The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Defining Who You Are By What You're Not: Organizational Disidentification and The National Rifle Association
Open accessAbstract
Through two exploratory studies, we develop and test an introductory framework of “organizational disidentification.” Our first study explores the concept of organizational disidentification through a qualitative investigation of cognitive relationships with the National Rifle Association (NRA). Findings suggest that organizational disidentification is a self-perception based on: (1) a cognitive separation between one's identity and the organization's identity, and (2) a negative relational categorization of oneself and the organization (e.g., categorizations such as “rivals” or “enemies”). Organizational disidentification appears to be motivated by individuals' desires to both affirm positive distinctiveness and avoid negative distinctiveness by distancing themselves from incongruent values and negative stereotypes attributed to an organization. Our findings also suggest that organizational disidentification can lead individuals to take action (either volunteer work or voicing their opinion) as a result of their perceived separation from the organization's identity. Results of our second study”a large-scale survey of public attitudes about the NRA”provide support for this framework.
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Cites
Evaluating Structural Equation Models with Unobservable Variables and Measurement Error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.2307/3151312 · Cited by 109,314
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
Alternative Ways of Assessing Model Fit
Browne, M. W., & Cudeck, R. (1992) · Sociological Methods & Research · doi:10.1177/0049124192021002005 · Cited by 27,200
Comparative fit indexes in structural models.
Bentler, P. M. (1990) · Psychological Bulletin · doi:10.1037/0033-2909.107.2.238 · Cited by 25,266
Stereotype threat and the intellectual test performance of African Americans.
Steele, C. M., & Aronson, J. (1995) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.69.5.797 · Cited by 9,020
The Social Self: On Being the Same and Different at the Same Time
Brewer, M. B. (1991) · Personality and Social Psychology Bulletin · doi:10.1177/0146167291175001 · Cited by 5,107
The Psychology of Self-Affirmation: Sustaining the Integrity of the Self
Steele, C. M. (1988) · Advances in Experimental Social Psychology · doi:10.1016/s0065-2601(08)60229-4 · Cited by 3,697
Who is this "We"? Levels of collective identity and self representations.
Brewer, M. B., & Gardner, W. (1996) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.71.1.83 · Cited by 3,401 · cited 1×
“…identity), although most participants discussed the importance of clear separation vs. a large or small separation of identities.2 Second, this definition suggests that disidentification is a form of “relational” categorization (Brewer and Gardner 1996) defining the organization and the individual.”
BackgroundTheory and research concerning social comparisons of personal attributes
Wood, J. V. (1989) · Psychological Bulletin · doi:10.1037/0033-2909.106.2.231 · Cited by 1,957
Statistical analysis of sets of congeneric tests
Jöreskog, K. G. (1971) · Psychometrika · doi:10.1007/bf02291393 · Cited by 1,481
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Bhattacharya, C. B., & Sen, S. (2003) · Journal of Marketing · doi:10.1509/jmkg.67.2.76.18609 · Cited by 3,336
Self-citationCorporate Social Responsibility and Marketing: An Integrative Framework
Maignan, I., & Ferrell, O. C. (2004) · Journal of the Academy of Marketing Science · doi:10.1177/0092070303258971 · Cited by 1,873
Group Faultlines: A Review, Integration, and Guide to Future Research
Thatcher, S. M. B., & Patel, P. C. (2012) · Journal of Management · doi:10.1177/0149206311426187 · Cited by 391
Lehman, D. W., O’Connor, K., Kovács, B., & Newman, G. E. (2019) · Academy of Management Annals · doi:10.5465/annals.2017.0047 · Cited by 317
Investigating the effects of smart technology on customer dynamics and customer experience
Foroudi, P., Gupta, S., Sivarajah, U., & Broderick, A. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2017.11.014 · Cited by 286
ORGANIZATIONAL PERCEPTION MANAGEMENT
Elsbach, K. D. (2003) · Research in Organizational Behavior · doi:10.1016/s0191-3085(03)25007-3 · Cited by 277
Self-citationNarcissistic Organizational Identification: Seeing Oneself As Central to the Organization's Identity
Galvin, B. M., Lange, D., & Ashforth, B. E. (2015) · Academy of Management Review · doi:10.5465/amr.2013.0103 · Cited by 263
Nationalism and Ideology in an Anticonsumption Movement
Varman, R., & Belk, R. W. (2009) · Journal of Consumer Research · doi:10.1086/600486 · Cited by 258
Beyond Performance: A Motivational Experiences Model of Stereotype Threat
Thoman, D. B., Smith, J. L., Brown, E. R., Chase, J., & Lee, J. Y. K. (2013) · Educational Psychology Review · doi:10.1007/s10648-013-9219-1 · Cited by 141
(2007) · D-Scholarship@Pitt (University of Pittsburgh) · Cited by 141
Negational Categorization and Intergroup Behavior
Zhong, C.-B., Phillips, K. W., Leonardelli, G. J., & Galinsky, A. D. (2008) · Personality and Social Psychology Bulletin · doi:10.1177/0146167208315457 · Cited by 81
Motivations to identify with social groups: A look at their positive and negative consequences.
Amiot, C. E., & Sansfaçon, S. (2011) · Group Dynamics: Theory, Research, and Practice · doi:10.1037/a0023158 · Cited by 75
Identity in Franchise Systems: The Role of Franchisee Associations
Lawrence, B., & Kaufmann, P. J. (2011) · Journal of Retailing · doi:10.1016/j.jretai.2010.11.003 · Cited by 67
Negative Partisanship in Latin America
Haime, A., & Cantú, F. (2022) · Latin American Politics and Society · doi:10.1017/lap.2021.54 · Cited by 25
Nabity-Grover, T., Matthews, K. D., Roth, P. L., Kwon, Y., & Thatcher, J. B. (2025) · Information Systems Research · doi:10.1287/isre.2020.0497 · Cited by 3
Metadata
- Authors
- Elsbach, K. D., & Bhattacharya, C. B.
- Venue
- Organization Science
- Published
- 2001-08
- Volume
- 12
- Issue
- 4
- Pages
- 393-413
- ISSN
- 1047-7039 (ISSN-L)
- 1526-5455
- OpenAlex
- W2109900598
- Cited by
- 632
- References
- 10
- FWCI
- 12.69
- Type
- journal article
- Language
- en
- Open access
- bronze
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