Digital Platform Capabilities for Transforming Cultural Heritage Business: Exploring the Mediating Role of Business Model Experimentation and Competitive Advantage
Bibliographic record
Abstract
Digitalisation has evolved as a multidimensional phenomenon and impacts the business world. SMEs heavily invest in digital platform capabilities to keep track of digital transformation, enabling them to perform business model experimentation to generate and develop innovation. This paper explores the role of these two crucial growth-promoting variables in the performance of art and craft-based firm’s performance. Through this paper, the researchers contest the argument that, although digital platform capabilities accelerate business model experimentation for firm performance, competitive advantage plays a significant mediating role. Along with these arguments, this study also explores the role of digital platform capability in business model experimentation. It examines the mediating role of business model experimentation in the forming of a competitive advantage. The research model under examination belongs to the explorative school of research; hence, the researchers have used partial least square–structural equation modelling (PLS-SEM) on a sample of 211 Indian firms belonging to the category of art and craft-based businesses. The hypothesis testing results facilitate exciting insights about the direct and indirect effects of digital platform capabilities, business model experimentation, and competitive advantage on firm performance. In light of the research findings, policymakers, SME consultants, and managers may obtain practical insights in order to develop an intervention mechanism. Researchers working in this area will glean a fresh look at the antecedents of SME performance as this model is explorative; future research may explore the testing of the model in different geographic locations and industry contexts.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".