Alignment at Work: Using Language to Distinguish the Internalization and Self-Regulation Components of Cultural Fit in Organizations

Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 603–612 Vancouver, Canada, July 30 - August 4, 2017. c⃝2017 Association for Computational Linguistics https://doi.org/10.18653/v1/P17-1056 Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics, pages 603–612 Vancouver, Canada, July 30 - August 4, 2017. c⃝2017 Association for Computational Linguistics https://doi.org/10.18653/v1/P17-1056 Alignment at Work: Using Language to Distinguish the Internalization and Self-Regulation Components of Cultural Fit in Organizations Gabriel Doyle Department of Psychology Stanford University gdoyle@stanford.edu Amir Goldberg Graduate School of Business Stanford University amirgo@stanford.edu Sameer B. Srivastava Haas School of Business UC Berkeley srivastava@haas.berkeley.edu Michael C. Frank Department of Psychology Stanford University mcfrank@stanford.edu Abstract Cultural fit is widely believed to affect the success of individuals and the groups to which they belong. Yet it remains an elu- sive, poorly measured construct. Recent research draws on computational linguis- tics to measure cultural fit but overlooks asymmetries in cultural adaptation. By contrast, we develop a directed, dynamic measure of cultural fit based on linguis- tic alignment, which estimates the influ- ence of one person’s word use on another’s and distinguishes between two encultura- tion mechanisms: internalization and self- regulation. We use this measure to trace employees’ enculturation trajectories over a large, multi-year corpus of corporate emails and find that patterns of alignment in the first six months of employment are predictive of individuals downstream out- comes, especially involuntary exit. Fur- ther predictive analyses suggest referential alignment plays an overlooked role in lin- guistic alignment. 1 Introduction Entering a new group is rarely easy. Adjusting to unfamiliar behavioral norms and donning a new identity can be cognitively and emotionally taxing, and failure to do so can lead to exclusion. But suc- cessful enculturation to the group often yields sig- nificant rewards, especially in organizational con- texts. Fitting in has been tied to positive career outcomes such as faster time-to-promotion, higher performance ratings, and reduced risk of being fired (O’Reilly et al., 1991; Goldberg et al., 2016). A major challenge for enculturation research is distinguishing between internalization and self- regulation. Internalization, a more inwardly fo- cused process, involves identifying as a group member and accepting group norms, while self- regulation, a more outwardly oriented process, en- tails deciphering the group’s normative code and adjusting one’s behavior to comply with it. Ex- isting approaches, which generally rely on self- reports, are subject to various forms of reporting bias and typically yield only static snapshots of this process. Recent computational approaches that use language as a behavioral signature of group integration uncover dynamic traces of en- culturation but cannot distinguish between inter- nalization and self-regulation. To overcome these limitations, we introduce a dynamic measure of directed linguistic accommo- dation between a newcomer and existing group members. Our approach differentiates between an individual’s (1) base rate of word use and (2) linguistic alignment to interlocutors. The former corresponds to internalization of the group’s lin- guistic norms, whereas the latter reflects the ca- pacity to regulate one’s language in response to peers’ …

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