A creator platform utilizes marketing tools and pricing policies to maximize customer acquisition and revenue, with trade-offs among the creators, their audience, and the platform itself. In chapter “Referral Program Design”, we focus on how a platform can leverage a referral program to acquire new subscribers and maximize profit, and quantify the financial consequences of referral marketing with a structural model. The return from implementing a referral program for the creator is a function of the size of the monetary reward for successful referrals, and the price charged for new subscriptions. We find a substantial contribution of applying referral marketing to creators’ revenue, but this varies for specific types of content with different referral effectiveness and operation costs. Further, we document inverted-U shaped relationships for profit and demand as the size of referral rewards increase. Using counterfactual analysis, we highlight the disparity of profit-optimizing referral marketing design between creators and the platform since creators focus on short term profit while the platform focuses on long run user base growth. In chapter “Platform Commission Design”, we study the impact of platform commission changes on creators’ pricing decisions and quality. The same creator platform underwent significant adjustments to its commission policy. In August 2019, the platform increased its commission from 5% to 20% for all creators, and later allowed eligible creators to revert to the original 5% commission. Using a difference-in-differences approach, we examine the consequences of subsidizing eligible creators with a lower commission, and find a significant price increase induced by the commission cut, which contrasts sharply with classic prediction from third-degree price discrimination. If the initial commission increase altered market concentration, the subsequent reduction in commission could incentivize some creators to raise prices in order to reveal their status as high-quality content providers. This study suggests that changes in commission should be approached with caution due to their irreversible effects. Increasing commission while discriminating against a segment of creators shifts content and price distribution, potentially disadvantaging subscribers and reducing their welfare.

The concept of “genuine hospitality” extends beyond the mere provision of tangible offerings and hospitable behaviors by the host. It requires true hospitableness on the part of the service providers themselves. However, like humans, can AI also serve as a provider capable of embodying hospitableness? This dissertation seeks to establish a comprehensive theoretical framework called the Consciousness Attribution Model of AI Hospitableness (CAMAH) which encompasses three interconnected aspects: (1) the mechanism of consciousness attribution by consumers towards AI-enabled service providers, (2) the necessity of such attributions in recognizing the symbolic value of AI hospitableness, and (3) a nuanced comparison between human and AI providers concerning their capacity to deliver genuine hospitability. Structured into three scholarly essays, this dissertation first undertakes a philosophical and conceptual exploration, culminating in the proposition of CAMAH. Extending the theoretical foundations established in Essay 1, the subsequent essays (2 and 3) delve into empirical investigations within specific service technology domains, focusing on service robots and AI avatars equipped with self-service technologies, respectively. The significance of this dissertation lies in its identification of a necessary condition for AI service providers to be recognized as hospitable hosts capable of imparting hospitality-oriented, symbolic value, while clearly delineating the key boundaries that distinguish AI service providers—notwithstanding their potential to equip with anthropomorphic behaviors/forms—to human counterparts. / Tourism and Sport

Thesis (Ph. D.)--University of Oregon, 2007. / Typescript. Includes vita and abstract. Includes bibliographical references (leaves 249-257). Also available for download via the World Wide Web; free to University of Oregon users.

Thesis PlanB (M.S.)--University of Wisconsin--Stout, 2008. / Includes bibliographical references.

Thesis (M. Sc.)--University of Hong Kong, 2005. / Also available in print.

Thesis (Ph.D.)--University of Hong Kong, 2002. / Includes bibliographical references. Also available in print.

Thesis (Ph. D.)--University of Wisconsin--Madison, 1969. / Typescript. Vita. eContent provider-neutral record in process. Description based on print version record. Includes bibliographical references (leaves 357-368).

Thesis (MBus)--University of South Australia, 1996

Thesis (Ph. D.)--University of Iowa, 2007. / Thesis supervisor: Nick Street. Includes bibliographical references (leaves 109-116).

Thesis PlanA (M.S.)--University of Wisconsin--Stout, 2008. / Includes bibliographical references.