Exploring interaction differences in Microblogging Word of Mouth between entrepreneurial and conventional service providers

In this study, we explore the interaction network properties of Microblogging Word of Mouth (MWOM), and how it is utilized by two different types of service providers, namely entrepreneurial and conventional. We use social network analysis, involving network metrics, sentiment, content and semantic analysis of real time data collected via Twitter, to compare two providers in terms of how they leverage MWOM in their social interactions. Results demonstrate that MWOM is utilized in an inherently different manner by an entrepreneurial provider, compared to a conventional one. Based on the findings, the study identifies distinctions between the entrepreneurial and conventional service providers in how they utilize MWOM on social media. Specifically, the entrepreneurial provider capitalizes on the interactive nature and dialogic capabilities of Twitter; whereas the conventional provider mostly relies on focal information sharing, thus neglecting the network members’ content creation and relationship building capability of social media networks. The study has significant implications as it provides key insights and lessons in terms of how companies should respond to emerging digital opportunities in their online social interactions

Entrepeneurial strategy making, dynamic capabilities & small firm growth

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Mapping the signaling environment between sustainability-focused entrepreneurship and investment inputs: A topic modeling approach

The need for climate action has increased attention to sustainability-focused entrepreneurship. In this context, entrepreneurial firms play a fundamental role in developing high-technology solutions for decarbonization but face funding gaps due to the liabilities of newness and smallness. Despite the importance of signaling in entrepreneurship, little is known about what and how to effectively signal to attract investor interest in small ventures that develop sustainable technologies. To address this gap, the present study is anchored in signaling theory and suggests a topic modeling solution to identify signals presented in company self-descriptions and areas of activity, alongside their investment inputs. Using data extracted from Crunchbase, a corpus of 5099 self-descriptions of small sustainable technology ventures over a period of 10 years, this study provides novel insights into the signaling environment of sustainability-focused entrepreneurship. The study's findings have implications for the sustainability ecosystem, namely, start-ups, small- and medium-sized enterprises, investors, and policymakers