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This paper investigates how respondents to conjoint experiments process information and choose among product profiles, and how this varies with their knowledge about the product. Models for estimating conjoint attribute weights are almost exclusively based on principles of compensatory decision making. The paper aims to explore to what extent and in what way these basic principles of conjoint modelling are violated.

Data were obtained from a verbal protocol study where 18 undergraduate students each performed a total of 28 stated choice tasks while “thinking aloud”.

Results show that cognitive operations consistent with compensatory decision rules constitute a majority of the total number of operations performed across tasks and respondents. However, few respondents exhibited a consistent use of compensatory‐type processes throughout their choice sets. Results suggest that individual preferences interact with characteristics of the choice sets to instigate changes in information processing. It also appears that complete strategies are seldom used. Finally, respondents' knowledge about the product influences the cognitive operations that respondents use in solving conjoint tasks.

Results are based on responses from 18 undergraduate students, which makes generalizations hard.

One implication of this work is that one should apply a more flexible model framework to allow detecting the existence of non‐compensatory strategies.

This paper is one of few which aim to implement findings in behavvioral decision research within the context of conjoint analysis.

Since the inception of the conjoint analysis technique in the year 1971, papers addressing the epistemological aspects of conjoint analysis are scant. Hence, this paper attempts to address the vacuum of qualitative discourse addressing the epistemological and methodological aspects of conjoint analysis including different issues, challenges, probable solutions, limitations and future direction of conjoint analysis in the recent decade.

For exploring the methodological and epistemological aspects of conjoint analysis, the seminal papers on conjoint analysis were reviewed. Moreover, the authors' experience for the state-of-art review was also taken into consideration.

The findings suggest that conjoint analysis that roots back since 1971 has not seen much exploration in Asian regions and is mainly used for new product development in the field of marketing or allied areas. Moreover, the reliability and validity of conjoint analysis is always a matter of concern for the researchers that hinders this technique's wider adaptability. Thus, the paper presents some probable solutions to address the focal issues useful for improved reliability and validity of the conjoint analysis technique.

This paper attempts to familiarize the researchers with epistemological and methodological aspects of conjoint analysis with certain solutions to evolve beyond existing conjoint analysis dimensions in terms of improved validity, reliability, epistemological and methodological aspects of conjoint analysis (CA). Moreover, it acts as a call for research in different research domains, especially in the Asian continent.

There exist certain seminal research papers on epistemological aspects of conjoint analysis. However, there is a dearth of such attempt in the recent decade addressing the application issues of conjoint analysis incorporating the recent issues as well. Therefore, this paper is an attempt to usher the future researcher to understand the methodological aspects of conjoint analysis. It may prevent them from violating the basic assumptions and methodological threshold. This research technique is preferred equally by academicians and practitioners, thus making it imperative to have clarity beforehand for improved research rigor.

To help further our understanding of how keymethodological issues in conjoint analysis influence outcomes, a choice‐based conjoint study measuring consumer preferences for pre‐packed apple selection packs was conducted. The role of stimuli presentation format was considered by comparing the performance of physical prototype stimuli and realistic pictorial representations. This indicated no substantial differences in the choice decisions made using the two presentation formats and suggested that photographic images may be used instead of prototype stimuli. A second issue pertained to the need for training and warm‐up exercises prior to the actual conjoint choice task. While this indicated some differences in choice strategies, a significant improvement in internal validity of choice decisions made with and without training was not achieved. One possible explanation for this finding may be that respondents made choices between apple products, a product category for which decision strategies are likely to be stable and well‐developed.

Behavioral scientists have long sought to capture how individuals’ understandings, perceptions and beliefs affect their decisions, often through examining the underlying cognitive processes that drive action (Schendel & Hofer, 1979). Economists, for example, are interested in how individuals’ utility functions influence their actions. Marketing researchers investigate how consumers’ preferences are reflected in their purchase behaviors. Organization researchers examine individual characteristics that influence outcomes such as job satisfaction, promotion, and turnover (Aiman-Smith et al., 2002).

Multiattribute utility models link the relative attractiveness of a given project to the utility functions of the decision makers. Conjoint analysis is industry's most commonly used marketing research method for measuring consumers' multiattribute utility functions (Green 1984). In this article, conjoint analysis is proposed as a methodology for measuring managers' utility functions regarding innovative capital budgeting proposals.

Conjoint analysis has become an increasingly popular approach to estimate the benefits derived from the attributes of a product. This decompositional method provides information about the structure of consumers' preferences, as obtained from the overall judgement of a set of alternative products defined as a combination of levels of different attributes. A two‐Limit Tobit Model approach for conjoint analysis has been used to examine and compare wine attribute preferences within and between different retail outlets (wine shops, direct‐from‐producer and supermarkets) in two Spanish regions: Aragon and Navarre. Three attributes have been used in the conjoint design: price, origin and vintage year. Among these attributes, price is the principal aspect for consumers who buy in wine shops, origin is the most important attribute for supermarket buyers and grape vintage has more utility for buyers who obtain wine directly from the producer. Finally, three wine consumer segments have been identified and characterised in the two regions.

This study was carried out to analyze the importance of consumer preference data in forecasting demand in apparel retailing.

To collect preference data, 729 hypothetical stock keeping units (SKU) were derived using a full factorial design, from a combination of six attributes and three levels each. From the hypothetical SKU's, 63 practical SKU's were selected for further analysis. Two hundred two responses were collected from a store intercept survey. Respondents' utility scores for all 63 SKUs were calculated using conjoint analysis. In estimating aggregate demand, to allow for consumer substitution and to make the SKU available when a consumer wishes to buy more than one item in the same SKU, top three highly preferred SKU's utility scores of each individual were selected and classified using a decision tree and was aggregated. A choice rule was modeled to include substitution; by applying this choice rule, aggregate demand was estimated.

The respondents' utility scores were calculated. The value of Kendall's tau is 0.88, the value of Pearson's R is 0.98 and internal predictive validity using Kendall's tau is 1.00, and this shows the high quality of data obtained. The proposed model was used to estimate the demand for 63 SKUs. The demand was estimated at 6.04 per cent for the SKU cotton, regular style, half sleeve, medium priced, private label. The proposed model for estimating demand using consumer preference data gave better estimates close to actual sales than expert opinion data. The Spearman's rank correlation between actual sales and consumer preference data is 0.338 and is significant at 5 per cent level. The Spearman's rank correlation between actual sales and expert opinion is −0.059, and there is no significant relation between expert opinion data and actual sales. Thus, consumer preference model proves to be better in estimating demand than expert opinion data.

There has been a considerable amount of work done in choice-based models. There is a lot of scope in working in deterministic models.

The proposed consumer preference-based demand estimation model can be beneficial to the apparel retailers in increasing their profit by reducing stock-out and overstocking situations. Though conjoint analysis is used in demand estimation in other industries, it is not used in apparel for demand estimations and can be greater use in its simplest form.

This research is the first one to model consumer preferences-based data to estimate demand in apparel. This research was practically tested in an apparel retail store. It is original.

A method is proposed for handling multi-attribute judgment problems with a large number of attributes such as mobile phone features. To minimize the complication of multi-attributes and reduce the consumers’ choice task burden, this paper aims to suggest an integrated hierarchical survey design (IHSD) with the Kano model. The author compared the utility of mobile phone’s attributes for each market and for customer segment by analyzing empirical data on wear obtained from six Middle East and African countries, five Asia-Pacific countries and three European countries. Based on an IHSD of 10,200 respondents, brand, camera, memory and LTE (4G) play vital roles in all regions. In contrast, Wi-Fi, file-editor, MMS, LCD size and phone type are displayed as the least important attributes. The results of this study were successfully implemented for product planning, product development and marketing strategy in terms of price setting, features prioritizing and optimal designing for new products in the mobile phone company.

The first step was to list all possible features with the product planning team, product development team and market research specialists. The second step divided the selected features for designing a mobile phone into subgroups based on their functional characteristics by using the Kano model. The method for classifying features was determined using Kano questionnaire. The third step incorporated a fractional factorial design for the “must-be” choice-based conjoint (CBC) (Oppewal et al., 1994) which includes two factors: whether customers required the “one-dimensional” feature or the “attractive” feature, along with the “must-be” attributes. The consumers who selected the “must-be” features could choose both the “one-dimensional” feature and the “attractive” feature groups or one of the two feature groups in no particular order. Fractional factorial design was applied to both the “one-dimensional” features and the “attractive” features for individual CBCs. Random sequences of the combinations of attribute levels were generated for each of the three types of CBC analyses (“must-be”, “one-dimensional” and “attractive”). At the same time, the fourth step conducted a survey of the individual groups for the conjoint analysis on the functional characteristics of a mobile phone. The analysis of the accumulated data obtained from all the feature groups was completed using conditional logit models as part of the fifth step. In addition, the “must-be” CBC design was linked with the “one-dimensional” and “attractive” CBC designs. The sixth step was to analyze the accumulated results obtained from all the feature groups and estimate the usefulness of each feature’s level in the context of the CBC. Based on the results of the sixth step, the importance and willingness-to-pay of each attribute were estimated in the seventh step.

Use of the conjoint important score is aimed to expand the market by finding the different consumers’ needs across the regions. In detail, attributes such as “FM Transmitter”, “Touch screen” and “Health (heart rate)” are considered consumers’ new crucial needs in Europe, which would enable the product to superiorly differentiate itself from others to dominate the current market. On the other hand, it is shown that attributes such as “brand”, “mobile TV”, “external memory”, “mobile tracker” and “4G” are more important in Asia-Pacific. Therefore, if mobile manufacturers develop this sector more, it will grant mobile manufacturers the opportunity to lead the market. The only difference of the Middle East and African consumers is that “NFC” has a higher importance while the rest of the needs are very similar to those of Asia-Pacific. Regarding willingness-to-pay (WTP) among countries, the highest scoring utility, besides brand, appeared to be associated with the camera function in all countries. Especially, relatively low utility value was given in Wi-Fi and File-editer, MMS, LCD size and Phone type. In a value-based approach, the price of a product is based on the perceived valuation by the target customers. The research in the field of pricing is of ample importance. This is because price is the only element of the marketing mix that generates income. All other elements, such as advertising and promotion, product development, selling effort, distribution and packaging, involve expenditures (Monroe, 2003). Regarding among regions, the needs for 3G and the internet-related feature (WAP, Wi-Fi, etc.) in the emerging market are low compared to those for 4G and internet-related feature in the mature market. Also, the needs for productivity and advanced features, such as camera and e-mail, are lower in Asia-Pacific than in Europe. It is therefore recommended that manufactures and marketers of mobile phones should consider producing and selling phones with modern technology features that are more durable and of highly quality.

The integrated hierarchical survey by function with the Kano model proves to be a highly useful, efficient and accurate methodology for understanding a consumer mobile phone behavior. Although the proposed method was applied to designs of mobile phones in the emerging and mature markets, its accuracy was not compared with the traditionally used methods such as CBC, adaptive conjoint analysis and hybrid method. This is left for further areas of research.

The results of this research study correspond with previous studies conducted (Pakola et al., 2010; Das, 2012; Malaasi, 2012, 2008; Dziwornu, 2013), which consider the features of mobile phone as a crucial factor in consumer buying decision in all countries. It is significant that this study made huge impact on mobile phone manufacturers in several ways. It has been converted into product development with consumer-oriented approach. The pricing policy has been changed from cost-based pricing into value-based pricing; and marketing strategy has been changed from an unsystematic function into a systematic and consistent one.

The proposed method with the Kano model proved to be a practical and efficient tool for decision-making, as it helped mobile manufacturers to better understand how customers evaluate and perceive quality attributes. The Kano model was used to explain how the quality attributes can be classified into mainly three categories of perceived quality: “must-be”, “one-dimensional” and “attractive”. It has lots of benefits in terms of cost and time reduction and is expected to bring a great effect into the industrial field.

Conjoint analysis is a class of techniques for analyzing consumers’ preferences and trade‐offs regarding their selection of products and services. Typically, conjoint analysis has been applied to established markets such as frequently purchased packaged goods, consumer durables, communication services, and business‐to‐business products. Recently, marketing researchers have extended conjoint methodology to cope with the measurement of buyer trade‐offs associated with “really new” products and services, for which there is little or no prior buyer knowledge or experience. The researcher’s task is twofold: to educate the potential buyer regarding the pros and cons of the new product/service while, at the same time, obtaining the respondent’s evaluation of the new product/service itself. This article describes the application of conjoint techniques to a new service, TrafficPulse, that enables subscribers to obtain continuous 24/7 updates on traffic conditions, travel times, and alternative routes, should congestion occur. In particular, describes how traditional conjoint analysis can be embellished to obtain relevant information about consumer evaluations of new goods and services prior to their actual use by prospective consumers. In short, the prospective consumer can be “educated” about the new product/service before obtaining evaluation of its potential worth. The paper also shows how conjoint analysis can be modified to accommodate restrictions on various attribute levels, how the use of BASES‐like norms can be incorporated, and how optimization algorithms can be used at either the single product or multiple product (i.e. product line) level.