Local Competence Building and International Venture Capital in Low-Income Countries:Exploring foreign high-tech investments in Kenya's Silicon Savanna

PurposeThe purpose of this paper is to shed light on the changing pattern and characteristics of international financial flows in the emerging entrepreneurial ecosystems of Sub-Saharan Africa (SSA), provide a novel taxonomy to classify and analyze them, and discuss how such investments contribute to competence building and sustainable development.Design/methodology/approachIn an exploratory study, the authors analyze the characteristics of international venture capital investors and the start-ups receiving funding in Kenya and map their interaction. The authors proceed by developing a novel taxonomy, classifying investors according to their main rationales (for-profit-for-impact), and start-ups according to the locus of needs and markets addressed by the start-up (local-global) and the locus of the start-ups capacity and knowledge (local-global).FindingsThe authors observe a new type of mainly western investors who support innovative ideas in SSA by identifying and investing in domestically developed technical innovations with the potential to address global market needs. The authors find such innovations to be mainly developed at the intersect of global and local knowledge.Originality/valueThe authors shed light on the – up to now – under-researched emerging phenomenon of international high-tech investments in SSA, and develop a novel taxonomy of technology investments in low-income countries, guiding further research on the conditions, impact, practical, and policy implications of this new form of finance flows.</jats:sec

Social impacts reflected in CSR reports:Method of extraction and link to firms innovation capacity

Assessing and comprehending the social impact of firms at global and local level is a pressing concern for both researchers and policy-makers. To address this concern, our paper contributes to the stream of literature that studies the content of Corporate Social Responsibility (CSR) reports (which are also referred to as non-financial statements, sustainability reports or parts of annual reports) using text mining methods. We present a novel approach called Standard-based Impact Classification method (SBIC method), which employs natural language processing (NLP) and supervised machine learning techniques to identify the types of social impacts reflected in CSR reports. We deploy a Random Forest model which we train on reports adhering to Global Reporting Initiative (GRI) framework, enabling the identification of social impact in the majority of CSR reports that do not conform to this standard. Our proposed SBIC method serves as a valuable tool for comparing the social impacts generated by firms, industries, or countries. We showcase an application of our approach by examining the relationship between a company’s social impact and its innovation capacity. Our findings support the existing literature consensus that CSR activities generally exhibit a positive correlation with a firm’s ability to innovate. Furthermore, we reveal that specific types of social impacts have a more pronounced influence on innovation capacity

Determinants of Cross-Border Venture Capital Investments in Emerging and Developed Economies: The Effects of Relational and Institutional Trust

Acknowledgments We would like to thank Bengt-Åke Lundvall, Jesper Lindgaard Christensen, Andreas Pyka, Tereza Tykvová, Mike Wright, Volker Seiler, Douglas Cumming, Wenxuan Hou, Edward Lee, two anonymous referees, and all participants of the JBE Special Issue Tibet Conference 2014, the Stanford Scancor Seminar Series 2014, the IKE Research Seminar Series 2013, and the UK IRC Cambridge Young Scholar Workshop 2012 for invaluable insight, comments, inspiration, and feedback. All opinions and errors remain our own.Peer reviewedPublisher PD