Dynamic Capabilities for Business Model Innovation in Logistics: The Role of Digital Technologies
Bibliographic record
Abstract
The rising competition among firms necessitates new ways of doing business, especially in this digital era. This is fundamentally true of logistics companies as they strive to innovate their business models using digital technologies. Nevertheless, the dynamic competence gained by logistics firms while using digital technologies for BMI has not received sufficient research attention. Driven by the expedient research question, how do firms leverage digital technologies to develop dynamic capabilities for BMI, this study teases out the pathways to BMI by investigating how logistics companies engage digital resources to gain dynamic capabilities. Following the procedures established in Gioia methodology, we perform thematic analysis on qualitative data from the whitepapers of 20 logistics companies prominent for technology-enabled business models. Results reveal that while engaging digital technologies for their business processes, logistics businesses and their managers can sense opportunities for business expansion; seize these opportunities by mobilizing digital resources as well as reconfigure their processes to continue to take advantage of the recognized opportunities. Our results contribute to the dynamic capabilities theory by building on its core arguments to explicate the theoretical foundations of BMI development. Additionally, three propositions emerge regarding the sources of dynamic capabilities in the utilization of digital technologies by digital logistics.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".