The Rawls College of Business Administration

Texas Tech University


ISQS 7342-002, Fall 2011 (Subject to updates)

ADVANCED TOPICS IN MIS – Recent Research Trends in Business Intelligence


Instructor: Dr. Zhangxi Lin

Office hours: 9:30-11:30a MWTh or by appointment, BA 708

Class Meeting: 9:00-11:50a Friday, BA375


Course objectives:

  1. To address currently most viable subjects in business intelligence research, letting students follow the dynamics of the MIS research
  2. To improve student’s ability in identifying the most promising research topics from the real world and extract appropriate research issues under the guideline of relevant theories
  3. To allow students study and practice the most-widely applied research methodologies and techniques for innovative BI/MIS research
  4. To develop and build up a number of basic research skills, such as research reference material search and summary, logical thinking and analysis, problem solving, research notes writing, and paper development.


Keywords characterizing the class: open source, online reputation, digital market, open innovation, social network, business intelligence, information security, network privacy, knowledge management, Internet marketing, sentiment analysis


Course Structure:

In general, this course will emphasize on the application of quantitative methods to MIS research, including business intelligence, optimization, computer experimentation, and decision sciences. There are a series of lectures on the latest research issues, named as Research Highlight.



1. Assignments (20%, not including class discussions), each worth 2%. There will be combinations of different types of questions in each assignment:

1)       A summary from a reading material (2-3 page, 600-1200 words),

2)       A comprehensive summary based on a number of reading materials (3-6 page, 800-1500 words), or

3)       A research proposal, 1-2 pages plus references

2. Class participation (20%).  Involvement in class discussion and weekly short presentations if any.

3. A research proposal with clearly defined research issues, relevant literature and applicable theories (6-10 pages plus references, double spacing, 12 point). (20%)

4. A final version of the extended proposal or working paper with a twenty-minute in-class presentation (40%)


Grading criteria of the deliverables:

1.       Relevance to the theme of this seminar course

2.       Creativity and insightfulness of the research subject

3.       Comprehensiveness and broadness of the supporting information

4.       Quality writing and conciseness of expressions

5.       Logically well-structured and methodologically sound contents

6.       Timeliness  of the submission


University Policies

Requirements:  Please contact me if you have any special requirements, or if I need to make special accommodations for you during the semester.  I encourage you to visit with me about your progress in the course at any time.

Integrity.  Academic dishonesty will not be tolerated.  All students are required to adhere to the Texas Tech University Policy on Academic Honesty

Civility in the Classroom.  “Students are expected to assist in maintaining a classroom environment which is conducive to learning.  In order to assure that all students have an opportunity to gain from time spent in class, unless otherwise approved by the instructor, students are prohibited from using cellular phones or beepers, eating or drinking in class, making offensive remarks, reading newspapers, sleeping or engaging in any other form of distraction.  Inappropriate behavior in the classroom shall result in, minimally, a request to leave class.” 

ADA Requirements.  Classroom accommodations will be made for students with disabilities, if requested.

Religious Holidays.  A student who intends to observe a religious holy day should make that intention known to the instructor prior to an absence. A student who is absent from classes for the observance of a religious holy day shall be allowed to take an examination or complete an assignment scheduled for that day within a reasonable time after the absence.




Schedule (subject to updates)




Deliverables  (due a week after the assignment)

Aug 25, 2011





Research Highlight: E-Market Structure

Presentation: Tianxi Dong, “An Improved Pearson-CI Decision Model for Procurement Projects”



  1. Andrea M. Matwyshyn, Discussion of Online Display Advertising: Targeting and Obtrusiveness by Avi Goldfarb and Catherine Tucker, Marketing Science, Vol. 30, No. 3, MayJune 2011, pp. 409412.
  2. Leonard M. Lodish, Americus Reed II, “When Is Less More, and How Much More? Thoughts on the Psychological and Economic Implications of Online Targeting and Obtrusiveness,” Marketing Science, Vol. 30, No. 3, MayJune 2011, pp. 405408.
  3. Avi Goldfarb and Catherine Tucker, ‘Online Display Advertising: Targeting and Obtrusiveness,” Marketing Science, May/June 2011 30:389-404


References (2011 reading list):

  1. Hevner et al, “Design Science in Information Systems Research,” MISQ, v28, n1, 2004, pp.75-105.
  2. Grandon Gill and Anol Bhattacherjee, “Issues and Recommendations for MIS Research from an Informing Science Perspective,” MISQ, v33, n2, 2009, pp. 217-235.

References (2009 reading list):

  1. Pinsonneault, Alain, and Kraemer, Kenneth L. “Survey Research Methodology in Management Information Systems: An Assessment” Journal of Management Information Systems, Vol. 10 No. 2, Fall 1993 pp. 75 - 106
  2. Allen S. Lee, Richard L. Baskerville, “Generalizing Generalizability in Information Systems Research,” Information Systems Research, V14. September 2003, N3. pp 0221-0243
  3. Pierre Berthon, Leyland Pitt, Michael Ewing, Christopher L. Carr, “Potential Research Space in MIS: A Framework for Envisioning and Evaluating Research Replication, Extension, and Generation,” Information Systems Research, V13. December 2002, N4. pp 0416-0427
  5. Vasant Dhar, Arun Sundararajan, “Information Technologies in Business: A Blueprint for Education and Research,” Information Systems Research, Vol. 18, No. 2, June 2007, pp. 125–141

Assignment 1

1)       Read the papers in next week’s reading list

2)       List two possible IT research issues.

3)       Summarize one paper for the next week’s presentations.

4)       Find 2 most relevant research references to next week’s topic; draw a single summary with no more than five sentences; and provide a list of them.

5)       Prepare for a 15-minute presentation on the assigned readings


Sep 2, 2011



Topic 1 - E-market Structure


Research Highlight: Binjie Luo, “Price dispersion in e-market”



  1. Michael E. Poster, “Clusters and New Economics of Competition” Harvard Business Review, Nov-Dec 1998, 77-90. (Tianxi)
  2. J. Yannis Bakos, “Reducing Buyer Search Costs: Implications for Electronic Marketplaces,” Management Science, 1997 43:1676-1692. (Siming)
  3. Anindya Ghose, and Yuliang Yao, “Using Transaction Prices to Re-Examine Price Dispersion in Electronic Markets,” Information Systems Research, Vol. 22, No. 2, June 2011, pp. 269–288 (Ajay)
  4. Dewan, Rajiv, Bing Jin, and Abraham Seidmann, “Adoption of Internet-Based Product Customization and Pricing Strategies,” JMIS, v17, n2, 2000, 9-28. (Roozmehr)
  5. Erik Brynjolfsson, Yu (Jeffrey) Hu, and Michael D. Smith, “Long Tails vs. Superstars: The Effect of Information Technology on Product Variety and Sales Concentration Patterns,” Information Systems Research, Vol. 21, No. 4, December 2010, pp. 736–747 (Jason)



  1. James D. Dana, Jr., “Equilibrium price dispersion under demand uncertainty: the roles of costly capacity and market structure,” RAND Journal of Economics, Vol. 30, No. 4, Winter 1999, pp. 632–660.
  2. Rahul Telang, Uday Rajan, Tridas Mukhopadhyay, “Market Structure for Internet Search Engines”, JMIS, 21(2), 137-160, 2004.
  3. Eric K. Clemons, Il-Horn Hann, and Lorin M. Hitt, “Price Dispersion and Differentiation in Online Travel: An Empirical Investigation,” Management Science, Vol. 48, No. 4, April 2002. pp. 534–549.
  4. Zhangxi Lin, Dahui Li, Balaji Janamanchi, and Wayne Huang, “Reputation Distribution and Consumer-to-Consumer Online Auction Market Structure,” Decision Support Systems, 41 (2006) 435-448.
  5. Glenn C. Loury, “Market Structure and Innovation,” The Quarterly Journal of Economics, Vol. 93, No. 3 (Aug., 1979), pp. 395-410
  6. Salop, Steven C., “Monopolistic Competition with Outside Goods,” The Bell Journal of Economics 10(1), Spring 1979, 141-150.
  7. Ramesh Sankaranarayanan, and Arun Sundararajan, “Electronic Markets, Search Costs, and Firm Boundaries,” Information Systems Research, Vol. 21, No. 1, March 2010, pp. 154–169
  8. Martin Bichler, Alok Gupta, and Wolfgang Ketter, “Designing Smart Markets,” Information Systems Research, Vol. 21, No. 4, December 2010, pp. 688699
  9. Ramnath K. Chellappa, Raymond G. Sin,  and S. Siddarth, “Price Formats as a Source of Price Dispersion: A Study of Online and Offline Prices in the Domestic U.S. Airline Markets,” Information Systems Research 2011 22:83-98


Assignment 2

1)       Read the papers in next week’s reading list

2)       Summarize one paper in next week’s reading list. You must draw a short paragraph as your comments on the paper.

3)       Find 2 most relevant research references to next week’s topic; draw a single summary with no more than five sentences; and provide a list of them.

4)       Prepare for a 15-minute presentation.

5)       Identify one research issue from the industry, with a viable research problem.



Sep 9, 2011





Topic 2 – E-commerce: Reputation, Trust, and Risk



  1. Akerlof, G. (1970). The Market for “Lemons”: Quality Under Uncertainty and the Market Mechanism. Quarterly Journal of Economics, Vol. 84, 488-500. (Siming)
  2. Nelson Granados, Alok Gupta, and Robert J. Kauffman, “Information Transparency in Business-to-Consumer Markets: Concepts, Framework, and Research Agenda,” Information Systems Research, Vol. 21, No. 2, June 2010, pp. 207–226 (Alaa)
  3. Dawn G. Gregg and Judy E. Scott, “The Role of Reputation Systems in Reducing On-Line Auction Fraud,” International Journal of Electronic Commerce, Spring 2006, Vol. 10, No. 3, pp. 95–120. (Roozmehr)
  4. Mikhail I. Melnik and James Alm, “Does a Seller's Ecommerce Reputation Matter? Evidence from eBay Auctions,” The Journal of Industrial Economics, Vol. 50, No. 3 (Sep., 2002), pp. 337-349 (Tianxi)
  5. Michael Luca, “Reviews, Reputation, and Revenue: The Case of,” AER Accepted paper, Nov 2010 (Ajay)
  6. Jong-Seok Lee, and Dan Zhu, “Shilling Attack Detection—A New Approach for a Trustworthy Recommender System,” INFORMS Journal on Computing, Articles in Advance, pp. 1–15, 2011 (Jason)


  1. C. Dellarocas, Digitization of Word-of-Mouth: Promise and Challenges of Online Feedback Mechanisms, Management Science 49 (2003) 1407-1424.
  2. Standifird, S. S. (2001). Reputation and e-commerce: eBay auctions and the asymmetrical impact of positive and negative ratings. Journal of Management, 27(3), 279-295.
  3. S. Tadelis, “What's in a Name? Reputation as a Tradeable Asset,” American Economic Review, 89(3) (1999) 48-563.
  4. Fombrun, C., & Shanley, M. (1990). What's in a Name? Reputation Building and Corporate Strategy. Academy of Management Journal, 33(2), 233-258.
  5. Chrysanthos Dellarocas, “Research Note - How Often Should Reputation Mechanisms Update a Trader’s Reputation Profile?,” Information Systems Research, Vol. 17, No. 3, September 2006, pp. 271–285
  6. Xiaorui Hu, Zhangxi Lin, Andrew B. Whinston, and Han Zhang, “Hope or Hype: On the Viability of Escrow Services As Trusted Third Parties in Online Auction Environments” Information Systems Research, September 2004, 236-249.


Assignment 3

1)       Read the papers in next week’s reading list

2)       Summarize one paper in next week’s reading list.

3)       Prepare for a 15-minute presentation.

4)       Find 2 most relevant research references to next week’s topic; draw a single summary with no more than five sentences; and provide a list of them.

5)       Pick one of the topics to develop a one-page research proposal.


Sep 16, 2011



Topic 3 – Social Network & Open Innovation

Research Highlight: Jung Kim, “Apple’s AppStore”



  1. Henry W. Chesbrough, and Melissa M. Appleyard, “Open Innovation and Strategy,” CALIFORNIA MANAGEMENT REVIEW VOL. 50, NO. 1 FALL 2007. (Tianxi)
  2. Nobuyuki Hanaki, Alexander Peterhansl, Peter S. Dodds, Duncan J. Watts, “Cooperation in Evolving Social Networks,” Management Science, Vol. 53, No. 7, July 2007, pp. 1036–1050 (Alaa)
  3. De Liu, Gautam Ray, and Andrew B. Whinston, “The Interaction Between Knowledge Codification and Knowledge-Sharing Networks,” Information Systems Research, Vol. 21, No. 4, December 2010, pp. 892–906 (Siming)
  4. Xiaoqing Jing, and Jinhong Xie, “Group Buying: A New Mechanism for Selling Through Social Interactions,” MANAGEMENT SCIENCE, Vol. 57, No. 8, August 2011, pp. 1354–1372 (Roozmehr)
  5. Alberto Galasso, and Timothy S. Simcoe, “CEO Overconfidence and Innovation,” MANAGEMENT SCIENCE, Vol. 57, No. 8, August 2011, pp. 1469–1484 (Jason)
  6. Mihnea C. Moldoveanu, and Joel A. C. Baum, “ ‘I Think You Think I Think You’re Lying’: The Interactive Epistemology of Trust in Social Networks,” MANAGEMENT SCIENCE, Vol. 57, No. 2, February 2011, pp. 393–412 (Ajay)


  1. Bo Yang, William K. Cheung, and Jiming Liu, “Community Mining from Signed Social Networks,” IEEE Transactions on Knowledge and Data Engineering, V 19, N 10, October 2007
  2. Nicos Nicolaou, Sue Birley, “Social Networks in Organizational Emergence: The University Spinout Phenomenon,” Management Science, Vol. 49, No. 12, December 2003, pp. 1702–1725
  3. Wolf, Timo; Schröter, Adrian; Damian, Daniela; Panjer, Lucas D.; Nguyen, Thanh H. D. “Mining Task-Based Social Networks to Explore Collaboration in Software Teams,” IEEE Software, Jan/Feb2009, Vol. 26 Issue 1, p58-66
  4. Elia Zureik and Abbe Mowshowitz, “Consumer Power in the Digital Society,” October 2005/Vol. 48, No. 10 COMMUNICATIONS OF THE ACM
  5. Jiangtao Qiu and Zhangxi Lin, “A Framework for Exploring Organizational Structure in Dynamic Social Networks,” Decision Support Systems, Feb 2011


Assignment 4

1)       Read the papers in next week’s reading list

2)       Summarize one paper in next week’s reading list.

3)       Prepare for a 15-minute presentation.

4)       Find 3 most relevant research references to next week’s topic; draw a single summary with no more than five sentences; and provide a list of them.


Sep 23, 2011



Topic 4 – Business Analytics

Research Highlight: Qiwei Gan, “The ‘helpfulness’ of online user reviews”



  1. Jong-Seok Lee, and Dan Zhu, “When Costs Are Unequal and Unknown: A Subtree Grafting Approach for Unbalanced Data Classification,” Decision Sciences, accepted 2011. (Alaa)
  2. Yinghui (Catherine) Yang, “Web user behavioral profiling for user identification,” Decision Support Systems, 49 (2010) 261–271. (Tianxi)
  3. Yinghui (Catherine) Yang, Balaji Padmanabhan, “Toward user patterns for online security: Observation time and online user identification,” Decision Support Systems 48 (2010) 548–558. (Siming)
  4. Ahmed Abbasi, Zhu Zhang, David Zimbra, Hsinchun Chen, and Jay F. Nunamaker, Jr., “DETECTING FAKE WEBSITES: THE CONTRIBUTION OF STATISTICAL LEARNING THEORY,” MIS Quarterly Vol. 34 No. 3, pp. 435-461/September 2010. (Roosmehr)
  5. Xiao-Bai Li, and Sumit Sarkar, “Against Classification Attacks: A Decision Tree Pruning Approach to Privacy Protection in Data Mining,” OPERATIONS RESEARCH, Vol. 57, No. 6, November–December 2009, pp. 1496–1509. (Ajay)
  6. Maytal Saar-Tsechansky, and Foster Provost, “Handling Missing Values when Applying Classification Models,” Journal of Machine Learning Research 8 (2007) 1217-1250. (Jason)


  1. Pei-yu Chen, Gaurav Kataria, and Ramayya Krishnan, “CORRELATED FAILURES, DIVERSIFICATION, AND INFORMATION SECURITY RISK MANAGEMENT,” MIS Quarterly Vol. 35 No. 2 pp. 397-422/June 2011.
  2. Marcus A. Maloof, “Learning When Data Sets are Imbalanced and When Costs are Unequal and Unknown,” Workshop on Learning from Imbalanced Data Sets II, ICML, Washington DC, 2003.
  3. Radha Appan, and Zhangxi Lin, “Sellers in Online Auction Markets: Introducing a Feedback-Based Classification,” Journal of International Technology and Information Management, 15(1), 27-42, 2006.


Assignment 5

Same as the previous

+ Refine the research proposal.

Sep 30, 2011



Topic 5 – Text Mining & Content Analysis




  1. Laura J. Kornish, and Karl T. Ulrich, “Opportunity Spaces in Innovation: Empirical Analysis of Large Samples of Ideas”, MANAGEMENT SCIENCE, Vol. 57, No. 1, January 2011, pp. 107–128 (Roozmehr)
  2. Nikolay Archak, Anindya Ghose, Panagiotis G. Ipeirotis, “Deriving the Pricing Power of Product Features by Mining Consumer Reviews,” MANAGEMENT SCIENCE, Vol. 57, No. 8, August 2011, pp. 1485–1509 (Alaa)
  3. Douglas L. Dean, Paul Benjamin Lowry, and Sean Humpherys, “PROFILING THE RESEARCH PRODUCTIVITY OF TENURED INFORMATION SYSTEMS FACULTY AT U.S. INSTITUTIONS,” MIS Quarterly Vol. 35 No. 1 pp. 1-15/March 2011 (Jason)
  4. Ahmed Abbasi, and Hsinchun Chen, “CYBERGATE: A DESIGN FRAMEWORK AND SYSTEM FOR TEXT ANALYSIS OF COMPUTER-MEDIATED COMMUNICATION,” MIS Quarterly Vol. 32 No. 4, pp. 811-837/December 2008. (Tianxi)
  5. Paul A. Pavlou, Angelika Dimoka, “The Nature and Role of Feedback Text Comments in Online Marketplaces: Implications for Trust Building, Price Premiums, and Seller Differentiation,” Information Systems Research, Volume Number: 17, Issues: Dec. (Ajay)
  6. Fletcher H. Glancy, Surya B. Yadav, “A computational model for financial reporting fraud detection  Original Research Article,” Decision Support Systems, Volume 50, Issue 3, February 2011, Pages 595-601 (Siming)



  1. Dennis, A. R., Valacich, J. S., Fuller, M. A., and Schneider, C. 2006. “Research Standards for Promotion and Tenure in Information Systems,” MIS Quarterly (30:1), pp. 1-12.
  2. Qing Li, Yuanzhu Chen, “Personalized Text Snippet Extraction Using Statistical Language Models,” Pattern Recognition, Pattern Recognition 43 (2010) 378 - 386.
  3. Maytal Saar-Tsechansky, Prem Melville, and Foster Provost, “Active Feature-Value Acquisition,” MANAGEMENT SCIENCE, Vol. 55, No. 4, April 2009, pp. 664–684
  4. “LATENT SEMANTIC ANALYSIS” DSCI 5910 – Text Mining Notes, University of North Texas, Spring 2007
  5. David M. Blei, Andrew Y. Ng, and Michael I. Jordan, “Latent Dirichlet Allocation” Journal of Machine Learning Research 3 (2003) 993-1022
  6. Peter H. Kim, “When Private Beliefs Shape Collective Reality: The Effects of Beliefs About Coworkers on Group Discussion and Performance,” Management Science, Volume Number: 49, Issues: Six.


Useful links:


Twitter Sentiment


Assignment 6

Same as the previous


Oct 7, 2011



Topic 6 – Internet Marketing



  1. Vasant Dhar, Anindya Ghose, “Research Commentary - Sponsored Search and Market Efficiency,” Information Systems Research, Vol. 21, No. 4, December 2010, pp. 760–772 (Alaa, Roozmehr)
  2. Avi Goldfarb, Catherine Tucker, “Search Engine Advertising: Channel Substitution When Pricing Ads to Context,” MANAGEMENT SCIENCE, Vol. 57, No. 3, March 2011, pp. 458–470 ()
  3. Glenn J. Browne, Mitzi G. Pitts, and James C. Wetherbe, “Cognitive Stopping Rules for Terminating Information Search in Online Tasks,” MISQ, (31:1), 2007. (Jason, Ajay)
  4. Gediminas Adomavicius and Young Ok Kwon, “Improving Recommendation Diversity Using Ranking-Based Techniques,” working paper (2009). (Tianxi, Siming)



  1. Johnson, Eric J.; Moe, Wendy W.; Fader, Peter S.; Bellman, Steven; Lohse, Gerald L., “On the Depth and Dynamics of Online Search Behavior,”  Management Science, Mar2004, Vol. 50 Issue 3, p299-308
  2. Nanda Kumar, Izak Benbasat, “Research Note - The Influence of Recommendations and Consumer Reviews on Evaluations of Websites,” Information Systems Research, Vol. 17, No. 4, December 2006, pp. 425–439
  3. Dirk Lewandowski, “Web searching, search engines and Information Retrieval,” Information Services & Use 25 (2005), pp.137–147.
  4. Morahan-Martin, Janet M., “How Internet Users Find, Evaluate, and Use Online Health Information: A Cross-Cultural Review,” CyberPsychology & Behavior, Oct2004, Vol. 7 Issue 5, pp.497-510.
  5. Collier, Harry, and Stephen E. Arnold, “Search Engines: Evolution and diffusion,” January 31, 2003
  6. Feng, J., Bhargava, H. and Pennock, D. “Implementing Sponsored Search in Web Search Engines: Computational Evaluation of Alternative Mechanisms” INFORM Journal on Computing, Vol. 19, No. 1, Winter 2007, pp. 137–148.


Assignment 7

Same as the previous


Oct 14, 2011



Topic 7 –Market Intelligence



  1. Qing Li, Jia Wang, Yuanzhu Chen, Zhangxi Lin, “User Comments for News Recommendation in Forum-based Social Media,” Information Sciences 180 (2010) 4929–4939. (Alaa)
  2. Robert Zeithammer, and Christopher Adams, “The Sealed-Bid Abstraction in Online Auctions,” Marketing Science , Vol. 29, No. 6, November–December 2010, pp. 964–987 (Ajay, Jason)


Commentary a: Kannan Srinivasan, and Xin Wang, “Bidders’ Experience and Learning in Online Auctions: Issues and Implications,” Marketing Science, Vol. 29, No. 6, NovemberDecember 2010, pp. 988993.


Commentary b: Ali Hortaçsu, and Eric R. Nielsen, “Do Bids Equal Values on eBay?” Marketing Science, Vol. 29, No. 6, NovemberDecember 2010, pp. 994997


  1. Wan-Shiou Yang and Jia-Ben Dia, “Discovering cohesive subgroups from social networks for targeted advertising,” Expert Systems with Applications, Volume 34, Issue 3, April 2008, Pages 2029-2038. (Siming, Tianxi)
  2. Hongbin Cai, Yuyu Chen, Hanming Fang, “Observational Learning: Evidence from A Randomized Natural Field Experiment,” Working Paper 13516, to appear in American Economic Review,



  1. Zwick, Rami; Rapoport, Amnon; Lo, Alison King Chung; Muthukrishnan, A. V. “Consumer Sequential Search: Not Enough or Too Much?” Marketing Science, Fall2003, Vol. 22 Issue 4, p503-519
  2. Ali Hortaçsu, and Eric R. Nielsen, “Do Bids Equal Values on eBay?” Marketing Science, Vol. 29, No. 6, NovemberDecember 2010, pp. 994997
  3. Avi Goldfarb and Catherine Tucker, “Online Display Advertising: Targeting and Obtrusiveness,” working paper, February 2010
  4. Benjamin Van Roy, Xiang Yan, ” Manipulation Robustness of Collaborative Filtering,” MANAGEMENT SCIENCE, Vol. 56, No. 11, November 2010, pp. 1911–1929
  5. Avi Goldfarb, Catherine E. Tucker, “Privacy Regulation and Online Advertising,” MANAGEMENT SCIENCE, Vol. 57, No. 1, January 2011, pp. 57–71
  6. Xiaojing Yang, Robert E. Smith, “Modeling the Persuasive and Emotional Effects of Advertising Creativity,” Marketing Science, Vol. 28, No. 5, September–October 2009, pp. 935–949


Assignment 8

Same as the previous assignment


Oct 21, 2011



Proposal discussion


Research proposal due

Oct 28, 2011



Topic 8 –Financial Intelligence




  1. Paul C. Tetlock, Maytal Saar-Tsechansky, and Sofus Macskassy, “More Than Words: Quantifying Language to Measure Firms’ Fundamentals,” Journal of Finance, 2009 (Tianxi, Siming)
  2. Xue Bai, Manuel Nunez, and Jayant R. Kalagnanam, “Managing Data Quality Risk in Accounting Information Systems,” Information Systems Research, Articles in Advance, pp. 1–21, 2011 (Jason, Ajay)
  3. Bruno Biais, Christophe Bisière, and Chester Spatt, “Imperfect Competition in Financial Markets: An Empirical Study of Island and Nasdaq,” MANAGEMENT SCIENCE, Vol. 56, No. 12, December 2010, pp. 2237–2250 (Roozmehr, Alaa)




  1. Ramayya Krishnan, James Peters, Rema Padman, David Kaplan, “On Data Reliability Assessment in Accounting Information Systems,” Information Systems Research, Vol. 16, No. 3, September 2005, pp. 307–326
  2. Gurdip Bakshi, and Liuren Wu, “The Behavior of Risk and Market Prices of Risk Over the Nasdaq Bubble Period,” MANAGEMENT SCIENCE, Vol. 56, No. 12, December 2010, pp. 2251–2264
  3. Jingguo Wang, Aby Chaudhury, and H. Raghav Rao, “A Value-at-Risk Approach to Information Security Investment,” Information Systems Research, Vol. 19, No. 1, March 2008, pp. 106–120
  4. Adam Fadlalla, and Chien-Hua Lin, “An Analysis of the Applications of Neural Networks in Finance,” INTERFACES 31: 4 July–August 2001 (pp. 112–122)
  5. Nhien-An Le-Khac, Sammer Markos, and Mohand-Tahar Kechadi, “Towards a New Data Mining-Based Approach for Anti-Money Laundering in an International Investment Bank,” ICDF2C 2009, LNICST 31, pp. 77–84, 2010.



Assignment 9

Same as the previous assignment


Nov 4, 2011



Topic 9 -  Computational Finance – Credit Scoring in the i-Age


ISQS 7339 term project: Credit Scoring for Online Firms


¨  Online market providers are now considering sponsoring their loyal online firms with a credit assurance program. This will resolve the risk when the project contracted by two online firms fails.

¨  Goal: To set up a model to estimate the credit points for each firm, and set a credit line for each project the firm involved.



A dataset from an e-commerce company is available for credit scoring if anyone will be interested in. Check \\TechShare\coba\d\isqs6347\isqs7339\Term%20Project




  1. Bee Wah Yap, Seng Huat Ong, Nor Huselina Mohamed Husain, “Using data mining to improve assessment of credit worthiness via credit scoring models,” Expert Systems with Applications 38 (2011) 13274–13283. (Tina & Tina)
  2. Ray Tsaiha, Yu-Jane Liu, Wenching Liu, Yu-Ling Lien, “Credit scoring system for small business loans,” Decision Support Systems 38 (2004) 91– 99 (Roozmehr & Alaa)
  3. Xiaohong Chen, Xiaoding Wang, Desheng Dash Wu, “Credit risk measurement and early warning of SMEs: An empirical study of listed SMEs in China,” Decision Support Systems 49 (2010) 301–310 (Siming)
  4. Michael Doumpos, and Constantin Zopounidis, “A Multicriteria Outranking Modeling Approach for Credit Rating,” Decision Sciences, Volume 42 Number 3, August 2011 (Jason & Ajay)




1.       Vincenzo Pacelli, Michele Azzollini, “An Artificial Neural Network Approach for Credit Risk Management,” Journal of Intelligent Learning Systems and Applications, 2011, 3, 103-112

2.       Nan-Chen Hsieh, “Hybrid mining approach in the design of credit scoring models,” Expert Systems with Applications 28 (2005) 655–665


(See more in the subdirectory of \references)



Assignment 10

Same as the previous assignment


Nov 11, 2011



Topic 10 – Business Optimization



  1. Ritu Agarwal, Guodong (Gordon) Gao, Catherine DesRoches, and Ashish K. Jha, “The Digital Transformation of Healthcare: Current Status and the Road Ahead,” Information Systems Research, Vol. 21, No. 4, December 2010, pp. 796–809
  2. Balaji Padmanabhan, and Alexander Tuzhilin, “On the Use of Optimization for Data Mining: Theoretical Interactions and eCRM Opportunities,” Management Science, Vol. 49, No. 10, October 2003, pp. 1327–1343
  3. KAIQUAN XU, “MINING and ANALYZING CUSTOMER OPINIONS/SENTIMENTS of WEB 2.0 for BUSINESS APPLICATIONS,” PhD Dissertation, Advisor: Stephen Liao, defended in July 2011, City University of Hong Kong.



Sudheer Chava, Catalina Stefanescu, and Stuart Turnbull, “Modeling the Loss Distribution,” Management Science, Vol. 57, No. 7, July 2011, pp. 1267–1287


Assignment 11

Same as the previous assignment


Nov 18, 2011



Topic 11 –  Miscellaneous Topics – the Latest ITs



  1. Susarla, Anjana, Barua, Anitesh, and Whinston, Andrew B., “Multitask Agency, Modular Architecture, and Task Disaggregation in SaaS,” Journal of Management Information Systems; Spring2010, Vol. 26 Issue 4, p87-117 (Tianxi, Jason)
  2. Surajit Chaudhuri, Umeshwar Dayal, and Vivek Narasayya, “An Overview of Business Intelligence Technology,” Communications of the ACM, Aug2011, Vol. 54 Issue 8, p88-98 (Roozmehr)
  3. ARMBRUST, MICHAEL et al. “A View of Cloud Computing,” Communications of the ACM, Apr2010, Vol. 53 Issue 4, p50-58 (Alaa)
  4. Luca de Al faro, Ashutosh Kulshreshtha, Ian Pye, and B. Thomas Adler, “Reputation Systems for Open Collaboration,” Communications of the ACM, Aug2011, Vol. 54 Issue 8, p81-87 (Ajay)
  5. Savage, Neil, “Twitter as Medium and Message,” Communications of the ACM, Mar2011, Vol. 54 Issue 3, p18-20 (Siming)



  1. N. Nan. “Capturing Bottom-Up IT Use Processes: A Complex Adaptive Systems Model,” MIS Quarterly, 35, 2, 2011, pp. 505-532. (received the Price College of Business Dean’s Excellence Research Paper Award).
  2. Massimo Massa, and Andrei Simonov, “Experimentation in Financial Markets,” Management Science, Vol. 55, No. 8, August 2009, pp. 13771390



Nov 25, 2011


No class

Dec 02, 2011

Term Paper Presentation



Dec 12, 2009


Term paper/report due