Does ICT Investment Affect Market Share and Customer Acquisition Cost? A Comparative Analysis of Domestic and Foreign Banks Operating in India
Bibliographic record
Abstract
Competitive banks aggressively invest in information and communication technologies (ICT) to enhance their market share and reduce Customer Acquisition Costs (CAC). This study examines the impact of cumulative stock of ICT investment on (a) deposit and loan market share and (b) CAC of banks operating in India. The analysis uses a longitudinal dataset of 84 domestic and 70 foreign banks from 2000 to 2020, employing a two-step system Generalized Method of Moment (GMM). It is found that ICT investment adversely affects the market share of domestic banks, indicating a need for these banks to strategically invest more in CAC. Conversely, foreign banks are able to increase their market share through ICT investment and reduced CAC, thereby demonstrating greater efficiency in utilizing ICT. The study underscores the strategic importance of cumulative stock of ICT investment for banks. Nonetheless, it is emphasized that ICT investment must be complemented with innovative marketing strategies to enhance customer experience, reduce CAC, and increase market share. Overall, while foreign banks are able to leverage ICT to boost efficiency, domestic banks must leverage ICT to implement targeted marketing strategies and strive to enhance their customer service.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".