Hiring and Cultural Fit |
Research Team: Avi Goyal, Sarah K. Stein, Amir Goldberg, Sameer B. Srivastava
Project Description: We examine whether and how job applicants’ pre-hire language use predicts who will get hired and, conditional on being hired, who will fit in culturally and perform well on the job. |
Cultural Integration Following Post-Merger Integration |
Research Team: Anjali Bhatt, Amir Goldberg, Sameer B. Srivastava
Project Description: We trace the cultural integration of three firms based on analyses of email content before and after their mergers and explore how patterns of cultural assimilation that individuals follow after the merger relate to their subsequent career outcomes. |
Identifying the
|
Research Team: Jesse Fagan, Richard Lu, Amir Goldberg, Giuseppe (Joe) Labianca, Sameer B. Srivastava
Project Description: We use machine learning techniques to identify from email content the “linguistic signature” of career mobility. |
Situated Cultural Fit |
Research Team: Richard Lu, Jennifer A. Chatman, Amir Goldberg, Sameer B. Srivastava
Project Description: We use machine learning techniques to identify the “linguistic signature” of two cognitive measures of cultural fit—perceptual accuracy (how closely perceptions of the culture match others' perceptions) and value congruence (how closely preferences for a work environment match perceptions of the existing environment)—based on responses to an indirect self-report, the Organizational Culture Profile (OCP). We then use longitudinal email data to impute perceptual accuracy and value congruence for all employees, including those who did not complete the OCP, and across time, including periods before the OCP was administered. This approach enables us to examine the situations under which value congruence and perceptual accuracy give rise to behavioral cultural fit. We also take advantage of a reorganization event that created quasi-exogenous shifts in employees' interlocutors to estimate the effect of peer influence on perceptual accuracy and behavioral conformity. |
Measuring Cultural Schemata Through Word Associations |
Research Team: Austin van Loon, Amir Goldberg, Sameer B. Srivastava
Project Description: We develop a series of word association games to measure schematic similarity in first-order, as well as higher-order, cultural beliefs. We validate this approach in laboratory settings, focusing on the cognitive divides among people with different political party affiliations. We then apply this technique to measuring cultural schemas in organizational settings and examine the consequences for individual attainment. |
Physical Spaces and Local Culture |
Research Team: Lara Yang, Amir Goldberg, Sameer B. Srivastava
Project Description: We draw on archival data from a firm that experienced changes in physical space layouts to understand how space affects the formation of local group cultures. We are also in process of designing experiments on workspace configuration to assess how these spatial changes affect local culture, as measured based on the linguistic style of email communication. |
Team Cultures and Performance |
Research Team: Katharina Lix, Amir Goldberg, Sameer B. Srivastava, Melissa A. Valentine
Project Description: We apply the tools of computational linguistics to develop a language-based, time-varying measure of discursive diversity in teams. We find that discursive diversity is generally associated with better team performance. However, levels of discursive diversity fluctuate significantly over teams’ life cycles. In more fine-grained analyses, we find that discursive diversity’s effects on performance are contingent on time: it is positive when a team’s next milestone is distant but turns negative as the next milestone approaches. |