Perceived Value, Social Bond, and Switching Cost as Antecedents and Predictors of Customer Loyalty in the B2B Chemical Industry Context: A Literature Review
Bibliographic record
Abstract
Industries have been emphasizing customer loyalty to ensure business sustainability. A lot of constructs influence customer loyalty, and this paper focuses on perceived value, social bond, and switching cost. What is actually behind customer’s retention with the firms becomes exciting topics to be explored since firms will employ the best strategy to retain customers, either through offering superior value, investing in a relationship, or in setting up switching costs. This study is designed to develop integrative constructs of customer loyalty and investigates their antecedents using the literature review method. Perceived value tends to be evaluated from an economic benefit perspective since this paper refers to some business practices in chemical industries that concern cost. Purposely, to achieve this economic value, both firms and customers need to work closely, transparently, and cooperatively since the beginning; hence, it needs an interpersonal relationship between parties. With more transferred information from the customer and more transparent communication, firms will be able to identify the customer’s need and deliver tailored superior value. This literature review finds that excellent value and the social bond become financial and relational switching costs for the customer. By understanding antecedents of loyalty, a firm can develop a social bond, superior value, and set up switching costs to create loyalty and build a sustainable business relationship.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".