Vipul Aggarwal, Elina Hwang, and Yong Tan. Information Systems Research 32(4):1214-1235. Awarded TISR Cutting-Edge Analytics Award 2021.
This study investigates the creative idea generation process in an open innovation platform. The idea generation process is simultaneously influenced by multiple activities: knowledge acquisition from participants’ interactions with each other’s ideas, deliberate practice through persistent participation, and learning through failures. Due to the dynamic interplay across these activities, it is challenging to identify each activity’s influence on creative ideation outcomes using reduced-form regression analysis. To overcome these challenges, we employ a comprehensive empirical framework, the mutually exciting spatiotemporal point process model with unobserved heterogeneity, which endogenizes the occurrences of these activities in continuous time and allows for user-dependent effects. By utilizing the activity stream data of 13,028 participants from 2010 to 2016 in an open innovation platform, we uncovered synergistic effects of these activities on creative outcomes. We find that knowledge acquired through interaction with others (i.e., stimulus ideas) plays a vital role in the creative ideation process, but their effect is more nuanced than what we have known so far. In contrast to the prior belief that distant analogies, stimulus ideas outside of a problem domain, spur creativity, we find that distant analogies lead to failures. Yet, we further find that such failures are indispensable to the creative ideation process because failures motivate idea generators (1) to acquire more knowledge by increasing their future interactions with other participants’ ideas (learning from others), and (2) to persist in generating ideas that lead to improvements in their ability to apply the acquired knowledge and to identify innovation tasks that are relevant to their stock of acquired knowledge (learning by doing). Our results indicate that failures are a stronger driver of the learning activities than successes. Based on our findings, we offer insights on how to cultivate creativity in an open innovation setting.
Structural Analysis of Bitcoin Cash’s Emergency Difficulty Adjustment Algorithm
Vipul Aggarwal, Lijia Ma, Yong Tan. Under review in Information Systems Research (Second Round Revision) (2025).
In this paper, we analyze the equilibrium behavior of miners involved in mining Bitcoin (BTC) and Bitcoin Cash (BCH) cryptocurrencies during the period of April 2017 - February 2018. BCH was born out of a contentious BTC fork in August 2017 and inherited BTC’s characteristics such as its mining and difficulty adjustment algorithms. BCH developers introduced the Emergency Difficulty Adjustment (EDA) algorithm to incentivize miners to process BCH’s transactions over that of BTC’s. Based on the strategic actions of the miners, difficulty changes brought through EDA resulted in dynamic changes to the difficulty adjusted profitability of BCH. This manifested in wild swings in their mining rates. Consequently, it leads to questions regarding the effectiveness of such emergency mechanisms and their economic validity. Therefore, we construct an equilibrium model of cryptocurrency mining to uncover miners’ profit motivations, with and without such mechanisms. We utilize a two-step structural approach to recover the model primitives. Using the publicly available chain data, we find that competition among miners is a major contribution to miners’ per-period payoffs. However, contribution of competition is dependent on the state variables affecting profits. The ability of EDA to affect such state variables leads to differing impacts of competition. Through our counterfactual simulations, we assess the sensitivity of miners’ profits to the EDA algorithm’s configuration and its impact on the cryptocurrency reward-halving schedules.
Status gains and subsequent effects on evaluations
Vipul Aggarwal, Michelle Kyungmin, Elina Hwang. Academy of Management Proceedings (2017).
This paper shows how gaining status can affect the status holder’s subsequent evaluations. Yelp reviews are used as a measure of evaluations. We use expectation states theory to hypothesize that a status gain will result in higher quality evaluations characterized by a wider range of topics discussed in subsequent reviews. We also hypothesize that newly minted elites will conform their reviews to match the reviews of other Yelp elites. Finally, we hypothesize that status gain will also result in more critical reviews in the form of lower star ratings. We use a difference-in-difference model to examine the treatment effect of status on subsequent evaluations. We match the control group with the treatment group using propensity score matching. We then use topic modeling to choose categories to characterize the diversity of reviews as a proxy for review quality. Our results indicate that status gain results in higher quality evaluations and conformity of reviews to other elite users’ reviews, but no significant difference in criticality of reviews.
Does Amazon Search Dream of Query Attributes? A Semi-Supervised Approach to Attribute Annotation.
Vipul Aggarwal, Vishnu Narayanan, Vaclav Petricek, & Rahul Bhagat. Amazon Machine Learning conference (2020).
Paper and abstract do not have public access.
ASIN2Vec: Ataxonomy-based embedding for products
Vipul Aggarwal, Andrey Volozin, M. Hidayath Ansari, Subhadeep Chakraborty, Avik Sinha. Amazon Machine Learning conference (2018).
Paper and abstract do not have public access.
Economic Impact of Facebook on the Evolving Market Structure in News Industry
Vipul Aggarwal, Mariia Mitkina, and Yong Tan. Secured Facebook Data Grant (2019).
Analysis of Demand for Information Goods from Online News Organization
Vipul Aggarwal, Stephanie Lee, and Yong Tan. Secured Facebook Data Grant (2019).
An Analysis of the Role of Advertisement on General Elections in India
Vipul Aggarwal, Uttara Ananthakrishnan and Yong Tan. Secured Facebook Data Grant (2019).