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	Scala di Equità del Prezzo	Scala di Fedeltà del Cliente
Campo	Gestione del marketing	Gestione del marketing
Famiglia	Process / pipeline	Process / pipeline
Anno di origine	2004	1994
Ideatore	Ling Xia, Kent B. Monroe, Jennifer L. Cox	Alan S. Dick, Kunal Basu
Tipo	Multi-dimensional price fairness scale	Multi-dimensional behavioral and attitudinal loyalty scale
Fonte seminale	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.
Alias	Pricing Justice Scale, Fair Price Perception Scale	Behavioral Loyalty Scale, Loyalty Commitment Scale
Correlati	3	4
Sintesi	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.