The Great Resignation has brought a renewed national focus on job quit rates throughout the United States (Gittleman, 2022; Serenko, 2023). Researchers have explored why non-faculty higher education professionals leave the field of student affairs, but few recent studies have considered departure among functional area-specific professionals, with the exception of residence life and housing. Further, while there are studies on burnout, the field lacks research about how the day-to-day reality of fraternity/sorority advising (FSL work) impacts the quitting behaviors of fraternity/sorority advising professionals (FSAs). This general qualitative study was rooted in the sensitizing concepts (Charmaz, 2003) of the weight of the work of FSAs, the complexity of the fraternity and sorority advising job, and the burn-through that exists in fraternity/sorority advising. Participants included nine former campus-based FSAs who left the field from January 2018-December 2023. Data were collected through participant interest forms and semi-structured Zoom interviews to answer the following research questions: 1. How do former campus-based fraternity/sorority advisors describe their former FSA positions and work experiences? 2. What aspects of the fraternity/sorority advising position led to former campus-based FSAs' decisions to quit their campus-based role? Thematic analysis and inductive coding methods were used to analyze the data. Findings included four major themes related to the experiences of former FSAs: unrealistic expectations, challenges that are specific to working in fraternity and sorority life, the impact of the campus environment, and lasting personal effects of the FSA experience. This study offers implications and recommendations for policy and practice, specifically around the areas of training and onboarding, employee well-being, and the resources that are necessary to support the work of FSAs. Further research should explore how various stakeholders define the value of fraternities and sororities and the prevalence and lasting impact of the student affairs practice of burning through and exhausting student affairs professionals for the sake of the college or university. / Doctor of Philosophy / The Great Resignation has brought a renewed national focus on job quit rates throughout the United States (Gittleman, 2022; Serenko, 2023). In higher education, there is research on why non-faculty higher education professionals leave the field of student affairs, but few studies have focused on functional area-specific student affairs professionals, except in residence life and housing. Further, while there are studies on burnout in student affairs, there is limited research on the day-to-day reality of fraternity/sorority advising work (FSL work) and how this reality impacts the quitting behaviors of fraternity/sorority advising professionals (FSAs). The purpose of this study was to explore how the complex nature of FSL work influenced the quitting behaviors of formerly campus-based fraternity/sorority professionals. Nine participants participated in a Zoom interview where they each shared details about their experiences as former FSAs, their ultimate decisions to leave campus-based work, and how their experiences and decisions to quit impacted their lives. Findings from this study include four major themes related to the experiences of former FSAs: unrealistic expectations, challenges that are specific to working in fraternity and sorority life, the impact of the campus environment, and lasting personal effects of the FSA experience. This study offers implications and recommendations for policy, practice and further research that apply not only to fraternity and sorority advising, but also more broadly to student affairs.

This paper is investigating the possibility to predict employee attrition on an individual level with machine learning. The study is divided into two parts, one qualitative part which were conducted by doing interviews with selected roles where the openness to which practitioners are willing to use machine learning models to predict employee attrition, and what effects such a model could have on an organization was investigated. The second part is a quantitative part where a random forest model, support vector machine model and a logistic regression model are compared in terms of accuracy in predicting employee attrition with the usage of large human resource data sets. Firstly, it was shown that people are willing to use machine learning models to predict employee attrition if the models were to be trusted, and if organizations that used such models were transparent in how the models were used, and to what purpose. The model comparison did not give any interesting results about the possibility to predict employee attrition with the chosen models. There were several reasons for that, where some of them were that the models were over fitted, the time of notice when a person quit was not accounted for enough and the choice of input data points. This resulted in that the accuracy could not be determined in a confident way. / Den här rapporten undersöker möjligheten att kunna förutse uppsägningar på en individuell nivå med hjälp av machine learning och vad utövare tycker om att använda sådana modeller. Den här avhandlingen är uppdelad i två delar. Den första är en kvalitativ del där intervjuer har gjorts för att undersöka öppenheten hos utövare som ska använda eller användas av en sådan modell, samt att undersöka vad folk tror att en sådan modell kan bidra med till organisationen. Den andra delen är en kvantitativ del där en random forest modell, en support vector machine modell och en logistisk regressionsmodell har ställts mot varandra för att titta vilken modell som kan predicera uppsägningar baserat på medarbetsundersökningar. För det första så visades det att utövare är öppna för att använda machine learning för att predicera uppsägningar så länge modellerna som används kan litas på. Dessutom visade det sig att transparens från organisationer som använder sig av sådana modeller är viktigt, där tydighet i hur modellen används och varför och vad den används till måste kommuniceras. Av den andra delen, där de tre modellerna ställdes mot varandra, visade det sig att det var svårt att predicera uppsägningar baserat på medarbetsundersökningar. Det gick inte med säkerhet att visa att den träffsäkerheten som uppnåddes faktiskt betyder något väsentligt utan istället så visade det sig att det fanns problem som gjorde att resultaten inte blev som förväntat. Dessa problem var bland annat över fitting, uppsägningstid togs inte med i beräkningarna tillräckligt mycket, och valet av input data visade sig inte vara bra nog.

Relationships between MMPI subscale scores and premature resignations among Southern Baptist foreign missionaries appointed in 1964 were investigated in an effort to develop a predictive model for attrition. Unsuccessful attempts were made at cross-validating the results of a previous related study, and two separate discriminant function analyses were undertaken. The first sorted subjects into three groups, defined by subjects' length of service before resignation. The second classified them according to the reason stated for their termination, if applicable. Both procedures failed to establish a statistically reliable classification system for relating MMPI scores with premature resignations. Although consistent success has been achieved with the MMPI as a screening instrument for psychopathology, it is suggested that the instrument is not adequately sensitive as a screening device for groups lying predominantly within the normal range of variability.

The purpose of this multiple case study was to explore strategies that managers in the retail grocery industry use in reducing short-tenured employee attrition in the West Midland states in the United Kingdom. Participants were 4 store managers who had managerial experience in the retail grocery industry, worked as a retail store manager in the West Midlands, and had experience implementing effective strategies to reduce employee attrition. The Herzberg 2-factor theory was the conceptual framework. Semistructured interviews were used to collect data. Data were analyzed using Yin's 5-step data analysis process. The major themes were: training and development, enriched job responsibility, human resources intervention, and employee recognition. Participants relied on training and development, enriched job responsibility, human resources intervention and, employee recognition to reduce employee attrition. The results may provide retail grocery leaders with strategies for reducing short-tenured employee attrition in grocery stores, which may reduce adverse effects on the industry's profitability. Implications for positive social change include improving the quality of life of the community and citizens; improved levels of satisfaction in quality of life translate into developing and maintaining positive relationships with family and friends, as well as helping the local communities and the economy.