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Fitting In or Standing Out? The Tradeoffs of Structural and Cultural Embeddedness

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  • Goldberg, Amir
  • Srivastava, Sameer B
  • Manian, Govind
  • Monroe, William
  • Potts, Christopher

Abstract

A recurring theme in sociological research is the tradeoff between fitting in and standing out. Prior work examining this tension has tended to take either a network structural or a cultural perspective. We instead fuse these two traditions to develop a theory of how structural and cultural embeddedness jointly relate to individual attainment within organizations. Given that organizational culture is hard to observe, we develop a novel approach to assessing individuals’ cultural fit with their colleagues in an organization based on the language expressed in internal email communications. Drawing on a unique data set that includes a corpus of 10.25 million email messages exchanged over five years among 601 employees in a high-technology firm, we find that network constraint impedes, while cultural fit promotes, individual attainment. More importantly, we find evidence of a tradeoff between the two forms of embeddedness: cultural fit benefits individuals with low network constraint (i.e., brokers), while network constraint promotes attainment for those with low cultural fit.

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  • Goldberg, Amir & Srivastava, Sameer B & Manian, Govind & Monroe, William & Potts, Christopher, 2016. "Fitting In or Standing Out? The Tradeoffs of Structural and Cultural Embeddedness," Institute for Research on Labor and Employment, Working Paper Series qt9bf631rg, Institute of Industrial Relations, UC Berkeley.
    • Handle: RePEc:cdl:indrel:qt9bf631rg
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    Cited by:

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    2. Zhang, Han, 2021. "How Using Machine Learning Classification as a Variable in Regression Leads to Attenuation Bias and What to Do About It," SocArXiv 453jk, Center for Open Science.
    3. Sameer B. Srivastava & Amir Goldberg & V. Govind Manian & Christopher Potts, 2018. "Enculturation Trajectories: Language, Cultural Adaptation, and Individual Outcomes in Organizations," Management Science, INFORMS, vol. 64(3), pages 1348-1364, March.
    4. van Loon, Austin, 2022. "Three Families of Automated Text Analysis," SocArXiv htnej, Center for Open Science.
    5. Andreas Schwab & Zhu Zhang, 2019. "A New Methodological Frontier in Entrepreneurship Research: Big Data Studies," Entrepreneurship Theory and Practice, , vol. 43(5), pages 843-854, September.
    6. Suss, Joel & Bholat, David & Gillespie, Alex & Reader, Tom, 2021. "Organisational culture and bank risk," Bank of England working papers 912, Bank of England.
    7. Katharina Lix & Amir Goldberg & Sameer B. Srivastava & Melissa A. Valentine, 2022. "Aligning Differences: Discursive Diversity and Team Performance," Management Science, INFORMS, vol. 68(11), pages 8430-8448, November.
    8. Michael Weinhardt, 2021. "Big Data: Some Ethical Concerns for the Social Sciences," Social Sciences, MDPI, vol. 10(2), pages 1-14, January.
    9. Hongjiang Lv & Xinghua Zhao & Man Cao & Jingjing Ding, 2023. "How can managers, acting as brokers, be ambidextrous? The effect of trust brokerage on managers’ ambidexterity," Asian Business & Management, Palgrave Macmillan, vol. 22(3), pages 1008-1034, July.
    10. Myra Sader & Barthélemy Chollet & Sébastien Brion & Olivier Trendel, 2021. "Supported, detached, or marginalized? The ambivalent role of social capital on stress at work," Post-Print hal-03167159, HAL.
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    12. Abigail Z. Jacobs & Duncan J. Watts, 2021. "A Large-Scale Comparative Study of Informal Social Networks in Firms," Management Science, INFORMS, vol. 67(9), pages 5489-5509, September.

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