Distant or Local? The Roles of Knowledge Search on General Purpose Technology Innovation in Emerging Industries

新兴市场 困境 业务 产业组织 概括性 开放式创新 新兴技术 营销 知识管理 经济 计算机科学 管理 财务 认识论 哲学 人工智能
作者
Jie Cen,Feng Fu,Yan Yang,Jingxin Yan,Jing Li
出处
期刊:Journal of Innovation & Knowledge [Elsevier BV]
卷期号:8 (2): 100331-100331 被引量:5
标识
DOI:10.1016/j.jik.2023.100331
摘要

Due to the inherent complex tensions in General Purpose Technology (GPT) in emerging industries, namely those between “generality” and “emergance,” firms in emerging industries often face the dilemma of choosing distant search or local search in GPT innovation. The core research question of this paper is: How do the different knowledge search strategies affect GPT innovation in emerging industries? And do industry alliances play a moderating role between them? Based on a knowledge-based view, this paper takes 380 firms in emerging industries as total samples and 196 firms in emerging industries as subsamples to examine the impact of distant, local, and balanced knowledge search on the GPT innovation in emerging industries, finding: (1) Different knowledge search strategies have differential effects on the GPT innovation in emerging industries. (2) The heterogeneity of industry alliances positively moderates the relationship between distant search, balanced search, and GPT innovation in emerging industries, and weakens the relationship between local search and GPT innovation in emerging industries. This paper clarifies the relationship between knowledge search strategies and GPT innovation in emerging industries, and also provides management inspiration for the emergence and improvement of GPT innovation.

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