Caught in an Expectations Trap: Risks of Giving Securities Analysts What They Expect

2023 | Organization Science | Citations: 0

Authors: Bascle, Guilhem; Jung, Jiwook

Abstract: Although recent research shows that there is mounting pressure on firms to achie ... Expand

Abstract: Although recent research shows that there is mounting pressure on firms to achieve earnings expectations of securities analysts, firms are far from being passive conformers; many firms proactively manage such pressure, particularly with earnings management tools. Yet why does the pressure to meet analyst expectations persist despite firms’ efforts to reduce it? To address the question, we develop an intertemporal model of the mutually reinforcing relationships between analyst expectations and firms’ strategic response, combining the behavioral theory of the firm and the concept of expectations trap. We argue that firms’ efforts to meet analyst expectations strengthen their salience as a predominant performance benchmark and, in doing so, ironically put them under greater pressure from analysts in three sequentially related steps—escalating future earnings expectations, imposing more severe penalties for failure to meet heightened expectations, and generating compensatory action for missed expectations. Our analysis, using data on more than 700 of the largest listed U.S. firms between 1986 and 2015, supports our arguments. Our study expands the scope of the behavioral theory of the firm, by demonstrating the increasing importance of performance feedback based on analyst expectations. Our study also contributes to the research on earnings pressure, by illuminating why the pressure persists despite firms’ efforts to reduce or evade it, and finally to the literature on strategic management of external expectations, by elaborating its unintended, long-term consequences. Supplemental Material: The e-companion is available at https://doi.org/10.1287/orsc.2021.1569. Collapse

Topics: strategic management organizational productivity Generally Accepted Accounting Principles IS downsizing accounting

Methods: longitudinal research statistical hypothesis test Compustat descriptive statistic robustness check

Theories: behavioral theory of the firm institutional theory

Software-Vendor Diversification: A Source of Organizational Rigidity in Adversity?

2023 | Journal of Management Information Systems | Citations: 0

Authors: Gong, Jing; Liang, Yi; Ramasubbu, Narayan

Abstract: Firms often assemble digital infrastructures using continuously evolving softwar ... Expand

Abstract: Firms often assemble digital infrastructures using continuously evolving software applications sourced from a multitude of vendors. Using the theoretical lens of the threat-rigidity thesis, we raise the possibility that during adverse environmental conditions, software-vendor diversification can be a source of organizational rigidity that may dampen firm performance. Empirical analysis using data on 918 large public U.S. firms operating during two severe environmental shocks, the global financial crisis and the burst of the dot-com bubble, lends strong support to our thesis. Results indicate that a variety of firm performance indicators (e.g., stock return and operating income measures) are negatively associated with software-vendor diversification during crisis periods. Mediation analysis highlights the role of IT-related material weakness in firms' internal controls in transmitting threat-rigidity effects that decrease performance. These results underscore the importance of software portfolio optimization for countering the dysfunctional effects of software-vendor diversification during adverse environmental shocks. Collapse

Topics: organizational productivity accounting computer hardware information technology infrastructure database system

Methods: mediation analysis structural equation modeling cross sectional research propensity score method longitudinal research

Scalable Sustainability Monitoring of Financial Reports: A Design Science Artifact

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Rath, Oliver; Haase, Frederic; Erlacher, Nina Annika; Schoder, Detlef

Abstract: The United Nations Climate Change (UNCC) report in 2022 warns that the likeliho ... Expand

Abstract: The United Nations Climate Change (UNCC) report in 2022 warns that the likelihood of global warming exceeding 1.5°C by 2026 has surpassed 50%. Climate change is a global problem that requires collective action, and companies have a significant role to play in mitigating climate change. Companies have already started to incorporate climate change risk and liabilities in their annual reports. Monitoring a company's progress in transitioning to sustainability can help stakeholders make informed decisions. However, existing approaches for sustainability monitoring of annual reports are limited in scalability, mainly because of the manual steps involved. Therefore, we designed and evaluated a more scalable artifact using state-of-the-art natural language processing (NLP) techniques to monitor companies' sustainability targets based on their annual reports. This work presents a prototype that improves upon the current state of practice and contributes to the body of knowledge by outlining key design decisions. Collapse

Topics: usability evaluation criteria information portal computer data storage artificial intelligence

Methods: natural language processing computational algorithm question answering design artifact machine learning

Explainable Risk Classification in Financial Reports

2023 | International Conference on Information Systems | Citations: 0

Authors: Tan, Xue Wen; Kok, Stanley

Abstract: Every publicly traded company in the US is required to file an annual 10-K fina ... Expand

Abstract: Every publicly traded company in the US is required to file an annual 10-K financial report, which contains a wealth of information about the company. In this paper, we propose an explainable deep-learning model, called FinBERT-XRC, that takes a 10-K report as input, and automatically assesses the post-event return volatility risk of its associated company. In contrast to previous systems, our proposed model simultaneously offers explanations of its classification decision at three different levels: the word, sentence, and corpus levels. By doing so, our model provides a comprehensive interpretation of its prediction to end users. This is particularly important in financial domains, where the transparency and accountability of algorithmic predictions play a vital role in their application to decision-making processes. Aside from its novel interpretability, our model surpasses the state of the art in predictive accuracy in experiments on a large real-world dataset of 10-K reports spanning six years. Collapse

Topics: cloud computing explainable artificial intelligence artificial intelligence logistics management risk management

Methods: machine learning word embedding term frequency–inverse document frequency experiment logistic regression

Attentive statement fraud detection: Distinguishing multimodal financial data with fine-grained attention

2023 | Decision Support Systems | Citations: 0

Authors: Wang, Gang; Ma, Jingling; Chen, Gang

Abstract: Financial statement fraud caused by listed companies directly jeopardizes the re ... Expand

Abstract: Financial statement fraud caused by listed companies directly jeopardizes the reliability the financial reporting process. Leveraging multimodal information for financial statement fraud detection (FSFD) has recently become of great interest to academic research and industrial applications. Unfortunately, the predictive ability of multi­ modal information in FSFD remains largely underexplored, particularly the fusion ambiguity embedded in and among multi-modalities. In this study, we propose a novel attention-based multimodal deep learning method, named RCMA, toward an accurate FSFD. RCMA synthesizes a fine-grained attention mechanism including three innovative attention modules, i.e., ratio-aware attention, chapter-aware attention, and modality-aware attention mechanism. The first two attention mechanisms help to liberate the extraordinary predictive power of the financial modality and the textual modality on FSFD, respectively. Moreover, the proposed modality-aware attention mechanism enables better coordination between the two modalities. Furthermore, to ensure effec­ tive learning on the attention-based multimodal embedding, we design a novel loss function named Focal and Consistency Loss, or FCL. It considers class-imbalance and modality-consistency simultaneously, to specialize the optimization of FSFD. The experimental results on the real-world dataset show that the proposed RCMA on FSFD task outperformed the state-of-the-art benchmarks. Furthermore, interpretation analysis visualizes the attention weights of different ratio groups, chapters, and modalities from RCMA, and illustrates how these interpretations influence stakeholders’ decision process for FSFD. Collapse

Topics: accounting fraud detection database system

Methods: machine learning deep learning financial report robustness check experiment

Value Drivers of Artificial Intelligence

2022 | Americas Conference on Information Systems | Citations: 2

Authors: Böttcher, Timo; Weber, Michael; Weking, Jörg; Hein, Andreas; Krcmar, Helmut

Abstract: Artificial intelligence (AI) holds great potential for firms to create new busi ... Expand

Abstract: Artificial intelligence (AI) holds great potential for firms to create new business models and gain competitive advantages. While some pioneers are effectively leveraging AI, most firms are struggling to capitalize on the opportunities for value creation. Previous research has highlighted the performance benefits, success factors, and challenges of adopting AI. However, the value drivers of AI, specifically regarding how AI creates value, remain unclear and need exploration so that firms can adapt their value creation to leverage the potential. To clarify how AI creates value, we conduct a case survey of 61 firms to identify six value drivers: efficiency, novelty, knowledge from data, ecosystem, personalization, and human resemblance. We discuss how these value drivers differ from other digital technologies. For practitioners, we provide valuable insights into the business value of AI and business model (BM) design opportunities to build on.The analysis of relationships between the six value drivers resulted in Figure 2. Efficiency, novelty, knowledge from data, personalization, and human resemblance all interact with each other, while ecosystem has an overarching role in value creation in digital contexts. Collapse

Topics: artificial intelligence value creation digital innovation organizational value competitive advantage

Methods: survey qualitative coding computational algorithm case study triangulation

When Do Firms Add Digital Platforms? Organizational Status as an Enabler to Incumbents’ Platformization

2022 | International Conference on Information Systems | Citations: 0

Authors: Gao, Mengjin; Kim, Young-Kyu; KIm, Jieun; Lee, Dongwon; Um, Sungyong

Abstract: ... Expand

Abstract: Collapse

Topics: digital platform organizational productivity database system digital transformation competitive advantage

Methods: longitudinal research proportional hazards model instrumental variables estimation experimental group hazard function

Digital attrition: The negative implications of the sharing economy for the digital options of incumbent firms

2022 | Information Systems Journal | Citations: 1

Authors: Meng, Tian; Ng, Evelyn; Tan, Barney

Abstract: One of the key competitive advantages of sharing economy platforms stems from th ... Expand

Abstract: One of the key competitive advantages of sharing economy platforms stems from their superior IT capabilities, which enable operations that are more efficient and/or effective than the incumbent firms. However, given that many of the incumbents tend to be established market leaders or resource-rich multi-nationals, their general inability to acquire the appropriate digital options (ie, the IT capabilities that enable the launch of, and response to, competitive actions) to meet the challenge of the sharing economy platforms is puzzling. This study explores this phenomenon by posing the research question: How do the “forces at work” associated with the sharing economy paradigm impact the digital options of incumbent firms? Based on the case of Qiangsheng Taxi and how its IT capabilities have been affected by the emergence of ridesharing platforms, the evidence uncovered suggests that the sharing economy can influence the digital options of incumbent firms through a process of digital attrition, which may be induced by a blend of unfavorable contextual influences (ie, an unbalanced regulatory regime, evolving market preferences and the resourcing advantages of sharing economy platforms). A framework is inductively derived from the data collected that depicts digital attrition as a process of three phases: (a) deinstitutionalization, (b) technological incapacitation, and (c) competitive erosion. In doing so, our study suggests that digital attrition may culminate in IT-induced competitive disadvantages for incumbent firms, which in turn, could exacerbate the unfavorable contextual influences to complete a vicious cycle that reinforces the negative influence of the sharing economy even further. Collapse

Topics: sharing platform information technology capability ride sharing platform mobile application IT investment

Methods: qualitative interview business process modeling qualitative coding case study grounded theory

A Model for Detecting Accounting Frauds by using Machine Learning

2022 | HICSS | Citations: 0

Authors: Nguyen, Minh; Nguyen Hoang To, Loan; Nguyen Viet, Hung

Abstract: This paper aims to develop a machine learning model that enables to predict sign ... Expand

Abstract: This paper aims to develop a machine learning model that enables to predict signs of financial statement frauds by combining the domain knowledge of machine learning and accounting. Inputs of this model is a published dataset of financial statements, and outputs involve the conclusions whether the predicted financial statements indicate the signs of financial statement frauds or not. Currently, XGBoost is recognized as one of the most popular classification methods with fast performance, flexibility, and scalability. However, its default properties are not suitable for fraudulent detecting of imbalanced datasets. To overcome this drawback, this research introduces a new machine learning model based on XGBoost technique, called f(raud)-XGBoost. The proposed model not only inherits XGBoost advantages but also enables it to detect financial statement frauds. We apply the Area Under the Receiver Operating Characteristics Curve and NDCG@k to perform the evaluation process. The experimental results show that the new model performs slightly better than three existing models including logistic regression model that is based on financial ratios, Support-vector-machine model, and RUSBoost model. Collapse

Topics: accounting missing data database system logistics management

Methods: machine learning XGBoost ensemble learning support vector machine experiment

In NVivo Veritas: How Information Systems Increase the Flexibility and Relevance of Strategic Management Accounting

2022 | International Conference on Information Systems | Citations: 0

Authors: Schulte, Stephan; Parra-Moyano, José; Schmedders, Karl

Abstract: One of the key tasks for strategic management accountants is to estimate the si ... Expand

Abstract: One of the key tasks for strategic management accountants is to estimate the size of the market in which their firm operates. For such an estimation to be correct, strategic management accountants need to have access to private information from the firm's competitors. Such access is impossible since no competitor is willing to share internal documents, resulting in a problem of imperfect information. This problem hinders strategic management accountants' efforts to perform their main tasks, forcing them to just approximate the size of their firms' markets. In this paper we show how, by applying text analysis techniques to publicly available documents from their firm's competitors, strategic management accountants can significantly increase the accuracy of their forecasts. This increased accuracy implies that the use of techniques from the Information Systems (IS) field can help mitigating the thus far unsolvable problem of imperfect information from which the strategic management accounting field has traditionally suffered. Collapse

Topics: management accounting accounting information system use critical success factor privacy

Methods: case study qualitative coding mathematical model qualitative content analysis computational algorithm