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849 items in total found

Journal Articles | 2022

Compact living or policy inaction? Effects of urban density and lockdown on the Covid-19 outbreak in the US

Andy Hong, and Sandip Chakrabarti

Urban Studies

The coronavirus pandemic has reignited the debate over urban density. Popular media has been quick to blame density as a key contributor to rapid disease transmission, questioning whether compact cities are still a desirable planning goal. Past research on the density–pandemic connection have produced mixed results. This article offers a critical perspective on this debate by unpacking the effects of alternative measures of urban density, and examining the impacts of mandatory lockdowns and the stringency of other government restrictions on cumulative Covid-19 infection and mortality rates during the early phase of the pandemic in the US. Our results show a consistent positive effect of density on Covid-19 outcomes across urban areas during the first six months of the outbreak. However, we find modest variations in the density–pandemic relationship depending on how densities are measured. We also find relatively longer duration mandatory lockdowns to be associated with lower infection and mortality rates, and lockdown duration’s effect to be relatively more pronounced in high-density urban areas. Moreover, we find that the timing of lockdown imposition and the stringency of the government’s response additionally influence Covid-19 outcomes, and that the effects vary by urban density. We argue that the adverse impact of density on pandemics could be mitigated by adopting strict lockdowns and other stringent human mobility and interaction restriction policies in a spatially targeted manner. Our study helps to inform current and future government policies to contain the virus, and to make our cities more resilient against future shocks and threats.

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Journal Articles | 2022

The impact of dominant IT infrastructure in multi-establishment firms: The moderating role of environmental dynamism

Franck Soh, and Pankaj Setia

Journal of the Association for Information Systems

Multi-establishment firms (MEFs) rely on digitized processes enabled by advanced IT infrastructure; however, environmental dynamism is a major influence on their operations. Environmental dynamism threatens the efficacy of current operations, requiring firms to evolve their processes. Firms’ IT infrastructure may catalyze or hinder their endeavors and performance as they respond to environmental dynamics. Little previous research has examined which IT infrastructure types are high-performing and whether their effects vary across environments. We investigate the impacts of IT infrastructure, examining microlevel implementation—the constitution of technical and human assets—across the establishments of a multi-establishment firm (MEF). Specifically, we use the notion of a dominant IT infrastructure to unravel the heterogeneity of IT infrastructure across establishments. We explore dominant IT infrastructures—technology, human, or both—and assess their impacts across environmental conditions. To test our hypotheses, we used a panel dataset from 2007 to 2009 comprising 355 unique firms. Our findings reveal that the impact of establishment-level IT infrastructure types on MEF performance is contingent on environmental dynamism. A technology-dominant IT infrastructure leads to greater MEF performance in less dynamic environments, while a human-dominant IT infrastructure leads to greater MEF performance in more dynamic environments. The MEF performance is enhanced through a combination of technology- and human-dominant IT infrastructures in more dynamic environments. We conclude by discussing the theoretical insights and managerial implications of our findings.

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Journal Articles | 2022

Exploring the culture–creativity–innovation triad in the handicraft industry using an interpretive approach

Subhadip Roy, and Subhalaxmi Mohapatra

Journal of Business Research

Researchers have independently studied the roles of culture, creativity, and innovation in the domain of handicrafts. In the present study, we aimed to understand the linkages between the three constructs in the same sector. We based our research background on the theory of cultural embeddedness and the componential view of creativity. To this end, we employed a multiple-case-study design of two handicraft forms in Odisha and one handicraft form in Maharashtra, India. The data consist of focus group discussions (FGDs) (n = 49), observations, and documentational evidence. The findings were a process model of the creativity and innovation in the handicraft sector that began with the cultural backdrop that influences creativity, which, in turn, influences innovation. We observed innovation influences the marketability, and we found that it has a reverse relationship with creativity. We found that cultural embeddedness and cultural clustering moderate the relationship between creativity and innovation. The study has implications for theory and policy.

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Journal Articles | 2022

Learning-by-hiring: How do rival firms learn from focal firm's hiring

Mayank Varshney

Research Policy

Previous studies provide evidence of learning from the mobility of scientists for the source and the hiring firms. However, we have a limited understanding of the competitive implications of such inter-firm mobility and associated learnings. Using a difference–in–difference approach on matched patents in the semiconductor industry in 1981–2010, we find that mobile scientists' patents receive more citations from rival firms after the mobility vis-à-vis before the mobility and vis-à-vis other similar patents. We conclude that rival firms respond to mobilities across other firms by attributing more attention to mobile scientists. Furthermore, the context of the mobility can determine the extent of response from rival firms. Rival firms are more likely to build on a mobile scientist's patents after mobility when the mobility occurs between technologically distant firms, the source firm or the hiring firm has low research experience, or the mobile scientist has considerable experience.

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Journal Articles | 2022

Determining the optimal release time of movies: A study of movie and market characteristics

Megha Sharma, Sumanta Basu, Soumyakanti Chakraborty, and Indranil Bose

Decision Support Systems

The over-the-top (OTT) industry has witnessed remarkable growth in recent years with a sharp increase in the number of subscribers, leading to increased competition among OTT platforms to acquire movie rights. Consequently, the gap between the theatrical and OTT releases has been diminishing over the last few years. An early release of a movie on an OTT platform fetches a higher distribution fee for a movie distributor (MD), however, it reduces the MD’s revenue from the theatrical release. Therefore, it becomes critical for the MD to determine the optimal release time and distribution fee combination. In this paper, we analytically solve the MD’s decision problem and provide a detailed analysis of how the optimal release time varies with changes in platform characteristics such as the proportion of ad revenue and the platform’s risk profile, movie characteristics such as success factor and suitability for OTT, and market characteristics such as broadband penetration, piracy rate and customers’ preferences for viewing channels. We compare our results with the actual release times of 243 movies released during 2015–2022. We find that the optimal release time increases with ad revenue proportion, broadband penetration, and piracy rate, whereas the optimal fee reduces non-linearly with release time and depends on OTT’s risk profile. Our findings also indicate that the optimal release time reduces for movies that do not provide any additional utility for theater goers, and as customers’ preference towards OTT increases. Our work provides much-needed guidelines for professionals dealing with movie releases on OTTs

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Journal Articles | 2022

Understanding the evolution of an emerging technological paradigm and its impact: The case of Digital Twin

Suparna Dhar, Pratik Tarafdar, and Indranil Bose

Technological Forecasting and Social Change

The interest of the academic and practitioner communities on the topic of Digital Twin has grown substantially in recent years. Bibliometric analysis can serve as a useful tool to explore the roadmap of the Digital Twin across various emergent themes over time. In this paper, we compare and analyze 1270 news articles and 4036 research publications to assess the evolution of the Digital Twin paradigm according to these sources from 2016 to 2021. We apply topic modeling and sentiment analysis on the textual corpora. Our analysis shows that certain topics related to applications, simulation, and enabling technologies for Digital Twin find greater coverage and generate higher positivity over time. We ascertain the coevolution and divergence in the number and sentiment of topics through curve matching metrics and determine whether they can rouse consumer interest, captured through online search trends. Our regression analysis shows that news on applications of Digital Twin and research on process evaluation through real-time simulation significantly impact the search frequency of consumers. Our research helps the digital product and service providers to understand the academia-industry gap in their effort to investigate Digital Twin and guides them on steps to take and themes to pursue for generating consumer interest.

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Journal Articles | 2022

The Influence of status on evaluations: Evidence from online coding contests

Swanand J. Deodhar, Yash Babar, and Gordon Burtch

MIS Quarterly

 

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Journal Articles | 2022

Evaluating the efficacy of demand-side communication interventions on claiming rights: evidence from an action research field experiment in India

Akshay Milap, and Ankur Sarin

Human Communication Research

Communication-based interventions increasingly characterize attempts to strengthen policy implementation, especially policies targeting disadvantaged populations who despite their eligibility often fail to access potential benefits. However, factors that determine their effectiveness remains an open empirical question. To examine elements of effective communication in the exercising of rights, we designed and implemented a randomized field experiment around a public informational assistance campaign, spanning an entire urban district in India as part of a larger action research initiative. Situated within the context of India’s ambitious “Right to Education” Act, our intensive campaign employed distinct instruments varying in terms of trustworthiness, expertise, and media richness—frontline public health workers, trained student volunteers, and an interactive voice response system—to assist individuals in the claiming process. While our results reiterate the value of information, we find these effects to be less pronounced for the most disadvantaged. Our results also emphasize the role of expertise in navigating complex administrative processes. However, our analysis points to the necessity of complementing communication-based interventions with other supply-side enabling measures that ensure they aid the most disadvantaged.

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Journal Articles | 2022

Sparsistent filtering of comovement networks from high-dimensional data

Arnab Chakrabarti, and Anindya S. Chakrabarti

Journal of Computational Science

Network filtering is a technique to isolate core subnetworks of large and complex interconnected systems, which has recently found many applications in financial, biological, physical and technological networks among others. We introduce a new technique to filter large dimensional networks arising out of dynamical behavior of the constituent nodes, exploiting their spectral properties. As opposed to the well known network filters that rely on preserving key topological properties of the realized network, our method treats the spectrum as the fundamental object and preserves spectral properties. Applying asymptotic theory of high-dimensional covariance matrix estimation, we show that the proposed filter can be tuned to interpolate between zero filtering to maximal filtering that induces sparsity via thresholding, while having the least spectral distance from a consistent (non-)linear shrinkage estimator. We demonstrate the application of our proposed filter by applying it to covariance networks constructed from financial data, to extract core subnetworks embedded in full networks.

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Journal Articles | 2022

Fairness over time in dynamic resource allocation with an application in healthcare

Andrea Lodi, Philippe Olivier, Gilles Pesant, and Sriram Sankaranarayanan

Mathematical Programming

Decision making problems are typically concerned with maximizing efficiency. In contrast, we address problems where there are multiple stakeholders and a centralized decision maker who is obliged to decide in a fair manner. Different decisions give different utility to each stakeholder. In cases where these decisions are made repeatedly, we provide efficient mathematical programming formulations to identify both the maximum fairness possible and the decisions that improve fairness over time, for reasonable metrics of fairness. We apply this framework to the problem of ambulance allocation, where decisions in consecutive rounds are constrained. With this additional complexity, we prove structural results on identifying fair feasible allocation policies and provide a hybrid algorithm with column generation and constraint programming-based solution techniques for this class of problems. Computational experiments show that our method can solve these problems orders of magnitude faster than a naive approach.

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