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Chih-Hao (Justin) Ku

Title: Assistant Professor

Department: Information Technology and Decision Sciences

College: College of Business

Curriculum Vitae

Curriculum Vitae Link

Education

  • MS, Claremont Graduate University, 2012
    Major: Information Systems and Technology
  • PhD, Claremont Graduate University, 2012
    Major: Information Systems and Technology

Current Scheduled Teaching

DSCI 5360.006Data VisualizationSpring 2025
DSCI 5360.007Data VisualizationSpring 2025
DSCI 5360.003Data VisualizationFall 2024
DSCI 5360.005Data VisualizationFall 2024

Previous Scheduled Teaching

DSCI 5360.006Data VisualizationSpring 2024 SPOT
DSCI 5360.007Data VisualizationSpring 2024 SPOT
DSCI 5360.003Data VisualizationFall 2023 SPOT
DSCI 5360.005Data VisualizationFall 2023 SPOT

Published Intellectual Contributions

    Book Chapter

  • Ku, C., Iriberri, A., Jena, G. (2016). Visual analytics for crime analysis and decision support. Data Mining Trends and Applications in Criminal Science and Investigations. 53--81. IGI Global.
  • Conference Proceeding

  • Chen, C., Chang, Y., Ku, C. (2022). Deep Neural Network-based Question Answering System for Customer Relationship Management. PACIS 2022 Proceedings. 62
  • Firoozi, M., Ku, C. (2019). Accountability on social media: CEO narratives and stakeholders' responses at the time of a crisis. https://www.uoguelph.ca/lang/news/2019/12/accounting-symposium-brings-together-faculty-across-world
  • Ku, C., Chang, Y., Wang, Y., Chen, C., Hsiao, S. (2019). Artificial intelligence and visual analytics: a deep-learning approach to analyze hotel reviews & responses. 52nd Annual Hawaii International Conference on System Sciences (HICSS).
  • Hsiao, S., Kao, T., Su, H., Ku, C. (2019). Effect of Social Influencer on Crowdfunding Project Efficiency. http://doti.is-research.com/
  • Ku, C., Chang, Y. (2016). Natural Language Processing: From Text Mining to Social Media Analysis.
  • Ku, C., Kwak, M., Yurov, K.M., Yurova, Y.V. (2014). A Study of the Influence of Gaming Behavior on Academic Performance of IT College Students.. AMCIS.
  • Ku, C., Leroy, G. (2013). Automated crime report analysis and classification for e-government and decision support. Proceedings of the 14th Annual International Conference on Digital Government Research. 18--27.
  • Ku, C., Nguyen, J.H., Leroy, G. (2012). TASC-Crime report visualization for investigative analysis: A case study. 2012 IEEE 13th International Conference on Information Reuse \& Integration (IRI). 466--473.
  • Ku, C., Iriberri, A., Leroy, G. (2008). Crime information extraction from police and witness narrative reports. 2008 IEEE Conference on Technologies for Homeland Security. 193--198.
  • Iriberri, A., Ku, C., Leroy, G. (2008). Enabling synergy between psychology and natural language processing for e-Government: crime reporting and investigative interview system.. DG. O. 399--400.
  • Ku, C., Iriberri, A., Leroy, G. (2008). Natural language processing and e-government: crime information extraction from heterogeneous data sources. Proceedings of the 2008 international conference on Digital government research. 162--170.
  • Journal Article

  • Ku, C., Chang, Y., Yichuan, W. (2024). How to Strategically Respond to Online Customer Reviews - A Strategy-Aware Deep Learning and Visual Analytics Approach. Information & Management.
  • Firoozi, M., Ku, C. (2023). Corporate accountability during crisis in the digitized era. Accounting, Auditing & Accountability Journal. 36 (3) 933--964.
  • Kao, T., Hsiao, S., Su, H., Ku, C. (2022). Deriving Execution Effectiveness of Crowdfunding Projects from the Fundraiser Network. Journal of Management Information Systems. 39 (1) 276--301.
  • Chang, Y., Ku, C., Le Nguyen, D. (2022). Predicting aspect-based sentiment using deep learning and information visualization: The impact of COVID-19 on the airline industry. Information & Management. 59 (2) 103587.
  • Zhu, J., Chang, Y., Ku, C., Li, S., Chen, C. (2021). Online critical review classification in response strategy and service provider rating: Algorithms from heuristic processing, sentiment analysis to deep learning. Journal of Business Research. 129 860--877.
  • Chang, Y., Ku, C., Chen, C. (2020). Using deep learning and visual analytics to explore hotel reviews and responses. Tourism Management.
  • Chang, Y., Ku, C., Chen, C. (2019). Social media analytics: Extracting and visualizing Hilton hotel ratings and reviews from TripAdvisor. International Journal of Information Management. 48 263--279.
  • Ku, C., Firoozi, M. (2019). The use of crowdsourcing and social media in accounting research. Journal of Information Systems. 33 (1) 85--111.
  • Ku, C., Leroy, G. (2014). A decision support system: Automated crime report analysis and classification for e-government. Government Information Quarterly. 31 (4) 534--544.
  • Yurov, K.M., Yurova, Y.V., Kwak, M., Ku, C. (2014). The effect of psychological and environmental factors on academic performance of video gamers. Issues in Information Systems.
  • Ku, C., Leroy, G. (2011). A crime reports analysis system to identify related crimes. Journal of the American Society for Information Science and Technology. 62 (8) 1533--1547.

Contracts, Grants and Sponsored Research

    Fellowship

  • Ku, C. (Other), "Doctoral Student Fellowship," sponsored by Claremont Graduate University, Other, $50000 Funded. (2007 - 2012).
  • Grant - Research

  • Ku, C. (Principal), "Faculty Scholarship Initiative (FSI) Grant," sponsored by Cleveland State University, Other, $5000 Funded. (2022 - 2023).
  • Ku, C., "Seed Grant," sponsored by Lawrence Technological University, Other, $5000 Funded. (2016 - 2017).
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Overall
Summative Rating
Challenge and
Engagement Index
Response Rate

out of 5

out of 7
%
of
students responded
  • Overall Summative Rating (median):
    This rating represents the combined responses of students to the four global summative items and is presented to provide an overall index of the class’s quality. Overall summative statements include the following (response options include a Likert scale ranging from 5 = Excellent, 3 = Good, and 1= Very poor):
    • The course as a whole was
    • The course content was
    • The instructor’s contribution to the course was
    • The instructor’s effectiveness in teaching the subject matter was
  • Challenge and Engagement Index:
    This rating combines student responses to several SPOT items relating to how academically challenging students found the course to be and how engaged they were. Challenge and Engagement Index items include the following (response options include a Likert scale ranging from 7 = Much higher, 4 = Average, and 1 = Much lower):
    • Do you expect your grade in this course to be
    • The intellectual challenge presented was
    • The amount of effort you put into this course was
    • The amount of effort to succeed in this course was
    • Your involvement in course (doing assignments, attending classes, etc.) was
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