Impact of digital transformation on the individual job performance of insurance companies in Peru
Bibliographic record
Abstract
The objective of this study was to analyze and determine the impact of digital transformation on the individual job performance of insurance companies in Peru. The deductive inferential scientific method of explanatory level was used, with a non-experimental design, to four insurance companies that operate in the regions of Arequipa, Cusco, Iquitos, Lima, Tacna and Trujillo. The results generated by structural equations show that customer service experience (CSE), based on digital transformation, had a positive impact on task performance (p ≤ 0.05) and contextual performance (p ≤ 0.05); in contrast, the customer service experience (CSE), based on digital transformation, was found to have no impact on counterproductive behavior (p≥ 0.05). In relation to the collaborator's capabilities (CC) based on digital transformation, the results reveal that it had a significant influence on task performance (p ≤0.05) and contextual performance (p ≤ 0.05), while it did not have any impact on counterproductive behavior (p ≥ 0.05). Likewise, processes based on digital transformation (P) significantly influence task performance (p ≤ 0.05) and contextual performance (p ≤ 0.05), unlike counter-productive behavior that did not present a causal link with the processes (p> 0.05). Finally, the business model based on digital transformation (BM) had no implications for task performance (p> 0.05), contextual performance (p> 0.05) and counterproductive behaviors (p> 0.05). The conclusion of the study indicates that the customer service experience, the collaborator's capabilities and processes based on digital transformation contribute to the performance and contextual performance of the workers of the insurance companies in Peru.
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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 | 0.001 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".