Manlu Liu

Professor, MIS

Department of MIS, Marketing, and Analytics

Saunders College of Business

Program Director, MS in Business Analytics

manluliu@saunders.rit.edu

585-475-3984

Office Location

LOW-A317

Manlu Liu

Professor, MIS

Department of MIS, Marketing, and Analytics

Saunders College of Business

Program Director, MS in Business Analytics

Education

MBA, The Hong Kong University of Science and Technology (Hong Kong); Ph.D., University of Arizona

Bio

Manlu Liu is the Benjamin Forman Endowed Professor for Research and the Director of Master in Business Analytics Program at the Saunders College of Business at Rochester Institute of Technology (RIT). Dr. Liu received her Ph.D. degree in Management Information Systems from Eller College of Management at the University of Arizona. She obtained an MBA degree major in Accounting and Finance from the Hong Kong University of Science & Technology. She had six years of working experience as a consultant and venture capital analyst. She also had financial analyst working experience at the investment banking.

Prior to join Rochester Institute of Technology, Dr. Liu was an Associate Professor at the School of Management at Zhejiang University in China. Her research interests include AI and Business Intelligence, Big Data, Accounting and Finance Analytics, Open Innovation, Blockchain Technology, and Health Informatics. She started to examine community-based open source phenomenon in 2005. She is one of the researchers who introduced this phenomenon to MIS academic field and developed research models for community source evolution and sustainability. Dr. Liu is passionate about how data analytics applies to accounting and finance field. She co-initiated the Advanced Certificate of Accounting and Financial Analytics. She designed and developed the graduate level Accounting Analytics course. She is the college representative of AI Task Force at RIT. Her research has been published in leading academic journals, including Accounting, Organizations and Society, Journal of Strategic Information Systems, Information and Organization, European Accounting Review, Information Systems Journal, Communications of the ACM, Journal of Global Information Management, Decision Support Systems, Journal of Systems and Software, Electronic Commerce Research etc. She served as a Guest Co-Editor for Journal of Electronic Commerce Research in 2012-2013. She serves as the member of the Editorial Review Board for the following journals: Electronic Commerce Research, Journal of Electronic Commerce Research, Electronic Government: An International Journal, and International Journal of Electronic Finance. She has been serving as a program chair/session chair/program committee member for various international conferences including the International Conference on Information Systems (ICIS), the Americas Conference on Information Systems (AMCIS), the Southwest Decision Science Institute Conference (SWDSI), Human Computer Interaction (HCI) International Conference, SIGBPS – Blockchain and Financial Analytics etc. She is co-chairing the Workshop on E-Business (WeB) 2024.

manluliu@saunders.rit.edu

585-475-3984

Areas of Expertise

Community Based Open Source

Accounting and Financial Analytics

Blockchain and Auditing

Open Innovation in Health IT

Currently Teaching

ACCT-745

Accounting Information and Analytics

3 Credits

This course covers data management and data analysis in accounting. As such, it covers technical aspects of accounting data exploration, accounting data acquisition, and data warehouse structure. Additionally, students learn accounting data analysis and reporting by using techniques such as slicing, dicing and queries. The course also covers data visualization techniques in Big Data to conduct descriptive and predictive analysis. The overall goal is to provide data analytic skills in the accounting context. A specific goal is to acquire techniques of accounting data exploration, data management and data analysis. A final goal is to use these techniques to turn accounting data into actionable insights and recommendations for the organization's key decision-makers.

BANA-785

Business Analytics Experience

3 Credits

Students apply their mathematical, data analytic, and integrative business analytics skills in a complex project involving real or simulated data. Under the supervision of an advisor, students work in teams to perform a stipulated task/project and write a comprehensive report at the end of the experience. Subject to approval by the program director, an individual student internship/coop followed by an in-depth report may obtain equivalent credit.

MGIS-650

Introduction to Data Analytics and Business Intelligence

3 Credits

This course serves as an introduction to data analysis including both descriptive and inferential statistical techniques. Contemporary data analytics and business intelligence tools will be explored through realistic problem assignments.

MGIS-805

Advanced Data Analytics

3 Credits

This Ph.D. research methodology course will introduce students to contemporary and advanced analytics techniques related to data acquisition, data preparation, data mining, and data reporting. Students will engage in hands-on experience with different techniques and will demonstrate the ability to carry a research project on their own using a combination of techniques taught in class.

MGIS-811

Qualitative Research Methods

3 Credits

In this course, students learn and apply qualitative data collection and analysis methods in the context of business research. The course provides an overview of prominent qualitative research designs, including case study, field study, and ethnography. Students learn critical qualitative data collection techniques, including interviewing, field observation, and historical analysis. Finally, students explore different techniques for qualitative data analysis, including grounded theory methodology, thematic analysis, discourse analysis, and conversation analysis. Students will engage in hand-on experiences in each of the analytical methods to demonstrate skills in managing selected design, data collection, analysis and writing strategies of qualitative research.