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| Indice Americano di Soddisfazione del Cliente (ACSI)× | Scala di Fedeltà del Cliente× | |
|---|---|---|
| Campo | Gestione del marketing | Gestione del marketing |
| Famiglia | Process / pipeline | Process / pipeline |
| Anno di origine≠ | 1996 | 1994 |
| Ideatore≠ | Claes Fornell, Michael D. Johnson, Eugene W. Anderson, Jaesung Cha, Barbara E. Bryant | Alan S. Dick, Kunal Basu |
| Tipo≠ | Structural equation model for satisfaction and loyalty | Multi-dimensional behavioral and attitudinal loyalty scale |
| Fonte seminale≠ | Fornell, C., Johnson, M. D., Anderson, E. W., Cha, J., & Bryant, B. E. (1996). The American Customer Satisfaction Index: Nature, Purpose, and Findings. Journal of Marketing, 60(4), 7-18. DOI ↗ | Dick, A. S., & Basu, K. (1994). Customer Loyalty: Toward an Integrated Conceptual Framework. Journal of the Academy of Marketing Science, 22(2), 99-113. DOI ↗ |
| Alias | ACSI, National Customer Satisfaction Index | Behavioral Loyalty Scale, Loyalty Commitment Scale |
| Correlati | 4 | 4 |
| Sintesi≠ | The American Customer Satisfaction Index (ACSI), developed by Fornell and colleagues in 1996, is a structural equation modeling-based approach to measuring and predicting customer satisfaction across industries and over time. ACSI assesses customer expectations, perceived value, perceived quality, complaints, and loyalty in a unified framework. Since 1994, ACSI data has been collected quarterly on thousands of customers across diverse U.S. industries, making it a key economic indicator and benchmark for organizational performance. | 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. |
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