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Publication date: 21 November 2024

Sonika Jha and Sriparna Basu

This study aims to examine the combinations of internal and external knowledge flows between research and development (R&D) incumbents and start-ups in the context of open…

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Abstract

Purpose

This study aims to examine the combinations of internal and external knowledge flows between research and development (R&D) incumbents and start-ups in the context of open innovation. While there is a growing body of knowledge that has examined how, in a knowledge economy, a firm’s knowledge and innovation activities are closely linked, there is no systematic review available of the key antecedents, perspectives, phenomenon and outcomes of knowledge spillovers.

Design/methodology/approach

The authors have conducted dual-stage research. First, the authors conducted a systematic review of literature (97 research articles) by following the theories–contexts–methods framework and the antecedent-phenomenon-outcomes logic. The authors identified the key theories, contexts, methods, antecedents, phenomenon and outcomes of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context. In the second stage, the findings of stage one were leveraged to advance a nomological network that depicts the strength of the relationship between the observable constructs that emerged from the review.

Findings

The findings demonstrate how knowledge spillovers can help incumbent organisations and start-ups to achieve improved innovation capabilities, R&D capacity, competitive advantage and the creation of knowledge ecosystems leading to improved firm performance. This study has important implications for practitioners and managers – it provides managers with important antecedents of knowledge spillover (knowledge capacities and knowledge types), which directly impact the R&D intensity and digitalisation driving open innovation. The emerging network showed that the antecedents of knowledge spillovers have a direct relationship with the creation of a knowledge ecosystem orchestrated by incumbents and that there is a very strong influence of knowledge capacities and knowledge types on the selection of external knowledge partners/sources.

Practical implications

This study has important implications for practitioners and managers. In particular, it provides managers with important antecedents of knowledge spillover (knowledge capacities and knowledge types), which directly impact the R&D intensity and digitalisation driving open innovation. This will enable managers to take important decisions about what knowledge capacities are required to achieve innovation outcomes. The findings suggest that managers of incumbent firms should be cautious when deciding to invest in knowledge sourcing from external partners. This choice may be driven by the absorptive capacity of the incumbent firm, market competition, protection of intellectual property and public policy supporting innovation and entrepreneurship.

Originality/value

Identification of the key antecedents, phenomenon and outcomes of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context. The findings from Stage 1 helped us to advance a nomological network in Stage 2, which identifies the strength and influence of the various observable constructs (identified from the review) on each other. No prior study, to the best of the authors’ knowledge, has advanced a nomological network in the context of knowledge spillovers between R&D-driven incumbents and start-ups in the open innovation context.

Details

Journal of Knowledge Management, vol. 29 no. 2
Type: Research Article
ISSN: 1367-3270

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Article
Publication date: 2 October 2024

Miriam Al Lily

This paper aims to explore the possible forms and characteristics of an artificial intelligence (AI) leader and discuss the potential applications of AI in political leadership…

68

Abstract

Purpose

This paper aims to explore the possible forms and characteristics of an artificial intelligence (AI) leader and discuss the potential applications of AI in political leadership and governance.

Design/methodology/approach

A categorization system consisting of three categories – the level of responsibility, the voting system and the bindingness of the AI’s decisions – was developed to better understand the various types of AI leaders. Additionally, to identify the main characteristics of an AI leader, a comprehensive literature review was conducted. The themes from the literature were then categorized and supplemented with additional discussions.

Findings

This paper identifies several potential AI leaders, including the AI President, the AI Dictator, the AI Minister and the AI Consultant. The key characteristics of an AI leader were also discussed. The primary strengths of AI lie in their intelligence and rationality, which could potentially lead our societies toward a peaceful and prosperous future. However, a significant drawback of AI is that it will always be limited by the capabilities and intentions of its programmer, whether human or AI.

Practical implications

Understanding the forms and characteristics of AI leaders may help policymakers and decision-makers explore the possibilities of integrating AI into political leadership and governance.

Originality/value

This paper contributes to the emerging field of AI in governance by exploring the forms and characteristics of AI leaders and discussing their potential applications in political leadership.

Details

foresight, vol. 27 no. 1
Type: Research Article
ISSN: 1463-6689

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