Accounting

T. Blackburne and Quinn, P., 鈥Disclosure Speed: Evidence from Nonpublic SEC Investigations鈥, The Accounting Review, 2022.
T. Blackburne and Quinn, P., 鈥Disclosure Speed: Evidence from Nonpublic SEC Investigations鈥, The Accounting Review, vol. 98, no. 1, pp. 55-82, 2022.
T. Blackburne, Armstrong, C., and Quinn, P., 鈥Are CEO's purchases more profitable than they appear?鈥, Journal of Accounting and Economics, vol. 71, no. 2-3, 2021.
T. Blackburne, Bernard, D., and Thornock, J., 鈥Information flows among rivals and corporate investment鈥, Journal of Financial Economics, vol. 136, no. 3, pp. 760-779, 2020.
T. Blackburne, Kepler, J., Quinn, P., and Taylor, D., 鈥Undisclosed SEC Investigations鈥, Management Science, 2020.
T. Blackburne and Armstrong, C., 鈥Discussion of "Do Voting Rights Matter? Evidence from the Adoption of Equity-Based Compensation Plans."鈥, Contemporary Accounting Research, vol. 29, pp. 1237-1248, 2012.
B. Marshall, Mortenson, K., Bourne, A., and Price, K., 鈥Visualizing Basic Accounting Flows: Does XBRL + Model + Animation = Understanding?鈥, International Journal of Digital Accounting Research, vol. 10, pp. 27-54, 2010.
C. Brown and Coakley, J., 鈥Financial neural Network applications: 1998-1999 update鈥, New Review of Applied Expert Systems and Emerging Technologies, vol. 7, pp. 167-182, 2001.
J. Coakley and Brown, C., 鈥Artificial Neural Networks in Accounting and Finance: Modeling Issues鈥, International Journal of Intelligent Systems in Accounting Finance and Management, vol. 9, no. 2, pp. 119-144, 2000.
C. Brown, Coakley, J., and Phillips, M. Ellen, 鈥Neural Networks Enter the World of Management Accounting鈥, Management Accounting, vol. LXXVI, no. 11, pp. 51-53, 56-57, 1995.
C. Brown, Coakley, J., and Phillips, M. Ellen, 鈥Neural Networks: Nuts and Bolts鈥, Management Accounting, vol. LXXVI, no. 11, pp. 54-55, 1995.
C. Brown, Coakley, J., and Eining, M. M., 鈥Conference Report: The Fourth International Symposium on Intelligent Systems in Accounting, Finance and Management鈥, International Journal of Intelligent Systems in Accounting Finance and Management, vol. 3, no. 3, pp. 223-235, 1994.
J. Coakley and Brown, C., 鈥Artificial Neural Networks Applied to Ratio Analysis in the Analytical Review Process鈥, International Journal of Intelligent Systems in Accounting, Finance and Management, vol. 2, no. 1, pp. 19-39, 1993.
J. Deng and Yang, J., 鈥Corporate Reputation and Hedging Activities鈥, Accounting and Finance, vol. 63, no. S1, pp. 1223-1247, 2023.
J. Deng and Laux, R. C., 鈥Income Shifting and U.S. International Trade in Goods Statistics鈥, Journal of Accounting and Public Policy, vol. 40, no. 5, 2021.
J. Deng, Steele, L., Lynch, D., and Gaertner, F. B., 鈥Proprietary Costs and the Reporting of Segment-level Tax Expense鈥, Journal of the American Taxation Association, vol. 43, no. 1, pp. 1-26, 2021.
J. Deng, 鈥Foreign Exchange Risk, Hedging, and Tax-Motivated Outbound Income Shifting鈥, Journal of Accounting Research, vol. 58, no. 4, pp. 953-987, 2020.
J. Elston and Weidinger, A., 鈥Internationalization and Regional Entrepreneurship in China鈥, Small Business Economics, 2019.
J. Elston, Chen, S., and Weidinger, A., 鈥The role of informal capital on new venture formation and growth in China鈥, Small Business Economics, vol. 46, no. 1, pp. 79-91, 2016.
M. Behm, Veltri, A., and Kleinsorge, I., 鈥The Cost of Safety: Cost Analysis Model鈥, Professional Safety, vol. 49, no. 4, pp. 22-29, 2004.
J. Drexler and Kleinsorge, I., 鈥Using total quality processes and learning outcome assessments to develop management curricula鈥, Journal of Management Education, vol. 24, no. 2, pp. 167-182, 2000.
I. Kleinsorge, 鈥A Benchmarking Study of the Current Practices with Regard to the Role of the Quantitative Curriculum in Business Schools鈥, International Journal of Operations and Quantitative Management, vol. 3, no. 2, pp. 125-138, 1997.
I. Kleinsorge, 鈥A Multicourse Practice Set: The Ultimate 'Messy' Problem鈥, Journal of Private Enterprise, vol. 7, no. 2, 1997.
I. Kleinsorge, 鈥Lessons Learned Using Total Quality Concepts for Accounting Curriculum Development鈥, vol. 2, no. 2, pp. 185-198, 1997.
I. Kleinsorge, 鈥Linking Accounting Courses with an Unstructured Project鈥, vol. 2, no. 2, pp. 199-211, 1997.
I. Kleinsorge, 鈥Financial and Efficiency Differences in Family-Owned and Non-Family Owned Nursing Homes An 麻豆传媒高清 Study鈥, Family Business Review, 1994.
H. Koenig and Kleinsorge, I., 鈥Perceptual Measures of Quality: A Tool to Improve Nursing Home Systems鈥, Hospital & Health Services Administration, vol. 39, no. 4, pp. 487-503, 1994.
I. Kleinsorge, 鈥TQM: Are Cost Accountants Meeting The Challenge鈥, Management Accounting, vol. 75, no. 10, pp. 65-67, 1994.
H. Koenig and Kleinsorge, I., 鈥The Silent Customers: Measuring Customer Satisfaction in Nursing Homes鈥, Journal of Health Care Marketing, vol. 11, no. 4, pp. 2-13, 1991.
B. Marshall, Curry, M., Correia, J., and Crossler, R. E., 鈥Machine Learning and Survey-based Predictors of InfoSec Non-Compliance鈥, ACM Transactions on Management Information Systems, vol. 13, no. 2, pp. 1-20, 2021.
B. Marshall, Curry, M., Correia, J., and Crossler, R. E., 鈥Machine Learning and Survey-based Predictors of InfoSec Non-Compliance鈥, ACM Transactions on Management Information Systems, 2021.
M. Curry, Marshall, B., Correia, J., and Crossler, R. E., 鈥InfoSec Process Action Model (IPAM): Targeting Insider's Weak Password Behavior鈥, Journal of Information Systems, vol. 33, no. 3, pp. 201-225, 2019.
M. Curry, Marshall, B., Crossler, R. E., and Correia, J., 鈥InfoSec Process Action Model (IPAM): Systematically Addressing Individual Security Behavior鈥, Data Base for Advances in Information Systems, vol. 49, no. SI, 2018.
M. Curry, Marshall, B., and Kawalek, P., 鈥A Normative Model for Assessing SME IT Effectiveness鈥, Communications of the IIMA, vol. 15, no. 1, 2017.
M. Curry, Marshall, B., and Kawalek, P., 鈥Improving IT Assessment with IT Artifact Affordance Perception Priming鈥, International Journal of Accounting Information Systems, vol. 19, pp. 17-28, 2015.
M. Curry, Marshall, B., and Kawalek, P., 鈥IT Artifact Bias: How exogenous predilections influence organizational information system paradigms鈥, International Journal of Information Management, vol. 34, no. 4, pp. 427-436, 2014.
R. Reitsma, Marshall, B., and Chart, T., 鈥Can Intermediary-based Science Standards Crosswalking Work? Some Evidence from Mining the Standard Alignment Tool (SAT)鈥, Journal of the Association for Information Science and Technology, vol. 63, pp. 1843-1858, 2012.
B. Marshall, Curry, M., and Reitsma, R., 鈥Organizational Information Technology Norms and IT Quality鈥, Communications of the IIMA, vol. 11, no. 4, 2011.
R. Reitsma, Marshall, B., and Zarske, M., 鈥Aspects of 'Relevance' in the Alignment of Curriculum with Educational Standards鈥, Information Processing & Management, vol. 46, no. 3, pp. 362-376, 2010.
B. Marshall, Mortenson, K., Bourne, A., and Price, K., 鈥Visualizing Basic Accounting Flows: Does XBRL + Model + Animation = Understanding?鈥, International Journal of Digital Accounting Research, vol. 10, pp. 27-54, 2010.
S. Kaza, Xu, J., Marshall, B., and Chen, H., 鈥Topological Analysis of Criminal Activity Networks: Enhancing Transportation Security鈥, IEEE Transactions on Intelligent Transportation Systems, vol. 10, no. 1, pp. 83 - 91, 2009.
B. Marshall, Chen, H., and Kaza, S., 鈥Using Importance Flooding to Identify Interesting Networks of Criminal Activity鈥, Journal of the Association for Information Science and Technology, vol. 59, no. 13, pp. 2099-2114, 2008.
K. D. Qui帽ones, Su, H., Marshall, B., Eggers, S., and Chen, H., 鈥User-Centered Evaluation of Arizona BioPathway: An Information Extraction, Integration, and Visualization System鈥, IEEE Transactions on Information Technology in Biomedicine, vol. 11, no. 5, pp. 527-536, 2007.
B. Marshall, Su, H., McDonald, D., Eggers, S., and Chen, H., 鈥Aggregating Automatically Extracted Regulatory Pathway Relations鈥, IEEE Transactions on Information Technology in Biomedicine, vol. 10, no. 1, pp. 100- 108, 2006.
B. Marshall, Chen, H., and Madhusudan, T., 鈥Matching Knowledge Elements in Concept Maps Using a Similarity Flooding Algorithm鈥, Decision Support Systems, vol. 42, no. 3, pp. 1290-1306, 2006.
B. Marshall, Chen, H., Shen, R., and Fox, E. A., 鈥Moving Digital Libraries into the Student Learning Space: the GetSmart Experience鈥, Journal on Educational Resources in Computing, vol. 6, no. 1, 2006.
T. Madhusudan, J. Zhao, L., and Marshall, B., 鈥A Case-based Reasoning Framework for Workflow Model Management鈥, Data and Knowledge Engineering, vol. 50, no. 1, pp. 87-115, 2004.
B. Marshall, McDonald, D., Chen, H., and Chung, W., 鈥EBizPort: Collecting and Analyzing Business Intelligence Information鈥, Journal of the Association for Information Science and Technology, vol. 55, no. 1, pp. 873-891, 2004.
D. McDonald, Chen, H., Su, H., and Marshall, B., 鈥Extracting Gene Pathway Relations Using a Hybrid Grammar: The Arizona Relation Parser鈥, Bioinformatics, vol. 20, no. 18, pp. 3370-8, 2004.
C. Norman, Obermire, K., Rose, A. M., Rose, J., and Frydenlund, N., 鈥Take Responsibility or Take Action: How Can Firms Recover from Information Technology Control Material Weakness Disclosures?鈥, International Journal of Accounting Information Systems, vol. 48, 2023.
K. Obermire, Cohen, J., and Johnstone, K., 鈥Audit Committee Members' Professional Identities: Evidence from the Field鈥, Accounting, Organizations and Society, vol. 93, 2021.
E. R. Condie, Obermire, K., Seidel, T. A., and Wilkins, M. S., 鈥Prior Audit Experience and CFO Financial Reporting Aggressiveness鈥, Auditing: A Journal of Practice and Theory, 2021.
P. Hurley, Mayhew, B., Obermire, K., and Tegeler, A., 鈥The Impact of Risk and the Potential for Loss on Managers' Demand for Audit Quality鈥, Contemporary Accounting Research, 2021.
D. Downey, Obermire, K., and Zehms, K., 鈥Toward an Understanding of Audit Team Distribution and Performance Quality鈥, Auditing: A Journal of Practice and Theory, vol. 39, no. 4, pp. 87-112, 2020.
P. Hurley, Mayhew, B., and Obermire, K., 鈥Realigning Auditors' Accountability: Experimental Evidence鈥, The Accounting Review, vol. 94, no. 3, pp. 233-250, 2019.
C. Chen, Pesch, H., and Wang, L., 鈥Selection benefits of below-market pay in social-mission organizations: effects on individual performance and team cooperation鈥, The Accounting Review, vol. 95, no. 1, pp. 57-77, 2020.
R. Reitsma, Marshall, B., and Chart, T., 鈥Can Intermediary-based Science Standards Crosswalking Work? Some Evidence from Mining the Standard Alignment Tool (SAT)鈥, Journal of the Association for Information Science and Technology, vol. 63, pp. 1843-1858, 2012.
B. Marshall, Curry, M., and Reitsma, R., 鈥Organizational Information Technology Norms and IT Quality鈥, Communications of the IIMA, vol. 11, no. 4, 2011.
R. Reitsma, Marshall, B., and Zarske, M., 鈥Aspects of 'Relevance' in the Alignment of Curriculum with Educational Standards鈥, Information Processing & Management, vol. 46, no. 3, pp. 362-376, 2010.
W. O'Grady, Akroyd, C., and Scott, I., 鈥Beyond Budgeting: Distinguishing Modes of Adaptive Performance Management鈥, Advances in Management Accounting, vol. 29, pp. 33-53, 2017.
J. Deng, Steele, L., Lynch, D., and Gaertner, F. B., 鈥Proprietary Costs and the Reporting of Segment-level Tax Expense鈥, Journal of the American Taxation Association, vol. 43, no. 1, pp. 1-26, 2021.
L. Steele, Campbell, J., and Lee, G., 鈥Express yourself: Why managers' disclosure tone varies across time and what investors learn from it鈥, Contemporary Accounting Research, vol. 37, no. 2, pp. 1140-1171, 2020.
L. Steele and Lee, G., 鈥Debt Structure and Conditional Conservatism鈥, Journal of Financial Reporting, 2019.
L. Steele, Gaertner, F., and Kauser, A., 鈥Negative accounting earnings and gross domestic product鈥, Review of Accounting Studies, 2019.
L. Steele, Bens, D., and Monahan, S., 鈥The Effect of Aggregation of Accounting Information via Segment Reporting on Accounting Conservatism"鈥, European Accounting Review, vol. 27, no. 2, pp. 237-262, 2018.
L. Steele, Dhaliwal, D., Lee, H. - S., and Pincus, M., 鈥Taxable Income and Firm Risk鈥, Journal of the American Taxation Association, vol. 39, no. 1, pp. 1-24, 2017.
L. Steele, Campbell, J., Chen, H., Dhaliwal, D., and Lu, H. -min, 鈥The information content of mandatory risk factor disclosures in corporate filings鈥, Review of Accounting Studies, vol. 19, no. 1, pp. 396-455, 2014.
J. Deng and Yang, J., 鈥Corporate Reputation and Hedging Activities鈥, Accounting and Finance, vol. 63, no. S1, pp. 1223-1247, 2023.
L. Dang and Yang, J., 鈥The choice between rights and underwritten equity offerings: Evidence from Chinese Stock Markets鈥, Journal of Multinational Financial Management, vol. 23, no. 3, pp. 235-253, 2013.

BIS

B. Marshall, Mortenson, K., Bourne, A., and Price, K., 鈥Visualizing Basic Accounting Flows: Does XBRL + Model + Animation = Understanding?鈥, International Journal of Digital Accounting Research, vol. 10, pp. 27-54, 2010.
J. Chen, Shao, R., and Fan, S., 鈥Destabilization and Consolidation: Conceptualizing, Measuring, and Validating the Dual Characteristics of Technology鈥, Research Policy, vol. 50, no. 1, pp. 104-115, 2021.
E. Coulter, Coakley, J., and Sessions, J., 鈥The Analytic Hierarchy Process: A Tutorial for Use in Prioritizing Forest Road Investments to Minimize Environmental Effects鈥, International Journal of Forest Engineering, vol. 17, no. 2, pp. 51-70, 2006.
M. K. Fiegener and Coakley, J., 鈥Making Sense of Strategic Alignment: An Interpretive View of Alignment Problems and Practices鈥, Journal of Information Technology Management, vol. XIII, no. 3-4, pp. 15-32, 2002.
C. Brown and Coakley, J., 鈥Financial neural Network applications: 1998-1999 update鈥, New Review of Applied Expert Systems and Emerging Technologies, vol. 7, pp. 167-182, 2001.
J. Coakley and Brown, C., 鈥Artificial Neural Networks in Accounting and Finance: Modeling Issues鈥, International Journal of Intelligent Systems in Accounting Finance and Management, vol. 9, no. 2, pp. 119-144, 2000.
C. K. Tyran and Coakley, J., 鈥The E-Commerce Course Project: Creating Educational Linkages with the Value Chain鈥, Journal of Informatics Education and Research, vol. 2, no. 2, pp. 59-70, 2000.
J. Coakley, Drexler, J. A., Kircher, A. E., and Larson, E. W., 鈥Using a Computer Based Version of The Beer Game - Lessons Learned,"鈥, Journal of Management Education, vol. 22, no. 3, pp. 416-424, 1998.
E. Larson, Coakley, J., and Drexler, J., 鈥Using a Computer-Based Version of the Beer Game鈥, Journal of Management Education, 1998.
J. Coakley, 鈥An Expert System to Diagnose Anemia and Report Results Directly on Hematology Forms鈥, Computers and Biomedical Research, pp. 16-26, 1996.
J. Coakley, 鈥Using Pattern Analysis Methods to Supplement Attention-Directing Analytical Procedures鈥, Expert Systems with Applications, no. December, pp. 513-528, 1996.
J. Coakley, 鈥CIO 'Impression Management:' Problems and Practices鈥, Journal of Systems Management, no. Nov/Dec, pp. 56-61, 1995.
C. Brown, Coakley, J., and Phillips, M. Ellen, 鈥Neural Networks Enter the World of Management Accounting鈥, Management Accounting, vol. LXXVI, no. 11, pp. 51-53, 56-57, 1995.
C. Brown, Coakley, J., and Phillips, M. Ellen, 鈥Neural Networks: Nuts and Bolts鈥, Management Accounting, vol. LXXVI, no. 11, pp. 54-55, 1995.
C. Brown, Coakley, J., and Eining, M. M., 鈥Conference Report: The Fourth International Symposium on Intelligent Systems in Accounting, Finance and Management鈥, International Journal of Intelligent Systems in Accounting Finance and Management, vol. 3, no. 3, pp. 223-235, 1994.
J. Coakley and Brown, C., 鈥Artificial Neural Networks Applied to Ratio Analysis in the Analytical Review Process鈥, International Journal of Intelligent Systems in Accounting, Finance and Management, vol. 2, no. 1, pp. 19-39, 1993.
J. Coakley, 鈥A Compari颅son of User Satisfaction with MIS Across Two Cultures鈥, International Journal of Logistics Management, vol. 2, pp. 22-29, 1991.
J. Coakley, 鈥The Expectations of Accounting Errors in Medium-Sized Manufacturing Firms鈥, Advances in Accounting, vol. 2, no. 199-245, 1985.
G. Shang, Ilk, N., and Fan, S., 鈥Need for Speed, but How Much Does It Cost? Unpacking the Fee-Speed Relationship in Cryptocurrency Transactions鈥, Journal of Operations Management, 2022.
J. Chen, Shao, R., and Fan, S., 鈥Destabilization and Consolidation: Conceptualizing, Measuring, and Validating the Dual Characteristics of Technology鈥, Research Policy, vol. 50, no. 1, pp. 104-115, 2021.
D. Pan, J. Zhao, L., Fan, S., and Zhang, Z., 鈥Dividend or No Dividend in Delegated Blockchain Governance: A Game Theoretic Analysis鈥, Journal of Systems Science and Systems Engineering, pp. 1-19, 2021.
N. Ilk, Shang, G., Fan, S., and J. Zhao, L., 鈥Stability of Transaction Fees in Bitcoin: A Supply and Demand Perspective鈥, MIS Quarterly, vol. 45, no. 2, pp. 563-692, 2021.
S. Fan and Ilk, N., 鈥A text analytics framework for automated communication pattern analysis鈥, Information & Management, vol. 57, no. 4, 2020.
N. Ilk and Fan, S., 鈥Combining Textual Cues with Social Clues: Utilizing Social Features to Improve Sentiment Analysis in Social Media鈥, Decision Sciences, 2020.
J. Wu, Fan, S., and J. Zhao, L., 鈥Community Engagement and Online Word of Mouth: An Empirical Investigation鈥, Information & Management, vol. 55, no. 2, pp. 258-270, 2018.
S. Fan, Li, X., and J. Zhao, L., 鈥Collaboration Process Pattern Approach to Improving Teamwork Performance: A Data Mining-Based Methodology鈥, INFORMS Journal on Computing, vol. 29, no. 3, pp. 438-456, 2017.
Z. Liu, Fan, S., Wang, H., and J. Zhao, L., 鈥Enabling effective workflow model reuse: A data-centric approach鈥, Decision Support Systems, vol. 93, pp. 11-25, 2017.
S. Fan, Xiao, J. I., Xie, K., and J. Zhao, L., 鈥Introduction to the special issue of ECR on E-business innovation with big data鈥, Electronic Commerce Research, 2017.
S. Fan, Hua, Z., Storey, V., and Zhao, J. L., 鈥A Process Ontology Based Approach to Easing Semantic Ambiguity in Business Process Modeling鈥, Data and Knowledge Engineering, vol. 102, 2016.
L. J. Zhao, Fan, S., and Yan, J., 鈥Overview of business innovations and research opportunities in blockchain and introduction to the special issue鈥, Financial Innovation, vol. 2, 2016.
S. Fan, Lau, R., and Zhao, J. L., 鈥Demystifying big data analytics through the lens of marketing mix鈥, Big Data Research, vol. 2, no. 1, pp. 28-32, 2015.

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