The B2Com Relationship: An Empirical Study of the Measure of Relationship Quality in a Business-to-Community Relationship
Bibliographic record
Abstract
The quality of relationship between Oil Producing Companies (OPC) and their Host Communities (HC) within the Niger Delta Region of Nigeria (NDRN) is a source of concern for different stakeholders such as the practitioners, government, communities, and OPC. Scholars in the field of marketing have studied relationship quality in different contexts, such as business-to-business, business-to-customer, customer-to-business, and interpersonal relationship. In this paper, the authors build on existing studies to develop a new relationship framework in a business-to-community (B2Com) context, intended to assess the degree of relationship quality between a business and its host community. The framework is supported by the results of a qualitative research study conducted using an in-depth semi-structured interview approach in exploring and assessing the various relationship elements and constructs impacting on the quality of a relationship developed between an OPC business and its host community in the Niger Delta of Nigeria. The findings showed that activity links, resources ties, actor bond, mutual benefit, communication, control mutuality, mutual goal and culture are the main antecedents of relationship quality, while trust, satisfaction, and commitment are the essential outcomes of relationship quality. The findings also showed that there is a linear relationship between trust, satisfaction and commitment.
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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 | 0.008 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".