We study the influence minimization problem: given a graph
\(G\)
and a seed set
\(S\)
, blocking at most
\(b\)
nodes or
\(b\)
edges such that the influence spread of the seed set is minimized. This is a pivotal yet underexplored aspect of network analytics, which can limit the spread of undesirable phenomena in networks, such as misinformation and epidemics. Given the inherent NP-hardness of the problem under the independent cascade (IC) and linear threshold (LT) models, previous studies have employed greedy algorithms and Monte Carlo Simulations for its resolution. However, existing techniques become cost-prohibitive when applied to large networks due to the necessity of enumerating all the candidate blockers and computing the decrease in expected spread from blocking each of them. This significantly restricts the practicality and effectiveness of existing methods, especially when prompt decision-making is crucial. In this paper, we propose the AdvancedGreedy algorithm, which utilizes a novel graph sampling technique that incorporates the dominator tree structure. We find that AdvancedGreedy can achieve a
\((1-1/e-\epsilon)\)
-approximation in the problem under the LT model. For the problem under the IC model, since the problem is APX-hard, we further propose a novel heuristic algorithm GreedyReplace, based on identifying the relationships among candidate blockers. Experimental evaluations on real-life networks reveal that our proposed algorithms exhibit a significant enhancement in efficiency, surpassing the state-of-the-art algorithm by three orders of magnitude, while achieving high effectiveness.
While prior research has primarily focused on the direct value of information technology (IT) and IT adoption by individuals and firms, this study explores the indirect value of IT in the form of public infrastructure technology. By exploiting a spatial discontinuity in water monitoring stations, we discover that firms located immediately upstream of water monitoring stations exhibit significantly lower levels of corruption than firms located immediately downstream. These findings are particularly noteworthy given that water monitoring stations have the potential to generate significant indirect value as they are not explicitly designed to mitigate corruption. Further analyses reveal that public infrastructure technology alone does not hold the key to mitigating corporate corruption. Instead, it is the synergistic interplay between public infrastructure technology and organizational change that drives the outcome. These findings contribute to a deeper understanding of the broader IT value landscape, emphasizing the indirect value of technological advancements in public infrastructure that were not originally intended for such benefit. Additionally, our findings highlight the benefits of leveraging existing infrastructure technology to address emerging societal needs.
The literature often explains female labor force participation through factors such as schooling, wage gaps, and fertility. In this study, we identify how technology-induced time savings from household chores increase female labor force participation in South Korea. Using a leads-and-lags difference-in-differences model, we find that the entry of online food delivery platform significantly increased the female employment rate in the three years following the platform’s entry, and the results still hold after excluding employment in the food and beverage sector. Our further analyses show that such digital platforms offered a pathway for women to break free from traditional household roles, thus granting them more time and the opportunity to decide whether to join the labor market or stay at home. We examine the positive externalities generated by the online food delivery platform and find that this new technology-induced female employment accounted for 0.27% of South Korea’s GDP, or 17 times the platform’s direct revenue.
Firms have been proactively holding data science competitions via online contest platforms to look for innovative solutions from the crowd. When firms are designing such competitions, a key question is: What should be a better contest design to motivate contestants to exert more effort? We model two commonly observed contest structures (one-stage and two-stage) and two widely adopted prize structures (high-spread and low-spread). We employ economic experiments to examine how contest design affects contestants’ effort level. The results reject the base model with rationality assumption. We find that contestants exert significantly more effort in both the first stage and the second stage of the two-stage contest. Moreover, it is better to assign most prizes to the winner in the two-stage contest while it does not matter in one-stage. To explain the empirical regularities, we develop a behavioral economics model that captures contestants’ psychological aversion to falling behind and continuous exertion of effort. Our findings demonstrate that it is important for contest organizers to account for the non-pecuniary factors that can influence contestants’ behavior in designing a competition.
“Product Search and Sourcing in Live-Commerce: Evidence from a Quasi-Experiment”, (with Chu Dang). 2024 ISMS Marketing Science Conference, June 2024, Sydney, Australia.
“Your Movement in a City Reveals Your Credit”, (with Youngsok Bang), Post-ICIS KrAIS Research Workshop 2021, December 2021. (KrAIS Best Student Paper Award)
“Your Movement in a City Reveals Your Credit”, (with Youngsok Bang), Korea Intelligent Information Systems Society Fall Conference 2021 (KIISS 2021), December 2021. (KIISS Best Paper Award)
“Risk Disclosure Policy in Crowdfunding”, (with Siqi Pei, Keehyung Kim), 17th Symposium on Statistical Challenges in Electronic Commerce Research (SCECR 2021), June 2021
“Online Food Delivery Platforms and Female Labor Force Participation”, (with Siqi Pei, Xiaoquan Michael Zhang) 31st Workshop on Information Systems and Economics (WISE), December 2020. (WISE Best Paper Award)
“Designing Multi-Stage Contests: Does the Contest Structure Matter?”, (with Keehyung Kim), 16th Symposium on Statistical Challenges in Electronic Commerce Research (SCECR 2020), June 2020
“Your Movement in a City Tells Your Credit: Credit Default Prediction based on Geolocation Information”, (with Youngsok Bang). 13th China Summer Workshop on Information Management (CSWIM), June 2019, Shenzhen, China
“Your Movement in a City Tells Your Credit: Credit Default Prediction based on Geolocation Information”, (with Youngsok Bang). 2018 INFORMS Marketing Science Conference, June 2018, Pennsylvania, USA