Key Factors Influencing Adoption of Online Dispute Resolution in Banking Sector: An Empirical Analysis
Bibliographic record
Abstract
Background: Online dispute resolution system helps banks to reduce expenses and time associated with dispute resolution while enhancing client satisfaction and loyalty However, it faces several challenges necessitating the establishment of standards to ensure consistency and security. Objectives: Many online dispute resolution (ODR) services like email, chat, and video conferencing offer global grievance solutions, yet individuals face challenges due to diverse regulations, leading to security, privacy, and connectivity issues. Thus, this study aims to understand how customers perceive online dispute resolution for resolving banking issues. Methods: The research adopts an explanatory research design and employs a convenient sampling method for data collection. It's grounded in the Unified Theory of Acceptance and Use of Technology theory. A total of 403 data points were gathered using structured questionnaires and were analyzed using both descriptive and inferential analysis. Results: This study reveals that the trust placed in ODR technology, trust in the bank's services, and the perceived ease of use (effort expectancy) are crucial determinants shaping individuals' intentions to engage with ODR. The challenges of limited technology access, lack of awareness and trust, and inadequate infrastructure pose constraints on online dispute resolution's effectiveness. Addressing these limitations involves enhancing access to technology, and infrastructure and organizing awareness programs as managerial solutions. Conclusion: Trust in ODR technology, trust in the bank's services, and perceived ease of use are key factors influencing individuals' intentions to utilize ODR. Challenges, including limited technology access, lack of awareness and trust, and inadequate infrastructure, pose significant constraints on the effectiveness of online dispute resolution. Managerial solutions should prioritize enhancing technology access, improving infrastructure, and implementing awareness programs to overcome these barriers and optimize the potential of ODR.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".