Emergent IT artifacts have opened new opportunities for various stakeholders in e-business, including expert traders on social trading platforms and retail sellers on e-commerce. Specifically, the advantages of certain IT artifacts, such as gamification on financial platforms and algo-pricing techniques, have been recognized for the users on platforms and online sellers, respectively. However, given the lack of in-depth examination of the effects of the novel artifacts, their impact on platforms and users remains uncertain. If improperly applied, these artifacts could lead to losses for consumers and platforms. This dissertation explores the effects of innovative IT artifacts, comprising two studies that investigate the effects of algo-pricing on e-commerce and a goal setting-related gamified system on a social trading platform.

The first study, “The Pitfalls of Goal Setting in Social Trading Platforms: Implications for User Investment Behaviors” concentrates on the goal gradient effects of the gamification setting on user investment under the social trading context. Drawing on goal gradient theory, it investigates the impact of goal gradient on users’ investment performance, mediated by investment behavioral features such as trading activities and gambling-trading propensity. Using data from a popular social trading platform, I found empirical support that induced by goal attainment, users who are close to the goal become more and more active in their investment activities with goal proximity. Simultaneously, these users significantly increase their lottery-like trading tendency to attract monetary flow and meet the upgrade requirements when approaching the goals. As a result, users’ investment performance gets worse with decreased distance to goal attainment. Therefore, adopting goal setting can cause some intended outcomes, i.e., users tend to take on some risky investment for their short-term benefits, at the cost of potential loss in investment return of the copiers.

The second study, “The Effects of Algorithmic Pricing on Market Structure and Seller Performance: Evidence from an Online Marketplace,” delves into the effects of algo-pricing on sellers and markets. Algorithmic pricing is a prevalent business practice that enhances merchants’ competitiveness. However, due to the novelty of algorithmic pricing and its ability to incorporate diverse pricing strategies, the public understanding of its impacts remains limited. The existing literature presents inconclusive evidence of its effects. One concern is that employing algorithmic pricing may adversely affect market competition and sellers’ welfare (e.g., market share). To shed light on this topic, our study examines the consequences of algorithmic pricing on market competition, including market price, market concentration, and sellers’ market share, using data from Amazon.com. Our findings indicate that algorithmic pricing leads to lower market concentration and lower market prices and confers an advantage to the sellers in gaining more market share. Specifically, algorithmic pricing with more unique price values over time generates higher premiums for sellers adopting it.

The dissertation contributes to the current literature in three folds. First, the first study contributes to goal gradient theory by demonstrating that goal-based gamification design can lead to the amplification of users’ investment engagement on a social trading platform and revealing the shortcomings of goal setting in a two-sided financial platform—users of the gamified system can perform risky investment operations for their short-term benefit in goal attainment at the cost of the copiers investment revenue. Second, the first study also contributes to the gamification literature. Few gamification studies in the finance context have carefully examined the impacts of a badge-based system under social trading. I found a mediator, lottery-like trading tendency, representing short-term behaviors that lead to negative investment outcomes of the gamification application. Such a result contributes to the literature by revealing a mediator and helps explain some mixed results of gamification design effectiveness. Third, the second study contributes to the algo-pricing literature by comprehensively showing the outcomes of algorithmic pricing on market competition and extending the application of innovation disruptive theory to the retailing industry.

From a managerial perspective, this dissertation contributes in four aspects. Firstly, the first study emphasizes the importance of utilizing long-term goal assessments to reduce risk-over-taking trading behaviors and foster sustainable investment for copiers for social trading platforms. Secondly, the first study indicates the value of gamification in enhancing user engagement, especially for those with goals and rewards. Thirdly, the second study suggests that algo-pricing can strengthen market competition, offering insights for policymakers on market structure regulation.