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Published in Walter R. Paczkowski, Deep Data Analytics for New Product Development, 2020
Conjoint analysis is a member of a family of choice methodologies designed to determine or estimate customer preference for one product versus another. This amounts to determining the optimal combination of attributes and their levels. This family is described in Paczkowski [2016] and Paczkowski [2018]. Another member of the family is discrete choice analysis which seeks to handle the same problem but the context of discrete choice differs from that of conjoint analysis. As members of the same family, they share similar features but differ in important ways. The common features are: reliance on attributes with discrete, mutually exclusive, and completely exhaustive levels;an experimental design to arrange or combine the levels into choice alternatives that are interpreted as products;a simple evaluative question used in a survey; andan estimation procedure applied to the data collected in the survey.
System and Software Beta and Usability Programs
Published in Ron S. Kenett, Emanuel R. Baker, Process Improvement and CMMI® for Systems and Software, 2010
Ron S. Kenett, Emanuel R. Baker
Full factorial and fractional factorial designs are used in development, beta tests, and general marketing applications. Suppose your development organization designed two types of service contracts to complement the product under beta test. We call them Standard and Platinum. We also consider two levels of annual fees for these types of service. The cost levels are $4000 and $7000, respectively, per year for an annual service contract. If cost is the only consideration, then the choice is clear: the lower-priced service is preferable. What if the only consideration in buying an annual service agreement is regular (within 1 day) or fast service (within 4 hours)? If service response time is the only consideration, then you would probably prefer a fast service with a promised response time of technicians within 4 hours. Finally, suppose you can take either a service contract with access to the technician’s hotline only between 9:00 a.m. and 5:00 p.m., or buy a service level with full-time accessibility, 24 hours a day, 7 days a week? Virtually everyone would prefer the full-time accessibility. In a real purchase situation, however, customers do not make choices based on a single attribute such as price, response time, or access to technicians. Rather, customers examine a range of features or attributes and then make judgments or trade-offs to determine their final purchase choice. Marketing experts call the application of factorial designs to these types of problems “conjoint analysis.” Conjoint analysis examines feature trade-offs to determine the combination of attributes that will be most satisfying to the customer. In other words, by using conjoint analysis, a company can determine the optimal features for its product or service. In addition, conjoint analysis will identify the best advertising message by identifying the features that are most important in product choice. Conjoint analysis therefore presents choice alternatives between products/services defined by sets of attributes. This is illustrated by the following choice: Would you prefer a service agreement with 1-day service that costs $4000 and gives you access to the service technicians only during working hours, or a service agreement that costs $7000, has 24/7 coverage, and takes a maximum of 4 hours for the technician to arrive? Extending this, there are potentially eight types of service (see Table 7.4).
Conjoint Analysis of Blockchain Adoption Challenges in Government
Published in Journal of Computer Information Systems, 2023
Sujeet Kumar Sharma, Yogesh K. Dwivedi, Santosh K. Misra, Nripendra P. Rana
Conjoint analysis, a multivariate statistical technique, has been used extensively in marketing research since the 1970s to understand the relative importance of features and trade-offs consumers consider when purchasing a product or service. Specifically, conjoint analysis is used to obtain information about how users rate different product features overall.62,63 For example, if we assign equal weight to each challenge, each challenge would contribute approximately 8.3% to Blockchain adoption in Government. Conjoint analysis facilitates the elicitation of individual responses. We assume that multiple challenges will be addressed simultaneously to realize Blockchain adoption in Government. The relative importance of each challenge can be calculated and understood using the methodology adopted. We conducted a pilot test of the survey questionnaire by emailing it to three experts involved in government blockchain use cases, and two professors who do research in the field of Blockchain. This pilot study aimed to determine how much time it would take to complete this survey and to get feedback. We emailed the attributes and levels of the challenges identified to the experts. They reported difficulties in ranking the profiles of the different orthogonal combinations, which were generated using SPSS 24.0 software.
Assessing performance of mined business process variants
Published in Enterprise Information Systems, 2021
Lucas Van Den Ingh, Rik Eshuis, Sarah Gelper
The approach leverages the knowledge of domain experts on ideal process performance. To extract this knowledge, the approach uses conjoint analysis (Green, Douglas Carroll, and Goldberg 1981; Hair et al. 2014) to estimate weights of different performance dimensions. Conjoint analysis is a standard technique used in new product development to identify how potential customers value the attributes of the new product. The advantage is that, by requesting the respondent to choose among fictitious products rather than rating each attribute separately, the relative importance of each attribute is unconsciously revealed. In our setting of process evaluation, domain experts are asked to choose among fictitious business processes in terms of performance according to their own judgement, which reveals the relative importance of the process dimensions. The weights that the experts implicitly assign to the performance dimensions are estimated from their choices. Once the weights are estimated, the overall performance of any process variant can be assessed.
The effect of gender on willingness to pay for mass customised running shoes
Published in Journal of Global Fashion Marketing, 2021
Hassan Daronkola Kalantari, Lester W Johnson, Chamila R. Perera
The data were analysed using conjoint analysis (Green & Rao, 1971; Rao, 2014). Conjoint analysis is a well-known method which allows inclusion and combinations of a number of attributes to describe a hypothetical situation in which participants evaluate the situation rather than evaluating each attribute individually. This method makes preference statements more realistic. Conjoint analysis first was proposed by Green and Rao (1971). There is growing interest in the use of conjoint analysis as a tool for estimating customer preferences within the economic evaluation of different products and services. It has been suggested that where cost is included as one of the attributes within the exercise, conjoint analysis can be used to estimate willingness to pay (Kulshreshtha et al., 2018). There are many studies that have used conjoint analysis to estimate the willingness to pay (e.g. Tokimatsu et al., 2016).