The value of values in business purchase decisions
Bibliographic record
Abstract
Purpose The purpose of this study is to develop and test a theoretical framework to examine business purchase decisions using the concept of “values” (personal values (PV), organizational values (OV) and values-congruency). Design/methodology/approach The data for the study were collected from members of the Supply Chain Management Association of Canada. The relationships between perceived PV/OV/ values-congruency (IVs) and perceived role values played in business purchase decisions (DV) were hypothesized. Three factors, namely, humanity, bottomline and convention were identified using exploratory factor analysis. The hypotheses were tested using polynomial regression, which is a preferred method for measuring congruency or fit (Edwards, 1994). Findings Perceived humanity (humaneness or benevolence) values of an organization were found to have a positive relationship with the perceived role that humanity and convention (risk aversion or compliance) values played in business purchase decisions. Perceived purchase function formalization within buying organizations was also found to have a positive relationship with the perceived role of humanity, bottomline and convention values played in business purchase decisions. Research limitations/implications The study drew a relatively small convenience sample from a single industry association/country with a low response rate. It used the perceived role of values instead of behavioral intention or actual behavior to measure business purchasing behavior. McDonald and Gandz’s (1991; 1993) list of values may be more suitable to measure OV than PV. The study only considered the buyer side of purchase decisions and values to have positive characteristics. Practical implications Buying organizations may consider formalizing their purchase functions, clarifying their humaneness/benevolence and risk aversion/compliance values to their employees and vendors and incorporating them in the purchasing criteria/process. Similarly, selling organizations may benefit from considering these values of customers to position their products and services for better sales outcomes and business relationships. Originality/value The study explores the role of values in business purchase contexts by proposing and testing a theoretical framework. The study has implications for practitioners and academics in the field and identifies several areas for future research.
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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 | 0.002 | 0.010 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 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".