Awareness and implementation of three high levels of conjoint management in patients with stroke by neurologists: A cross-sectional questionnaire survey from a tertiary general hospital in Southwest China
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Conjoint Analysis
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The aim of this paper is to demonstrate the use of conjoint analysis (CA) in health services research. Conjoint analysis is first explained, with emphasis on the history of the technique, followed by an explanation of how to carry out such a study and how the results from such a study can be used. The technique is demonstrated with reference to a study that looks at the benefits of in vitro fertilization. It is shown how CA can be used to estimate the relative importance of attributes, the trade-offs individuals make between these attributes, willingness to pay if cost is included as an attribute, and utility or benefit scores for different ways of providing a service. The paper then considers the potential advantages of CA over other, more commonly used benefit assessment instruments. Finally, there is discussion of the issues raised in the design and analysis of CA studies. It is concluded that these issues must be addressed before the technique becomes an established instrument for technology assessment.
Conjoint Analysis
Health Technology
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Objective To understand how to measure patients′ preferences of community health services with conjoint analysis by means of review of origin,development,methods and steps of conjoint analysis.Methods The way of literature review was taken to introduce the conjoint analysis,and to analyze what kind of community health services the patients prefer by considering them as a special consumer group,so as to provide a reference for application of conjoint analysis in the future.Results The attributes and levels could be determined according to characteristics of community health service.The data could be collected with full-profile method,and analyzed with vector model.And finally the most preferred type of community health services that the patients choose could be interpreted with the utility values of product of different services.Conclusion Conjoint analysis method can be widely used in various fields.For community health services,the conjoint analysis can be taken to describe the patient′s preferences and choices of health services.
Conjoint Analysis
Community Health
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Conjoint Analysis
Warrant
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최근 들어, 호텔 고객들이 호텔을 선택할 때 해당 호텔들과 관련하여 기존 고객들이 온라인상에 남긴 경험후기를 참고하는 것이 통상화 되어 가고 있으며, 이러한 추세에 따라 기존 호텔들은 고객들이 온라인상에 남긴 고객 의견 정보를 잘 활용하여 이를 보다 효과적으로 마케팅전략에 반영할 필요성이 한층 더 커지고 있다. 본 연구는 호텔 마케팅 전략 수립에 있어 기존 고객들의 온라인 고객경험 후기 정보가 차지하는 비중이 더욱 중요해 지고 있는 현실을 반영하여, 온라인사이트에서 제공되고 있는 정보체계에서 고객들이 그들의 호텔 선택행동에서 보다 중요하게 고려할 수 있는 요인들을 찾아내고, 이러한 요인들이 가지는 특성과 관련된 정보를 컨조인트분석(Conjoint Analysis)을 활용한 최적 효용값(optimum utilities) 도출을 통해 분석해 보고자 한다. 본 연구에서는 온라인 고객들이 남긴 리뷰내용을 구성하는 주요 요인들(factors)과 요인들이 가질 수 있는 수준 값들(levels)을 중심으로 선택속성의 조합(conjoint)을 구성한 후, 이를 기반으로 고객들이 선택 가능한 프로파일(profile) 제시를 통해 자료를 수집하고 컨조인트 분석을 하여, 호텔고객 온라인리뷰 선택 속성과 수준 값들에 대한 효용 값과 이들의 최적조합(mix)을 도출해 보고자 한다. 이러한 분석은 사전에 준비된 속성들에 대한 설문조사 고객 응답을 토대로 속성 중요도를 결정하고자 하는 기존의 많은 연구들과는 달리, 고객들의 최종 구매 행동과 관련된 자료를 토대로 부분가치(part-worth)를 도출하는 방식으로 주요 속성과 그들의 수준에 대한 중요도를 산출함으로써, 호텔 마케터들에게 고객의 구매행동을 기반으로 한 보다 현실적으로 도움이 될 수 있는 속성 중요도를 도출하는 방안을 제시하게 될 것으로 사료된다. 궁극적으로, 본 연구에서는 호텔 경영자와 마케터들에게 고객정보를 바탕으로 그들의 호텔 서비스 상품을 결정하고 설계하며, 이와 함께 호텔들이 그들의 사이트 내에 구축되는 고객 온라인 리뷰 사이트의 구조와 내용을 결정하고 이후 이들 정보를 관리 및 활용하는데 필요한 시사점을 제시하고자 한다.
Conjoint Analysis
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Conjoint Analysis
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In this paper, the general principles and methodology of conjoint analysis are discussed. When the effectiveness function is obtained through conjoint analysis, the forecast of product sales can be made to determine the best combination of product features. Based on the discussion, the bright prospect of the application of conjoint analysis in forecasting technology is suggested.
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Reviews the state-of-the-art in the conjoint analysis paradigm for stated preference research. Communalities and deferences among different conjoint analysis techniques are discussed, including the assumptions required to use the techniques and to simulate individuals choices. The discussion is organized around rating, ranking and discrete response methods of collecting conjoint data. Recent advances in conjoint analysis are discussed, and particular attention is given to the design and analysis of discrete choice experiments. Limitations and problems in the use of conjoint techniques are outlined, and suggestions are made about directions for future research.
Conjoint Analysis
Discrete choice
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In this paper we compare the validity of a new type of adaptive hybrid conjoint analysis called Customized Computerized Conjoint Analysis (CCC) withe the traditional Adaptive Conjoint Analysis (ACA) and two self-explicated models. CCC combines self-explicated preference structure measurement with individually designed full-profile conjoint analysis in a fully computerized adaptive interview. For a representative sample of almost 500 German potential customers of refrigerators we found (similar to Srinivasan/Park, 1997) surprisingly robust results of the self-explicated approaches compared to CCC as welll as ACA.
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Abstract This article chapter provides an up‐to‐date review of methods that have come to be called conjoint analysis . These methods enable marketing researchers to determine trade‐offs among attributes of a new product based on responses of stated preferences and stated choices. These trade‐offs can assist in product design, pricing, market segmentation, and similar marketing decisions. There are essentially four types of conjoint analysis; these are traditional conjoint analysis that uses stated preferences, choice‐based conjoint analysis (CBCA) that uses stated choices, self‐explicated conjoint analysis that uses direct elicitation of attribute importances and ratings on attribute levels, and adaptive conjoint analysis (ACA) which involves a staged and adaptive data collection. Over several thousand conjoint studies were conducted since the introduction of this method in the early 1970s. The chapterarticle also covers significant advances in estimation methods and design of stimuli (profiles and choice sets) over these years. Essentially, this methodology is alive and thriving well.
Conjoint Analysis
Market Segmentation
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