FINANCIAL STATEMENT FRAUD DETECTION USING TEXT MINING: A SYSTEMIC FUNCTIONAL LINGUISTICS THEORY PERSPECTIVE

Fraudulent financial information made by public companies not only cause significant financial loss to broad shareholders but also result in a great loss of confidence to capital market. Conventional auditing practices, which primarily focus on statistical analysis of structured financial ratios in auditing process, work not so well with the presence of misleading financial reports. This research tries to tap the power of huge amount of largely ignored textual contents in financial statements. With the theoretical guidance of Systemic Functional Linguistics theory (SFL), we develop a systematic text analytic framework for financial statement fraud detection. Seven information types, i.e., topics, opinions, emotions, modality, personal pronouns, writing style, and genres are identified based on ideational, interpersonal, and textual metafunctions in SFL. Under the analytic framework, Latent Dirichlet Allocation algorithm, computational linguistics, term frequency-inverse document frequency method, are integrated to create a synergy for extracting both word-level and document-level features. All these features serve as the input of Liblinear Support Vector Machine classifier. Finally, with application to detect fraud in 1610 firm-year samples from U.S. listed companies, the analytic framework makes a classification with average accuracy at 82.36% under ten-fold cross validation, much better than baseline method using financial ratios

The Impact of Competitive Threats from the Product Market on Data Breaches

In this digital era, the concern about data breaches is rapidly increasing among consumers and firms. When the firm is facing competitive threats from the product market, would it perform well in data protection? Does the presence of a CIO/CTO help prevent a data breach? Would the impact of competitive threats on data breach vary by the severity of the data breach? In this study, by examining a sample of data breaches from 2005 to 2018, we find that competitive threats from the product market are positively associated with the likelihood of data breaches. And the presence of a CIO/CTO will also increase the likelihood of a data breach. It’s somewhat different from prior studies. We plan to further explore the competitive threats’ impact on different types of data breaches as well as their latent mechanism. Our study contributes a lot to IS cybersecurity and management literature

ENHANCING WORK PERFORMANCE IN STABLE POST-ADOPTIVE STAGE: A SYSTEM USE-RELATED BEHAVIORS PERSPECTIVE

The success of enterprise systems (ES) hinges on the work performance of system users in the stable post-adoptive stage. With a high failure rate of ES implementation, it is crucial to explore factors that could enhance users’ work performance. Drawing on literature on IS post-adoption and system use-related behaviors, this study proposes a theoretical model to understand how different types of ES use-related behaviors (i.e., technology interaction behaviors, task-technology adaptation behaviors and individual adaptation behaviors) can induce better performance in the stable phase of post-adoption. A field survey involving 250 physicians was conducted to test the proposed research model. The results showed different effects of ES use-related behaviors on improving users’ work performance. Individual adaptation behaviors enhanced the user performance, while technology interaction behaviors and task-technology adaptation behaviors did not show significant effect on performance. Interestingly, individual adaptation and task-technology adaptation behaviors could moderate the relationship between system use and performance, yet in an opposite manner. This study offers important contributions to ES researchers and practitioners

A Bayesian Network-Based Framework for Personalization in Mobile Commerce Applications

Providing personalized services for mobile commerce (m-commerce) can improve user satisfaction and merchant profits, which are important to the success of m-commerce. This paper proposes a Bayesian network (BN)-based framework for personalization in m-commerce applications. The framework helps to identify the target mobile users and to deliver relevant information to them at the right time and in the right way. Under the framework, a personalization model is generated using a new method and the model is implemented in an m-commerce application for the food industry. The new method is based on function dependencies of a relational database and rough set operations. The framework can be applied to other industries such as movies, CDs, books, hotel booking, flight booking, and all manner of shopping settings

The Effect of Online Review Portal Design: The Moderating Role of Explanations for Review Filtering

The flood of non-constructive and fake online consumer reviews erects a considerable barrier to consumers making efficient decisions. Various review filtering algorithms have been developed to address this challenge, but the design of post-development review portals continues to lack a consensus. In review portals, disclosing more transparent reviews is efficient for enhancing users’ trust. However, it will cause users’ diminished focus on recommended reviews, leading to sub-optimal decisions. A research model is then developed to investigate users’ cognitive processes in their responses to three review exhibition designs (i.e., informed silent display design, filtered review display design, and composite display design) regarding trust in the review portal and perceived decision quality. We also suggest that explanations for review filtering play a moderating role in users’ perceptions, which appears to be a viable resolution to this dilemma. This paper provides significant theoretical and practical insights for the review portal design and implementation

Mining Sequential Relations from Multidimensional Data Sequence for Prediction

By analyzing historical data sequences and identifying relations between the occurring of data items and certain types of business events we have opportunities to gain insights into future status and thereby take action proactively. This paper proposes a new approach to cope with the problem of prediction on data sequence characterized by multiple dimensions. The proposed relation mining approach improves the existing sequential pattern mining algorithm by considering multidimensional data sequences and incorporating time constraints. We demonstrate that multidimensional relations extracted by our approach are an enhancement of single dimensional relations by showing significantly stronger prediction capability, despite of the substantial work done in the latter area. In addition, matching algorithm based on the obtained relations is proposed to make prediction. The effectiveness of the proposed methods is validated by experiments conducted on a mobile user context dataset

Exploring the Role of AI Explanations in Delivering Rejection Messages: A Comparative Analysis of Organizational Justice Perceptions between HR and AI

The increasing use of AI decision systems in recruitment processes has created challenges, including potential resistance from job applicants. To address this issue, drawing on organizational justice theory, we identify dimensions of AI explanations in the employment context and examine their impact on job applicants\u27 perceptions of organizational justice. We conducted an experiment to understand applicants\u27 reactions to AI versus HR managers without explanations and examined the impact of AI explanations on organizational justice perceptions and acceptance intention. Our findings show that without explanation, AI is perceived as lower organizational just and acceptance intention compared to HR managers. Organizational justice mediates the effects between outcome/process explanations of AI on acceptance intention. However, outcome explanations have a stronger impact compared to process explanations. Our study contributes to understanding explanation structures for AI-based recruitment and offers practical implications for developing explanations that improve the perceived justice of AI recruitment systems