Relatedness in External Corporate Venturing: Analysis, Reconceptualization, and Effects on Venture Governance Mode Choice
Bibliographic record
Abstract
External corporate venturing has received increasing attention on the agendas of leading high technology companies and on the research agenda of strategy scholars during the past years. Due to the global and networked economy companies very rarely can survive in, let alone outperform, their markets without interorganizational collaboration. Acquisitions, joint ventures, alliances and corporate venture capital investments are important vehicles for corporate renewal, and the success of high-tech companies in particular is affected heavily by the decisions of with whom and what kind of ventures they undertake. It is widely understood that the business scope of a prospective partner, relative to the scope of the focal firm, is one of the strategic factors affecting the choice of governance mode in their collaboration, but yet these effects have rarely been formally studied - and even when they have, assessment of the relatedness of the partner's business has been rather shallow. This thesis presents an attempt to provide a more profound understanding of these effects by utilizing a multi-faceted approach to establish the key factors of relatedness pertaining to the business scope of a partner, and reflecting these factors on a broad theoretical basis to predict expected outcomes. This thesis aims to 1) clarify the concept of relatedness through critical analysis of the contextual prerequisites of relatedness in terms of essence and structure, 2) building on the preceding reconceptualization, propose measures of business relatedness in terms of product-market scope based on technological and industry contexts of relatedness, and finally 3) analyze how relatedness of a prospective partner's business affects the venture governance mode preferences of leading U.S. firms in the information and communication technology (ICT) sector, utilizing the proposed measures of technological and industry relatedness. It is hypothesized that both technological and industry relatedness increase the preference for modes providing more control and also requiring more corporate involvement. Empirical analysis is conducted utilizing data on the acquisitions, alliances, joint ventures and corporate venture capital investments of 110 of the largest U.S. companies in the ICT sector in 1995-2001. The relatedness measures used are based on commodity flow data from U.S. industry input-output tables, industry employment data from the Occupational Employment Survey, and patent applicability data from the Canadian Patent Office. The hypotheses are tested using multinomial logit regression analysis. The results of the analysis provide partial support for the hypotheses, but also include some contradicting findings suggesting industry-specific dynamics, and strategies related to intellectual property rights. At any rate, the proposed measures of business relatedness are found to offer deeper insights into the governance mode choice decision than traditional measures of relatedness utilized in prior research.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".