Building Strong Relationships: Strategies for Managing Marketplace Interactions
Interactions in the marketplace refer to the value exchanges between buyers and sellers. Typically, these interactions occur in two ways: in-person, face-to-face interactions, such as those that occur in a B2B setting, or virtually online, such as those between users on an online platform. Managing interactions lie at the core of building relationships between buyers and sellers. By engaging in effective interactions, firms can update their knowledge about their customers' needs and preferences. This can help firms provide personalized offerings which can help provide better experiences. Effective and efficient management of interactions between firms and their customers has become a source of lasting competitive advantage. Therefore, companies invest a significant amount of time and resources to control and fine-tune these interactions to ensure customer satisfaction and loyalty. The abundance of research examining various aspects of managing interactions is not surprising. These research studies typically focus on B2C settings, such as retail, or traditional B2B settings, which involve salespeople approaching buyers. However, despite the wealth of research, scholars in this field face new challenges in understanding interactions. There is a lack of research on two vital aspects of interaction management: firstly, identifying and implementing strategies that improve (or diminish) the quality of interactions; and secondly, comprehending how activities can be utilized to enhance buyer interest. These issues are crucial for both traditional business models, such as business-to-business (B2B) contexts, and emerging business models, like customer-to-customer (C2C) platforms. My dissertation essays make important theoretical and managerial contributions two different areas - network effects and sales management. The first essay advances our understanding on how the similarity between consumers shapes the way they interact with other consumers on online platforms. While prior studies have investigated the effects of network size on engagement, this essay focuses on network homogeneity, or the degree of similarity between network users. We theorize that homogeneity improves engagement because it improves the quality of user interactions. We test our theory using daily gamer-level data from a massively-multiplayer-online-game that introduces five for-purchase add-ons. Each add-on introduction creates separate networks of add-on adopters and non-adopters. We find that engagement increases with network homogeneity and size. Our results show that if network size grows without concomitant increases in network homogeneity, then user engagement is reduced. This creates conditions where small homogeneous networks generate greater platform engagement compared to large heterogeneous networks. Indeed, we find that add-on non-adopters increase their engagement after an add-on is launched even though the add-on non-adopter network shrinks as add-on adopters migrate to their new network. This occurs because the add-on non-adopter network becomes more homogeneous as add-on adopters leave. Lastly, greater network homogeneity increases the likelihood a user purchases the videogame sequel. Our results have implications for platform managers designing strategies that improve the quality of user interactions. The second essay contributes to the literature on sales management by examining how sales representatives balance various activities while interacting with buyers. B2B firms are increasingly adopting sales activity tracking technologies to manage their sales pipeline, but there is limited knowledge on how to effectively implement these activities within ongoing relationships. This study aims to bridge this gap by investigating how salespeople can use activities to enhance buyer interest during interactions. We utilize a unique longitudinal dataset that captures interactions between buyers and salespeople stored in textual format. We leverage novel text analysis techniques such as Generative Pretrained Transformer models (popularly known as ChatGPT) and automated topic modeling to process text data and subsequently identify of four distinct activities (information sharing, problem-solving, data analyzing, and interaction planning). We differentiate the activities based on their temporal orientation focus and their role in the trajectory of the relationship. These theoretical distinctions enable predictions about the independent and joint impact of salesperson activities on buyer interest at the interaction level. Results indicate that present-focused activities are sufficient in enhancing buyer interest, while past and future-focused activities are less effective. Moreover, past and future activities have complementary roles, moderating the impact of present-focused activities on buyers interest. Sales managers can leverage these insights to train salespeople to improve the effectiveness of their activities.