Identifying lead users in a living lab environment

This paper emphasizes the identification process of lead users within a living lab environment. Lead users are seen as important contributors to the living lab methodology since they express needs before the general market does. Additionally they generate ideas with a high level of novelty. Living Lab researchers have focused on the added value of involving these users in their research, but research on how to identify these lead users is still lacking. Therefore this paper will focus on the identification process of lead users by means of a Living Lab case study in the world of movie theaters

Explanation Needs in App Reviews: Taxonomy and Automated Detection

Explainability, i.e. the ability of a system to explain its behavior to users, has become an important quality of software-intensive systems. Recent work has focused on methods for generating explanations for various algorithmic paradigms (e.g., machine learning, self-adaptive systems). There is relatively little work on what situations and types of behavior should be explained. There is also a lack of support for eliciting explainability requirements. In this work, we explore the need for explanation expressed by users in app reviews. We manually coded a set of 1,730 app reviews from 8 apps and derived a taxonomy of Explanation Needs. We also explore several approaches to automatically identify Explanation Needs in app reviews. Our best classifier identifies Explanation Needs in 486 unseen reviews of 4 different apps with a weighted F-score of 86%. Our work contributes to a better understanding of users' Explanation Needs. Automated tools can help engineers focus on these needs and ultimately elicit valid Explanation Needs

Integration of innovative users as source of service innovations

In this study we research user integration in the German service industry. Goal is to explore the industrial practice with regard to four dimensions of user integration, i.e., why, whom, how, and how successful the service industry integrates to find novel service ideas. Data is collected from a large-scale survey sent out to 2,905 service companies and posted in various user groups related to service innovations. Drawing from data gathered from 301 respondents in our study, we present explorative findings for each distinct dimension of user integration. To better understand the interrelation of these dimensions, we also create a structural equation model using partial least square for estimation of direction and strength of relationships between those. Results show that service companies like companies from other industries actively pursue the development of radical innovations. We find that service companies do not integrate users by random. Instead a service company's level of importance for radical innovation significantly determines both, choice of users integrated as well as choice of integration instruments deployed. In our study we can also show, that many of the beliefs brought forward by service companies for not integrating users cannot be sustained in the light of our findings. We can demonstrate that user contributions provide true value to those companies integrating the latter, and also that using tools which are considered easy and versatile to apply can still have a significant impact on the attractiveness of user ideas. --

Extracting Business Intelligence from Online Product Reviews: An Experiment of Automatic Rule-Induction

Online product reviews are a major source of business intelligence (BI) that helps managers and market researchers make important decisions on product development and promotion. However, the large volume of online product review data creates significant information overload problems, making it difficult to analyze users’ concerns. In this paper, we employ a design science paradigm to develop a new framework for designing BI systems that correlate the textual content and the numerical ratings of online product reviews. Based on the framework, we developed a prototype for extracting the relationship between the user ratings and their textual comments posted on Amazon.com’s Web site. Two data mining algorithms were implemented to extract automatically decision rules that guide the understanding of the relationship. We report on experimental results of using the prototype to extract rules from online reviews of three products and discuss the managerial implications

Sentiment Analysis of Spanish Words of Arabic Origin Related to Islam: A Social Network Analysis

With the arrival of Muslims in 711 till their expulsion in the 1600s, Arabic language was present in Spain for more than eight centuries. Although social networks have become a valuable resource for mining sentiments, there is no previous research investigating the layman’s sentiment towards Spanish words of Arabic etymology related to Islamic terminology. This study aim at analyzing Spanish words of Arabic origin related to Islam. A random sample of 4586 out of 45860 tweets was used to evaluate general sentiment towards some Spanish words of Arabic origin related to Islam. An expert-predefined Spanish lexicon of around 6800 seed adjectives was used to conduct the analysis. Results indicate a generally positive sentiment towards several Spanish words of Arabic etymology related to Islam. By implementing both a qualitative and quantitative methodology to analyze tweets’ sentiments towards Spanish words of Arabic etymology, this research adds breadth and depth to the debate over Arabic linguistic influence on Spanish vocabulary