Frank Goethals

Gaining insight into student satisfaction using comprehensible data mining techniques

As a consequence of the heightened competition on the education market, the management of educational institutions often attempts to collect information on what drives student satisfaction by e.g. organizing large scale surveys amongst the student population. Until now, this source of potentially very valuable information remains largely untapped. In this study, we address this issue by investigating the applicability of different data mining techniques to identify the main drivers of student satisfaction in two business education institutions. In the end, the resulting models are to be used by the management to support the strategic decision making process. Hence, the aspect of model comprehensibility is considered to be at least equally important as model performance. It is found that data mining techniques are able to select a surprisingly small number of constructs that require attention in order to manage student satisfaction.

Differences in e-commerce behavior between neighboring countries: the case of France and Belgium

It is often (implicitly) assumed that e-commerce behavior is similar in countries with a similar culture in terms of Hofstedes dimensions. Through a study of e-commerce behavior of 969 private consumers and company representatives on 50 products, this paper proves there are actually significant differences in e-commerce behavior (information finding, suppler selection, and purchase) between people from two neighboring, culturally similar countries: Belgium and France. Furthermore, contrary to what was found in various US studies, very few differences in e-commerce behavior were detected between men and women in these two countries. Furthermore, clear differences were detected between age groups for product information finding and supplier selection but not for product purchase. Finally, to a big extent differences in e-commerce behavior between private consumers of two countries also exist between company representatives of the two countries.

Using Social Network Classifiers for Predicting E-Commerce Adoption

This paper indicates that knowledge about a person’s social network is valuable to predict the intent to purchase books and computers online. Data was gathered about a network of 681 persons and their intent to buy products online. Results of a range of networked classification techniques are compared with the predictive power of logistic regression. This comparison indicates that information about a person’s social network is more valuable to predict a person’s intent to buy online than the person’s characteristics such as age, gender, his intensity of computer use and his enjoyment when working with the computer.

Perceived Social Influence in Watching Online Theory Presentations

This paper investigates the intent of students of a Paris business school to watch online theory videos. The research model builds upon the UTAUT and extends it with other antecedents of the behavioral intention. Using 379 filled out questionnaires, the significance of the new constructs is shown. We also develop a new way to measure the perceived social influence. While the social influence is insignificant when measured in the classic way, it is significant when using our richer measure.

Predicting software revision outcomes on GitHub using structural holes theory

Many software repositories are hosted publicly online via social platforms. Online users contribute to the software projects not only by providing feedback and suggestions, but also by submitting revisions to improve the software quality. This study takes a close look at revisions and examines the impact of social media networks on the revision outcome. A novel approach with a mix of different research methods (e.g., ego-centric social network analysis, structural holes theory and survival analysis) is used to build a comprehensible model to predict the revision outcome. The predictive performance is validated using real life datasets obtained from GitHub, the social coding website, which contains 32,962 pull requests to submit revisions, 20,399 distinctive software project repositories, and a social network of 234,322 users. Good predictive performance has been achieved with an average AUC of 0.84. The results suggest that a repository hosts position in the ego network plays an important role in determining the duration before a revision is accepted. Specifically, hosts that are positioned in between densely connected social groups are likely to respond more quickly to accept the revisions. The study demonstrates that online social networks are vital to software development and advances the understanding of collaboration in software development research. The proposed method can be applied to support decision making in software development to forecast revision duration. The result also has several implications for managing project collaboration using social media.

Exploring the Choice for Default Systems

This paper investigates the role the default effect plays in choosing IT systems. This paper presents two studies concerning the acceptance of Gmail (vs. a self-hosted mailing system) and of legal (vs. illegal) movie downloading. Classic factors such as the perceived usefulness and ease of use of a system hardly explain the intent to use default systems. Including the perception of an alternative system in the model significantly increases the R2 of the intent to use the default system. Social influence plays a much bigger role in explaining the intent to use default systems than in the intent to use alternative systems.

An Exploration of the Research/Teaching Trade‐Off in the Perception of Business Students

Most business education institutions (BEIs) try to hire good researchers for teaching. However, it is controversial to say that excellent researchers are always excellent instructors. This paper explores the students’ perception of the teaching delivered by both researchers and non‐researchers. Our data suggests that the pure possession of a PhD is negatively related to measured aspects of students’ satisfaction with classes. However, the instructors publication record is positively related to most – but not all – aspects. Having more teaching experience, another element that is typically considered in an instructors job interview, is not positively related to the students’ satisfaction, but rather it is the specific teaching experience in that specific BEI that counts. Moreover, smaller group sizes, a key element in a BEIs cost structure, are appreciated by students. Finally, compulsory and optional classes are perceived to be equally useful, although the latter seem to overall better satisfy and entertain students.

Drivers for RFID implementations

This workshop paper studies drivers for RFID (Radio Frequency IDentification) adoption. The research in progress reported here puts forward several types of mindless and mindful decision making drivers. Hypotheses are tested using a questionnaire that was answered by 122 Chinese companies. The promise to reach higher efficiency seems to be the most important driver for mindful RFID adoption decisions. The early implementers were more driven to adoption by the promise of higher efficiency, better supply chain monitoring and better collaboration than late implementers. Tests show that mindless decision making shows up in RFID adoption.