The Determinants of PayTech’s Success in the Mobile Payment Market—The Case of BLIK
Bibliographic record
Abstract
FinTech and its interaction with banking is widely discussed today as a new phenomenon notwithstanding the relationship between technology and financial services is not a new topic. Most of the research focuses on innovations and determinants of their adoptions including among other innovations in the payment system. The studies dedicated directly to PayTechs as a special kind of a FinTech entity and its market activity are a relatively new field of research. This paper aims to fill this gap. The multidimensional character of this exploratory research causes the necessity to apply various research methods, including both inductive and deductive methods, together with comparative analysis. The theoretical analysis conducted in the paper for defining PayTechs from the perspective of business model and market behavior was based on an in-depth literature review. In this section, the inductive method and comparative analysis were mostly applied. The empirical part of the paper includes the analysis of quantitative data published by the National Bank of Poland (NBP), Central Statistical Office (GUS), and Bank for International Settlements (BIS). The subject of the case is the Polish Payment Standard referred to as BLIK implemented in Poland in 2015 for mobile payments. The BLIK diffusion is measured by the number of entrants and acceptants as well as the scope of transactions while the adoption by the number of customers using BLIK in everyday transactions. The results present the market behavior of BLIK as an open business model and the key success factors of BLIK adoption and diffusion and the determinants for further open payment innovations’ development. The newly developed definition of PayTechs, the identification of the major components of the PayTech open business model, as well as the indication of the key success factors of adoption and diffusion of m-payments, constitute the original contribution of the paper.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".