Exploring organizational pressures on data driven decision making in social service nonprofits : a Grounded Theory study
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In most sectors across the United States, individuals within organizations use data-driven decision making to understand their outcomes and make decisions about the future of their organization's work (Brynjolfsson et al., 2011). This is rarely happening in the approximately 112,000 human service nonprofits in the United States (Bopp et al., 2017; National Center for Charitable Statistics [NCCS], 2020). Recognizing these barriers is crucial for progress. Data-driven decision making (DDDM) involves three stages: defining relevant data, establishing measurement protocols, and utilizing data for decision-making (Julnes and Holzer, 2001; Mandinach, 2012). The study seeks to build a theoretical foundation lacking in previous literature by interviewing leaders and front-line workers at social service nonprofit organizations in Boone County, Missouri to better understand influences on their utilization of data in decision making. Employing a non-experimental Grounded Theory approach, the study explores the relationship between organizational state and actions within social service nonprofits, aspects of DDDM. Qualitative semi-structured interviews and quantitative demographic data collection were used to develop a theoretical model for DDDM in this sector. Twenty organizations were randomly selected from a sample frame of 55 social service nonprofits, resulting in 34 participants from 18 nonprofits. Qualitative data, gathered through interviews, provided insights into participants' experiences and perceptions of DDDM effectiveness. A Grounded Theory analysis was employed to analyze interview transcripts. Quantitative data, including organizational information from IRS reports and individual self-report surveys, supplemented the qualitative findings, allowing for robust validation of qualitative results. The study revealed three key findings: 1) the differing influences of internal and external stakeholders on organizational actions; 2) the significant role of organizational capacity in shaping data definitions and processing; 3) and the primacy of organizational factors over individual ones in influencing DDDM. These findings lay a foundation for future research and practical interventions aimed at fostering effective DDDM in social service nonprofits. By addressing gaps in current literature and offering insights into challenges and opportunities for DDDM implementation, this research contributes to the advancement of effective, equitable, and sustainable practices within social service nonprofits.
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Ph. D.