The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
Guilt appeals: Persuasion knowledge and charitable giving
Abstract
This paper applies the persuasion knowledge model to explain consumers' responses to charity guilt appeals. With data obtained through a stimuli‐driven survey, the research examines the relationships between knowledge of persuasion tactics and charities, and the level of felt guilt experienced in response to an advertisement and subsequent donation intentions. The findings show that guilt arousal is positively related to donation intention, and that persuasion and agent knowledge impact the extent of guilt aroused. The research confirms that consumers are active rather than passive processors of marketing communications by revealing the role of persuasion and agent knowledge as methods of coping with and informing responses to guilt appeals. Specifically, the research finds that manipulative intent and the respondents' skepticism toward advertising tactics in general are negatively related to guilt arousal but that their affective evaluation and beliefs about a charity are positively related to feelings of guilt. However, it also shows that there is a positive direct relationship between perceived manipulative intent and the intention to donate. © 2007 Wiley Periodicals, Inc.
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“Social norms and guilt are important in increasing consumers' ethical intentions and prosocial behavior (Steenhaut and Van Kenhove, 2006 ; Hibbert et al., 2007)”
From Understanding the antecedents of consumers' attitudes towards doggy bags in restaurants: Concern about food waste, culture, norms and emotions (2017) · cites this paper as Hibbert et al., 2007
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Cites
On the Evaluation of Structural Equation Models
Bagozzi, R. P., & Yi, Y. (1988) · Journal of the Academy of Marketing Science · doi:10.1007/bf02723327 · Cited by 26,163
A Paradigm for Developing Better Measures of Marketing Constructs
Churchill, G. A. (1979) · Journal of Marketing Research · doi:10.1177/002224377901600110 · Cited by 16,549
Lazarus, R. S. (1991) · doi:10.1093/oso/9780195069945.001.0001 · Cited by 12,691
The Persuasion Knowledge Model: How People Cope with Persuasion Attempts
Friestad, M., & Wright, P. (1994) · Journal of Consumer Research · doi:10.1086/209380 · Cited by 3,811
Bagozzi, R. P. (1975) · Journal of Marketing · doi:10.2307/1250593 · Cited by 1,648
Campbell, M. C., & Kirmani, A. (2000) · Journal of Consumer Research · doi:10.1086/314309 · Cited by 1,342
Research works printed but not linked: 51
- 10.1037/0033-2909.103.3.411 · DOI · Paper page
- 10.2307/1252162 · DOI
- Bagozzi R. P., 1980, Causal models in marketing
- 10.1037/0022-3514.41.4.607 · DOI · Paper page
- 10.1287/opre.31.1.128 · DOI
- 10.1111/j.2044-8309.1995.tb01076.x · DOI
- 10.2307/1252251 · DOI
- 10.1086/209063 · DOI · Paper page
- 10.2307/1251840 · DOI
- 10.1111/j.0265-0487.1996.00021.pp.x · DOI
- 10.1080/02650487.1998.11104734 · DOI
- 10.1057/palgrave.jt.5740088 · DOI
- 10.1086/209390 · DOI
- 10.1111/j.1467-6494.1986.tb00391.x · DOI
- Bruce I., 1994, Meeting need: Successful charity marketing
- 10.1108/07363769410065454 · DOI
- 10.2307/3151726 · DOI
- 10.1207/s15327663jcp0403_02 · DOI
- 10.1037/0022-3514.34.5.907 · DOI
- 10.1037/0021-9010.80.6.697 · DOI
- DeVellis R. F., 1991, Scale development: Theory and applications
- Eagly A. H., 1993, The psychology of attitudes
- 10.1111/j.2044-8309.1990.tb00915.x · DOI
- 10.1086/209124 · DOI
- 10.1362/026725798784959408 · DOI
- 10.1177/002224378101800104 · DOI · Paper page
- 10.1086/209435 · DOI
- 10.1002/(SICI)1520-6793(199903)16:2<185::AID-MAR7>3.0.CO;2-N · DOI
- Hair J. F., 1995, Multivariate data analysis
- 10.1002/mar.20043 · DOI
- 10.1002/nvsm.6090020308 · DOI
- 10.1080/00913367.1997.10673521 · DOI
- 10.1007/BF00382660 · DOI
- 10.2307/3172904 · DOI
- 10.1080/00913367.1997.10673528 · DOI
- 10.1111/j.1432-2277.2005.00183.x · DOI
- 10.1002/mar.4220110404 · DOI
- 10.2307/1251413 · DOI · Paper page
- 10.2307/3172593 · DOI
- 10.1086/209305 · DOI
- 10.1080/00913367.1996.10673498 · DOI
- Nunnally J. C., 1978, Psychometric theory
- 10.1177/001872679504800605 · DOI
- 10.1086/208666 · DOI
- 10.1002/mar.20001 · DOI
- 10.1086/208922 · DOI
- 10.1362/026725799784870351 · DOI
- 10.1002/mar.20021 · DOI
- 10.1086/209411 · DOI
- 10.1016/0167-8116(91)90027-5 · DOI · Paper page
- 10.3758/BF03337260 · DOI
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Sorted by citations
Boerman, S. C., Willemsen, L. M., & Van Der Aa, E. P. (2017) · Journal of Interactive Marketing · doi:10.1016/j.intmar.2016.12.002 · Cited by 581
Consumer Responses to AI-Generated Charitable Giving Ads
Arango, L., Singaraju, S. P., & Niininen, O. (2023) · Journal of Advertising · doi:10.1080/00913367.2023.2183285 · Cited by 243
Persuasion for Good: Towards a Personalized Persuasive Dialogue System for Social Good
Wang, X., Shi, W., Kim, R., Oh, Y., Yang, S., Zhang, J., & Yu, Z. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1566 · Cited by 222
Sirieix, L., Lála, J., & Kocmanová, K. (2017) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2016.10.004 · Cited by 175
Effects of Persuasive Dialogues: Testing Bot Identities and Inquiry Strategies
Shi, W., Wang, X., Oh, Y. J., Zhang, J., Sahay, S., & Yu, Z. (2020) · Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3313831.3376843 · Cited by 93
Artificial Intelligence Disclosure in Cause-Related Marketing: A Persuasion Knowledge Perspective
Qiu, X., Wang, Y., Zeng, Y., & Cong, R. (2025) · Journal of Theoretical and Applied Electronic Commerce Research · doi:10.3390/jtaer20030193 · Cited by 35
Metadata
- Authors
- Hibbert, S., Smith, A., Davies, A., & Ireland, F.
- Venue
- Psychology & Marketing
- Publisher
- Wiley
- Published
- 2007-06-19
- Volume
- 24
- Issue
- 8
- Pages
- 723-742
- ISSN
- 0742-6046
- 1520-6793
- Cited by
- 300
- References
- 6
- Language
- en
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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