Who Takes You to the Dance? How Partners’ Institutional Logics Influence Innovation in Young Firms

By Emily Cox Pahnke, Riitta Katila, Kathleen M. Eisenhardt. 2015

350 citations recorded by the source corpus.

Subfields: Accounting, Computer Science Applications, Management of Technology and Innovation

View Paper: https://www.semanticscholar.org/paper/4f3a923ccf60a03c8573959cac69a0a87d244fa0

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In Computer Science Applications

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Citations in the corpus

Funding Financial Inclusion: Institutional Logics and the Contextual Contingency of Funding for Microfinance Organizations (2016, background): “Another potential issue is that, while there is precedent for analyzing overall investment levels in both the management (e.g., Pahnke et al., 2015; Wry et al., 2014) and microfinance literatures (Gonzalez, 2010; Sapundzhieva, 2011), there are other potential ways to measure funding.”
Turning Lead into Gold: How Do Entrepreneurs Mobilize Resources to Exploit Opportunities? (2018, positive): “The literature on institutional logics provides a useful language for describing the heterogeneous motives underlying economic action (Pahnke, Katila, & Eisenhardt, 2015; Thornton, Ocasio, & Lounsbury, 2012).”
Signaling by early stage startups: US government research grants and venture capital funding (2018, background): “These competitions not only apply the internal scientific and technical capabilities of public agencies but also draw on highly prestigious external review partners that are able to better inform the process (Pahnke et al., 2015; Howell, 2014).”
The Role of Accelerator Designs in Mitigating Bounded Rationality in New Ventures (2018, background): “Also, keeping important information private helps firms maintain their competitive advantage (Pahnke et al., 2015).”
An institutional logics approach to social entrepreneurship: Market logic, religious diversity, and resource acquisition by microfinance organizations (2016, background): “…when variables of interest vary greatly across countries but are constant or slowly changing over time for each country (as in our case for the religious diversity variable), fixed-effects estimators are also less precise and produce large standard errors (Beck, 2001; Plumper and Troeger, 2007).”