of age, farm assets, irrigation facility and information about SRI. The analysis showed that small and marginal farmers are more likely to adopt SRI as compared to large farmers. The National Food Security Mission (NFSM) came out to be significant and positive only in the case of few practices in some States. The welfare outcome results showed that the adoption increased the yield and income of three out of four practices-plant plus water, plant plus soil and plant plus water plus soil management. Briefly, the results show that the adoption of SRI especially full adoption of SRI has greater impact on the yield and income. © 2019, © 2019 Informa UK Limited, trading as Taylor & Francis Group.

Order release policies such as a card-controlled CONWIP policy aim to improve system responsiveness and minimize system-wide inventory levels. It is not clear if order release policies (without card control) can be equally effective under certain settings of the production system design parameters. In this research, we study the performance of alternate order release (material control policies) such as CONWIP and scheduled start time policy under a variety of design parameter settings such as number of stations, station service time characteristics, availability of raw material, and the location of the bottleneck station using queuing network models. Using an exhaustive numerical study, we observe that throughput times in CONWIP systems are about 2–50% less compared to open control systems; however, for a balanced system, the Deterministic scheduled start time policy performs equally well for a large set of production system designs. We further identify that the number of stations and the location of the bottleneck station affect the choice of the order release policy. Additionally, we also analyze the system for a variety of demand inter-arrival times and check its effect on the expected number of backlogs and system-wide expected waiting times. © 2018

In this paper, we explore a popular flexible work arrangement (FWA), home-based telework, in the Indian IT industry. We show how IT managers used the dominant meanings of telework to portray telework as an employee benefit that outweighed the attendant cost—intensified work. While using their discretion to grant telework, the managers drew on this portrayal to orchestrate a negotiated exchange with their subordinates. Consequently, the employees consented to accomplish the intensified work at home in exchange of telework despite their opposition to the intensified work in the office. Thus, whereas the extant studies consider work intensification as an unanticipated outcome of using FWAs, we show how firms may use FWAs strategically to get office-based intensified work accomplished at home. While the dominant argument is that employees reciprocate the opportunity to telework with intensified work, we show a discursively orchestrated negotiation that favors management. A corrective policy measure is to frame telework as an employee right. © 2017, Springer Science+Business Media Dordrecht.

Firms utilise both internal and external knowledge reservoirs in order to innovate. This study explores the drivers of innovation specifically, role of business groups, alliances, degree of internationalisation and financial slack on innovation. Hypotheses are tested using patent data on a sample of Indian firms. Group affiliation, financial slack and degree of internationalisation are found to positively impact innovation output. This study contributes towards the nascent literature on innovation in the Indian context. © 2019 World Scientific Publishing Europe Ltd.

Big Data Analytics is an important and flexible tool available for data analysis and informed decision making. In this paper, we look at the use of Big Data Analytics in asset liability management and asset allocation in uncertain economic situations using stochastic linear programming (SLP). In particular, this paper is an extension of our earlier work and we contribute to the existing literature by conducting experiments on the stochastic model through DSS. In particular, for this SLP based DSS, we address issues like the optimal number of scenarios required for good results, and the impact of the change in the number of scenarios on the stability of the model. The paper also addresses the impact of the change in the number of scenarios on the policy holders’ as well as shareholders’ reserves. In particular, we show the relevance of employing a larger number of scenarios and also present the experimental design developed to test the relevance of this model. We also show that a stochastic model employing fewer scenarios produced marked improvements in both return side measures as well as risk side measures compared to a mean value model or a partial mean value model. © 2018 Elsevier Ltd

A new family of attitudinal discrete choice models is proposed by considering the attitudinal character and the weight vector, both of which are specific to a decision maker (DM). Given the attribute values of different alternatives, the proposed models give varying choice probabilities, as per the DM's-specific attitudinal character and the weight vector. It is also shown that the conventional discrete choice models are the special cases of the proposed attitudinal models. The proposed choice models are also generalized through an additional parameter to add to their capabilities. An application on real data is included to demonstrate their usefulness in the real world. © 2019 Wiley Periodicals, Inc.

[No abstract available]

[No abstract available]

Many Internet service providers (ISPs) operate under network neutrality regulations that forbid differential QoS or differential pricing. This leads to lower profitability for ISPs. However, increasingly bandwidth-hungry content is making the consumers demand significantly improved ISP infrastructure. With the risk of poor consumer experience on them, ISPs have to invest in their infrastructure, but with limited scope for monetization. Hence, they are asking content providers (CPs) to contribute towards ISP capacity expansion. In this paper, we explore network neutral capacity expansion sponsored by voluntary contributions from CPs. We consider the scenario where CPs contribute to ISP capacity expansion via a voluntary payment. Since ISP capacity expansion can benefit all the CPs, and possibly even the ISP, selfish CPs will determine their contributions strategically. We consider three models for CPs to interact in determining their payments—a cooperative model, a non-cooperative model, and a bargaining model. Our analysis reveals a rather surprising result. We show that the bargaining model leads to a higher investment in the ISP infrastructure than even the cooperative model. This leads us to recommend the creation of transparent platforms for CPs to contribute directly to Internet capacity expansion. © 2019 Elsevier B.V.

This study provides an overview of the scholastic literature on cause-related marketing (CRM) during the period 1988–2016. The purpose of this study was to first identify significant knowledge gaps and hence encourage researchers to close the gaps through future studies. Second, the objective was to provide the researchers insights about the constructs examined, theories applied and managerial dimensions addressed. A review of 202 full-length articles across peer-reviewed academic journals, published in the literature for 28 years, was undertaken to provide researchers with a reference guide. A structured approach, to accumulate relatively complete census of articles, was followed, and information was extracted from six major online e-journal databases. Every article was classified by year, journal, country, the methodology used, theories adopted and managerial dimensions addressed. Journal of Nonprofit & Public Sector Marketing was found to publish maximum CRM related articles. Experimental methodology was adopted by large number of research papers. Attribution theory was found to be the most popular theory for understanding the CRM phenomenon. Majority of the articles were found to address the dimension of planning. In contrast to past CRM literature reviews, this paper identified and recorded the theoretical foundations applied to understand consumer behavior towards CRM. Further, the present paper extends the scope of past CRM review studies by examining and discussing managerial dimensions addressed in CRM domain. Implications and agenda for future research are also discussed. © 2019, © 2019 Taylor & Francis.

This paper investigates country-of-origin (CO) effects as they relate to celebrity endorsements. Across multiple studies in emerging markets, the authors show that consumers’ evaluations depend on the match between (1) celebrity CO and consumer CO (termed consumer CO fit), and (2) celebrity CO and brand CO (termed brand CO fit). If there is a trade-off between consumer CO fit and brand CO fit, the authors identify contingencies (e.g., ethnocentrism levels) that determine which type of CO fit leads to higher evaluations. Furthermore, the authors develop prescriptions for segmentation in emerging markets and specify when these prescriptions differ from those prescribed by prior international business research. © 2019, Academy of International Business.

Urbanization is globally rampant; in developing countries such as India, it is driven in a phenomenal pace by an aspiring, young society and ambitious state policies to achieve a competitive space in global economy. “Smart cities” is a new-age term coined specifically to control, monitor, measure, and evaluate our urban growth, in a way to understand how conducive are our cities for running businesses. Global borders are diminishing, and world-wide economies compete on multiple dimensions in a level playing field to participate and prosper in global markets. Cities are centers of economic activities, and the economic growth of a country largely depends on safe and supportive cities. Therefore, the need for smart cities is not difficult to establish. The more difficult questions are, “do we really know what a smart city is?” or “what are the elements that go into making an existing city smart?” Such questions are pertinent and timely. This paper attempts to answer these questions in a descriptive manner based on theoretical and empirical observations. We adopt an integrative approach of design conceptualization, policy building, and framework development, substantiated using an Indian city, namely, Kakinada in Andhra Pradesh State as an illustrative case. © 2018 John Wiley & Sons, Ltd.

The evaluation of the alternatives against multiple criteria is of the utmost importance in a multi-criteria decision making (MCDM) problem. It is often the case that the experts have a varying degree of confidence in their evaluations. That is, an evaluation has an associated degree of credibility. To take into account this crucial piece of information in determining the best choice, we present a new data structure. More specifically, we present a confidence-based soft set. We also extend the same to the fuzzy and intuitionistic fuzzy domains. The proposed concepts are elucidated through a number of illustrative examples. We establish their usefulness in a real case-study. © 2018 Elsevier Ltd

In this paper, we analyze a major controversy regarding the allocation of water for Indian Premier League (IPL) cricket matches during a period of extreme drought in the state of Maharashtra in India. We use a discursive approach to public policy to understand water policy production and to analyze the competing narratives on water advanced by opposing discourse coalitions during the ‘IPL vs. drought’ controversy. We find that the neoliberal view of ‘water as an economic good’ is dominant and institutionalized in the water allocation priorities determined by the Maharashtra State government. This is resisted by civil society actors like Loksatta and by the Indian judiciary, who view ‘water as a Human Right.’ Our reading of the ‘IPL vs. drought’ public interest litigation (PIL) shows that Loksatta's decision to target water allocation for the IPL through the Courts leverages the popularity of the IPL in the Indian media, as well as the uneven unfolding of neoliberalism across institutions of the state in India. At the same time, Loksatta's PIL focuses solely on the IPL and does not pay attention to the water allocation to larger users like industry and sugarcane cultivation that best represent the institutionalization of the neoliberal view of water in Maharashtra. We argue that the focus on the IPL makes it the site of contesting water policy on ideological grounds. We conclude by examining the challenge provided by Loksatta's PIL to the dominant neoliberal view of water in Maharashtra. © 2019 Elsevier Ltd

Purpose: The purpose of this paper is to demonstrate the utility of corpus linguistics and digitised newspaper archives in management and organisational history. Design/methodology/approach: The paper draws its inferences from Google NGram Viewer and five digitised historical newspaper databases – The Times of India, The Financial Times, The Economist, The New York Times and The Wall Street Journal – that contain prints from the nineteenth century. Findings: The paper argues that corpus linguistics or the quantitative and qualitative analysis of large-scale real-world machine-readable text can be an important method of historical research in management studies, especially for discourse analysis. It shows how this method can be fruitfully used for research in management and organisational history, using term count and cluster analysis. In particular, historical databases of digitised newspapers serve as important corpora to understand the evolution of specific words and concepts. Corpus linguistics using newspaper archives can potentially serve as a method for periodisation and triangulation in corporate, analytically structured and serial histories and also foster cross-country comparisons in the evolution of management concepts. Research limitations/implications: The paper also shows the limitation of the research method and potential robustness checks while using the method. Practical implications: Findings of this paper can stimulate new ways of conducting research in management history. Originality/value: The paper for the first time introduces corpus linguistics as a research method in management history. © 2019, Emerald Publishing Limited.

Since its discovery in 1989, there has been extensive research on endothelin (ET)-1 physiology, as well as pathology. Accordingly, there is considerable research on the discovery of therapeutics based around ET-1, amongst which current treatment options include endothelin receptor antagonists. These target the ET-1 receptors, which are G-protein–coupled receptors (GPCRs). We have effectively developed a soluble form of a GPCR that binds to ligands, by constructing a fusion polypeptide of different endothelin receptor ligand binding domains. Phage experiments identified strong binders to ET-1. We then constructed Fc-fusions of the top binders and further binding assays revealed a K D of 21.2 nM for the Fc-ETtr1 construct and K D of 77.3 nM for the Fc-ETtr2 construct. These constructs are soluble and have the ability to bind and potentially sequester elevated ET-1 levels that are prevalent in different diseases. These results provide a novel approach to targeting GPCR–binding ligands, and thereby to contribute to a very important class of therapeutics. © 2017, Springer Science+Business Media, LLC, part of Springer Nature.