In order to understand the antecedents of costumer satisfaction, businesses can analytically utilise the growing amount of customer's information. Unstructured text data can be used to uncover important information owing to developments in Natural Language Processing and text analytics approaches. In this paper, we focus on customer reviews posted on e-commerce shopping platforms. We perform manual data annotation to determine the sentiment of the review with respect to the most important aspects of the customer journey. The 14 extracted aspects are grouped into three constructs that correspond to the stages of the customer's interaction with the e-commerce platform. We make use of a configurational approach, Fuzzy-set Qualitative Comparative Analysis, to understand how the sentiment with respect to the three stages combine to achieve positive customer satisfaction. The outcomes of the analysis show that all the three stages of customer journey play important roles in determining the final evaluation of a customer, leading to a positive or a negative sentiment. The theoretical and practical implications are discussed.@en

Entrepreneurship education is critical for developing the entrepreneurial skills of tomorrow’s entrepreneurs and leaders. In this paper, we aim to identify factors influencing entrepreneurship educators’ pedagogical choices, in particular, factors influencing their preferences to become either a teacher-centric or a student-centric educator. Our analysis includes job satisfaction, self-efficacy, and department support as the antecedent variables influencing the outcome. The data are collected from 289 global entrepreneurship educators, and fuzzy-set comparative qualitative analysis (fsQCA) was used to obtain multiple configurations of conditions leading to either a teacher-centric or student-centric model. The fsQCA analysis reveals that for teacher-centric educators, job satisfaction and more than 10 years of teaching experience are the most important factors, whereas for student-centric educators, teaching experience is not important factor, but self-efficacy and entrepreneurship teaching training are influential factors. In the article we discuss the important theoretical and practical contributions resulting from the analysis.

@en

This paper offers step-by-step guidance and best practices for using fuzzy-set qualitative comparative analysis (FsQCA) in entrepreneurship research. We analyzed publications in which FsQCA was utilized as the primary methodological choice, focusing our scope on 10 top entrepreneurship journals. Our review reveals that inconsistencies exist regarding how to use the method, what processes to follow, and how to interpret results. Specifically, there is a need for an improved understanding regarding (a) considerations for performing direct calibration, (b) performing (necessity) analysis for both the outcome of interest and its negation, and (c) assessing the sensitivity of the resulting configurations to the selected threshold and cutoff values. To illustrate best practices, we conclude by analyzing GEM data from 43 countries to demonstrate how to best use the method, what to consider, and how the findings change if best practices are not followed.

@en

In this article, we focus on mobile wellness and health-related applications from the perspective of the level of imprecision present in the data used in the recommendation systems. We propose a general fuzzy optimization model based on chanced constrained optimization to design recommendation systems that can take into consideration (i) the imprecision in the data and (ii) the imprecision by which one can estimate the effect of a recommendation on the user of the system. Our proposal is one of the first to use fuzzy optimization models in health-related decision making problems and the first to define a chance constrained optimization problem for interval-valued fuzzy numbers. The proposed approach identifies a set of actions to be taken by the users in order to optimize general health-related and/or wellness condition of the user from various perspectives. The model is illustrated through the example of walking speed optimization, with an additional numerical experiment offering a comparison with traditional methods.@en

This paper focuses on multi-attribute design problems in which several quantitative and/or qualitative attributes aresimultaneously involved. Preference elicitation methods dealing with decision-makers’preferences are often employed tosolve such problems. The present paper proposes a conceptual framework that considers preference elicitation as a processshaped by the interaction of different factors including the researcher, methodology, questionnaire and respondent in a givencontext, at the same time. The aim of the framework is to assist in choosing a decision support tool among various multi-attribute decision-making methods, as well as in evaluating and understanding preference elicitation results. To illustrateand exemplify the use of the framework, it is applied to a real-world case of end user preferences of mobile serviceplatforms. We make use of two methods that are suitable for such a setting: analytic hierarchy process and conjointanalysis. Albeit with considerable discrepancies, the results show that both methods provide insightful information onthe principle. Nonetheless, to a large degree, these methods perform differently in the case of non-complex tasks andunknowledgeable respondents, which the process view aids in evaluating and understanding. Copyright © 2015 JohnWiley & Sons, Ltd. @en

This paper proposes an approach denoted visual conjoint analysis (VCA). Conjoint analysis is commonly used in marketing to understand consumers’ decision criteria, particularly why consumers prefer and select certain products and their variations. Yet, little efforts have been made to provide visual means for exploring and visualizing preferences and utilities of consumers. In this paper, we propose an approach that enables identifying a low-dimensional topology of consumer profiles and their demographic characteristics. Through a two-step approach, VCA makes use of techniques for (i) data reduction and (ii) dimension reduction in combination with conjoint analysis. It provides a two-dimensional representation (dimension reduction) of a small number of respondent segments (data reduction). This provides means for two key tasks: (i) identifying the topology of multivariate respondent profiles in a lower dimension, focusing on neighborhood relations, and (ii) visual representations of information describing the respondent profiles, as well as the combination of the two tasks. The approach is applied to a real-world case of consumers’ preferences of mobile platform ecosystems.@en