Essays on Stakeholder Networks in Marketing
Open Access
- Author:
- Schmid, Franziska
- Graduate Program:
- Business Administration
- Degree:
- Doctor of Philosophy
- Document Type:
- Dissertation
- Date of Defense:
- June 27, 2022
- Committee Members:
- J. Andrew Petersen, Chair & Dissertation Advisor
Stefan Wuyts, Major Field Member
Arvind Rangaswamy, Major Field Member
Brent Ambrose, Program Head/Chair
Bruce Desmarais, Outside Unit, Field & Minor Member - Keywords:
- social networks
business-to-business sales
community detection
social media - Abstract:
- This dissertation examines the importance of how stakeholder networks can generate insights for marketers. Although there has been ample research on some types of stakeholder networks in the marketing literature (e.g., customers), not all stakeholder networks have been given equal attention. In the introduction of this dissertation, I discuss the different types of stakeholder networks, including networks of customers, employees, and suppliers, as well as networks of firms’ followers on social media. The following essays address gaps in the marketing literature by examining how data on two specific networks of stakeholders (salespeople and social media followers) can aid firms in identifying potentially valuable marketing strategies. The first essay uses novel data to identify network ties between salespeople at a business-to-business (B2B) firm. Using this data on network ties, I measure salespeople’s social capital within the firm. I then distinguish how the effect of social capital on B2B sales is distinct from other categories of intellectual capital (i.e., human and organizational capital). I find that categories of intellectual capital have heterogenous impacts on salespeople’s success across different selling types (e.g., customer acquisitions, rebuying, and cross-selling). Among selling types, human capital is most beneficial for rebuying and social capital for cross-selling. In the second essay, I demonstrate how to use standard network analysis methods and community detection algorithms common in computer science to identify segments of followers using a large organization’s social media account. Many firms only apply a mass marketing strategy without targeting content to specific follower segments. I use four community detection methods to identify segments of social media followers with common connections or interests. My results show that among all methods tested, the algorithm that utilizes both network connections and follower interests as inputs to detect “communities” generates segments with the most specific community characteristics. These results suggest that firms can leverage their existing social network data to detect communities based on follower connections and interests, enabling more granular marketing strategies. I conclude by discussing future directions for stakeholder network research.