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Опубліковано: 14 березня 2023

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Статті в журналах з теми "Choice-Based Conjoint Analysi"

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Kim, Jinmin, Kwangtae Park, and Hosun Rhim. "Analysis of Product-Service Integration Effect using Choice-based Conjoint Analysis." Journal of the Korean Operations Research and Management Science Society 39, no. 1 (March 31, 2014): 101–12. http://dx.doi.org/10.7737/jkorms.2014.39.1.101.

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Cunningham, Charles E., Ken Deal, and Yvonne Chen. "Adaptive Choice-Based Conjoint Analysis." Patient: Patient-Centered Outcomes Research 3, no. 4 (December 2010): 257–73. http://dx.doi.org/10.2165/11537870-000000000-00000.

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BARBOSA, Eduardo Campana, Carlos Henrique Osório SILVA, Moysés NASCIMENTO, Fabyano Fonseca e. SILVA, Valéria Paula Rodrigues MINIM, Rosires DELIZA, and Suzana Maria DELLA LUCIA. "Choice-Based Conjoint Analysis: A Bayesian Approach." REVISTA BRASILEIRA DE BIOMETRIA 36, no. 1 (April 3, 2018): 1. http://dx.doi.org/10.28951/rbb.v36i1.102.

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This paper presents a Bayesian approach to Frequentist Logit Multinomial model used in Choice-based Conjoint Analysis. The analysis was conducted with data of choice for the assessment by 144 consumers, eight samples of light strawberry flavored yogurts, ranging information from three ingredients (sugar, fat and protein) in a full factorial design. the results and inferences obtained by the Bayesian approach are presented in terms of estimating the main effect of the attributes, the choice probabilities and choice ratio. The frequentist results are also reported and discussed. The Bayesian analysis showed similar results to frequentist and allowed the construction of credibility intervals for choice probabilities and choice ratio, allowing statistically compare such quantities. About the practical results, the most likely choice was associated with yoghurt containing strawberry flavor light information "0% sugar", "0% fat" and "bioactive proteins enriched"
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Vilikus, Ondřej. "Hybrid Approach to Choice-Based Conjoint Analysis." Acta Oeconomica Pragensia 21, no. 4 (August 1, 2013): 3–19. http://dx.doi.org/10.18267/j.aop.407.

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Desarbo, Wayne S., Venkatram Ramaswamy, and Steven H. Cohen. "Market segmentation with choice-based conjoint analysis." Marketing Letters 6, no. 2 (March 1995): 137–47. http://dx.doi.org/10.1007/bf00994929.

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Kim, Bu-Yong. "New Design of Choice Sets for Choice-based Conjoint Analysis." Korean Journal of Applied Statistics 25, no. 5 (October 31, 2012): 847–57. http://dx.doi.org/10.5351/kjas.2012.25.5.847.

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Toubia, Olivier, John R. Hauser, and Duncan I. Simester. "Polyhedral Methods for Adaptive Choice-Based Conjoint Analysis." Journal of Marketing Research 41, no. 1 (February 2004): 116–31. http://dx.doi.org/10.1509/jmkr.41.1.116.25082.

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Meißner, Martin, and Reinhold Decker. "Eye-Tracking Information Processing in Choice-Based Conjoint Analysis." International Journal of Market Research 52, no. 5 (September 2010): 593–612. http://dx.doi.org/10.2501/s147078531020151x.

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Schlereth, Christian, and Bernd Skiera. "Schätzung von Zahlungsbereitschaftsintervallen mit der Choice-Based Conjoint-Analyse." Schmalenbachs Zeitschrift für betriebswirtschaftliche Forschung 61, no. 8 (December 2009): 838–56. http://dx.doi.org/10.1007/bf03373670.

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Verma, Vivek Kumar, and Bibhas Chandra. "Sustainability and customers’ hotel choice behaviour: a choice-based conjoint analysis approach." Environment, Development and Sustainability 20, no. 3 (April 5, 2017): 1347–63. http://dx.doi.org/10.1007/s10668-017-9944-6.

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Дисертації з теми "Choice-Based Conjoint Analysi"

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Barbosa, Eduardo Campana. "Choice-Based Conjoint Analysis: um enfoque bayesiano." Universidade Federal de Viçosa, 2015. http://www.locus.ufv.br/handle/123456789/7179.

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A presente dissertação teve como objetivo principal demonstrar um enfoque Bayesiano para a metodologia Choice-Based Conjoint Analysis (CBCA). Apresenta-se no texto uma ampla revisão sobre a CBCA (Capítulo 1), sobre o modelo Logit Multinomial [desenvolvimento do modelo, procedimentos de estimação de parâmetros, probabilidades e razões de escolha (Capítulo 2)] e sobre o enfoque de estimação Bayesiano [distribuição a priori utilizada, aproximação de Laplace para a função de verossimilhança, distribuições a posteriori e detalhes sobre o algoritmo MCMC empregado (Capítulo 3)]. No Capítulo 4 apresenta-se um exemplo hipotético, no intuito de demonstrar os resultados e inferências que podem ser obtidos por meio desta recente abordagem (Bayesiana), sendo também apresentados os resultados do enfoque Frequentista. O tratamento em estudo foi um tipo de refrigerante e avaliou-se o efeito de três fatores (A, B e C) na intenção de compra de 96 consumidores, por meio de dados simulados. As análises estatísticas foram conduzidas no software livre R, cujos scripts encontram-se disponibilizados nos apêndices desta dissertação. Concluiu-se que a abordagem Bayesiana para CBCA apresentou resultados interessantes e satisfatórios, com estimativas similares às Frequentistas e mostrando-se uma alternativa metodológica viável para os estudos de CBCA. Adicionalmente, a abordagem proposta possibilitou ainda ao pesquisador construir intervalos de credibilidade (percentis das distribuições a posteriori) para as probabilidades e razões de escolha, no intuito de comparar estas quantidades ou testar hipóteses sobre estas. Quanto aos resultados práticos, a maior probabilidade de escolha estava associada ao tratamento 4, composto pelo nível do fator A, nível do fator B e nível do fator C.
This dissertation main goal is to demonstrate the Bayesian approach to Choice-Based Conjoint Analysis (CBCA). We present a comprehensive review of the CBCA methodology (Chapter 1), on the Multinomial Logit model [model development, parameter estimation procedures, probabilities of choice ratios (Chapter 2)] and on the Bayesian estimation approach [prior distribution, Laplace approach to the likelihood function, posterior distributions and details about the MCMC algorithm we applied (Chapter 3)]. In Chapter 4 we present a hypothetical example, in order to demonstrate the results and inferences that can be obtained through this recent approach (Bayesian), and we also present the results of the frequentist approach. The treatment for the study was a type of refrigerant (soda or soft drink) and we evaluated the effect of three factors (volume, type and color) on purchase intention of 96 consumers, using simulated data. Statistical analyzes were conducted with the free software R, whose scripts are provided in the appendices of this dissertation. It was concluded that the Bayesian approach to CBCA presented interesting and satisfactory results, with estimates similar to the frequentist ones, therefore proved to be a viable alternative methodology for CBCA studies. Additionally, the proposed approach also allows the researcher to build credibile intervals (percentiles of the posterior distributions) for the probabilities and choice ratios, in order to compare these quantities or test hypotheses about them. In terms of practical or applied results, the highest estimated probability of choice was obtained for treatment 4, with a1 level of factor A, b2 level of factor B and C1 level of factor C.
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Wirth, Ralph. "Best-Worst Choice-Based Conjoint-Analyse : eine neue Variante der wahlbasierten Conjoint-Analyse /." Marburg : Tectum-Verl, 2010. http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&doc_number=018939410&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA.

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Wirth, Ralph. "Best-Worst Choice-Based Conjoint-Analyse eine neue Variante der wahlbasierten Conjoint-Analyse." Marburg Tectum-Verl, 2009. http://d-nb.info/1000027414/04.

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Natter, Martin, and Markus Feurstein. "Improving predictive validity of choice-based conjoint models." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2000. http://epub.wu.ac.at/1372/1/document.pdf.

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Up to date, it is unclear how Choice-Based Conjoint (CBC) models perform in terms of forecasting (external) real world aggregate shop data. In this contribution, we measure the performance of a Latent Class CBC model - not with an experimental holdout sample - but with aggregate real world scanning data. We find that the CBC model does not accurately predict real world market shares. In order to improve the forecasting performance, we propose a correction scheme based on external scanner data. Our analysis based on 8 brands shows that the use of the proposed correction vector improves the performance measure considerably. (author's abstract)
Series: Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Natter, Martin, and Markus Feurstein. "Real world performance of choice-based conjoint models." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2001. http://epub.wu.ac.at/376/1/document.pdf.

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Conjoint analysis is one of the most important tools to support product development, pricing and positioning decisions in management practice. For this purpose various models have been developed. It is widely accepted that models that take consumer heterogeneity into account, outperform aggregate models in terms of hold-out tasks. The aim of our study is to investigate empirically whether predictions of choice-based conjoint models which incorporate heterogeneity can successfully be generalized to a whole market. To date no studies exist that examine the real world performance of choice-based conjoint models by use of aggregate scanner panel data. Our analysis is based on four commercial choice-based conjoint pricing studies including a total of 43 stock keeping units (SKU) and the corresponding weekly scanning data for approximately two years. An aggregate model serves as a benchmark for the performance of two models that take heterogeneity into account, hierarchical Bayes and latent class. Our empirical analysis demonstrates that, in contrast to the performance using hold-out tasks, the real world performance of hierarchical Bayes and latent class is similar to the performance of the aggregate model. Our results indicate that heterogeneity cannot be generalized to a whole market and suggest that aggregate models are sufficient to predict market shares. (author's abstract)
Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Jahanbin, Semco. "The impact of consumer and product characteristics on change in attribute-weights over time and its implications for new product sales forecasting using choice-based conjoint analysis." Thesis, University of Bath, 2015. https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.681043.

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One of the major demand related risks for companies that produce consumer electronics goods is change in consumer preferences over time as reflected in the weights they attach to the attributes of products. This contributes to the difficulty of predicting whether consumers will purchase a new product or not and the accuracy of such forecasts can have significant ramifications for companies’ strategies, profitability and even their chances of survival. Knowledge of attribute-weights and accurate forecasts of new products can give companies better insights during the product development stages, inform go-no-go decisions on whether to launch a developed product and also support decisions on whether a recently launched product should be withdrawn or not due to poor early stage sales. Despite the important implications of change in attribute-weights, no research has investigated the extent to which such changes occur and impact on the accuracy of forecasts of the future market share of these products. Prior to the current research, it was assumed that the weights are constant over time – even when the nature of the attributes was assumed to change. To investigate these concerns choice based conjoint (CBC) was applied to data gathered in a longitudinal survey of consumer choices relating a range of consumer electronic products, where innovation has different rates and the product life cycles are various. This allowed an assessment of the extent to which the weights of attributes of choice-based conjoint models change over a six months period for consumer durable products and the degree to which this variability is dependent on the nature of the product. It demonstrates that the change in weights is greater for products that have high technological complexity and shorter lifecycles and also links the changeability of weights to the characteristics of potential consumers. The results of thesis demonstrate that the assumption of constant weights can potentially lead to inaccurate market share forecast for high-tech, short life-cycle products that are launched several months after the choice-based modelling has been conducted.
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Natter, Martin, and Markus Feurstein. "Correcting for CBC model bias. A hybrid scanner data - conjoint model." SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, 2001. http://epub.wu.ac.at/880/1/document.pdf.

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Choice-Based Conjoint (CBC) models are often used for pricing decisions, especially when scanner data models cannot be applied. Up to date, it is unclear how Choice-Based Conjoint (CBC) models perform in terms of forecasting real-world shop data. In this contribution, we measure the performance of a Latent Class CBC model not by means of an experimental hold-out sample but via aggregate scanner data. We find that the CBC model does not accurately predict real-world market shares, thus leading to wrong pricing decisions. In order to improve its forecasting performance, we propose a correction scheme based on scanner data. Our empirical analysis shows that the hybrid method improves the performance measures considerably. (author's abstract)
Series: Report Series SFB "Adaptive Information Systems and Modelling in Economics and Management Science"
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Davids, Aayesha. "Investigating the use of generational cohort theory to identify total reward preferences." Master's thesis, Faculty of Commerce, 2018. https://hdl.handle.net/11427/31608.

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Background: Anecdotal accounts of stereotypes and/or generalisations about perceived generational differences within the workplace have become commonplace. Generational cohort theories are often used to identify generational cohorts of employees that are argued to be different, including having differing expectations, needs, preferences and even values. In addressing and/or accommodating such individual differences organisations are increasingly adopting strategies and interventions that take such generational differences amongst employees into account (Costanza & Finkelstein, 2015). Addressing generational differences within the workplace has particularly become popular in the design and implementation of total reward or remuneration and recognition strategies, policies and practices. Understanding generational and/or demographic characteristics, specifically differences, that create distinct cohorts allow organisations to design reward and recognition packages that create distinctly unique value for their employees. Offering tailored or more focused reward strategies and practices, designed with individual differences in mind are believed to enhance attraction, employee engagement and retention and so allow an organisation to bolster its competitive advantage and contribute to sustained organisational success (Snelgar, Renard, & Venter, 2013). In support of this notion, empirical studies are showing promising results for targeted reward strategies and practices. Rationale for the Research Study Effective talent management, i.e. attracting, engaging and retaining sought-after highly skilled employees is critical for the success of any organisation. However, organisations are increasingly experiencing challenges in recruiting, motivating and retaining scarce human capital, colloquially referred to as talent (Barkhuizen, 2014). Failure on the part of organisations to understand and adapt to differences in the workforce may result in them not being able to attract the talent required; keep employees motivated and engaged; and experience unintended employee turnover which is associated with notable direct and indirect costs for them (Westerman & Yamamura, 2007). Organisations, therefore, are constantly searching for new and innovative approaches to more effectively attract, retain and engage employees (Snelgar et al., 2013). There is a growing body of research (Haynes, 2011; Snelgar, Renard, & Venter, 2013) that has shown that identifying distinct reward and recognition preferences amongst cohorts of employees and targeting reward and recognition strategies accordingly, is showing promising potential in this regard. When designing and implementing targeted approaches to reward and recognition, employee cohorts are most often identified using generational cohort theory, i.e. using various established guidelines to group employees into generational cohorts that are believed to be distinctly different to one another, while those within these groups being more similar than not. Results obtained from studies using these various employee cohorts as a framework have been used to inform the design of targeted reward and recognition practices and policies. Generational cohort theory is, however, mostly grounded on a set of historical events that took place in the United States of America (USA). Despite this, the American-based framework used to identify individuals belonging to various generations has been adopted globally, both within organisations and even used in research studies published in peer-reviewed literature. However, several authors have criticised the indiscriminate use of a popular American-based generational framework, i.e. focusing on events affecting Americans arguing that this has resulted in a somewhat narrow or even skewed view generational cohorts. These authors have gone as far as to argue that the American-based generational framework may not be appropriate or ineffective outside of the USA at all (Close, 2015). Following this reasoning, they have called for alternative frameworks that create distinct generational cohorts relevant in contexts outside of America, i.e. based on different events and criteria more applicable to those contexts. Aim of the research study The aim of the present study was to investigate the reward preferences of a broad range of employees in an effort to assess whether the popular generational model of Strauss and Howe (1991) is relevant and/or as effective in a non-American context, as well as to possibly find support for alternative perspectives or approaches to identify distinct generational cohorts in organisations that may be more appropriate and/or effective when designing reward offerings for different cohorts of employees. Given time and cost constraints, South Africa was chosen to investigate this claim given that it is a developing economy (vs the USA being a developed economy) and has a different set of notable events that have shaped its history to that which is applicable to the USA. Given the aim of the present study, an exploratory research design was considered most appropriate to investigate generational cohort theory within a non-American context as a framework to identify employee groups/cohorts that have distinctly different total reward preferences. For the purposes of the present study, it was decided that a quantitative approach would be followed as it is most useful to draw conclusions or inferences related to the total reward preferences of employee groups/cohorts. The present study followed a non–probability or convenience sampling approach with a realised sample of 169 respondents. The majority of respondents were Coloured and were further female, with majority of attaining a qualification post matric. Main results and findings A one-way Analysis of Variance (ANOVA) revealed no statistically significant difference between the generational groups based on the popular generational model of Strauss and Howe (1991), nor for a proposed generational cohort framework that was designed for the purposes of the present study and which was based on notable South African historical events. Following a data-driven exploratory approach, cluster analysis, on the other hand, yielded three distinct generational cohorts based on their perceived reward preferences for typical total rewards elements. Significant differences in the total reward preferences of respondents born after 1994 and those before 1994. Choice-based modelling (choice-based conjoint analysis) revealed that most respondents considered financial rewards as being the two most preferred total reward elements for them, including remuneration (guaranteed pay) followed by benefits and then non-financial rewards (work-life balance being the most preferred non-financial reward preference). Theoretical and Practical Implications Numerous research studies have made use of the popular American-based generational model to identify the reward preferences of cohort groups, without taking into account context-specific variables. There is further a dearth of empirical research that has been conducted to investigate generational cohort theory specifically, while none were found that were conducted in developing economies, such as South Africa. The present study address this gap in current literature. The use of choice-based modelling or choice-based conjoint analysis, furthermore, makes a methodological contribution given that this method is seldom found in total reward preference studies. This method was shown to identify total reward preferences that could not be determined using a field-survey or questionnaire. Choice-based modelling is different to typical survey approaches in that it is better able to replicate human decision making, i.e. assessing relative importance of attributes and levels based on combinations of choices and related sacrifices that humans deal with when making a choice-decision. In terms of the practical contribution of the present study, the results provide insights for organisations that may be incorporated when designing differentiated total reward strategies to accommodate and/or address the needs of the different generational groups.
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Theysohn, Sven. "Innovative Einsatzmöglichkeiten und Modifikationen der Choice-based-conjoint-Analyse zur Entwicklung und Optimierung von Preisstrategien /." Hamburg : Kovač, 2007. http://www.verlagdrkovac.de/978-3-8300-3282-3.htm.

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Van, Eijk Jeff. "The relationship between cultural orientation and reward preference: a study conducted in South Africa and the Netherlands." Master's thesis, University of Cape Town, 2016. http://hdl.handle.net/11427/23769.

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Orientation: Organisations operating in multiple countries and continents, referred to as multinationals, often experience cultural barriers when interacting with employees from the host county. These barriers, in turn, frequently result in counterproductive outcomes for the organisation. Being able to adapt Human Resource (HR) policies and practices to the cultural values and norms of the host country, multinationals will be better able to attract, motivate and retain their host country employees and achieve the strategic objectives they have set. Research purpose: The aim of the current study was to investigate the relationship between an individual's reward preference and his/her cultural orientation by means of a novel methodological approach, while further investigating this relationship in two culturally distinct countries to allow for a comparison to be made. Motivation for the study: Research linking rewards to cultural orientation is characterised by mixed findings, which could be related to cross-cultural measurement issues (for example, issues of level of analysis and the reference-group effect). By measuring at an individual level of analysis and exploring the use of choice-based conjoint analysis, the present study aimed to advance the field of cross-cultural remuneration research. The study aimed to show that, by linking cultural orientation and reward preference, multinationals can be helped to optimize their remuneration policies and practices in a way that brings about desired organisational outcomes. Research design: A descriptive research design using quantitative methods was employed. Data was collected from employees in both South Africa (n = 132) and the Netherlands (n = 152). Survey items, responded to on a Likert-type response scale were used to measure an individual's reward preference and cultural orientation. To explore the potential bias introduced by the reference-group effect in cross-cultural reward research, a choice-based conjoint analysis was included to measure reward preference. Data from the field survey was analysed using descriptive and inferential statistics. Choice-based conjoint analysis was used to determine the relative importance of each reward element. Main findings: The results of the Multiple Regression analysis revealed that certain cultural orientations were significantly positively related to reward preference. These included the relationship between collectivism and group bonuses; uncertainty avoidance and job security; uncertainty avoidance and base pay; and long-term orientation and future oriented rewards. Uncertainty avoidance and long-term orientation were positively related to financiallyoriented reward elements. The conjoint analysis allowed for further differentiation between these elements. Practical implications: Multinationals will be able to better align their reward policies and practices with the preferences of employees who come from different cultures and who therefore possess differing cultural orientations. By doing so, multinationals will be able to improve their capability to attract, motivate and retain employees that come from distinct cultural backgrounds. Research contributions: By taking a different methodological approach using choice-based conjoint analysis, this study showed that the preference for particular reward packages can not be solely reduced to linear relationships. In contrast to previous studies, this study was able to incorporate a single sample for both the dependent and the independent variables by measuring the cultural orientations at an individual level of analysis.
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Книги з теми "Choice-Based Conjoint Analysi"

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B, Wiley James, and Chitturi Pallavi, eds. Choice-based conjoint analysis: Models and designs. Boca Raton, FL: Chapman & Hall/CRC, 2011.

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Raghavarao, Damaraju, Pallavi Chitturi, and James B. Wiley. Choice-Based Conjoint Analysis. Taylor & Francis Group, 2010.

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Raghavarao, Damaraju, Pallavi Chitturi, and James B. Wiley. Choice-Based Conjoint Analysis: Models and Designs. Taylor & Francis Group, 2010.

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Raghavarao, Damaraju, Pallavi Chitturi, and James B. Wiley. Choice-Based Conjoint Analysis: Models and Designs. Taylor & Francis Group, 2010.

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McFadden, Daniel, Moshe Ben-Akiva, and Kenneth Train. Foundations of Stated Preference Elicitation: Consumer Behavior and Choice-Based Conjoint Analysis. Now Publishers, 2019.

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Частини книг з теми "Choice-Based Conjoint Analysi"

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Eggers, Felix, Henrik Sattler, Thorsten Teichert, and Franziska Völckner. "Choice-Based Conjoint Analysis." In Handbook of Market Research, 1–39. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-05542-8_23-1.

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Eggers, Felix, Henrik Sattler, Thorsten Teichert, and Franziska Völckner. "Choice-Based Conjoint Analysis." In Handbook of Market Research, 781–819. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-57413-4_23.

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Rao, Vithala R. "Choice Based Conjoint Studies: Design and Analysis." In Applied Conjoint Analysis, 127–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-540-87753-0_4.

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Giesen, Joachim, and Eva Schuberth. "The Combinatorial Structure of Polyhedral Choice Based Conjoint Analysis." In Conjoint Measurement, 259–71. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71404-0_13.

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Elrod, Terry, and Keith Chrzan. "The Value of Extent-of-Preference Information in Choice-based Conjoint Analysis." In Conjoint Measurement, 235–49. Berlin, Heidelberg: Springer Berlin Heidelberg, 2003. http://dx.doi.org/10.1007/978-3-540-24713-5_10.

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Elrod, Terry, and Keith Chrzan. "The Value of Extent-of-Preference Information in Choice-based Conjoint Analysis." In Conjoint Measurement, 209–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-662-06392-7_9.

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7

Elrod, Terry, and Keith Chrzan. "The Value of Extent-of-Preference Information in Choice-based Conjoint Analysis." In Conjoint Measurement, 209–23. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-662-06395-8_9.

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Elrod, Terry, and Keith Chrzan. "The Value of Extent-of-Preference Information in Choice-Based Conjoint Analysis." In Conjoint Measurement, 133–44. Berlin, Heidelberg: Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-71404-0_8.

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9

Gensler, Sonja. "Präferenzerfassung und Präferenzanalyse mittels Conjoint Analyse und Choice-Based Conjoint Analyse." In Heterogenität in der Präferenzanalyse, 11–79. Wiesbaden: Deutscher Universitätsverlag, 2003. http://dx.doi.org/10.1007/978-3-322-82002-0_2.

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10

Giesen, Joachim, Klaus Mueller, Bilyana Taneva, and Peter Zolliker. "Choice-Based Conjoint Analysis: Classification vs. Discrete Choice Models." In Preference Learning, 297–315. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14125-6_14.

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Тези доповідей конференцій з теми "Choice-Based Conjoint Analysi"

1

Ren, Yi, and Panos Y. Papalambros. "Enhanced Adaptive Choice-Based Conjoint Analysis Incorporating Engineering Knowledge." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34790.

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Анотація:
Conjoint analysis from marketing has been successfully integrated with engineering analysis in design for market systems. The long questionnaires needed for conjoint analysis in relatively complex design decisions can become cumbersome to the human respondents. This paper presents an adaptive questionnaire generation strategy that uses active learning and allows incorporation of engineering knowledge in order to identify efficiently designs with high probability to be optimal. The strategy is based on viewing optimal design as a group identification problem. A running example demonstrates that a good estimation of consumer preference is not always necessary for finding the optimal design and that conjoint analysis could be configured more effectively for the specific purpose of design optimization. Extending the proposed method beyond a homogeneous preference model and noiseless user responses is also discussed.
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2

Razu, Swithin S., and Shun Takai. "An Approach to Modeling Customer Preference Uncertainty by Applying Bootstrap to Choice-Based Conjoint Analysis Data." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-28231.

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Analysis of customer preferences is among the most important tasks in a new product development. How customers come to appreciate and decide to purchase a new product affects the products market share and therefore its success or failure. Unfortunately, when designers select a product concept early in the product development process, customer preference response to the new product is unknown. Conjoint analysis is a statistical marketing tool that has been used to estimate market shares of new product concepts by analyzing data on the product ratings, rankings or concept choices of customers. This paper proposes an alternative to traditional conjoint analysis methods that provide point estimates of market shares. It proposes two approaches to model market share uncertainty; bootstrap and binomial inference applied to choice-based conjoint analysis data. The proposed approaches are demonstrated and compared using an illustrative example.
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3

Swamy, Surya, Seth Orsborn, Jeremy Michalek, and Jonathan Cagan. "Measurement of Headlight Form Preference Using a Choice Based Conjoint Analysis." In ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2007. http://dx.doi.org/10.1115/detc2007-35409.

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The measurement and understanding of user aesthetic preference for form is a critical element to the product development process and has been a design challenge for many years. In this article preference is represented in a utility function directly related to the engineering representation for the automobile headlight. A method is proposed to solicit and measure customer preferences for shape of the automobile headlight using a choice task on a main-effects conjoint survey design to discover and design the most preferred shape.
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4

Sylcott, Brian, Jeremy J. Michalek, and Jonathan Cagan. "Towards Understanding the Role of Interaction Effects in Visual Conjoint Analysis." In ASME 2013 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/detc2013-12622.

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We investigate consumer preference interactions in visual choice-based conjoint analysis, where the conjoint attributes are parameters that define shapes shown to the respondent as images. Interaction effects are present when preference for the level of one attribute is dependent on the level of another attribute. When interaction effects are negligible, a main-effects fractional factorial experimental design can be used to reduce data requirements and survey cost. This is particularly important when the presence of many parameters or levels makes full factorial designs intractable. However, if interaction effects are relevant, a main-effects design creates biased estimates and potentially misleading conclusions. Most conjoint studies assume interaction effects are negligible; however, interactions may play a larger role for shape parameters than for other types of attributes. We conduct preliminary tests on this assumption in three visual conjoint studies. The results suggest that interactions can be either negligible or dominant in visual conjoint, depending on both consumer preferences and shape parameterization. When interactions are anticipated, it is possible in some cases to re-parameterize the shape such that interactions in the new space are negligible. Generally, we suggest that randomized designs are better than fractional factorial designs at avoiding bias due to the presence of interactions and/or the organization of profiles into choice sets.
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5

Ueda, Masashi. "An Analysis on Smartphone Adoption for University Students by using Choice-Based Conjoint Analysis." In the The 3rd Multidisciplinary International Social Networks Conference. New York, New York, USA: ACM Press, 2016. http://dx.doi.org/10.1145/2955129.2955155.

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6

Nickkar, Amirreza, Nashid K. Khadem, and Hyeon-Shic Shin. "Willingness to Pay for Autonomous Vehicles: An Adaptive Choice-Based Conjoint Analysis Approach." In International Conference on Transportation and Development 2020. Reston, VA: American Society of Civil Engineers, 2020. http://dx.doi.org/10.1061/9780784483138.001.

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7

Yelamati, Srilatha, Dammalapati Ravi Kiran, V. V. S. Sasank, and Chitturi Prasad. "Simulating Nash Equilibrium Market Outcomes with Bayesian Analysis of Choice-Based Conjoint Data." In 2021 5th International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, 2021. http://dx.doi.org/10.1109/iceca52323.2021.9675883.

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8

Dagher, Antoine, Jean-Franc¸ois Petiot, and Herve´ Guyon. "Elicitation and Modeling of Customers’ Preferences in Industrial Design: A Comparative Study on Vehicle Front End." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49637.

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Анотація:
The understanding of customer preference, in relation to product attributes, is a key challenge in industrial design. It is based on preference measurement, which is a complex task, crucial for sound design decisions. In this paper, we study experimentally the influence of the preference elicitation method on the results of the test. For this, we designed a case study concerning the perception of vehicle’s front-end by a panel of subjects. Two different preference elicitation methods were used in the same session: direct rating on a scale, and pairwise comparison. Conjoint Analysis was used next to model the preference and to compute the part worth of the different design factors. Two conjoint methods were examined: rating based and choice based. The paper presents a comparison of the results of the different modeling with conjoint analysis, in order to assess the reliability and the validity of the models.
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9

Sylcott, Brian, Seth Orsborn, and Jonathan Cagan. "The Effect of Product Representation in Visual Conjoint Analysis." In ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2014. http://dx.doi.org/10.1115/detc2014-34443.

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Анотація:
When most designers set out to develop a new product they solicit feedback from potential consumers. These data are incorporated into the design process in an effort to more effectively meet customer requirements. Often these data are used to construct a model of consumer preference capable of evaluating candidate designs. Although the mechanics of these models have been extensively studied there are still some open questions, particularly with respect to models of aesthetic preference. When constructing preference models, simplistic product representations are often favored over high fidelity product models in order to save time and expense. This work investigates how choice of product representation can affect model performance in visual conjoint analysis. Preference models for a single product, a table knife, are derived using three different representation schemes; simple sketches, solid models, and 3D printed models. Each of these representations is used in a separate conjoint analysis survey. The results from this study showed that consumer responses were inconsistent and potentially contradictory between different representations. Consequently, when using conjoint analysis for product innovation, obtaining a true understanding of consumer preference requires selecting representations based on how accurately they convey the product details in question.
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Zaps, R., and S. M. Chankov. "Are Consumers Ready for Flying Taxis? A Choice-based Conjoint Analysis of eVTOLs in Germany." In 2022 IEEE International Conference on Industrial Engineering and Engineering Management (IEEM). IEEE, 2022. http://dx.doi.org/10.1109/ieem55944.2022.9989956.

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