The importance of customer focus for organizational performance: a study of Chinese companies
Bibliographic record
Abstract
Purpose The main objective of this paper is to empirically investigate the linkage among organizational customer orientation, customer relationship practices, and organizational outcomes. Design/methodology/approach The sampling frame of the study consists of 143,000 Chinese companies, each with revenue of more than 5 million RMB (Chinese currency). The target companies were randomly selected from 29 Chinese provinces using the stratified probability proportional to sizes (PPS) method. Structure equation modelling was utilized to analyze data. Findings It is found that organizational customer orientation affects customer relationship practices, which subsequently influence production performance and customer satisfaction. Production performance and customer satisfaction lead to financial performance. Research limitations/implications The study has some limitations that provide directions for future research. Data were only collected from China. Therefore, the research findings might reflect unique aspects of Chinese companies. Caution should be exercised when generalizing these research findings to other nations. The study also focused only on manufacturing firms' customer focus practices. Practical implications Companies need to promote customer orientation in their organization, in order to successfully implement customer relationship practices. Only when they effectively utilize the knowledge that they collect to improve production performance can they enhance customer satisfaction and their financial outcomes. Originality/value The paper maintains that customer focus practices should consist of two elements: organizational customer orientation and customer relationship practices. This offers new directions to researchers and practitioners for improving customer focus practices.
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 | 0.002 | 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 | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".