Rules all the way down: Consumer behaviour from the standpoint of the 'ONE behavioural' research programme.
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| Title: | Rules all the way down: Consumer behaviour from the standpoint of the 'ONE behavioural' research programme. |
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| Authors: | Earl, Peter E. (AUTHOR) |
| Source: | Journal of Consumer Behaviour. May/Jun2023, Vol. 22 Issue 3, p531-546. 16p. |
| Subjects: | Consumer behavior, Rational choice theory, Behavioral research, Evolutionary economics, Consumer research, Interdisciplinary research, Heterodox economics |
| Abstract: | This article sets out a unified behavioural research programme that integrates compatible elements of old, new and evolutionary behavioural approaches to economics as an alternative to the dominant unified approach to economics based on rational choice theory and a Walrasian view of market coordination. However, the proposed programme can also be viewed as a general framework for interdisciplinary research on consumer behaviour. It employs the view of scientific research programmes proposed by Lakatos, setting out groups of 'hard‐core' propositions and their associated 'do' and 'do not' rules for the conduct of researchers. The unifying theme is that evolution in the economy (and in human systems more generally) entails the creation, adoption and abandonment of rules for dealing effectively with open‐ended choice problems that are bedevilled by infinite regress problems and cognitive challenges that people seek to address via personal repertoires of hierarchically related rules. To anticipate behaviour, researchers need to develop knowledge of these rules (including heuristics and routines), their functionality and the processes by which they get changed or prove difficult to change even where they cause problems. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Consumer Behaviour is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Psychology and Behavioral Sciences Collection |
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