Success Factors of New Product Launch: The Case of iPhone Launch
Bibliographic record
Abstract
The continuous globalization and new product launch can represent essential drivers for the company performance. For many years has been conducted conceptual and empirical research in order to identity the success factors for new product launch. Therefore, the purpose of this paper is to identify and analyze the critical success factors for launching a new product using a marketing approach. The contribution to the science of this article is to create an understanding framework related to the factors that have an impact on the success of a new product launch; it could help companies in planning the new products launch. This paper followed a case-study methodology–the iPhone launch case. After iPhone was successfully launched, millions of iPhones was sold, turned it in one of the most popular mobile phone ever launched. Considering that Apple is now the leader on smartphone market, overtaking Samsung in the fourth quarter of 2014, the purpose of this case study is to explain how a company as Apple can enter in a saturated market, have success, and after eight years became the leader market. In order to involve all crucial drivers of product success, we attempted to summarize the findings considering three essential levels–consumers, company, and environmental. Regarding the case study conclusion it can be said that iPhone changed the way that consumers interact with the mobile phones; it built a connection with the consumer and influenced their behavior regarding the information access and digital lifestyle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 | 0.000 |
| 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".