Essays on Innovation Adoption and Distribution in Digital Era
| dc.contributor.advisor | Mela, Carl | |
| dc.contributor.advisor | Guo, Tong | |
| dc.contributor.author | Xu, Boya | |
| dc.date.accessioned | 2024-06-06T13:44:39Z | |
| dc.date.available | 2024-06-06T13:44:39Z | |
| dc.date.issued | 2024 | |
| dc.department | Business Administration | |
| dc.description.abstract | In the rapidly digital era, the generation and market penetration of innovation are critical for economic and societal advancement. The rise of social media and two-sided e-commerce marketplaces has transformed the communication, distribution, and adoption of innovations, raising significant questions for producers, investors, adopters, and digital platforms. These challenges include deciphering the impact of information on innovation adoption, strategizing the optimal matching of innovations with potential adopters in digital channels, and understanding how distribution mechanisms like recommendation systems influence producers’ innovation generation decisions. This thesis addresses these issues to illuminate the dynamics of innovation in the digital era and its implications for management and policy. Chapter 2 examines the role of social media publicity in the early adoption of new products by local businesses, with a case study on Impossible Meat. It introduces a novel adoption metric based on social media announcements to track local business decisions and employs Natural Language Processing to analyze marketing communication. By exploiting the quasi-random variations of county-quarter-level news production for different topics, this chapter establishes a causal relationshipbetween social media exposure and product adoption. Findings demonstrate that local social media news coverage on innovation significantly boosts the adoption of impossible meat products by local entrepreneurs, especially the news content about producer financials. Furthermore, this chapter outlines the implications for content marketing strategy, drawing on the rich heterogeneity patterns by topic, geographical location, and timing. Chapter 3 contributes to solving challenges on the scalable distribution of innovation in online two-sided markets. In many digital contexts such as online news and e-tailing with many new users and items, recommendation systems face severalchallenges: i) how to make initial recommendations to users with little or no response history (i.e., cold-start problem), ii) how to learn user preferences on items (test and learn), and iii) how to scale across many users and items with myriad demographics and attributes. While many recommendation systems accommodate aspects of these challenges, few if any address all. This paper introduces a Collaborative Filtering (CF) Multi-armed Bandit (B) with Attributes (A) recommendation system (CFB-A) to jointly accommodate all of these considerations. Empirical applications including an offline test on MovieLens data, synthetic data simulations, and an online grocery experiment indicate the CFB-A leads to substantial improvement in cumulative average rewards (e.g., total money or time spent, clicks, purchased quantities, average ratings, etc.) relative to the most powerful extant baseline methods. Chapter 4 proposes future research on how recommendation systems affect content creation on social media. It explores how these systems influence creators’ genre choices. It outlines a model and experiments to optimize recommendation policiesfor platform monetization and user engagement, highlighting the interplay between content creation and distribution. Together, these chapters explore key aspects of innovation dynamics in the digital age, offering insights into how digital platforms can influence and enhance the adoption and distribution of new products and services. | |
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| dc.subject | Marketing | |
| dc.title | Essays on Innovation Adoption and Distribution in Digital Era | |
| dc.type | Dissertation |