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Higher education institutions' complexity and changing societal roles have increased interest in organizational culture as a research topic, combined with the heightened pressures on higher education institutions brought on by external conditions (e.g., the COVID-19 pandemic) and the need for more quantitative cultural research in higher education, have increased recognition of culture and the importance of organizational culture for consideration in management and effectiveness of institutions. Therefore, the study sought to identify and explore the typology of various levels of the administrative subculture and its relationship to the dominant administrative culture type. Using the Competing Values Framework (CVF) and The Organizational Culture Assessment Instrument (OCAI) created by Cameron and Quinn (2011). The descriptive study used a quantitative comparative analysis research approach, and data was collected via Dillman's (2000) Tailored design method (TDM). Participants were distributed into two groups, upper and lower administration. The study resulted in the current dominant overall dominant typology being identified as Bureaucratic (hierarchy), yet the preferred dominant culture typology being identified as Collegial (clan).

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Institutions of higher education across the country have partnered with predictive analytics companies to improve data-informed decision making on campus. These institutions are making sweeping changes to decision making processes based on predictive algorithms with the goal of improving student persistence and graduation rates. The purpose of this qualitative inquiry was to explore the experience of leaders using predictive analytics software working at a midsized open access state college in the Southeast. Following a purposeful sampling process, interviews were conducted to gather data. Bolman and Deal's (2017) four-frame model was employed to understand how higher education leaders construct meaning around their experiences with data. Through a phenomenological analysis of leadership experiences, Bolman and Deal's (2017) four-frame leadership model helped uncover the ways in which leadership approaches emerged as part of a data-informed decision-making process. The questions addressed by this study relate to the effect of using predictive analytics as part of participants' administrative work analyzing data. The research participants and location were selected due to access and familiarity. Additionally, only a limited amount of research exploring the experiences of higher education leadership using predictive analytics exists. The findings of this study add to the emerging literature on the use of predictive analytics in higher education.

Public universities in the United States have experienced continual state budget reductions since 2008. These cuts have forced public universities to defer maintenance on existing facilities and delay new facility construction. Consequently, public university administrators have sought alternative financing approaches to meet their facility needs. Several public universities have turned to social infrastructure public-private partnerships (P3s). Using a multiple case study approach, this exploratory, descriptive research study examines the use of social infrastructure P3 projects by public universities to determine: (1) what public university facility needs are being addressed, (2) what types of P3s are being utilized, and (3) what role private sector financing has in public university social infrastructure P3 projects. The cases within this study aligned with popular media sources that highlight public university use of social infrastructure P3s to meet facility needs beyond student housing. Of the twenty cases examined, sixteen included a private financing component. Among these cases, the social infrastructure P3 projects were most often financed through tax-exempt bonds provided by state economic development agencies and private partner equity. These financing approaches differ from traditional P3 models. The creative financing methods employed by public universities may have implications for other types of social infrastructure.

An Excess Credit Hour policy "assesses a tuition surcharge for any credits taken beyond a predetermined threshold" (Smith, 2017, para. 1). In 2009, the State of Florida implemented legislation on excess credit hours at four-year public institutions, Fla. Stat. § 1009.286 (State of Florida, 2012). Legislation found under title forty-eight (XLVIII) of the K-20 Education Code, Fla. Stat. § 1009.286, relating to "Educational Scholarships, Fees, and Financial Assistance," discussed the "intent of the Legislature to encourage each undergraduate student who enrolls in a state university to complete the student's respective baccalaureate degree program in the most efficient way possible…" (State of Florida, 2018, para. 1). Throughout the duration of the policy, various entities were deficient in monitoring or delivering an assessment of this legislation to determine its effectiveness. Minimal legislative follow up and a small number of empirical studies have tried to confirm the proficiency of this policy in the State of Florida (Carvajal, 2021; State of Florida, 2018). A lack of empirical research exists regarding the relationship between first time-in-college students (FTIC) in the mechanical and aerospace engineering disciplines at a Large Metropolitan University (LMU) and the Excess Credit Hour Surcharge policy's effect on student demographics and baccalaureate degree completion. This quantitative study will use pre-existing data to analyze the complex relationship between variables in collaboration with descriptive statistics. The data collection methods will report specific data points and demographics of students in mechanical and aerospace engineering at LMU. Findings from this study will provide further insight into the implementation of this policy and impart confirmation regarding its objective of increasing graduation rates while attempting to minimize credit hour utilization among students at State of Florida universities.

This study sought to understand the lived experiences of mathematics faculty teaching during the COVID-19 pandemic's rapid shift from on-campus instruction to online education. Faculty are the primary influencers at a college. Their perspectives on the challenges and changes to teaching practices may determine a college's response to improving teaching and learning during a crisis. At the time of the COVID-19 transition, many faculty had no experience creating and teaching an online course, and some had previously been resistant to teaching online. Using a phenomenological approach, nine participants at a large state college were interviewed using the Teacher-Centered Systemic Reform (TCSR) model (Gess-Newsome et al., 2003) as the framework. Five themes emerged from the initial layer of contextual awareness. The themes described participants' inner turmoil based on the pandemic environment and the subsequent changes. The participants described the phenomena in five overlapping themes of emotional context: a) professional emotion challenges, (b) professional learning, (c) pedagogy, (d) teacher culture, and (e) instructional values. The findings revealed a new category to the Teacher-Centered Systemic Reform (TCSR) model, emotional context. The study filled a gap in the literature regarding math faculty experiences and provided recommendations for both faculty and administrators.

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The purpose of this qualitative phenomenological study was to explore how STEM identity development influences the experiences of women STEM faculty who obtained federal grant funding. Data was collected from semi-structured interviews conducted with ten full-time women faculty in STEM employed at U.S. postsecondary institutions who were or were previously lead investigator on a federal grant. Herlihy and Campbell's (2018) socio-cultural-STEM identity theoretical framework informed the data analysis. Findings from the study revealed four primary ways in which participants were positively influenced in STEM including: (a) having supportive interactions with others; (b) being encouraged by others; (c) finding connections with others; and (d) being recognized by others. Participants described navigating challenging experiences, including (a) having negative interactions with others and (b) developing ways to push through challenging experiences to persist in STEM. Results of this study further revealed that participants' grant seeking experiences were influenced by their personal values, with most participants indicating they sought grants for intrinsic reasons. Implications of the results of this study are made for higher education administrators and stakeholders along with recommendations for future research.