The Critical Role of Race-related Stress and Racial Activism on STEM Graduate Students’ Career Aspirations: An Intersectional Perspective

This study explores the intersectionality of race, gender, and racialized experiences on career aspirations of racially underrepresented STEM students. Racial activism predicts academic career interest, while gender disparities persist. Race-related stress impacts entrepreneurship and mental health. Tailored support mechanisms are needed to address challenges faced by female and Black students. Understanding these dynamics promotes equity in STEM

A Critical Study of Information System Use in Ghana\u27s Public Sector

This paper uses a critical perspective (i.e., one that is grounded in historical conditions) via the actor network theory (ANT) framing and methodological approach to explain innovation adoption process of a novel data and knowledge management system in a public sector context of Ghana, West Africa

The Howard-Harvard effect: Institutional reproduction of intersectional inequalities

The US higher education system concentrates the production of science and scientists within a few institutions. This has implications for minoritized scholars and the topics with which they are disproportionately associated. This paper examines topical alignment between institutions and authors of varying intersectional identities, and the relationship with prestige and scientific impact. We observe a Howard-Harvard effect, in which the topical profile of minoritized scholars are amplified in mission-driven institutions and decreased in prestigious institutions. Results demonstrate a consistent pattern of inequality in topics and research impact. Specifically, we observe statistically significant differences between minoritized scholars and White men in citations and journal impact. The aggregate research profile of prestigious US universities is highly correlated with the research profile of White men, and highly negatively correlated with the research profile of minoritized women. Furthermore, authors affiliated with more prestigious institutions are associated with increasing inequalities in both citations and journal impact. Academic institutions and funders are called to create policies to mitigate the systemic barriers that prevent the United States from achieving a fully robust scientific ecosystem

Establishing a Data Science for Good Ecosystem: The Case of ATLytiCS

Data science for social good (DSSG) initiatives have been championed as worthy mechanisms for transformative change and social impact. However, researchers have not fully explored the systems by which actors coordinate, access data, determine goals and communicate opportunities for change. We contribute to the information systems ecosystems and the nonprofit volunteering literatures by exploring the ways in which data science volunteers leverage their talents to address social impact goals. We use Atlanta Analytics for Community Service (ATLytiCS), an organization that aids nonprofits and government agencies, as a case study. ATLytiCS represents a rare example of a nonprofit organization (NPO) managed and run by highly-skilled volunteer data scientists within a regionally networked system of actors and institutions. Based on findings from this case, we build a DSSG ecosystem framework to describe and distinguish DSSG ecosystems from related data and entrepreneurial ecosystems

A cross country investigation of social enterprise innovation: a multilevel modelling approach

This dissertation presents a multilevel model of national-level factors and their impact on the organizational-level characteristics of social enterprises and their innovations. This study builds on the foundations of two theoretical frameworks: the national systems of innovation, which recognizes economic competitiveness to be a product of several interrelated institutions (e.g. financial, educational, cultural, historical) and where organizational-level innovation drives country level competitiveness; and the comparative social enterprise framework, which contends that national-level institutions (e.g., economic competitiveness, models of civil society) drive the size and shape of the social enterprise sector of a country. Data for this study were collected from multiple secondary global datasets representing 54 countries across seven world regions. Research questions and hypotheses are examined using ordinal and logistic hierarchical generalized linear modeling, two analytical techniques capable of explaining variation at one level (i.e., organizations) as a consequence of factors at another level of analysis (i.e., countries) for non-normally distributed dependent variables. Findings indicate that economic competitiveness, welfare spending, culture and quality of life significantly impact the odds of a business being a social enterprise. Fewer significant relationships were found social enterprise innovations. Conclusions and policy implications are discussed in light of data limitations and the current state of the field.Ph.D

Data Science Intelligence: Mitigating Public Value Failures Using PAIR Principles

In this article, we introduce the term “data science intelligence” as the verified and validated qualitative and quantitative outcomes of the data science workflow. This framing marries the disciplines of science policy and data science in order to empirically ground a way forward for mitigating public value failures resulting from the implementation and use of data science algorithms and practices. After identifying the public value failures in the data science ecosystem, we discuss two public value failures which offer significant challenges and opportunities for data scientists and the organizations they serve. Finally, we pose the Participation, Access, Inclusion and Representation (PAIR) principles framework for organizations seeking to minimize the impacts of these failures via the creation of a taxonomy capable of deploying data science that reflects the values of the communities they aim to serve. Preliminary quantitative outcomes are shared while future work will engage its qualitative aspects

Impact of COVID-19 on the Career Trajectories of Black, Indigenous, and Latinx IT Graduate Students and Professionals

This study utilizes an explanatory sequential mixed-methods design to examine the impact of COVID-19 on the career trajectories of information technology (I.T.) graduate students and professionals of color. Building on individual differences theory in the initial quantitative phase, data from a national survey of 356 STEM graduate students and professionals of color (Black, Indigenous, and Latino) were analyzed to investigate intersectional differences among I.T. and non-I.T. STEM graduate students and professionals by race/ethnicity, gender, and socio-economic characteristics. Findings suggest differential impacts of COVID-19 on I.T. graduate students and I.T. professionals. Among STEM graduate students, financial strain significantly affected their career plans, whereas among professionals, gender was a significant predictor. Qualitative evidence from I.T. respondents clarified quantitative findings. I.T. graduate students (n=239) were more concerned about research setbacks and career instability, while I.T. professionals (n=117) were concerned with setbacks in professional roles and networks, work/life stability, and increased desires for entrepreneurship