Chris Snijders

Effects of different types of progress indicators on drop-out rates in web surveys

The present study analyzes whether and how different types of progress indicators affect the tendency of respondents to continue filling out a web survey, focusing on whether the progress indicators effects depend on the position of the respondent in the questionnaire. Using a sample of 2460 respondents of a Dutch online access panel, we analyze three kinds of progress indicators (linear, fast-then-slow, slow-then-fast, and a control condition) using survival analysis. The results show that the effect of the indicators on the completion rate is either negative or nonexistent, depending on the questionnaire length. Moreover, the effect of an indicator does not depend on the position of the respondent in the answering process. We interpret our findings in terms of the implicit narrative between survey designer and respondent.

External Knowledge and Innovation Performance in Clusters: Empirical Evidence from the Uruguay Software Cluster

The importance of geographic proximity for innovation has been widely stressed in the cluster literature. Yet, new insights from the inter-organisational network and cluster literatures underline the role of non-local linkages in enabling firms in networks to enhance learning and to innovate. This paper contributes to this literature by examining the importance of local and non-local knowledge networks for the innovation performance of firms in clusters. Our analysis is based on primary data from a survey of 95 software firms clustered in Montevideo, Uruguay. The results highlight that the most innovative firms in clusters heavily rely on non-local knowledge networks. Moreover, the number of indirect local ties to other firms is a better predictor of innovative performance than the number of direct local ties. Finally, our findings confirm the presence of knowledge gatekeepers in clusters and emphasise their role in enhancing innovation in clusters by absorbing knowledge from extra-cluster sources and diffusing it to other local firms.

Predicting Student Performance from LMS Data: A Comparison of 17 Blended Courses Using Moodle LMS

With the adoption of Learning Management Systems (LMSs) in educational institutions, a lot of data has become available describing students’ online behavior. Many researchers have used these data to predict student performance. This has led to a rather diverse set of findings, possibly related to the diversity in courses and predictor variables extracted from the LMS, which makes it hard to draw general conclusions about the mechanisms underlying student performance. We first provide an overview of the theoretical arguments used in learning analytics research and the typical predictors that have been used in recent studies. We then analyze 17 blended courses with 4,989 students in a single institution using Moodle LMS, in which we predict student performance from LMS predictor variables as used in the literature and from in-between assessment grades, using both multi-level and standard regressions. Our analyses show that the results of predictive modeling, notwithstanding the fact that they are collected within a single institution, strongly vary across courses. Thus, the portability of the prediction models across courses is low. In addition, we show that for the purpose of early intervention or when in-between assessment grades are taken into account, LMS data are of little (additional) value. We outline the implications of our findings and emphasize the need to include more specific theoretical argumentation and additional data sources other than just the LMS data.

Modeling customer-centric value of system architecture investments

System architecture investments aim at improving the quality of the system in alignment with (current and future) business goals. While the costs of architecture changes are routinely calculated, identifying benefits of architecture changes and translating them to a monetary value has been a challenge in practice. Currently, architecture value estimation is largely based on cost-savings or on risk mitigation, without much reliance on potential customer benefits. This article reports on our experience in modeling the customer value and evaluating its potential use in choosing between different system architectures in two case studies conducted in an organization developing healthcare systems. To model the customer value, we exploit best practices in management and marketing. Management tools, in particular strategy maps and balanced scorecards, are used to identify customer-centric benefits caused by architecture design decisions. Furthermore, two marketing concepts, customer value-in-use and customer segments, are adopted to quantify the value of architecture changes for a single customer and multiple customers, respectively. The paper shows that using the customer value in addition to the existing value indicators in the organization has several advantages but also calls for future improvements to be adopted in practice.