Consensus Formation in Nonprofit and Philanthropic Studies: Networks, Reputation, and Gender

Abstract

The research field of nonprofits and philanthropy has grown exponentially. To what extent do nonprofit scholars share a common language? Answering this question is crucial to assessing the field’s intellectual cohesiveness. We studied how coauthor networks, scholarly reputation, and the prevalence of female authors influence consensus formation. We found that the degree of consensus for all major research topics in the field has increased over time—For every 10% growth in the volume of literature, shared language increased by 1.4%. A cohesive research community on nonprofits and philanthropy has been forming since the early 2000s. Female scholars are fewer in number and less cited than males; their presence did not exceed 40% for most topics. The citation counts of scholars and small-world property of networks are positively associated with consensus, suggesting that star researchers and knowledge brokers bridging different intellectual communities are key to sharing research interests and language.

Institutional factors influencing knowledge production for practice: Evidence from nonprofit studies

“…Table 4 supports this point. Those researching the most productive topics can build their research primarily on paradigms from psychology, finance, and management—the areas where more research paradigms are shared and consensus level is high [ 42 ]. For research topics with less established theoretical paradigms, such as social enterprise, fewer articles are published.…”

Neutral, Non-Disruptive, and Native: Why Do Chinese Nonprofit Scholars Cite English Articles?

“…This study takes a disciplinary development approach to advance our understanding of this field’s development in different linguistic communities. It builds on curated datasets from or according to existing studies (Ma, 2023; Ma & Bekkers, 2023; Ma et al, 2023; Smith, 2013; Walk & Andersson, 2020). It also applies advanced and novel computational methods such as network analysis and multilingual topic modeling in natural language processing.…”