ARTUR WUERGES, Universidade Federal de Santa Catarina (UFSC)
Email: awuerges@gmail.com
JOSE ALONSO BORBA, Universidade Federal de Santa Catarina (UFSC) - Accounting Department
Email: jalonso@cse.ufsc.br

There are problems in accounting and finance that can't be solved easily though conventional techniques. Some examples are bankruptcy prediction and the development of strategies to trade (profitably) in stock exchanges. In these cases, one of the alternatives is the use of artificial intelligence systems. This article analyses empirical articles published in journals between 2000 and 2008 that present applications of neural networks, fuzzy logic and genetic algorithms to problems in finance and accounting. After the analysis of 57 articles, this article finds that the most used method is the artificial neural networks (40 articles) and most of the applications are in finance (49 articles). There were just 8 articles about AI systems applied to accounting, what leads to the conclusion that there is still room for new developments in this field of knowledge.

SERGIO DAVALOS, affiliation not provided to SSRN
Email: sergiod@u.washington.edu
FEI LENG, University of Washington, Tacoma
Email: fleng@u.washington.edu
EHSAN H. FEROZ, University of Washington - Tacoma-Milgard School of Business, Vernon Zimmerman Center, University of Illinois, US Government Accountability Office
Email: ehf2@u.washington.edu
ZHIYAN CAO, University of Washington - Tacoma
Email: zhiyan.cao@aya.yale.edu

This paper develops an adaptive ensemble model for bankruptcy classification of firms cited in the SEC's Accounting and Auditing Enforcement Releases (AAER). We develop a Genetic Algorithm (GA) model for bankruptcy classification of AAER firms. Our research contributes to the bankruptcy literature in several ways. First of all, it fills a gap in the bankruptcy literature by developing a domain specific model for AAER firms. Secondly, by using financial and non-financial variables, the GA model generates and optimizes a set of 'if-then' comprehensible rules for the financial failure classification of AAER firms. A Genetic Algorithm model can provide a greater degree of accuracy in predicting financial failure of firms than classical statistical models. Thirdly, we develop a model using bagging that incorporates the output from different models or sources. Finally, we demonstrate the key role of the fitness function in determining the successful performance of a financial failure GA model.

MARKO JÄRVENPÄÄ, affiliation not provided to SSRN
Email: marko.jarvenpaa@econ.jyu.fi
AAPO ILMARI LÄNSILUOTO, affiliation not provided to SSRN
Email: aapo.lansiluoto@seamk.fi

This study focuses on the (un)routinization of environmental performance measures (EPMs). It studies how new environmental measures were implemented within the new environmental management system (EMS) and how they became connected with the financial measures, like cost savings and profitability. By doing so, they were connected to the taken-for-granted profit orientation of the case company in order to overcome these values and to make them succeed in organization. Later on these new measures were also included in the new BSC. In conditions of the profit oriented taken-for-granted values, the BSC locked the rule status of the environmental measures, but it locked them at the operational level, while they were not included in the more strategic level scorecard. Neither the profit connection nor the BSC were able to make the environmental activities to be enacted as the routine organizational action. The ‘power of the system’, the institutionalized profit and cost orientation was too much for the environmental agenda to overcome. We found also several sub stages in ‘rules’ or ‘routinization’. EMS rule, BSC rule, routinization of environmental reporting and finally, routinization of environmental action. The routinization of reporting preceded the routinization of action because the routinization of reporting was much easier than the routinization of environmental action. Therefore, future studies could consider two different processes of routinization in more detail by building on the framework of Burns and Scapens (2000).

SUBHASH CHANDER, Guru Nanak Dev University
Email: subh_chander@rediffmail.com
MANJINDER SINGH SAINI, affiliation not provided to SSRN
Email: mssaini77@yahoo.co.in

The World Wide Web (www) has been adopted as a new medium for disseminating corporate information. It is a medium that offers numerous benefits for distributing business information to stakeholders over traditional means like the printed annual reports. The present paper analyzes the perceived usefulness of corporate disclosure through the Web by surveying retail investors taken from the selected cities of Punjab and Chandigarh (union territory). The data is collected from the selected respondents by personally administering the pretested questionnaire. Factor analysis has been applied to identify the structure of the Web-based factors, which have the potential to enhance the usefulness of corporate information. Improvement in quality of information, better decision making, increased usefulness of information, better evaluation, and enhanced competition have been identified as the factors explaining the perceived usefulness of corporate disclosure through the Web.

MARGARET H. CHRIST, University of Georgia
Email: mchrist@uga.edu
SCOTT A EMETT, Brigham Young University
Email: scottemett@gmail.com
SCOTT L. SUMMERS, Brigham Young University - School of Accountancy
Email: summers@byu.edu
DAVID A. WOOD, Brigham Young University - School of Accountancy
Email: davidwood@byu.edu

Given the vital role of internal controls in organizations, it is important to understand how different classes of control vary in terms of effectiveness and costliness. We examine how two classes of automated application controls, preventive (which deter problems before they arise) and detective controls (which discover problems after they occur), affect users’ task effectiveness (accuracy) and two important behavioral costs: efficiency (i.e., time spent) and intrinsic motivation. Preventive controls differ from detective controls in terms of the intensity and the timeliness of the feedback they provide. We argue that the increase in intensity and timeliness of feedback can focus an individual’s attention on the control objective (e.g., to perform the task accurately), even if achievement of this objective is not explicitly compensated. Therefore, we predict users exposed to preventive controls will be more effective at a given task (i.e., accurate) than users facing detective controls. Using a data-entry task, we find that the key determinant of control effectiveness is the timeliness of the feedback. We also find that preventive controls and detective controls with immediate feedback are equally effective and that both are significantly more effective than detective controls with delayed feedback or than a no control sample. When considering the costs of the different classes of control, we find no difference in control conditions for efficiency; however, preventive controls significantly diminish intrinsic motivation, which leads to lower willingness to participate in organizational citizenship behaviors (OCBs).