Exploring Coupled Open Innovation for Digital Servitization in Grocery Retail: From Digital Dynamic Capabilities Perspective
Bibliographic record
Abstract
Open innovation and digital servitization have been hot topics in existing research. Moreover, the latest research in entrepreneurship and general management justifies that the performance results of specific innovation strategies are usually influenced by dynamic capabilities. However, there is little empirical research on the linkage of open innovation, digital servitization, and micro-foundations of digital dynamic capabilities that affect alliance performance. The emerging literature on open innovation provides partial insight into the micro-foundations of digital dynamic capabilities. Based on it, from a dynamic capability perspective, this paper constructs a conceptual model of research including coupled open innovation of collaborative partners, alliance’s formation phases, and dynamic digital capabilities and their micro-foundations which impact alliance performance in grocery retail. The paper aims to provide an overarching view of the digital servitization process of grocery retailers and unpack the micro-foundations of the digital transformation of their business models to sustain advantages. Thus, the paper contributes to the research on open innovation, blockchain technology, artificial intelligence, and dynamic capabilities and provides two theoretical propositions. Then, having employed two illustrative case studies, this paper empirically tests theoretical propositions and justifies the role of coupled open innovation strategies for digital servitization and its micro-foundations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".