Mobile banking adoption: a systematic review
Bibliographic record
Abstract
Purpose This study is a systematic review of mobile banking services. Its main objective is to provide a state-of-the-art review of this particular growing type of services. It inventories and assesses the most significant determinants of and barriers to consumers' adoption of mobile banking. Moreover, it identifies the most common consequences of this adoption. Design/methodology/approach By using three major academic databases (ABI/INFORM global, Web of Science and Business Source Premier), this paper selected 76 manuscripts and produced a systematic review that exposes the main theories, conceptual frameworks and models used to explain consumers' adoption of mobile banking. Findings The results show that the TAM (technology of acceptance model), followed by the UTAUT (unified theory of acceptance and usage of technology), are still the main conceptual frameworks and models adopted and adapted by scholars to explain consumers' use or intention of using mobile banking. Using the vote counting method, a myriad of antecedents and consequences that are frequently used in the literature of mobile banking are reported. These were categorized into five main perspectives: (1) m-banking attributes-based perspective, (2) customer-based perspective, (3) social influence-based perspective, (4) trust-based perspective and (5) barriers-based perspective. Originality/value An integrated model regrouping and relating the five perspectives is proposed, leading to intriguing implications for both academics and practitioners.
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 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 | 0.010 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.018 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".