Economic and Societal Impacts of Technology, Data, and Algorithms

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    The Effects of Quote Retweet on Subsequent Posting Behavior and Morality Expression on Social Media
    ( 2023-01-03) Wu, Yan ; Du, Qianzhou ; Zhang, Xiaohui ; Zhang, John
    Guided by the Threshold model and Self-justification theory, we propose and test a research model regarding the impact of online discussion activity on users’ behaviors on social media. Specifically, we examine the effects of quote retweeting a tweet related to immigration policies and border issues on users’ subsequent posting behaviors and morality expression. In addition, we test the moderating effect of individual threshold level and behavior-opinion inconsistency on the main effect. Results indicate that individuals, who quote retweeted the selected-topic tweets, are likely to post more topic-related tweets and express more on morality. This impact can be strengthened when individuals have higher threshold levels or larger behavior-opinion inconsistency. These findings provide both theoretical and practical implications for social media governance.
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    ICU Outcome Predictions Using Real-Time Signals with Wavelet-Transform-based Convolutional Neural Network
    ( 2023-01-03) Jiang, Yiqun ; Wang, Shaodong ; Li, Qing ; Zhang, Wenli
    Intensive care units (ICUs) serve patients with life-threatening conditions. The limited ICU resources cause severe economic and healthcare burdens worldwide. It is critical to conduct ICU outcome predictions at an early stage and promote efficient use of ICU resources. However, all the current prediction methods have limitations such as unsatisfactory accuracy and depending on resource-demanding laboratory tests or expert domain knowledge. In this research, we design a wavelet-transformed-based convolutional neural network, WTCNN, which only requires patients’ vital sign series and information at ICU admission for real-time ICU outcome predictions. The model is evaluated using a large real-world ICU database and outperforms state-of-art baselines on both ICU mortality and length-of-stay prediction tasks. We conduct LIME for model interpretation and prescriptive analysis. Our work provides an efficient tool for ICU outcome predictions, allowing healthcare providers to take action promptly on patients at risk and reduce the negative impacts on patient outcomes.
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    Firm Profiling and Competition Assessment: A Heterogeneous Occupation Network–based Method
    ( 2023-01-03) Zhong, Hao ; Liu, Chuanren
    Extensive efforts have been made by both academics and practitioners to understand inter-firm competitive relationship owing to its profound impacts on multiple key business goals. However, it has never been an easy task to fully characterize firms and assess the competitive relationship among them mainly due to the challenge of information heterogeneity. In this regard, we propose a novel IT artifact for firm profiling and inter-firm competition assessment guided by Information System Design Theory (ISDT). We start by constructing a Heterogeneous Occupation Network (HON) using employees’ occupation details and education attainments. Then we adopt a Methpath2Vec-based heterogeneous network embedding model to learn firms’ latent profiles (embeddings). Using the firm embeddings as input, we train multiple supervised classifiers to assess the competitive relationship among the firms. Following the logic of design as a search process, we demonstrate the utility of our IT artifact with extensive experimental study and in-depth discussions. Our study also reveals that employees’ occupation and education information significantly contribute to the identification of the focal firm’s potential competitors.
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    How Does Fundraiser-claimed Product Innovation Influence Crowdfunding Outcomes
    ( 2023-01-03) Zhang, Min ; Tong, Ling ; Liu, Jiazi ; Liu, Wei ; Fan, Weiguo (Patrick)
    The crowdfunding platforms have always been dedicated to supporting and inspiring innovative, and creative campaigns. However, limited research has been done to examine the fundraiser-claimed product innovation in campaign descriptions and its relation to fundraising performance. In this paper, we aim to tackle this important yet understudied problem. More specifically, we adopt a deep learning-based approach to extract sentences that contain innovation claims from project descriptions. We then conduct an empirical analysis to study the relation between fundraiser-claimed product innovation and crowdfunding performance by using a large sample consisting of 11,521 projects collected from Kickstarter across 4 project categories. Findings show a statistically significant association between fundraiser-claimed product innovation and crowdfunding performance. Additionally, the number of focal project innovation claims has a curvilinear relationship (inverted ‘U’ shape) with crowdfunding performance. Our study contributes to both product innovation detection and crowdfunding literature by demonstrating the association between product innovation presentation and crowdfunding performance.
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    AI Assistant in Online Pharmacy
    ( 2023-01-03) Shen, Tong ; Liang, Chen ; Peng, Jing ; Guan, Mengcheng ; Li, Jianbin
    Artificial intelligence (AI) has been increasingly popular in diagnosing diseases and recommending drugs in digital healthcare platforms. Leveraging the introduction of an AI-powered medical assistant to one drug category in an online pharmacy platform, we investigate how the adoption of AI affects users’ purchase behaviors using a difference-in-differences design. We find that the adoption of the AI assistant significantly increases users’ purchases in the platform, even for drugs not recommended by the AI assistant. Furthermore, we find that the positive effect of the AI assistant adoption is stronger for early technology adopters, inexperienced users, and users with higher privacy concerns, likely because these users tend to perceive higher value from AI. Finally, our mediation analysis shows that the AI feature increases users’ purchases by increasing their engagement levels in the platform. Our results have important implications for designing and evaluating AI features in online platforms.