Analysis of non-deceptive copycatting

Junsong Bian, Chenchen Yang, Xiaolong Guo, Richard Tay and Suzhen Liang

Journal of the Operational Research Society, 2024, vol. 75, issue 12, 2421-2442

Abstract: This paper examines the impact of non-deceptive copycats considering the authentic firm’s effort in fighting copycats. We study how non-deceptive copycatting affects the authentic firm’s quality investment, demand, profitability, and consumer welfare. We reveal that, in contrast to the benchmarking scenario of no copycatting, non-deceptive copycats do not necessarily reduce the quality and market share of the genuine product, depending on the extent of copycat imitation and the quality investment efficiency of the genuine firm. Closer copycat imitation can either enhance or reduce consumer and social welfare, contingent upon the extent of copycat imitation and the quality investment efficiency of the genuine product. Furthermore, the authentic firm benefits from the presence of such copycats with sufficiently efficient anti-copycat effort and copycat penalty. In addition, we demonstrate that the genuine firm can drive copycat products out of the market with sufficiently efficient brand quality investment and low imitability, which further differentiates the quality between the genuine product and the copycat, and thus shrinks the demand for the copycat product. Finally, the main model is also generalised to study multiple scenarios: Stackelberg competition, two-feature copycat imitation, and with different production costs.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2024.2323024 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:75:y:2024:i:12:p:2421-2442

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2024.2323024

Access Statistics for this article

Journal of the Operational Research Society is currently edited by Tom Archibald

More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().