Perspectives of the App Economy: Tenets of the Innovative Phenomenon
Bibliographic record
Abstract
The paper aims to explore the App Economy drawing on a configurational multiple-theory perspective (Meyer, Tsui & Hinings 1993; Miller, 1996), using the lens of Transaction Costs Theory, Regulation, Disruptive Innovation Theory and Systemic Approach. These theories are examined in the form of tenets. The choice of these theories as dimensions of our model is the output of two different activities. The first regards an ex-ante analysis of the previous studies in this field in order to find less investigated perspectives and find a connection between the topic of the App Economy and the main management theories; the second refers to a debate with some strategic management scholars in order to identify and choice the main theories for this research. This paper contributes to the existing literature by proposing an original interpretation of the App Economy and it tries to add new knowledge in this emerging research field by adding new tenets. The results of study are the formulation of eight different tenets: two for Transaction Costs Theory, one for Regulation, three for Disruptive Innovation Theory and two for Systemic Approach. These results have confirmed the linked between chosen theories and the new research field of the App Economy. In any case, this paper is a preliminary study to develop a theoretically grounded approach to understanding the emergence of the App Economy and how manage the changes that it brings into the markets. This study has implications for several stakeholders (such as managers, enterprises, institutions, Authorities, app developers, operators, platform managers and other organizations that work in this field), and for several industries being impacted by developments induced by this innovative sharing economy.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.026 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.004 | 0.005 |
| 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".