New product success through big data analytics: an empirical evidence from Iran
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
Purpose Innovative firms leverage big data analytics (BDA) benefits in optimising value creation, particularly in business-to-business (B2B) contexts. Examples of this are found in new product success and product innovation performance. However, knowledge of how innovative firms and their corporate customers generate insights from big data, develop new products and gain higher-quality service from intra- and inter organisations' resources is limited. This knowledge manifests in the form of opportunities available in BDA and through the adoption of the co-creation approach to generate value in the form of new product innovation. BDA reflects an excellent means of enhancing a firm's customer agility, but how this is possible remains largely unknown. Design/methodology/approach In this research, the authors hypothesise that new product success is a function of a firm's customer agility and product innovation performance moderated by environmental turbulences. In turn, the firm's customer agility is enhanced by the effect of big data aggregation and analytical tools. These hypotheses have been confirmed by a survey in an emerging market. Findings The authors use structural equation modelling to test the authors’ hypotheses. The main contribution of this research is the conceptualisation and test of an integrative framework identifying the links among a firm's customer agility, new product success and BDA capabilities. Practical implications The study established that BDA tools – the effective use of data aggregation tools and the effective use of data analysis tools – shape customer agility in achieving new product success. This study contributes to one’s understanding of the relevance of BDA in B2B value creation contexts. Originality/value The study findings show that BDA shapes a firm's customer agility in achieving new product success.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.011 |
| Open science | 0.001 | 0.001 |
| 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