The Influence of Lifecycle in Shaping the Underlying Project Portfolios in the High-Technology Market
Bibliographic record
Abstract
High-technology markets have a tendency to be extremely dynamic, undergo periods of rapid market growth, and exhibit market-based developments that align with a firm’s lifecycle, which is generally based upon the influence(s) exerted upon them by innovation within the market space. Likewise, how firms navigate the market will depend on the types of innovation(s) they experience, their corporate size, the amount of time spent within selected market positions, and the projects they ultimately implement. These various elements will ultimately shape how a firm manages projects and its associated organizational structures. The question is how managers do and entrepreneurs know what to do in the face of periods of changing market conditions? This paper explores the rational underlying the change from agile early start-up companies that grow and mature into increasingly rigid organizational structures that influence the types of projects as the firms progress along the high-tech lifecycle. The type and attitude of customers also evolves through time and this also has to be taken into consideration as each stage of the lifecycle will have customers with different drivers and requirements influencing their purchasing decisions. This paper uses the Lifecycle Theory to analyze this phenomenon and explain how and why this evolutionary process occurs within the market, including the factors and characteristics associated with projects during various market phases throughout the lifecycle. Ultimately, this exploration illustrates the evolutionary process firms undergo in relation to the implementation of innovation strategies within high-tech market spaces. The implications to industry include enabling entrepreneurs and managers to recognize and react to the complexity of the multi-dimensional changes in the market, strategically plan their next steps for their compay’s project management approach, product development, portfolio development, and to gain insight to competitor’s actions.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".