Goal Multiplicity and Innovation: How Social and Economic Goals Affect Open Innovation and Innovation Performance
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Integrating insights from the strategic goal literature and the knowledge‐based view of the firm, this article proposes that the pursuit of social and economic strategic goals by commercial firms affects their innovation performance through different knowledge sourcing activities. The strategic goals, knowledge sourcing practices, and innovation performance of 1257 Belgian firms are investigated. Results show that both social and economic strategic goals are associated with the use of external information sources, but only the pursuit of social goals inspires firms to engage in external collaboration. No evidence is found of an inherent conflict between social and economic strategic goals. Instead, the two types of goals are independent of each other, that is, an emphasis on social goals does not preclude an emphasis on economic goals and vice versa. Moreover, firms’ external knowledge sourcing and innovation performance benefit most when strongly held social goals align with strongly held economic goals. These findings offer new insight into the nature and the effects of goal multiplicity among commercial firms. They open up a new perspective on the potential positive effects of the joint pursuit of social and economic strategic goals instead of seeing them as inherently conflicting, as past research has typically done. We illustrate how social strategic goals can deliver unique benefits to a firm, independently of and in addition to economic strategic goals. Our findings also contribute to the open innovation literature by revealing strategic goals as a driver of firms’ knowledge sourcing practices. Our findings suggest that solely emphasizing economic goals may be one reason why firms struggle to implement open innovation practices and do not reap their full benefits. The practical implications of our research are discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it