Driving Open Innovation with Twitter: Strategies for Engaging Communities and Maximizing Impact

Open innovation (OI) is crucial for organizations in today's interconnected and rapidly changing technological landscape. Companies must leverage external knowledge and collaborations to stay competitive, and social media platforms have emerged as powerful tools for facilitating OI practices. However, despite the recognized importance of both OI and social media, there remains a significant gap in our understanding of how these two forces intersect, particularly on Twitter, a platform characterized by its real-time communication, global reach, and diverse user base. This dissertation addresses this gap by investigating the dynamics of OI communication on Twitter, employing a mixed-methods approach to analyse the sentiment, language, thematic structures, and network dynamics that shape discussions surrounding OI. Traditional closed innovation models, with their reliance on internal R&D, have proven increasingly inadequate in today's fast-paced environment. OI, conceptualized by Chesbrough (2003), recognizes the value of external knowledge and collaboration, encouraging firms to actively seek outside partnerships and ideas. This shift requires effective communication strategies to facilitate knowledge exchange, build relationships, and foster a culture of collaboration. This study examines how these strategies are employed on Twitter, a platform offering unique opportunities and challenges for OI communication. This dissertation employs a mixed-methods approach, combining quantitative analysis of a large Twitter dataset with qualitative case study insights and predictive modelling. This multifaceted approach provides a more comprehensive understanding of the OI discourse on Twitter, addressing a crucial gap in the literature by empirically investigating how OI is communicated and practiced on this platform. The findings reveal several key insights. Sentiment analysis indicates a generally positive tone associated with OI communication, suggesting favourable perceptions of this approach among Twitter users. Linguistic analysis uncovers distinct patterns in language use, including specialized vocabulary and an emphasis on collaboration. Topic modelling identifies eight key themes, ranging from funding and investment to emerging technologies and sustainability. Network analysis reveals a fragmented ecosystem, with clusters and influential actors shaping the conversation. Furthermore, a novel language-based cluster analysis identifies distinct communities characterized by unique communication styles, providing a more nuanced perspective than traditional hashtag analysis. A feed-forward neural network model successfully predicts tweet view count based on user characteristics, content, and timing, offering practical implications for optimizing communication strategies. This research contributes to both theory and practice. It deepens our understanding of OI communication dynamics on Twitter, informing the literature on OI, social media, and organizational communication. The findings offer practical guidance for organizations seeking to leverage Twitter for OI, enabling them to tailor their communication to specific audiences, build relationships with key actors, and foster a culture of collaboration. The predictive model offers a valuable tool for maximizing message reach and impact. The study acknowledges limitations, primarily its focus on a specific timeframe and platform. Future research could extend the analysis to other platforms, like LinkedIn, or conduct longitudinal studies to capture evolving trends. Further exploration of advanced machine learning techniques and integration of qualitative methods could provide even richer insights. This research serves as a first foundation for future investigations into the dynamic intersection of social media and open innovation.