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Open innovation ecosystems: A multi-method assessment of knowledge transfer success / EMILY BACON

Swansea University Author: EMILY BACON

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DOI (Published version): 10.23889/SUthesis.59511

Abstract

This research conducts an empirical investigation into the conditions for knowledge transfer success across open innovation ecosystems. The ecosystem approach has received significant theoretical attention in recent decades, with such a popularity echoed in its practical applications across a variet...

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Published: Swansea 2020
Institution: Swansea University
Degree level: Doctoral
Degree name: Ph.D
Supervisor: Williams, Michael ; Davies, Gareth
URI: https://cronfa.swan.ac.uk/Record/cronfa59511
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last_indexed 2022-03-08T04:28:40Z
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spelling 2022-03-07T12:25:32.8613085 v2 59511 2022-03-07 Open innovation ecosystems: A multi-method assessment of knowledge transfer success 84f0c2ffe0fb470a621b5f9960334be5 EMILY BACON EMILY BACON true false 2022-03-07 This research conducts an empirical investigation into the conditions for knowledge transfer success across open innovation ecosystems. The ecosystem approach has received significant theoretical attention in recent decades, with such a popularity echoed in its practical applications across a variety of organisations. Despite effective information exchange significantly supporting the ecosystem infrastructure, existing literature has established very limited insights into the variances between transfer practices of diverse ecosystem partner types, thus failing to increase knowledge of how knowledge transfer can be achieved successfully within these contexts. In remedying this absence, this study adopts a two-phase, multi-method approach to establish how knowledge transfer conditions are maintained and prioritised between ecosystem partners, in order to accelerate the development of this approach to innovations, both theoretically and practically. Through developing a novel conceptual framework, this study detects seven knowledge transfer conditions, categorising them into three groupings that reflect the nature of the relationship; the knowledge exchanged; and the characteristics of the recipient organisation. Their relevance for innovation ecosystems is ascertained through conducting a qualitative, two-phase investigation into partner perceptions of the identified conditions. Based upon qualitative questionnaires with key stakeholders from an ecosystem orchestrator, results from Interpretive Structural Modelling (ISM) reveal that the conditions retain equal importance, necessitating further analyses to illuminate their relationships with knowledge transfer success. To engender more nuanced understandings, fuzzy-set Qualitative Comparative Analysis (fsQCA) is utilised to analyse semi-structured interviews with 30 ecosystem partners, segregated into threefold analyses representing the common organisational typologies within an ecosystem: multinationals, small-and-medium sized enterprises (SMEs) and universities. Results reveal distinct, mutually exclusive solutions for success, grounded upon combinations of conditions that manifest within causal configurations. These findings affect how information exchange should be construed and accomplished by practitioners engaging in ecosystem approaches. E-Thesis Swansea 28 2 2020 2020-02-28 10.23889/SUthesis.59511 Due to Embargo and/or Third Party Copyright restrictions, this thesis is not available via this service.ORCiD identifier: https://orcid.org/0000-0001-9933-3902 COLLEGE NANME COLLEGE CODE Swansea University Williams, Michael ; Davies, Gareth Doctoral Ph.D European Social Fund through the European Union’s Convergence programme 2022-03-07T12:25:32.8613085 2022-03-07T11:13:04.9362415 Faculty of Humanities and Social Sciences School of Management - Business Management EMILY BACON 1
title Open innovation ecosystems: A multi-method assessment of knowledge transfer success
spellingShingle Open innovation ecosystems: A multi-method assessment of knowledge transfer success
EMILY BACON
title_short Open innovation ecosystems: A multi-method assessment of knowledge transfer success
title_full Open innovation ecosystems: A multi-method assessment of knowledge transfer success
title_fullStr Open innovation ecosystems: A multi-method assessment of knowledge transfer success
title_full_unstemmed Open innovation ecosystems: A multi-method assessment of knowledge transfer success
title_sort Open innovation ecosystems: A multi-method assessment of knowledge transfer success
author_id_str_mv 84f0c2ffe0fb470a621b5f9960334be5
author_id_fullname_str_mv 84f0c2ffe0fb470a621b5f9960334be5_***_EMILY BACON
author EMILY BACON
author2 EMILY BACON
format E-Thesis
publishDate 2020
institution Swansea University
doi_str_mv 10.23889/SUthesis.59511
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Business Management{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Business Management
document_store_str 0
active_str 0
description This research conducts an empirical investigation into the conditions for knowledge transfer success across open innovation ecosystems. The ecosystem approach has received significant theoretical attention in recent decades, with such a popularity echoed in its practical applications across a variety of organisations. Despite effective information exchange significantly supporting the ecosystem infrastructure, existing literature has established very limited insights into the variances between transfer practices of diverse ecosystem partner types, thus failing to increase knowledge of how knowledge transfer can be achieved successfully within these contexts. In remedying this absence, this study adopts a two-phase, multi-method approach to establish how knowledge transfer conditions are maintained and prioritised between ecosystem partners, in order to accelerate the development of this approach to innovations, both theoretically and practically. Through developing a novel conceptual framework, this study detects seven knowledge transfer conditions, categorising them into three groupings that reflect the nature of the relationship; the knowledge exchanged; and the characteristics of the recipient organisation. Their relevance for innovation ecosystems is ascertained through conducting a qualitative, two-phase investigation into partner perceptions of the identified conditions. Based upon qualitative questionnaires with key stakeholders from an ecosystem orchestrator, results from Interpretive Structural Modelling (ISM) reveal that the conditions retain equal importance, necessitating further analyses to illuminate their relationships with knowledge transfer success. To engender more nuanced understandings, fuzzy-set Qualitative Comparative Analysis (fsQCA) is utilised to analyse semi-structured interviews with 30 ecosystem partners, segregated into threefold analyses representing the common organisational typologies within an ecosystem: multinationals, small-and-medium sized enterprises (SMEs) and universities. Results reveal distinct, mutually exclusive solutions for success, grounded upon combinations of conditions that manifest within causal configurations. These findings affect how information exchange should be construed and accomplished by practitioners engaging in ecosystem approaches.
published_date 2020-02-28T04:16:53Z
_version_ 1763754128939417600
score 11.035655

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