Sima Sajjadiani
CSOM 3-300M
Curriculum Vitae (110.93 KB)

Update Profile

Sima Sajjadiani

PhD Candidate
Work & Organizations


  • Masters of Art 2014
    Human Resources and Industrial Relations University of Minnesota- Carlson School of Management

  • Master of Business Administration 2008
    Business Administration University of Tehran

  • Bachelor of Science 2005
    Electrical Engineering K.N.Toosi University of Technology


  • Employee Selection
  • Employee Turnover
  • Incentives Design
  • Machine Learning Applications in HRM

I am a Ph.D. candidate at the Carlson School of Management, University of Minnesota with a concentration in Human Resource Management and Organizational Behavior. I mainly examine the determinants and consequences of different HR practices such as employee selection, employee turnover, and incentives design and how these practices can be optimized to improve individual, team, and organization-level work outcomes. Informed by a multidisciplinary perspective, my research draws on organizational psychology and personnel economic theories. In addressing my research questions, I use a diverse range of research methods and statistical techniques, such as archival panel data, structural equation modeling, longitudinal and multilevel models, text-mining, and machine learning.

Current Activities

  • Working Papers

    Sajjadiani, S., Sojourner, A., & Kammeyer-Mueller, J. "Predicting Work Outcomes Using Prehire Work History: Who Is Fit to Teach?" Target: Journal of Applied Psychology. (Status: Final revisions before submission) (Dissertation Essay 1 & Job Market Paper)

    Summary. Job applicants’ work history, especially in the form of resumes and job applications,are commonly used in practice to screen job applicants; yet, there is little consensus within the employee selection literature regarding how to systematically model work history to translate information about one’s past into predictions about the future work outcomes. Drawing on and expanding the extant literature and applying machine learning (ML) techniques, my colleagues and I theoretically develop novel, indirect, and objective measures to predict, prior to hire, the subsequent subjectively and objectively evaluated performance as well as voluntary and involuntary turnover. We empirically examine our theoretical model on a sample of 16,071 applicants for teaching positions, among whom 15% were hired. ML enables us to enrich work history data by connecting applicants’ previous jobs to their occupational characteristics through the U.S. Department of Labor’s O*NET occupational information system and measure applicants’ fit for the job (Demands-abilities Fit). The analysis of reasons for leaving previous jobs using ML reveals information about applicants’ passion for job, propensity for involuntary turnover, and propensity for job dissatisfaction. Consistent with our theoretical model, we find that unobservable determinants of hiring, expected to be highly correlated with recruiters’ decision-making biases, as well as demographic variables and application quality only predict subjective evaluations of teacher performance. Additionally, we show that demands-abilities fit and pre-hire propensity for job dissatisfaction are linked to subjective and objective performance evaluations, as well as turnover. We also demonstrate that our model can improve the quality of the selection process, while lowering the risk of adverse impact.

    Sajjadiani, S., Benson, A., & Kammeyer-Mueller, J. "Esprit de Corps: Mediating Effects of Team Affective Tone on the Relationship between Staffing Events and Work Outcomes", Target: Journal of Applied Psychology. (Status: Data Analysis) (Dissertation Essay 2)

    Summary. In the second essay of my dissertation (Sajjadiani, Benson, & Kammeyer-Mueller, Status: Data Analysis), we use longitudinal and multilevel personnel, financial, and daily mood data collected from a large U.S. retailer to examine how staffing events impact fluctuations in collective team affect, and how changes in team affect impact performance and turnover. Our data include financial performance and staffing changes among 468,091 workers in 6,603 teams at 1844 stores, from 2014 through 2016, as well as team level daily mood data for 1,895,078 location-team-days. The stores and teams are structurally similar and comparable. Since the workplace is not isolated from the larger economic context, we scrutinize the economic backdrop against which employees function (unemployment rate and minimum wage), and whether the economic environment moderates the relationships among staffing events, affective tone, and work outcomes. In 2015, the organization started an organization-wide daily ritual to improve team cohesion and engagement. We also examine whether positive rituals mitigate the negative effects of staffing events, such as layoffs.