방법 비교

	Means-End Chain Laddering	Implicit Reaction-Time Brand Measures
분야	마케팅	마케팅
계열	Process / pipeline	Process / pipeline
기원 연도	1982	1986
창시자	Jonathan Gutman (means-end model); Thomas Reynolds & Jonathan Gutman (laddering method)	Russell Fazio (affective priming); Anthony Greenwald, Brian Nosek & Mahzarin Banaji (D-score scoring)
유형	Depth-interview pipeline linking attributes to consequences to values	Response-latency measurement pipeline for implicit brand associations
원전	Gutman, J. (1982). A Means-End Chain Model Based on Consumer Categorization Processes. Journal of Marketing, 46(2), 60-72.	Fazio, R. H., Sanbonmatsu, D. M., Powell, M. C., & Kardes, F. R. (1986). On the automatic activation of attitudes. Journal of Personality and Social Psychology, 50(2), 229-238.
별칭	Laddering, Means-End Chain Analysis, Attribute-Consequence-Value Analysis, Hierarchical Value Mapping	Implicit Brand Association Measures, Response-Latency Brand Testing, Affective Priming for Brands, Implicit Brand Attitude Measurement
관련	3	3
요약	Means-end chain analysis explains consumer choice by linking the concrete attributes of a product to the consequences of using it and ultimately to the personal values those consequences serve. Jonathan Gutman's 1982 model proposed that consumers categorize products by the desirable consequences they deliver, and that these consequences are valued because they help attain higher-order life values, so a chain runs attribute to consequence to value. Laddering, formalized by Thomas Reynolds and Jonathan Gutman, is the interviewing technique that uncovers these chains by repeatedly asking why a feature matters until the respondent reaches the underlying values. The resulting ladders are content-coded into attributes, consequences, and values, then summarized in an implication matrix counting how often each element leads to another. Applying a cutoff to that matrix yields a hierarchical value map (HVM), a network showing the dominant attribute-consequence-value pathways for the category. The approach reveals not just what consumers want but why, providing a values-grounded foundation for positioning and advertising strategy.	Implicit reaction-time brand measures use how fast people respond, rather than what they say, to gauge the associations a brand automatically triggers. The logic comes from Russell Fazio's demonstration that strong attitudes are activated automatically: when a brand acts as a prime, it speeds responses to evaluatively congruent targets and slows responses to incongruent ones, and the size of that facilitation indexes the brand's implicit evaluation. Building on this, response-latency tasks pair brands with positive or negative words, with attribute categories, or with competing brands, and read off implicit associations from millisecond differences in reaction time. Anthony Greenwald, Brian Nosek, and Mahzarin Banaji's improved scoring algorithm turns these latency differences into a standardized D-score that is comparable across people and tasks. Because the measures tap associations that operate before deliberate editing, they capture brand equity that consumers may be unwilling or unable to report. The result is a behaviorally grounded, hard-to-fake complement to survey-based brand tracking.