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The purpose of this paper is to demonstrate how a web‐based virtual ideation tool, dialogr.com, can be used to capture insights from consumers and to gain an understanding of consumer satisfaction with package design during and after product use. The authors also demonstrate how the resulting rich qualitative data can be combined with output from traditional survey research, to provide insight into the impact of satisfaction with package design on purchase intent.

A mixed‐methods approach was employed that captured qualitative insights from a large number of consumers regarding their experience using product packaging and combined the quantitative rigor of survey research to capture brand familiarity and perceptions, as well as brand preference and loyalty.

Results demonstrate that design is important to consumers and also that consumers are increasingly design literate. The perceived quality of the package design does impact brand evaluation. Negative reactions to product packaging adversely impacted intent to purchase and intent to recommend the product to others.

Results show that the mixed model is a promising method for gaining feedback on new package designs. Substantive implications include: design drives purchase, consumers want to be involved in design, and getting user input on design is important.

Consumer packaged goods companies often test packaging prototypes in a limited way – they either ignore qualitative measurement completely or use small sample focus groups. This quali‐quant method offers two advantages over the methodologies most commonly used to study package design; it has the capacity to engage large numbers of consumers and it can be set up to gather data from consumers during or immediately after product use.

Factor analysis is the most used tool in organizational research and its widespread use in scale validations contribute to decision-making in management. However, standard factor analysis is not always applied correctly mainly due to the misuse of ordinal data as interval data and the inadequacy of the former for classical factor analysis. The purpose of this paper is to present and apply the Bayesian factor analysis for mixed data (BFAMD) in the context of empirical using the Bayesian paradigm for the construction of scales.

Ignoring the categorical nature of some variables often used in management studies, as the popular Likert scale, may result in a model with false accuracy and possibly biased estimates. To address this issue, Quinn (2004) proposed a Bayesian factor analysis model for mixed data, which is capable of modeling ordinal (qualitative measure) and continuous data (quantitative measure) jointly and allows the inclusion of qualitative information through prior distributions for the parameters’ model. This model, adopted here, presents considering advantages and allows the estimation of the posterior distribution for the latent variables estimated, making the process of inference easier.

The results show that BFAMD is an effective approach for scale validation in management studies making both exploratory and confirmatory analyses possible for the estimated factors and also allowing the analysts to insert a priori information regardless of the sample size, either by using the credible intervals for Factor Loadings or by conducting specific hypotheses tests. The flexibility of the Bayesian approach presented is counterbalanced by the fact that the main estimates used in factor analysis as uniqueness and communalities commonly lose their usual interpretation due to the choice of using prior distributions.

Considering that the development of scales through factor analysis aims to contribute to appropriate decision-making in management and the increasing misuse of ordinal scales as interval in organizational studies, this proposal seems to be effective for mixed data analyses. The findings found here are not intended to be conclusive or limiting but offer a useful starting point from which further theoretical and empirical research of Bayesian factor analysis can be built.

The purpose of this paper is to show how consumer researchers have used experimental design of ideas to understand emotions.

The approach was “quali‐quant,” mixing the insights of qualitative research with the numerical rigor of quantitative research. Consumers were presented with small, easy to understand test concepts created by experimental design to obtain their reactions. The paper looks at the impact of emotion‐language on acceptance, through individual‐level regression modeling.

Models were developed for each respondent. It was found that the emotion elements are not key drivers of interest in the product. The paper did, however, find individuals who were sensitive to some emotion elements, but not to others, leading to the conclusion that responses to emotion in concepts may emerge out of the interaction of individual respondents and the concept elements.

The key limitation of this type of research is that it requires a specific internet‐based program (IdeaMap.Net) which does all the combinations, acquires the data, and then analyzes it.

The paper shows how to better understand the role of emotions in written concepts.

The paper presents a totally new treatment of emotion, by looking at emotion in terms of the stimulus and the respondent together. It shows that emotion is an emergent or new entity, not inherent in the predisposition of a group of individuals, nor in the nature of the stimulus.

This paper aims to present the assessment of different self-reported approaches that can be used to identify and measure consumers’ emotional responses towards brands. The goal is to determine whether visual and spontaneous protocols are able to generate deeper insights than only closed groups of scales, and to consider pictorial tools as innovative and challenging measurement techniques for brand value assessment.

Three versions of the same consumer online survey dedicated to identifying some brand-related consumer emotional insights were created to be compared in terms of quality of responses, interviewees’ evaluation and richness of insights.

Visual protocols provide more specific emotional responses and are considered as useful for “capturing deep contextual meanings of consumer experience”. They clearly provide deeper insights and better emotional granularity. It can be generally concluded that different emotions’ self-report measurements are adapted to some diverse research questions or situations.

This paper is based on research with a limited number of participants. It focuses on the use and consumer emotional insights delivered by three tested protocols rather than detailed analysis of the specific profiles of consumers.

The authors provide some recommendations of different research techniques, which can be used to identify and measure consumers’ emotional reactions towards brands.

Their paper encourages a critical reflection about researchmethods that are deployed for marketing and consumer behaviour purposes. Their work promotes a hybrid and not dogmatic approach, centred around the perception andmotivation of the respondentsmore than on the expectations of the researcher only.

Research outcomes among different self-reported protocols using internet technologies are compared. Quality of responses and richness of insights are measured in a quite innovative and comprehensive way. The paper also gives detailed recommendations to researchers interested in consumers’ emotional reactions towards brands measurements.