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Understanding the online consumer: A typology of online relational norms and behavior
Abstract
Relational norms governing Web-based interaction provide the basis for a typology of relationship orientation manifested by consumers online. Clustering techniques are applied to survey data collected online to identify four groups: Transactional Community Members, Socializers, Personal Connectors, and Lurkers. Cluster assignment is validated by examination of segment means, under volitional versus constraint-based marketing contexts. Relational profiles are developed, differentiated by the strength of relational norms and exhibited interactive behavior. Results suggest investment in community-building infrastructure (i.e., chat rooms, bulletin boards, interactive events), has a positive effect on the future loyalty intentions of highly relational patrons. Implications and future research directions are discussed.
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Cites
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
The commitment-trust theory of relationship marketing
Morgan, R. M., & Hunt, S. D. (1994) · Journal of Marketing · doi:10.1177/002224299405800302 · Cited by 22,355
The norm of reciprocity: A preliminary statement
Gouldner, A. W. (1960) · American Sociological Review · doi:10.2307/2092623 · Cited by 12,001
Consumers and Their Brands: Developing Relationship Theory in Consumer Research
Fournier, S. (1998) · Journal of Consumer Research · doi:10.1086/209515 · Cited by 7,542
Determinants of Long-Term Orientation in Buyer-Seller Relationships
Ganesan, S. (1994) · Journal of Marketing · doi:10.1177/002224299405800201 · Cited by 6,741
Work and/or Fun: Measuring Hedonic and Utilitarian Shopping Value
Babin, B. J., Darden, W. R., & Griffin, M. (1994) · Journal of Consumer Research · doi:10.1086/209376 · Cited by 6,020
Muniz, A. M., & O'Guinn, T. C. (2001) · Journal of Consumer Research · doi:10.1086/319618 · Cited by 3,998
Moorman, C., Zaltman, G., & Deshpande, R. (1992) · Journal of Marketing Research · doi:10.1177/002224379202900303 · Cited by 1,901
Recipient's mood, relationship type, and helping.
Clark, M. S., Oullette, R., Powell, M. C., & Milberg, S. (1987) · Journal of Personality and Social Psychology · doi:10.1037/0022-3514.53.1.94 · Cited by 531
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Valenzuela, S., Park, N., & Kee, K. F. (2009) · Journal of Computer-Mediated Communication · doi:10.1111/j.1083-6101.2009.01474.x · Cited by 2,229
Introducing COBRAs: Exploring motivations for brand-related social media use
Muntinga, D. G., Moorman, M., & Smit, E. G. (2011) · International Journal of Advertising · doi:10.2501/ija-30-1-013-046 · Cited by 1,468
An Experimental Study of the Relationship between Online Engagement and Advertising Effectiveness
Calder, B. J., Malthouse, E. C., & Schaedel, U. (2009) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2009.07.002 · Cited by 1,040
Social media research: Theories, constructs and conceptual frameworks
Ngai, E. W. T., Tao, S. S. C., & Moon, K. K. L. (2015) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2014.09.004 · Cited by 760
Effect of image interactivity technology on consumer responses toward the online retailer
Fiore, A. M., Kim, J., & Lee, H.-H. (2005) · Journal of Interactive Marketing · doi:10.1002/dir.20042 · Cited by 495
Metadata
- Authors
- Mathwick, C.
- Venue
- Journal of Interactive Marketing
- Publisher
- SAGE Publications
- Published
- 2002-02
- Volume
- 16
- Issue
- 1
- Pages
- 40-55
- ISSN
- 1094-9968 (ISSN-L)
- 1520-6653
- OpenAlex
- W2098755877
- Cited by
- 316
- References
- 10
- FWCI
- 45.56
- Type
- journal article
- Language
- en
- Open access
- closed
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