השוואת שיטות

	סולם הוגנות המחיר	סולם נאמנות לקוחות
תחום	ניהול שיווק	ניהול שיווק
משפחה	Process / pipeline	Process / pipeline
שנת המקור	2004	1994
הוגה השיטה	Ling Xia, Kent B. Monroe, Jennifer L. Cox	Alan S. Dick, Kunal Basu
סוג	Multi-dimensional price fairness scale	Multi-dimensional behavioral and attitudinal loyalty scale
מקור מכונן	Campbell, M. C. (2005). Perceived Price Fairness. MIT Sloan Management Review, 46(3), 30-35.	Dick, A. S., & Basu, K. (1994). Customer Loyalty: Toward an Integrated Conceptual Framework. Journal of the Academy of Marketing Science, 22(2), 99-113.
כינויים	Pricing Justice Scale, Fair Price Perception Scale	Behavioral Loyalty Scale, Loyalty Commitment Scale
קשורות	3	4
תקציר	The Price Fairness Scale (PFS), developed by Xia, Monroe, and Cox (2004), measures customer perception of whether a charged price is fair and reasonable relative to value received and market comparison. Price fairness assessment differs from absolute price satisfaction: customers may perceive a price as high but fair if quality justifies it, or as low but unfair if they suspect price discrimination or exploitation. The PFS captures three dimensions of price fairness judgment: Distributive Fairness (whether the price-value ratio is equitable), Procedural Fairness (whether the pricing process is transparent and non-discriminatory), and Interactional Fairness (whether pricing explanations are respectful). The scale is critical for premium pricing strategy, price increases, and dynamic pricing implementation.	The Customer Loyalty Scale (CLS) measures customer loyalty as a combination of attitudinal commitment and behavioral intention. Developed by Dick and Basu (1994), the scale distinguishes between behavioral loyalty (repeat purchases) and attitudinal loyalty (emotional commitment), recognizing that true loyalty involves both. CLS captures multiple dimensions of loyalty including repurchase intention, word-of-mouth advocacy, and resistance to competitive offerings. The instrument is widely used in marketing research to predict customer lifetime value and identify at-risk customers.