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Previous studies on IT investment using event studies investigated different aspects of IT. For example, Dos Santos et al. (1993) studied IT innovativeness andChatterjee et al. (2002) focused on different functionality aspects of IT (e.g., infrastructure and application). Apart from general IT investment, there were also a few studies specific to IT applications (e.g., ERP, e-business, and security). Nevertheless, the steps of the event study adopted in previous studies were similar. All previous studies first analyzed abnormal return based on subsamples followed by subsampling analysis. Most studies adopted a three-day event window around IT investment announcements. Both parametric and nonparametric tests (e.g., sign test and Corrado's rank test) were used to determine the level of significance of abnormal return in the estimation period. Furthermore, most of the sample data were from the United States. Meng and Lee (2007) was the only study that compared the reaction of IT investment in the United States with that in China. A summary of previous research on IT investment using event studies is shown in Table A1.

Summary: Correlating disease mutations with clinical and phenotypic information such as drug response or patient survival is an important goal of personalized cancer genomics and a first step in biomarker discovery. HyperModules is a network search algorithm that finds frequently mutated gene modules with significant clinical or phenotypic signatures from biomolecular interaction networks.Availability and implementation: HyperModules is available in Cytoscape App Store and as a command line tool at www.baderlab.org/Sofware/HyperModules.Contact: Juri.Reimand@utoronto.ca or Gary.Bader@utoronto.caSupplementary information: Supplementary data are available at Bioinformatics online

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Online learning is of growing importance to institutions and learners, and the COVID-19 pandemic has underscored its importance even more. Because learner autonomy is relatively high in these online environments, they must engage in self-regulated learning processes to achieve successful learning outcomes, but studies show that most learners are not able to do so. Hence, in this longitudinal field experiment, using a massively open online course (MOOCs), a type of online learning environment, we investigate whether gamified interventions through the learning platform can foster learners to engage in self-regulated learning processes and improve their learning outcomes. We find that gamification interventions are indeed useful, but for these gamification interventions to succeed, they must be designed to provide personalized feedback to learners that match with their learning goal-orientation. Overall, our findings point to the fact that gamification designs in online learning platforms can enhance learners’ engagement and learning outcomes, but they must be personalized. A one-size-fits-all approach to gamification design in online learning just does not work and may even backfire to reduce the engagement of some learners.