Paolo Bordignon

Consumer preferences in food packaging: CUB models and conjoint analysis

Purpose – Packaging features have been shown to be of great importance for the consumer final choice of fresh products (Silayoi and Speece, 2007). Packaging is an extrinsic attribute, which consumers tend to rely on, when relevant intrinsic attributes of the product are not available. In the current literature, studies on the influences of packaging features on consumer preferences are mainly related to classical preference evaluation methods like conjoint analysis (CA). The purpose of this paper is to apply both CA and the less known combination of uniform discrete and shifted binomial distributions (CUB) models to food packaging evaluations. Design/methodology/approach – Starting from a real case study in this field, along with CA, the author apply CUB models (Iannario and Piccolo, 2010) as a useful tool to evaluate preferences. CUB models can grasp some psychological characteristics of consumers related to the “feeling” toward packaging attributes and related to an inherently “uncertainty” that affects...

Statistical tests in medical research: traditional methods vs. multivariate npc permutation tests

Within medical research, a useful statistical tool is based on hypotheses testing in terms of the so-called null, that is the treatment has no effect, and alternative hypotheses, that is the treatment has some effects. By controlling the risks of wrong decisions, empirical data are used in order to possibly reject the null hypotheses in favour of the alternative, so that demonstrating the efficacy of a treatment of interest. The multivariate permutation tests, based on the nonparametric combination – NPC method, provide an innovative, robust and effective hypotheses testing solution to many real problems that are commonly encountered in medical research when multiple end-points are observed. This paper discusses the various approaches to hypothesis testing and the main advantages of NPC tests, which consist in the fact that they require much less stringent assumptions than traditional statistical tests. Moreover, the related results may be extended to the reference population even in case of selection-bias, that is non-random sampling. In this work, we review and discuss some basic testing procedures along with the theoretical and practical relevance of NPC tests showing their effectiveness in medical research. Within the non-parametric methods, NPC tests represent the current “frontier” of statistical research, but already widely available in the practice of analysis of clinical data.

Cheese perception in the North American market: Empirical evidence for domestic vs. imported Parmesan

Purpose – The purpose of this paper is to detect market segments where consumers have a different knowledge of domestic and imported Parmesan cheese in USA and Canada. The results may be helpful in understanding to what extend North America consumers appreciate Parmesan cheese and brands, Parmesan consumption and price while recognizing market segments according to consumer awareness, involvement and covariate effects. Design/methodology/approach – A class of mixture models, known as combination uniform binomial (CUB), is applied to survey data collected in USA and Canada. A questionnaire, filled out by 540 restaurant customers, collects opinions about consumption, purchase features and price. The CUB model estimates the two latent variables, known as feeling and uncertainty, explaining the respondent’s behavior as awareness and involvement variability while the CUB clustering procedure detects market segments. Findings – CUB results show that the Parmesan is a well-known cheese but also that a small shar...

Ranking Multivariate Populations

The need to define an appropriate ranking of several populations of interest, i.e. processes, products, and so on is very common within many areas of applied research such as Food Science, Chemistry, Engineering, Biomedicine, etc.

Customer Satisfaction Heterogeneity

The measurement of the customer satisfaction concerns the gap between the customer expectations about the product or service and the perceptions of the customer after the consumption or use. In other words, the customer satisfaction is closely related to the concept of “perceived quality”. According to the definition of Montgomery [24], it depends on how much the products or services meet the requirements of the consumers/users and it is directly connected to the homogeneity of the performance of the production process or service provision process.

The CUB Models

The CUB model [12], where CUB stands for Combination of a discrete Uniform and a shifted Binomial distributions assumes the involvement of two latent variables during an evaluation process, that have been called feeling and uncertainty. In order to justify the names for latent variables, consider the way you choose an evaluation grade from a set of 9. The final choice reflects your feeling about the evaluated item, your past experience, your knowledge about it, and so on. On the other hand, there are some aspects concern with a basic uncertainty about the evaluated item, for example you are asked to deal with it for the first time and you don’t know what grade to choose, maybe the task is too difficult or the task is annoying you. These two main components are supposed to move your final choice and they are supposed to follow respectively a shifted Binomial distribution and a Uniform distribution [12, 18].

Composite Indicators and Satisfaction Profiles

Evaluating the satisfaction about public services, organizations or products is very important in order to have a measure of their efficiency and effectiveness.

Analyzing Survey Data Using Multivariate Rank-Based Inference

Data from customer satisfaction surveys are multivariate—there are several questions resulting in as many endpoints. Furthermore, survey data typically don’t fit into simple parametric models. Indeed, the endpoints or response variables may be measured on different types of scales (metric, ordinal, binary). For these two reasons, one requires multivariate inference methods, and specifically methods that can deal with a mix of response variable types. Additionally, it would be advantageous if the procedures also performed well for small to moderate numbers of respondents, as not every survey can afford to obtain responses from hundreds of participants.