Strategies of Knowledge Pricing and the Impact on Firms’ New Product Development Performance

• Chuanrong Wu,
• Ning Tan,
• Zhi Lu,
• Xiaoming Yang,
• Mark E. McMurtrey,

Vol. 15, No. 8, August 31, 2021
10.3837/tiis.2021.08.020, Download Paper (Free):

• Knowledge transaction
• two-part tariff pricing
• lump-sum pricing
• royalty pricing
• subscription pricing
• pay-per-use pricing

Abstract

The economics of big data knowledge, especially cloud computing and statistical data of consumer preferences, has attracted increasing academic and industry practitioners' attention. Firms nowadays require purchasing not only external private patent knowledge from other firms, but also proprietary big data knowledge to support their new product development. Extant research investigates pricing strategies of external private patent knowledge and proprietary big data knowledge separately. Yet, a comprehensive investigation of pricing strategies of these two types of knowledge is in pressing need. This research constructs an overarching pricing model of external private patent knowledge and proprietary big data knowledge through the lens of firm profitability as a knowledge transaction recipient. The proposed model can help those firms who purchase external knowledge choose the optimal knowledge structure and pricing strategies of two types of knowledge, and provide theoretical and methodological guidance for knowledge transaction recipient firms to negotiate with knowledge providers.

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