My name is Mahdi Mirhoseini (Shortened from SeyedMohammadMahdi Mirhoseini) and I am a postdoctoral fellow at the DeGroote School of Business at McMaster University . In 2018 I earned my Ph.D. in Business Administration from the HEC Montréal under the supervision of professors Pierre-Majorique Léger and Sylvain Sénécal at the Tech3lab UX lab. I received my M.Sc. in Management Science and B.Sc. in Electrical Engineering from Sharif University of Technology, Tehran, Iran.
My primary research interests fall into the emerging and growing field "Neuro IS" which lies in the intersection of Information Systems, Neuroscience, Cognitive Psychology, and Human-Computer Interaction. As a member of the McMaster Digital Transformation Research Centre (MDTRC) team, I study users’ interaction with technology by combining the traditional psychometric scales with neurophysiological measures such as EEG and Eye tracking to fully understand users’ cognition, emotion, and behavior. I have also a keen interest in developing and introducing robust data analysis methods into the field of NeuroIS. For example, I have employed advanced Machine Learning and Signal Processing models for analyzing massive EEG datasets.
While not working on NeuroIS, I read history and classic novels and enjoy playing and watching football (soccer), volleyball, snooker, and chess. I am also a huge advocate for the underrepresented groups in academia and strive to provide a safe and inclusive space for students in the classes that I teach. During my studies, I was the president of several student-run organizations and have been involved in building a positive community in the institutions that I have worked.
most of my research revolves around the endeavour to fully comprehend users’ interaction with technology. I primarily rely on theories from Cognitive Psychology and Neuroscience to explain information technology users’ behavior and put forward solutions to improve users’ performance. Towards this aim, I employ laboratory experiments to collect behavioral and neurophysiological data, develop survey tools, and analyze large panel data sets using state-of-art data analytic methods to uncover the effects of information technology (IT) artifacts on users.
For more information on my ongoing projects, please click on the diamond markers below.
Click here for my list of publications on Google Scholar.
Helping Users to handle IT Interruptions
In this paper, we propose that the executive functions framework can be used to explain how our brain handles IT interruptions. In two large-scale research projects, we aim at 1-understanding how our brain handles IT interruptions and 2-designing an IT-based mobile application that helps users to strengthen the executive functioning of their brain and improve their performance in the face of IT interruptions.
Disinformation on Social Media
Recent public discourse about the phenomenon of “fake news” has exposed the vulnerability of people to the widespread broadcast of misinformation on social media. In this project, my goal is to 1) identify the IT factors that contribute to users’ belief in manipulated information and 2) propose solutions to social media designers in the form of IT interventions to help users in identifying disinformation.
Cognitive Load as a Criteria for Designing Information Technology Artifacts
In this work, I measured users’ instantaneous cognitive load over an online shopping task and extracted three features: average load, accumulated load, and peak load. The traditional subjective workload was also measured in order to compare it with other metrics. Two task factors (task difficulty and task uncertainty) were manipulated in an online shopping task to test their effect on four different types of workload (three objective and one subjective). The results show that while all four measures were sensitive to task difficulty, accumulated load was the only one that could capture the effect of task uncertainty. We therefore suggested that accumulated load is the most comprehensive measure among these four because it simultaneously captures both workload and time dimensions.
The following is a link to a PDF version of my full CV.