Article
Management
Ali Aouad, Daniela Saban
Summary: Motivated by online labor markets, this paper focuses on the online assortment optimization problem faced by a two-sided matching platform. The study investigates how platforms should design online assortment algorithms to maximize the expected number of matches in such settings. The results show that a simple greedy algorithm is 1/2-competitive against an optimal clairvoyant algorithm. However, no randomized algorithm can achieve a better competitive ratio, even in asymptotic regimes. The study further explores structured settings and develops new preference-aware balancing algorithms to improve the competitive ratios. The findings highlight the importance of suppliers' choices in designing online assortment algorithms for two-sided matching platforms.
MANAGEMENT SCIENCE
(2023)
Article
Computer Science, Artificial Intelligence
Yunmiao Gui, Dingbo Tan, Zhi Liu, Feng Dong
Summary: This study examines the impact of fairness concerns on pricing, matching, and profits in competitive two-sided markets. The findings show that platforms adjust their pricing strategy based on users' fairness concerns and exploit the disparity mentality to obtain higher pricing power. Additionally, users' fairness concern behaviors affect the platform's matching rate.
EXPERT SYSTEMS WITH APPLICATIONS
(2024)
Article
Business
Haksin Chan, Morgan X. Yang, Kevin J. Zeng
Summary: This research focuses on the strategic design of ratings and reviews systems on multi-sided platforms to ensure a steady flow of buyer-generated product knowledge. It presents a theoretical model and identifies exemplary practices through exploratory observations, contributing to theory and practice in the field of multi-sided platforms.
JOURNAL OF BUSINESS RESEARCH
(2022)
Article
Economics
Oguzhan Celebi, Joel P. Flynn
Summary: The researchers studied how to optimally coarsen an underlying score in centralized matching markets, and found that for any continuous objective function, the optimal design can be achieved by splitting agents into at most three indifference classes for each object under stable matching mechanisms. They provided insights into this design problem in three applications: distance-based scores in Boston Public Schools, test-based scores for Chicago exam schools, and income-based scores in New York public housing allocation.
REVIEW OF ECONOMIC STUDIES
(2022)
Article
Economics
Arim Park, Roger Chen, Soohyun Cho, Yao Zhao
Summary: Analyzing transactional data, we find that allowing price adjustments, longer lead time for shippers, and customer revisions in shipment information can improve successful matches in online freight platforms.
TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW
(2023)
Article
Computer Science, Artificial Intelligence
Yongxin Tong, Yuxiang Zeng, Bolin Ding, Libin Wang, Lei Chen
Summary: This paper investigates the Global Online Micro-task Allocation problem in spatial crowdsourcing and proposes the TGOA algorithm and its variants based on the random order model. By considering the average performance, algorithms with different competitive ratios are obtained and validated through experiments on synthetic and real datasets.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING
(2021)
Article
Mathematics, Interdisciplinary Applications
Sahin Telli, Hongzhuan Chen
Summary: This study investigates the relationship between the crypto markets of Bitcoin, Ethereum, Litecoin, XRP, and public attention on Reddit and Wikipedia. The analysis reveals varying degrees of multifractality and anti-persistent behavior in different time periods. Cross-correlations between social platform attention and market performances differ, with altcoins sharing similar dynamics with Bitcoin in terms of anti-persistence and multifractality.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Information Science & Library Science
Geng Sun, Huseyin Cavusoglu, Srinivasan Raghunathan
Summary: Membership-based free shipping (MFS) program is an important innovation in reducing shipping cost in online retailing. The program benefits the platform when the shipping cost is less than a threshold, but may stimulate demand from consumers with low consumption utility relative to the shipping cost, which could have negative social impact.
INFORMATION SYSTEMS RESEARCH
(2023)
Article
Business
Yonghong Sun
Summary: This study investigates the profitability and optimal number of versions for online dating platforms, as well as the impact of gender-based price discrimination. The findings suggest that providing multiple versions of services is more profitable than offering a single version. The optimal number of versions is determined as two: basic and premium. Additionally, a ban on gender-based price discrimination does not significantly affect versioning strategies.
ELECTRONIC COMMERCE RESEARCH
(2023)
Article
Management
Musa Eren Celdir, Soo-Haeng Cho, Elina H. Hwang
Summary: This paper examines the popularity bias in online dating platform recommendations and its impact on users' likelihood of finding dating partners. The study finds that while there may be bias against unpopular users, recommendations that maximize platform revenue and successful matches are not necessarily contradictory. Popular users can help the platform generate more revenue and successful matches as long as they do not become inaccessible.
M&SOM-MANUFACTURING & SERVICE OPERATIONS MANAGEMENT
(2023)
Review
Business
Marcello M. Mariani, Matteo Borghi, Benjamin Laker
Summary: By deploying big data analytical techniques, this study examines the impact of mobile devices on online consumer reviews and their satisfaction with services. Using more than 2.7 million OCRs from TripAdvisor.com and Booking.com, the research shows that the use of mobile devices positively influences OCR ratings on Booking.com, while the opposite effect is observed on TripAdvisor. These findings highlight the importance of online review policies in understanding consumer satisfaction and have implications for digital platforms, big data analytics, electronic word of mouth, and marketing research.
TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE
(2023)
Article
Management
Yi Liu, Bowen Lou, Xinyi Zhao, Xinxin Li
Summary: Recent advancements in matching technologies for gig-economy platforms have improved the matching between workers and employers, but they may also have unintended negative effects. Our study shows that better-matched jobs assigned by advanced matching technologies can unintentionally disclose labor demand information to workers, resulting in unfavorable participation decisions and a revenue loss for platforms, especially when demand is low. We extend our model to consider factors such as revenue sharing, continuous improvement of matching quality, competition between platforms, and worker competition. Our findings provide insights into the optimal matching quality a platform should pursue and suggest approaches to mitigate the potential negative effects.
MANAGEMENT SCIENCE
(2023)
Article
Computer Science, Information Systems
Mareike Mohlmann, Lior Zalmanson, Ola Henfridsson, Robert Wayne Gregory
Summary: This paper explores the nature of platform work on Online Labor Platforms (OLPs) and the role of algorithmic management in organizing how such work is conducted. The study reveals that platform workers experience tensions related to work execution, compensation, and belonging in the context of both algorithmic matching and algorithmic control, triggering market-like and organization-like response behaviors. This research contributes to the emerging literature on OLPs.
Article
Computer Science, Information Systems
Yi Yang, Yurong Cheng, Ye Yuan, Guoren Wang, Lei Chen, Yongjiao Sun
Summary: This paper presents the Privacy-preserving Cooperative Online Matching (PCOM) method to address the privacy concerns in online task assignment. By designing a PCOM framework and proposing two privacy-preserving algorithms, the effectiveness and efficiency of the approach are verified through extensive experiments.
PROCEEDINGS OF THE VLDB ENDOWMENT
(2022)
Review
Green & Sustainable Science & Technology
Marcello Mariani, Matteo Borghi
Summary: By analyzing over 2.7 million reviews, this study reveals that environmental discourse on digital platforms positively influences tourists' satisfaction with tourism and hospitality services. The findings contribute to our understanding of the impact of environmental concerns expressed in online reviews.
JOURNAL OF SUSTAINABLE TOURISM
(2023)
Article
Information Science & Library Science
Behnaz Bojd, Xiaolong Song, Yong Tan, Xiangbin Yan
Summary: This paper examines the effect of gamified challenges on weight loss outcomes. The study finds that participating in gamified challenges has a positive and significant impact on weight loss. Effective challenges are those that do not include numeric weight goals, focus on exercise-only behavioral goals, and have a large active group size. Additionally, leaderboards play a role in inducing social comparison and motivating users. These findings have implications for the design of gamified systems in online weight-loss communities.
INFORMATION SYSTEMS RESEARCH
(2022)
