The Quality Analytics Consortium (QAC) is an industrial affiliate program that connects organizations with leading quality analytics research faculty and students within the Pamplin College of Business. Pooling the resources of multiple quality-focused organizations, QAC can provide consortium members with affordable access to valuable reports from Virginia Tech’s proprietary internal quality analytics software tools – PamTAT and PamTag. Under sponsored-research contracts, members may sponsor the acquisition of proprietary commercial datasets consisting of millions of industry-specific online discussions, or may supply their own internal textual sources, such as reviews and emails, for quality scoring and analysis. Consortium members benefit from Virginia Tech’s nationally recognized expertise in defect discovery and innovation opportunity identification.

QAC is supported by the Pamplin College’s Center for Business Intelligence and Analytics. QAC’s research priorities are guided by an Advisory Tier of Consortium Members. A variety of membership tier levels with varying benefits are available to cater to different budgets and needs.

For more information, contact Dr. Alan Abrahams, Associate Professor, Business Information Technology, or 540-231-5887.

Smoke Words: Media Mining to Find Vehicle Defects  - Can social media postings by consumers be a source of useful information about vehicle safety and performance defects for automobile manufacturers?

Researchers Mine Online Consumer Reviews to Identify Unsafe Toys - A Virginia Tech research project led by Alan Abrahams has found that text mining can help researchers make more effective use of the data in millions of consumer reviews posted online to identify toys with potential hazards.

Academic Publications

America Business Law Journal

Healthy Predictions? Questions for Data Analytics in Healthcare

Janine S. Hiller

American Business Law Journal

Volume 53, Issue 2, pages 251-314, Summer 2016

Intelligence and Security Informatics

Predicting Vehicle Recalls with User-Generated Contents: A Text Mining Approach

X. Zhang, S. Niu, D. Zhang, G.A. Wang, W. Fan (2015).

Intelligence and Security Informatics,

Lecture Notes in Computer Science Volume 9074, pp 41-50

DOI: 10.1007/978-3-319-18455-5_3


Production and Operations Management

An Integrated Text Analytic Framework for Product Defect Discovery

Production & Operations Management  Published online 3 Nov 2014.

DOI: 10.1111/poms.12303

5-year ISI impact factor (2013):  2.378.

Production and Operations Management is among Business Week’s 20 Premier Journals and among Financial Times Research’s 45 Premier Journals.

Decision Support Systems

What’s buzzing in the blizzard of buzz? Automotive component isolation in social media postings.

Abrahams AS, Jiao J, Fan W, Wang GA, Zhang Z (2013).

Decision Support Systems, 55 (4) 871-882.

DOI: 10.1016/j.dss.2012.12.023

5-year ISI impact factor (2013): 2.651.

Decision Support Systems

Vehicle Defect Discovery from Social Media.

Abrahams AS, Jiao J, Wang GA, and Fan W (2012).

Decision Support Systems, 54 (1) 87-97.

DOI: 10.1016/j.dss.2012.04.005

5-year ISI impact factor (2013): 2.651.

Decision Support Systems

A Decision Support System for Patient Scheduling in Travel Vaccine Administration

Alan S. Abrahams, Cliff T. Ragsdale

Decision Support Systems

Volume 54, Issue 1, pages 215-225

Popular Press

The New York times logo
Automotive News logo

Other Faculty Research

  • “The Proximity Paradox: Why People Donate Less to Those “Close to Home”, J Jiang

A review of how geographic distance affects donations using a data collected from Findings: a counterintuitive result that people donate less to beneficiaries in close proximity (e.g., in the same zip code) than more distant ones (e.g., those in different zip codes).

  • “The Role of Feedback in Crowdsourcing Contest: A Theoretical and Empirical Study”, J Jiang

Development of a game-theoretic model of feedback in innovation contests. We show that feedback plays an informational role in mitigating the information asymmetry between the seeker and solvers, thereby inducing solvers to increase their effort level. We also show that the incentive mix of the contest (including contest reward, solver expertise, and solver population) has a direct effect on solver effort; that is, solvers’ effort level increases with the contest reward and solver expertise but decreases with solver population.

Interestingly, by endogenizing the seeker’s choice of giving feedback, we find that the seeker gives out more feedback comments when contest reward is higher, solver expertise is higher and solver population is smaller. Thus, the incentive mix also has an indirect effect on solvers’ effort level through feedback volume, indicating that feedback also has a mediating role in the relationship between the incentive mix and solvers’ effort.

  • “Do Customer Reviews Matter in Product Innovation – An Empirical Study of the Google Play Store”, Z. Qiao, A. Wang, P. Fan

This study investigates how customer knowledge impacts product innovation using data collected from Google Play store. Guided by Elaboration Likelihood Model of persuasion theory, our research shows that long and easy-to-read user reviews with mildly reviews can increase the frequency of product innovation activities. Our findings highlight the need for researchers to explore user reviews in the context of customer-driven product innovation. (Runner for Best Paper Award in Pre-ICIS SIGDSA Workshop)

  • “Identify Product Defects from User Complaints Using a Bayesian Mixture Model”, X. Zhang, Z. Qiao, P. Fan, A. Wang

This research develops a Bayesian Mixture Model to identify the most complained issues of a product from vast volume of customer reviews. Product defect is taken as a latent variable, and user reviews are used as observations in this model. Multinomial distribution and Gaussian distribution are incorporated to formulate the key entities of customer reviews, such as symptom words and incident date. EM algorithm is applied to infer the probability of defects, their most probable associated entities, and the representative complaints of defects. The evaluation results demonstrate that the proposed model is effective in finding essential product defect information from a large number of user complaints.

  • “Project Communications on Crowdfunding Success: An Empirical Study Based on Elaboration Likelihood Model”, J. Zhou, P. Fan, A. Wang, L. Wallace

This study introduces a dual-processing theory – the Elaboration Likelihood Model (ELM) – into crowdfunding domain. We employ ELM and text mining techniques to investigate influential characteristics related to funding success and to better understand the information processing procedures used by backers to make funding decisions. First, we identify three argument quality variables (length, readability, and sentiment) and two source credibility variables (past experience and past expertise) that are influential on funding success through central and peripheral routes. Second, we find a positive interaction effect on funding success between argument quality and source credibility. Third, we find a substitution effect on funding success between information disclosed in text and multimedia content. Fourth, we find that backer involvement has positive moderating effects on the both central and peripheral routes. Our results are robust across different measures of funding success (funding status, funding ratio, and funds pledged) and a variety of settings. Additionally, we reconcile the mixed results in the literature regarding the influence of duration on funding success. Specifically, we find that duration has positive impacts on successful projects but negative impacts on failed projects, suggesting that successfully funded projects have different behavior than failed projects.

  • “An Examination of the Readability of Tax Footnotes: Determinants and Implications for the Valuation of Tax Avoidance”, P. Fan, K. Inger, M. Meckfessel, J. Zhou

This paper investigates the information provided to stakeholders in corporate disclosures by examining the readability of tax footnotes. We find a positive association between tax avoidance and tax footnote readability for firms with relatively low levels of tax avoidance, suggesting managers highlight good performance by providing straightforward disclosures and conceal poor performance by providing information that is difficult to process. In contrast, there is no association between tax avoidance and tax footnote readability in firms with relatively high levels of tax avoidance. Consistent with these results, we find that investors discount (place a premium on) tax avoidance when the tax footnote is harder to read in low (high) avoidance firms. We provide evidence that the readability of the tax footnote has informational value beyond the overall readability of the annual report.

  • A Comparative Analysis of Major Online Review Sites: Implications for Social Media Analytics in Hospitality and Tourism”, P. Xiang, Y. Ma, Q. Du, P. Fan

The growing impact of user-generated content especially online reviews has been widely documented in hospitality and tourism. However, existing studies tend to use single data sources and the quality of data is anecdotal or based upon popularity of the websites. This project compares information quality of three major review sites, namely Tripadvisor, Expedia and Yelp, using the entire hotel population in Manhattan, NYC. We examined information quality in terms of the site’s representation of the hotel product from both the supply and demand sides. Online reviews’ linguistic features, prominent topics, distribution of helpful reviews, review features that contribute to satisfaction rating are compared across the three sites. Results clearly show the consistency and discrepancies in consumers’ experience and evaluation of the hotel product. This study offers implications for developing social media analytics in hospitality and tourism.

I. Lanham, M., Badinelli, R. “Developing a Rebalancing Parameter Table for Binary Classification Modeling.”

2. Lanham, M., Badinelli, R. “Merging Business KPIs With Predictive Model KPTs for Binary Classification Model Selection.”

I. Abrahams, A., and C. Ragsdale, “A Decision Support System for Patient Scheduling in Travel Vaccine Administration,” Decision Support Systems, Vol. 54, No. 1, pp. 215-225, 2012.

I. Winkler M, Abrahams AS, Gruss R, and Ehsani J (2016). Toy Safety Surveillance from Online Reviews. Decision Support Systems, Forthcoming. Accepted 20 May 2016.  5-year ISI Impact Factor (2015): 3.271

2. “Computational Rhetoric via Sequence Alignment: An Innovative Text Analytics Approach for Quantifying Persuasion” Şeref, MMH, Şeref, O, Abrahams, A, Warnick, Q, Hill, S. (Presented at the 2014 CBIA Workshop)

We develop a new methodology to identify rhetorical moves in large text data. We perform an initial qualitative analysis of a random sample of text to train our tagging algorithm. We are then able to quickly tag large amounts of text and therefore quantify the number of moves for different types of persuasion. This quantitative data can then be used for classification approaches in understanding and predicting decision behaviors. We demonstrate our method by analyzing pitch arguments used to justify stock decisions in an online community.

3. “Misdirection in Tax Reports for Corporate Inversion” MMH Seref, D Salbador. (Presented at the 2015 CBIA Symposium on Analytics)

We use new developments in text analytics, including computational rhetoric, to identify patterns of language in tax reports that may indicate compliance or fraud.

4. “Computational Rhetoric via Sequence Alignment: An Innovative Text Analytics Approach for Quantifying Persuasion” Şeref, MMH, Şeref, O, Abrahams, A, Warnick, Q, Hill, S.

5. “Mapping the Defect Genome: Improving Automated Defect Detection from Big Data using Techniques from Computational Biology” A Abrahams, O Seref, MMH Seref.

6. “A Song is Worth 1000 Words: Evaluating Potential Country Song Hits using Computational Rhetoric” C Ragsdale, MMH Seref, O Seref, A Abrahams.

7. “Critical Analysis of Accounting Reports: Do the Words Match the Numbers?” MMH Seref, J Brozovsky.

8. “Impact of Financial Media on IPO Investment Decisions” MMH Seref, R Barkhi.

9. “Misdirection in Tax Reports for Corporate Inversion” MMH Seref, D Salbador.

Research Support for Faculty

CBIA has limited funding for undergraduate and graduate research assistants. Assistants must be supervised by a faculty member in the Pamplin College of Business and the research must be related to business intelligence and analytics.

The process to request funding for undergraduate student research assistance is as follows:

1. Faculty members must complete the appropriate request form below. Either form can be returned electronically to

2. Upon notification of the grant award, the faculty member will work with his/her home department to hire the students. The faculty member’s home department will handle all hiring documents. At the end of the student’s employment period the faculty member will submit a project report (including student compensation details) to the department head and the center director. Upon on receipt of the project report the center director will notify the college accountant to transfer the grant funds to the home department.

Undergraduate Request Form

Pamplin faculty may apply for a grant in the amount of $2,000 to hire undergraduate and/or graduate student(s) to assist with research. The total grant must not exceed $2,000 for the above referenced fiscal year. To qualify, the faculty member’s home department must agree to provide 30% of the wage costs using departmental funding and/or the university work-study program. A report concerning research outcomes associated with the grant must be submitted within one year of the award and will be used to determine future grant awards. Undergraduate students must limit class enrollment to no more than 12 credit hours during the semester or session in which they are employed to assist faculty members with research. Exceptions to the enrollment limit must be submitted to the hiring faculty member and reviewed/approved by the department head and center director.

Graduate Request Form

The Center for Business Intelligence & Analytics (CBIA) has limited funding for Graduate Research Assistantships (GRA’s).  The GRA must supervised by a faculty member in the Pamplin College of Business and the research must be related to business intelligence and analytics.  Pamplin faculty may apply for full or part-time GRA funding.

CBIA’s first preference will be to fund graduate students pursuing graduate degrees in the Pamplin College of Business.  Tuition and fee waivers are limited. It is CBIA’s expectation that the faculty member’s department may need to cover the tuition and fee waiver. A report concerning research outcomes associated with the GRA’s research must be submitted at the end of the academic year and has implications for future funding.

For additional questions, contact Dr. Oldham at