Network Stability: The Role of Geography and Brokerage Structure Inequity

The effects of knowledge base characteristics on patent pledge financing: the moderating effects of technological munificence and dynamism

“…Firstly, alternative models are adopted to test the hypotheses. In this study, the xtlogit model, which is widely used when the dependent variables are binary and the relogit model, which can avoid rare event bias, are used to estimate the hypotheses and we found similar results (Kumar and Zaheer, 2022;Yang et al, 2021;King and Zeng, 2001). Secondly, we replace the measures of the theorised variables.…”

“…The cloglog model is preferred to the logit or probit models when the dependent variable has a skewed distribution, such as ours (i.e. a high percentage of zeros in the sample) (Kumar and Zaheer, 2022; King and Zeng, 2001). Our estimated sample had only 0.45% of pledge cases (5,006 events) of all observations, exhibiting extreme asymmetry.…”

What factors contribute to the formation of interorganizational collaboration networks: A longitudinal study in response to COVID‐19 emergency

“…If this information is unavailable, we then identify its business scope through Google. Geographical proximity is defined as $$${D}_{{ij}}=C\{\mathrm{across}[\sin ({\mathrm{lat}}_{i})\sin ({\mathrm{lat}}_{{\rm{j}}})]+\cos ({\mathrm{lat}}_{i})\cos ({\mathrm{lat}}_{j})\cos (|{\mathrm{long}}_{i}-{\mathrm{long}}_{j}|)\},$$$where $${D}_{{ij}}$$ is the straight‐line distance between organization i and j ; lat and long represent the latitude and longitude of the organization, respectively; and C is the coefficient that converts the Earth's arc into surface geographic distance ( C = 3437) (Kumar & Zaheer 2022; Sorenson & Audia, 2000). (4) Social proximity in stages T2–T6 is measured based on the number of collaborative relationships set during the prior stage of the emergency response process (Maskell & Malmberg, 1999).…”

Network Dynamics and Organizations: A Review and Research Agenda

“…In micro research, context-based drivers of network dynamics include formal organizational designs and work processes (e.g., Argyres et al, 2020; Caimo & Lomi, 2015; Clement & Puranam, 2018), geographic and physical location (e.g., Sailer & McCulloh, 2012), performance feedback (Parker et al, 2016), and disruptive events, such as corporate downsizing (Aalbers, 2020; Shah, 2000). In macro research, context-based drivers include changes in regulatory environments (e.g., Aggarwal et al, 2020; Zhang, Tan, & Tan, 2016), geographic proximity and colocation (e.g., Ghosh, Ranganathan, & Rosenkopf, 2016; Kim, Howard, Pahnke, & Boeker, 2016; Kumar & Zaheer, 2021), and mergers and acquisitions (e.g., Hernandez & Shaver, 2019).…”

“…Whereas some studies of interorganizational network dynamics have focused on characteristics that distinguish one firm from one another, others have emphasized general tendencies across firms, such as the general preference to build new ties with trusted partners (e.g., Baum, Rowley, Shipilov, & Chuang, 2005), a preference for partners who possess superior social capital (e.g., Hernandez & Shaver, 2019), and the quest for power and control (e.g., Howard et al, 2017). However, actor's attempts to develop ties that increase their power and control over other actors can trigger counteractions by those actors, suggesting that network dynamics evolve as a function of moves and countermoves rather than just unilateral action (e.g., Kumar & Zaheer, 2021; Rogan & Greve, 2015). Given the heightened possibility of opportunism in market relations, risk reduction is a common reason for network change in macro research.…”