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Abstract The Institutional Grammar (IG) is a rigorous tool for analyzing the laws and policies governing nonprofit organizations; however, its use was limited due to the time-consuming nature of hand-coding. We introduce an advance in Natural Language Processing using a semantic role labeling (SRL) classifier that reliably codes rules governing and guiding nonprofit organizations. This paper provides guidance for how to hand-code using the IG, preprocess text for machine learning, and demonstrates the SRL classifier for automated IG coding. We then compare the hand-coding to the SRL coding to demonstrate its accuracy. The advances in machine learning now make it feasible to utilize the IG for nonprofit research questions focused on inter-organizational collaborations, government contracts, federated nonprofit organizational compliance, and nonprofit governance, among others. An added benefit is that the IG is adaptable for different languages, thus enabling cross-national comparative research. By providing examples throughout the paper, we demonstrate how to use the IG and the SRL classifier to address research questions of interest to nonprofit scholars. 

ABSTRACT Institutional arrangements that guide collective action between entities create benefits and burdens for collaborating entities and can encourage cooperation or create coordination dilemmas. There is an abundance of research in public policy, public administration, and nonprofit management on cross‐sector alliances, co‐production, and collaborative networks. We contribute to advancing this research by introducing a methodological approach that combines two text‐based methods: institutional network analysis and cost–benefit analysis. We utilize the Institutional Grammar to code policy documents that govern relationships between actors. The coded text is then used to identify Networks of Prescribed Interactions to analyze institutional relationships between policy actors. We then utilize the coded text in a cost–benefit analysis to assess benefit and burden distributive effects. This integrated methodological framework provides researchers with a tool to elucidate both the institutional patterns of interaction and distributive implications embedded in policy documents, revealing insights that single‐method approaches cannot capture. We then utilize the coded text in a cost–benefit analysis to assess benefit and burden distributive effects. This integrated methodological framework provides researchers with a tool to elucidate both the institutional patterns of interaction and distributive implications embedded in policy documents, revealing insights that single‐method approaches cannot capture. To demonstrate the utility of this integrated approach, we examine the policy design of two nonprofit open‐source software (OSS) incubation programs with contrasting characteristics: the Apache Software Foundation (ASF) and the Open Source Geospatial Foundation (OSGeo). We select these cases because: (1) they are co‐production alliances and have policy documents that articulate support for collective action; (2) their policy documents and group discussions are open access, creating an opportunity to advance text‐based policy analysis methods; and (3) they represent juxtaposed examples of high and low risk for collaboration settings, thereby providing two illustrative cases of the combined network and cost–benefit text‐based methodological approach. The network analysis finds that ASF policies, as a high‐risk setting, emphasize bonding structures, particularly higher reciprocity, which creates a context for cooperation. OSGeo, a low‐risk setting, has policies creating a context for bridging structures, evident in high brokerage efficiency, to facilitate coordination. The cost–benefit analysis finds that ASF policies balance the distribution of costs and benefits between ASF and projects, while in OSGeo, projects bear both costs and benefits. These findings demonstrate that the combination of network and cost–benefit analysis is an effective tool for utilizing text to compare policy designs. 

Recent work on open source sustainability shows that successful trajectories of projects in the Apache Software Foundation Incubator (ASFI) can be predicted early on, using a set of socio-technical measures. Because OSS projects are socio-technical systems centered around code artifacts,we hypothesize that sustainable projects may exhibit different code and process patterns than unsustainable ones, and that those patterns can grow more apparent as projects evolve over time. Here we studied the code and coding processes of over 200 ASFI projects, and found that ASFI graduated projects have different patterns of code quality and complexity than retired ones. Likewise for the coding processes – e.g., feature commits or bug-fixing commits are correlated with project graduation success. We find that minor contributors and major contributors (who contribute <5%, respectively >=95% commits) associate with graduation outcomes, implying that having also developers who contribute fewer commits are important for a project’s success. This study provides evidence that OSS projects, especially nascent ones, can benefit from introspection and instrumentation using multidimensional modeling of the whole system, including code, processes, and code quality measures, and how they are interconnected over time. 

Sustainable Open Source Software (OSS) forms much of the fabric of our digital society, especially successful and sustainable ones. But many OSS projects do not become sustainable, resulting in abandonment and even risks for the world's digital infrastructure. Prior work has looked at the reasons for this mainly from two very different perspectives. In software engineering, the focus has been on understanding success and sustainability from the socio-technical perspective: the OSS programmers' day-to-day activities and the artifacts they create. In institutional analysis, on the other hand, emphasis has been on institutional designs (e.g., policies, rules, and norms) that structure project governance. Even though each is necessary for a comprehensive understanding of OSS projects, the connection and interaction between the two approaches have been barely explored. In this paper, we make the first effort toward understanding OSS project sustainability using a dual-view analysis, by combining institutional analysis with socio-technical systems analysis. In particular, we (i) use linguistic approaches to extract institutional rules and norms from OSS contributors' communications to represent the evolution of their governance systems, and (ii) construct socio-technical networks based on longitudinal collaboration records to represent each project's organizational structure. We combined the two methods and applied them to a dataset of developer digital traces from 253 nascent OSS projects within the Apache Software Foundation (ASF) incubator. We find that the socio-technical and institutional features relate to each other, and provide complimentary views into the progress of the ASF's OSS projects. Refining these combined analyses can help provide a more precise understanding of the synchronization between the evolution of institutional governance and organizational structure. 

Mentoring has been a subject of study for 50 years. Most studies of mentoring programs evaluate the effect of the program on the participants but do not evaluate if different mentors have different effects on mentees. Open-source software (OSS) is software with a license that allows it to be freely used by other people. Such software has become foundational to the world economy. However, many OSS projects get abandoned by their creators. Various nonprofit organizations have arisen to help OSS projects become sustainable. One of the key services offered by many of these nonprofit organizations is a mentorship program where experienced OSS developers advise nascent projects on how to achieve sustainability. We use data from the Apache Software Foundation Incubator program where 303 mentors have mentored 286 projects, with most mentoring more than one project, to address this question: Is who a project has as a mentor associated with variation in project success? Who a project has as a mentor accounts for 45% of the variation in project outcomes, with some mentors being associated with positive and some with negative outcomes. These mentors could offer insights into how to improve the mentoring program. This result also demonstrates, more broadly, that the nature of specific mentoring relationships may be important to understanding how mentors impact outcomes in other mentoring programs.