563
Views
4
CrossRef citations to date
0
Altmetric
Design & Manufacturing

Product positioning and pricing decisions in a two-attribute disruptive new market

&
Pages 285-297 | Received 25 Jan 2019, Accepted 18 Apr 2020, Published online: 04 Jun 2020
 

Abstract

In disruptive innovation, a new entrant firm with fewer resources challenges the established incumbent firms. The new entrant firm offers an innovative product that is considered superior in new features appealing to a group of new customers, but inferior along the traditional performance attributes valued by mainstream customers. Previous research primarily focuses on the product strategies that could benefit the incumbent firms, thus, it is less unclear how the new entrant firm should strategize the innovative product. This article investigates the product pricing and positioning strategies for the new entrant firms who wholly invest in a single product. Our analytical model incorporates two horizontally distinct product attributes, where potential consumers have different preferences and reservation prices toward the two product attributes. We identify four product pricing and positioning strategies, and their corresponding optimal conditions. Our analysis shows that the product position is closely related to the customer valuation gap between the two attributes. As the innovative product will improve sufficiently on the traditional performance attributes, results from this study can be applied at different stage of product development by the new entrant firm.

Notes

1 To simplify our analytical result, we do not model price and quality competition from incumbent firms. There are ample real-life examples where new entrant firms face little competition in revolutionary product innovation (Qian, Citation2011). These include high-technology software (such as Adobe, Microsoft Office, and other major commercial software); new patented products; and new medical equipment with high-end technologies.

Additional information

Notes on contributors

Yuwen Chen

Dr. Yuwen Chen (PhD, University of Florida) currently is an associate professor of supply chain management at the University of Rhode Island. Yuwen’s research interest and curiosity is inspired by observing changes in life then contemplating the solutions to react to the changes. He encourages students to understand the complexity and interconnectivity of the business world. His research papers are shown in the areas of new product development, operations and supply chain management, and logistics/transportation. His publications have been published in the journals of Production and Operations Management, Decision Sciences, Supply Chain Management: An International Journal, Decision Sciences in Innovative Education, Interfaces, Journal of Business Research, and European Journal of Operations Research.

John Z. Ni

John Z. Ni (PhD, Indiana University) is an assistant professor of supply chain management at Farmer School of Business, Miami University. His research interest is in quality management, product recall, and supply chain disruption. Before coming back to academia, he had extensive industrial experience in quality system certification and supplier assessment. His research has been published in the Journal of Supply Chain Management, European Journal of Operations Research, International Journal of Operations and Production Management, International Journal of Production Economics, International Journal of Production Research, and Quality Management Journal among others.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 202.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.