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626 items in total found

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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Journal Articles | 2022

Cross-border environmental regulation and firm labor demand

Pavel Chakraborty, Anindya S. Chakrabarti, and Chirantan Chatterjee

Journal of Environmental Economics and Management

In 1994, due to environmental concerns, Germany banned a chemical called ‘Azo-dyes’, a primary input for the leather and textiles firms in India (a key exporter). Exploiting this as a quasi-natural experiment, we examine the effects of this cross-border regulatory change on labor compensation, particularly managerial, for both Indian upstream (dye-producing) and downstream (leather and textile) firms. We find that the regulation increased compensation of managers by 1.3%–18% in dye-producing firms compared to other chemical firms. This is due to the combination of changes such as investing in R&D, product churning, import of high-quality intermediates, due to the ban, which led to this change in within-firm labor composition. This increase in overall compensation is driven only by fixed component (wages), consistent with the effects of a long-run shock. We find no such effects for downstream firms. We believe, our study is one of the first to show that just like tariff, non-tariff barriers (NTBs) can also significantly affect within-firm labor composition.

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Journal Articles | 2022

Understanding “reverse” knowledge flows following inventor exit in the semiconductor industry

Mayank Varshney, and Amit Jain

Technovation

Organizational learning research suggests that employee exit lowers firm performance by eroding its human and social capital. We have a rather limited understanding of the conditions under which exit from a focal firm, defined as the firm from which exit takes place, may stimulate learning and reverse knowledge flows from the hiring firm. We developed a model of learning-by-exit to address this gap and tested it using a long panel of data (1985–2012) from the semiconductor industry. Our model suggests that the focal firm is likely to benefit more from reverse knowledge flows from the hiring firm when it is less aware of the latter. A focal firm is less aware of the hiring firm when there have been no prior inter-firm interactions between them, and when they are separated by a larger geographic and technological distance. Econometric analysis of our data using zero-inflated Poisson regressions provides empirical support for our model. This research contributes to our understanding of knowledge spillovers by highlighting the criticality of firm heterogeneity in the relationship between employee exit and reverse knowledge flows.

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Journal Articles | 2022

Intervening on impostor phenomenon: prospective evaluation of a workshop for health science students using a mixed-method design

Shine Chang, Hwa Young Lee, Cheryl Anderson, Kava Lewis, Devasmita Chakraverty, and Melinda Yates

BMC Medical Education

Unaddressed impostor feelings that impede developing interest in science and self-efficacy in conducting research have a dispiriting effect that perpetuates unsatisfactory diversity in the health science workforce when such feelings are experienced more by those historically underrepresented in the workforce. This warrants effective interventions to reduce the impact of impostor feelings and related factors that diminish career resilience. We examined the effects of a 90-minute workshop on impostor perceptions and growth mindset to raise awareness of impostor phenomenon (IP) and develop skills to manage IP successfully for students attending a 10-week summer research experience program.

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IIMA