The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
How can open innovation ecosystem modes push product innovation forward? An fsQCA analysis
Abstract
Abstract Firms in open innovation ecosystems expand organizational resources and allow for collaborations across organizations, which can promote the flow, aggregation, and integration of resources within business network ecosystems. Yet, particular gaps in the literature limit our understanding of how open innovation ecosystems contribute to firm product innovation. Therefore, this study explores the causal recipes of how open innovation ecosystem modes promote product innovation by using both grounded theory and fuzzy set qualitative comparative analysis (fsQCA). First, by taking four innovative firms as case studies and applying grounded theory, we identify six specific open innovation ecosystem modes: “firm-university-institute cooperation,” “interfirm cooperation,” “firm-intermediary cooperation,” “firm-user cooperation,” “asset divestiture,” and “technology transfer.” Then, based on the data of 226 innovative firms, we employ fsQCA to explore the causal configurations of the open innovation ecosystem modes that consistently improve product innovation. The findings reveal that three particular combinations can explain the high level of product innovation: (1) interfirm cooperation, firm-intermediary cooperation, and technology transfer combined with asset divestiture; (2) interfirm cooperation, firm-intermediary cooperation, and technology transfer combined with firm-university-institute cooperation; and (3) interfirm cooperation, firm-intermediary cooperation, and firm-university-institute cooperation combined with firm-user cooperation. Overall, this work contributes to the theoretical literature on open innovation ecosystems and provides practical information for firms looking to achieve value creation.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Grounded theory research: Procedures, canons, and evaluative criteria
Corbin, J. M., & Strauss, A. (1990) · Qualitative Sociology · doi:10.1007/bf00988593 · Cited by 11,133
Configurational Comparative Methods: Qualitative Comparative Analysis (QCA) and Related Techniques
Rihoux, B., & Ragin, C. (2009) · SAGE Publications · doi:10.4135/9781452226569 · Cited by 3,089
Ecosystem as Structure: An Actionable Construct for Strategy
Adner, R. (2017) · Journal of Management · doi:10.1177/0149206316678451 · Cited by 2,600
A set-theoretic approach to organizational configurations
Fiss, P. C. (2007) · Academy of Management Review · doi:10.5465/amr.2007.26586092 · Cited by 2,225
Set Relations in Social Research: Evaluating Their Consistency and Coverage
Ragin, C. C. (2006) · Political Analysis · doi:10.1093/pan/mpj019 · Cited by 1,784
Standards of Good Practice in Qualitative Comparative Analysis (QCA) and Fuzzy-Sets
Schneider, C. Q., & Wagemann, C. (2010) · Comparative Sociology · doi:10.1163/156913210x12493538729793 · Cited by 1,473
Coordinating and competing in ecosystems: How organizational forms shape new technology investments
Kapoor, R., & Lee, J. M. (2013) · Strategic Management Journal · doi:10.1002/smj.2010 · Cited by 502
Assessing the Robustness of Crisp-set and Fuzzy-set QCA Results
Skaaning, S.-E. (2011) · Sociological Methods & Research · doi:10.1177/0049124111404818 · Cited by 407
Cited by
Robot will take your job: Innovation for an era of artificial intelligence
Rampersad, G. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.05.019 · Cited by 240
Zhou, Y., Zuo, Y., & Yang, Y. (2026) · International Journal of Management Science and Engineering Management · doi:10.1080/17509653.2026.2616016
Metadata
- Authors
- Xie, X., & Wang, H.
- Venue
- Journal of Business Research
- Publisher
- Elsevier BV
- Published
- 2020-01
- Volume
- 108
- Pages
- 29-41
- ISSN
- 0148-2963 (ISSN-L)
- OpenAlex
- W2990589719
- Cited by
- 325
- References
- 8
- FWCI
- 36.70
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.