“Dear CEO and Board”: How Activist Investors’ Confidence in Tone Influences Campaign Success

2023 | Organization Science | Citations: 0

Authors: Brauer, Matthias; Wiersema, Margarethe; Binder, Philipp

Abstract: Activist hedge funds represent the most potent form of financial activism. Yet w ... Expand

Abstract: Activist hedge funds represent the most potent form of financial activism. Yet we do not fully understand how these activist investors, despite holding only a small stake in target firms, are able to influence management and the board to acquiesce to their demands, especially given the large uncertainty that their demands will improve shareholder value. Building on impression management (IM) theory, we propose that activist investors who express their concerns and demands with high confidence in their letters to target firms are likely to be perceived as knowledgeable and competent by the firm’s other shareholders, thus influencing the response of the firm’s management and board. In support of this theoretical proposition, results of our empirical analysis of 475 U.S. activist campaigns against U.S. companies between 2007 and 2019 suggest that confidence in tone in an activist’s letter, as a form of self-promotion, is positively associated with campaign success. We also observe that the positive association between confidence in tone in activist letters and campaign success is less for activists with greater success in prior campaigns and in campaigns with multiple activists. Our paper contributes to financial activism research by showing that activists’ verbal impression management can serve as an effective influence tactic in their campaigns. Our study also contributes to the emerging research stream on verbal IM by introducing a language attribute, confidence in tone, that has not been studied in management research and is distinct from past constructs examined in verbal IM research. Collapse

Topics: corporate governance strategic management organizational productivity decision making database system

Methods: descriptive statistic qualitative content analysis theory development linguistic analysis regression analysis method

Theories: behavioral theory

Automated Deductive Content Analysis of Text: A Deep Contrastive and Active Learning Based Approach

2023 | International Conference on Information Systems | Citations: 0

Authors: De Oliveira Silveira, Alysson; Ray, Arindam; Ebrahimi, Mohammadreza; Bhattacherjee, Anol

Abstract: Content analysis traditionally involves human coders manually combing through t ... Expand

Abstract: Content analysis traditionally involves human coders manually combing through text documents to search for relevant concepts and categories. However, this approach is time-intensive and not scalable, particularly for secondary data like social media content, news articles, or corporate reports. To address this problem, the paper presents an automated framework called Automated Deductive Content Analysis of Text (ADCAT) that uses deep learning-based semantic techniques, an ontology of validated construct measures, a large language model, human-in-the-loop disambiguation, and a novel augmentation-based weighted contrastive learning approach for improved language representations, to build a scalable approach for deductive content analysis. We demonstrate the effectiveness of the proposed approach to identify firm innovation strategies from their 10-K reports to obtain inferences reasonably close to human coding. Collapse

Topics: knowledge representation human in the loop human computer interaction innovation management organizational innovation

Methods: ontological modelling machine learning language model large language model qualitative content analysis

Prior Experience of Managers and Maladaptive Responses to Performance Feedback: Evidence from Mutual Funds

2023 | Organization Science | Citations: 0

Authors: Gaba, Vibha; Lee, Sunkee; Meyer-Doyle, Philipp; Zhao-Ding, Amy

Abstract: In this study, we examine how the prior experiences of decision makers systemati ... Expand

Abstract: In this study, we examine how the prior experiences of decision makers systematically influence their assessment of and responses to negative performance feedback. We posit that, although greater and more specialized experiences enable managers to build relevant knowledge and expertise in specific domains, they also make them overconfident in their abilities and strategies. Such experience-induced overconfidence further leads to distortions in the performance assessment process, hindering a firm’s ability to recognize and respond to poor performance. We empirically test these arguments in the context of U.S. mutual fund managers making investment decisions in response to fund performance below aspirations. As hypothesized, we find that more experienced and more specialized fund managers change their investment decisions less when faced with negative performance feedback than managers who are less experienced and less specialized. In additional analyses, we further show that the lower responsiveness of more experienced (specialized) managers is associated with the fund’s lower future performance, supporting our proposed theoretical mechanism (overconfidence). This study augments existing performance feedback research by showing how decision makers’ prior experience can impede problem-solving behavior in organizations. It also contributes to the literature on human capital and organizational learning by documenting an unintended consequence of accumulated human capital on firm adaptive behavior. Funding: This work was supported by INSEAD research & development funds. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2022.1605. Collapse

Topics: IT career organizational productivity human resource management unintended consequence problem solving

Methods: robustness check descriptive statistic theory development qualitative content analysis SEC EDGAR

Theories: theory of bounded rationality behavioral theory of the firm

Extraction of Forward-looking Financial Information for Stock Price Prediction from Annual Reports Using NLP Techniques

2023 | HICSS | Citations: 0

Authors: Glodd, Alexander; Hristova, Diana

Abstract: Annual reports are one of the most important sources of information for financia ... Expand

Abstract: Annual reports are one of the most important sources of information for financial decisions. They contain forward-looking statements (FLS), which describe future trends and expectations. Thus, several studies deal with the automated identification of FLS, where the latest ones involve a combination of a rule-based approach and machine learning classification. In this paper, we extend this research with state-of-the-art NLP methods. We use DistilBERT for FLS identification and determine their sentiment with FinBERT. The result is processed by a Random Forest model for stock price growth prediction of different periods. Our evaluation shows that DestilBERT achieves higher accuracies on FLS identification than existing methods. For short-term stock price rate prediction, the extracted FLS information together with historical stock data outperforms the sole use of historical stock data. For mid-term prediction, using FLS alone with DestilBERT shows the best result. Finally, in the long-term, FLS provide no benefit. Collapse

Topics: Python decision making

Methods: BERT support vector machine natural language processing DistilBERT literature study

When Do Firms Crack Under Pressure? Legal Professionals, Negative Role Models, and Organizational Misconduct

2023 | Organization Science | Citations: 0

Authors: Gonsalves, Leroy

Abstract: Strain theory has long been invoked to explain organizational misconduct, with u ... Expand

Abstract: Strain theory has long been invoked to explain organizational misconduct, with underperformance creating pressure for firms to engage in morally objectionable activities. In this paper, I examine whether underperformance increases the risk of organizational misconduct. Drawing on institutional arguments about professions and social learning, I further predict that when experiencing performance strain, legal professionals will push the boundaries of the law, increasing the risk of misconduct if they have influence over decision making. However, industry peers caught engaging in misconduct should serve as negative role models, reducing the risk of the firm resorting to misconduct to overcome performance shortfalls. I test and find support for these predictions using longitudinal data on material legal claims filed against S&P 1500 firms between 2000 and 2017. The study extends the strain theory of organizational misconduct, identifying how legal professionals and negative role models shape firms’ strategic responses to performance pressure. Collapse

Topics: social media Slack decision making database system missing data

Methods: longitudinal research descriptive statistic robustness check SEC EDGAR Compustat

Theories: general strain theory

Decoding Digital Risk from Corporate Disclosure: A Neural Network Approach

2023 | Americas Conference on Information Systems | Citations: 0

Authors: Long, Yuan; Rai, Arun

Abstract: Digital risk-or the likelihood of losses from organizational context, digital i ... Expand

Abstract: Digital risk-or the likelihood of losses from organizational context, digital infrastructure, IS sourcing, data management and IS applications, is a key consideration in the valuation of firms. We apply a neural network approach to construct and measure digital risks by extracting linguistic relations from the 10-K disclosure (Section "Item 1A"). We develop novel firm-level digital risk measures based on these linguistic relations from three perspectives: (i) presence (whether the digital risk is mentioned or not), (ii) intensity (text coverage of digital risk relative to other issues), and (iii) diversity (the different types of digital risk that are mentioned). We validate our measures for digital risk by demonstrating that they correlate significantly with firm risk, as proxied by the volatility of the stock market. Overall, our findings suggest that the utility of leveraging this type of text-based measure for digital risk is practically feasible, scalable, and economically meaningful. Collapse

Topics: IT risk management Python database system accounting IT security

Methods: artificial neural network BERT regression analysis method descriptive statistic SEC EDGAR

Soft but Strong: Software-Based Innovation and Product Differentiation in the IT Hardware Industry

2022 | Management Information Systems Quarterly | Citations: 0

Authors: Kim, Keongtae; Lee, Jeongsik “Jay”; Gopal, Anandasivam

Abstract: It has been argued in recent years that software is a significant value-creating ... Expand

Abstract: It has been argued in recent years that software is a significant value-creating factor within the manufacturing sector, leading to an increased interest in understanding the mechanisms by which firms may benefit from software-based innovation. Empirical work in evaluating these mechanisms, however, remains underdeveloped. In this paper, we examine one such process through which software-based innovation may provide value for information technology hardware firms in the U.S. Specifically, we examine the influence of software patents on the extent to which firms are able to benefit from product differentiation in their product markets. Using data on over 380,000 patent grants for innovations filed by 730 public IT hardware firms over the time period 1996-2015, we find that greater levels of software-based innovation within the firm are associated with higher levels of product differentiation in product markets: increases in the intensity of software patents within the firm’s patent stock, which we refer to as software intensity, are associated with lower total similarity of product offerings, relative to those offered by rivals, as well as fewer effective competitors in the market. Furthermore, using a different dataset consisting of 23,000 new IT hardware product announcements, we show that firms with greater software intensity subsequently launch a higher number of new products. Moreover, these firms are more likely to launch products that are not only differentiated from those of rivals but also distinct from their own product lines. Our research contributes to the emerging literature on software-based innovation by substantiating an important mechanism through which software helps transform industry sectors, specifically through product differentiation. Collapse

Topics: innovation management computer hardware product differentiation software patent digital innovation

Methods: topic model term frequency–inverse document frequency longitudinal research robustness check difference in differences

DIGITAL CONVERGENCE: EXAMINING THE DISSOLUTION OF INDUSTRIAL AND TECHNOLOGICAL BOUNDARIES

2022 | European Conference On Information Systems | Citations: 0

Authors: Müller, Lukas; Hund, Axel; Wagner, Heinz-Theo

Abstract: Digital convergence is frequently discussed in research. The concept of converg ... Expand

Abstract: Digital convergence is frequently discussed in research. The concept of convergence describes how formerly separate areas are increasingly merging. So far, however, we have only a rudimentary understanding of digital convergence for several reasons. First, digital convergence is not clearly conceptualized and used differently across contexts. Second, we have little insight into what is converging and at what pace. We conceptualize digital convergence by arguing that its sociotechnical nature requires jointly considering technical and social aspects. Our analysis of a longitudinal patent data set covering 31 years and 677,045 patents from 124 industries shows that (1) industry boundaries defined by the Standard Industrial Classification (SIC) are dissolving as companies interact with technological knowledge outside their industrial boundaries. (2) Specific technology classes defined in the International Patent Classification (IPC) increasingly cite -and converge with -other technology classes. We close by highlighting promising avenues for future research on digital convergence. Collapse

Topics: website innovation management digital innovation database system spreadsheet

Methods: longitudinal research SEC EDGAR longitudinal data analysis

Theories: information systems theory

Digital Innovation Units: An Empirical Investigation of Performance Implications

2022 | International Conference on Information Systems | Citations: 0

Authors: Schumm, Max; Hanelt, André; Firk, Sebastian

Abstract: Digital innovation is both a necessary and a challenging endeavor for most firm ... Expand

Abstract: Digital innovation is both a necessary and a challenging endeavor for most firms. To achieve progress in this regard, firms across contexts increasingly set up digital innovation units (DIUs). Despite its popularity in practice, the prospects of this initiative are to date unclear. Building upon dynamic capabilities theory, we hypothesize the performance implications of DIUs and employ panel data regressions to a longitudinal and cross-industry data-set to investigate our predictions. We find that DIUs increase performance and that this effect is strengthened by the presence of digital ventures in the industry as well as the degree to which the industry relies on tangible assets. Our additional analyses provide a nuanced perspective on the implications of DIU establishments. On the base of these findings, we derive important implications for IS research about digital innovation and transformation as well as DIUs and provide recommendations for managerial practice. Collapse

Topics: organizational productivity electronic market innovation management digital transformation digital innovation

Methods: longitudinal research LexisNexis propensity score matching descriptive statistic SEC EDGAR

Theories: dynamic capabilities theory

The Information Content of ESG Disclosures and the Public Perception of Credit Risk

2021 | International Conference on Information Systems | Citations: 0

Authors: Bariz, Historei; Kiesel, Florian; Schreiber, Nicolas

Abstract: Environmental, social, and governance (ESG) topics are becoming omnipresent in ... Expand

Abstract: Environmental, social, and governance (ESG) topics are becoming omnipresent in capital markets. Despite the increasing importance of ESG-related risks, markets still exhibit a high degree of information asymmetry due to the mostly qualitative nature of ESGrelated information. Using natural language processing and 2,300 annual reports, we examine how ESG-related content affects the market's credit risk perception. We find that market participants integrate the tone and provision of numerical ESG-related information into their credit risk assessment. Additional evidence suggests that executives engage in strategic impression management, particularly thematic manipulation and manipulation through numerical information in their presentation of ESG content in annual reports, to improve the market's perception of a firm's ESG activities and thus to positively influence public perception of a firm's credit risk. Collapse

Topics: social media strategic management green IS open source data breach

Methods: sentiment analysis financial report qualitative content analysis regression analysis method computational algorithm

Theories: lemon market theory