New product success through big data analytics: an empirical evidence from Iran
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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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 | 0.006 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".