Battles of mobile payment networks: The impacts of network structures, technology complementarities and institutional mechanisms on consumer loyalty
Bibliographic record
Abstract
Abstract Most information systems (IS) research takes for granted that consumers' adoption and the use of mobile payment (MP) applications are motivated by generic factors such as perceived usefulness and perceived ease of use. Challenging this assumption, we argue that the salient contextual characteristics of MP applications compel a reconsideration and problematization of research on MP adoption and use. Drawing on network effect theory, we examined how contextual network effects and contextual network types determine MP consumer loyalty. Using a mixed methods design, we find that direct network effects (i.e., network size, network centrality, network capability), indirect network effects (i.e., platform–application complementarity, application–service complementarity, service–strategy complementarity) and negative network effects (i.e., general institutional structure, general structural assurance, local institutional structure and local structural assurance) are key determinants of perceived benefits, which further promote MP consumer loyalty. Furthermore, except for general institutional structure and general structural assurance, all of the network effects are important predictors of switching costs, which influence MP consumer loyalty. Finally, the impacts of network effects on MP consumer loyalty differ between consumer‐ and service‐oriented networks. Our study enriches the IS literature by problematizing the core assumption underlying the MP adoption and use research and offering a contextual explanation of MP consumer loyalty. Our work also provides practitioners with insights into how to better leverage network effects on MP consumer loyalty.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".