Back Conjoint analýza Czech Conjoint-Analyse German Análisis conjunto Spanish Analyse conjointe French Analisi congiunta Italian コンジョイント分析 Japanese 컨조인트 분석 Korean Analisis Konjoin Malay Vignettenmethode Dutch Konjunktanalyse NB
Conjoint analysis is a survey-based statistical technique used in market research that helps determine how people value different attributes (feature, function, benefits) that make up an individual product or service.
The objective of conjoint analysis is to determine what combination of a limited number of attributes is most influential on respondent choice or decision making. A controlled set of potential products or services is shown to survey respondents and by analyzing how they make choices among these products, the implicit valuation of the individual elements making up the product or service can be determined. These implicit valuations (utilities or part-worths) can be used to create market models that estimate market share, revenue and even profitability of new designs.
Conjoint analysis originated in mathematical psychology and was developed by marketing professor Paul E. Green at the Wharton School of the University of Pennsylvania. Other prominent conjoint analysis pioneers include professor V. "Seenu" Srinivasan of Stanford University who developed a linear programming (LINMAP) procedure for rank ordered data as well as a self-explicated approach, and Jordan Louviere (University of Iowa) who invented and developed choice-based approaches to conjoint analysis and related techniques such as best–worst scaling.
Today it is used in many of the social sciences and applied sciences including marketing, product management, and operations research. It is used frequently in testing customer acceptance of new product designs, in assessing the appeal of advertisements and in service design. It has been used in product positioning, but there are some who raise problems with this application of conjoint analysis.
Conjoint analysis techniques may also be referred to as multiattribute compositional modelling, discrete choice modelling, or stated preference research, and are part of a broader set of trade-off analysis tools used for systematic analysis of decisions. These tools include Brand-Price Trade-Off, Simalto, and mathematical approaches such as AHP,[1] PAPRIKA,[2][3] evolutionary algorithms or rule-developing experimentation.
- ^ Ijzerman MJ, van Til JA, Bridges JF (212). "A comparison of analytic hierarchy process and conjoint analysis methods in assessing treatment alternatives for stroke rehabilitation". The Patient. 5 (1): 45–56. doi:10.2165/11587140-000000000-00000. PMID 22185216. S2CID 207299893.
- ^ Liberman AL, Pinto D, Rostanski SK, Labovitz DL, Naidech AM, Prabhakaran S (2019). "Clinical decision-making for thrombolysis of acute minor stroke using adaptive conjoint analysis". The Neurohospitalist. 9 (1): 9–14. doi:10.1177/1941874418799563. PMC 6327243. PMID 30671158.
- ^ Al-Isma'ili A, Li M, Shen J, He Q (2016). "Cloud computing adoption decision modelling for SMEs: a conjoint analysis". International Journal of Web and Grid Services. 12 (3): 296–327. doi:10.1504/IJWGS.2016.079157.