Ronald C. W. Tsang, Amelia A. Baldwin, Joseph F. Hair, Ermanno Affuso, K. Lahtinen
This study examines the degree to which Loughran and McDonald (LM) word lists are informative at the item level of SEC filings, such as risk factors (RF) and management’s discussion and analysis (MDA) disclosures in 10-X reports. In this context, we explore if sentiment types are informative when associated with other material events, namely cybersecurity breaches. Our results support the assertion that sentiment types, beyond positive and negative, are informative at the individual disclosure item level, as tested in the RF and MDA sections. We also find that investors respond to different types of sentiment between RF and MDA. We find an economically significant estimated average economic impact of $469 million/firm. We further contribute to the literature by applying novel statistical methods that advance empirical accounting literature. Data Availability: Data are available from the public sources cited in the text.
{"title":"The Informativeness of Sentiment Types in Risk Factor Disclosures: Evidence from Firms with Cybersecurity Breaches","authors":"Ronald C. W. Tsang, Amelia A. Baldwin, Joseph F. Hair, Ermanno Affuso, K. Lahtinen","doi":"10.2308/isys-2022-014","DOIUrl":"https://doi.org/10.2308/isys-2022-014","url":null,"abstract":"\u0000 This study examines the degree to which Loughran and McDonald (LM) word lists are informative at the item level of SEC filings, such as risk factors (RF) and management’s discussion and analysis (MDA) disclosures in 10-X reports. In this context, we explore if sentiment types are informative when associated with other material events, namely cybersecurity breaches. Our results support the assertion that sentiment types, beyond positive and negative, are informative at the individual disclosure item level, as tested in the RF and MDA sections. We also find that investors respond to different types of sentiment between RF and MDA. We find an economically significant estimated average economic impact of $469 million/firm. We further contribute to the literature by applying novel statistical methods that advance empirical accounting literature.\u0000 Data Availability: Data are available from the public sources cited in the text.","PeriodicalId":42112,"journal":{"name":"African Journal of Information Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91384080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We propose new measures of financial disclosure quality based on XBRL standard and extension tag counts. XBRL is designed to allow users to analyze more financial statement detail, thereby increasing reporting transparency. Thus, firms that use more XBRL standard tags should produce more detailed and comparable financial reports. To test this assertion, we examine the association between XBRL tag counts and stock return synchronicity, which measures the market’s reaction to the quality of firms’ disclosures. We find that firms that use more standard taxonomy tags in their 10-K reports have higher stock return synchronicity after the 10-K filing. However, we find that stock return synchronicity decreases as XBRL extension tag counts increase, suggesting that the use of extension tags reduces disclosure quality. Our findings contribute to the growing body of research on XBRL disclosure. Data Availability: Data are available from the public sources cited in the text.
{"title":"Does XBRL Tagging Indicate Disclosure Quality? The Relationship Between XBRL Standard and Extension Tags and Stock Return Synchronicity","authors":"Jee-Hae Lim, V. Richardson, Rodney E. Smith","doi":"10.2308/isys-2022-027","DOIUrl":"https://doi.org/10.2308/isys-2022-027","url":null,"abstract":"\u0000 We propose new measures of financial disclosure quality based on XBRL standard and extension tag counts. XBRL is designed to allow users to analyze more financial statement detail, thereby increasing reporting transparency. Thus, firms that use more XBRL standard tags should produce more detailed and comparable financial reports. To test this assertion, we examine the association between XBRL tag counts and stock return synchronicity, which measures the market’s reaction to the quality of firms’ disclosures. We find that firms that use more standard taxonomy tags in their 10-K reports have higher stock return synchronicity after the 10-K filing. However, we find that stock return synchronicity decreases as XBRL extension tag counts increase, suggesting that the use of extension tags reduces disclosure quality. Our findings contribute to the growing body of research on XBRL disclosure.\u0000 Data Availability: Data are available from the public sources cited in the text.","PeriodicalId":42112,"journal":{"name":"African Journal of Information Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78773277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Determining whether constituent opinion agrees or disagrees with proposed regulation is crucial to improving our understanding of standard-setting practices. However, the constituent feedback mechanisms provided by regulators to constituents results in large-scale unstructured datasets—thus establishing an obstacle in examining differences of opinion between parties. Utilizing publicly available documents of the FASB, this study trains machine-learning models to efficiently and effectively categorize the level of agreement and disagreement on proposed regulation between the regulator and its constituent base. We employ three different approaches—a lexicon-based approach using the dictionary method and two participant-based approaches leveraging human raters (AMT and AS). We find that the machine-learning models demonstrate more accuracy in correctly classifying observations as compared to human raters. Further, the analysis indicates that the machine-learning models using the participant-based approach and the lexicon-based approach achieve similar accuracy in predicting constituent agreement and disagreement with proposed regulation. Data Availability: Data available upon request.
{"title":"Constituent Input on Regulatory Initiatives: A Machine-Learning Approach to Efficiently and Effectively Analyze Unstructured Data","authors":"Daniel P. Ferguson, M. K. Harris, L. T. Williams","doi":"10.2308/isys-2021-032","DOIUrl":"https://doi.org/10.2308/isys-2021-032","url":null,"abstract":"\u0000 Determining whether constituent opinion agrees or disagrees with proposed regulation is crucial to improving our understanding of standard-setting practices. However, the constituent feedback mechanisms provided by regulators to constituents results in large-scale unstructured datasets—thus establishing an obstacle in examining differences of opinion between parties. Utilizing publicly available documents of the FASB, this study trains machine-learning models to efficiently and effectively categorize the level of agreement and disagreement on proposed regulation between the regulator and its constituent base. We employ three different approaches—a lexicon-based approach using the dictionary method and two participant-based approaches leveraging human raters (AMT and AS). We find that the machine-learning models demonstrate more accuracy in correctly classifying observations as compared to human raters. Further, the analysis indicates that the machine-learning models using the participant-based approach and the lexicon-based approach achieve similar accuracy in predicting constituent agreement and disagreement with proposed regulation.\u0000 Data Availability: Data available upon request.","PeriodicalId":42112,"journal":{"name":"African Journal of Information Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80914105","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The purpose of this study is to review a text topic modeling methodology, latent semantic analysis (LSA), and provide researchers with the requisite knowledge to allow them to learn and implement their own accounting research study using LSA. The authors first provide a brief literature review of prior business and accounting research studies that have utilized the LSA methodology. Using a provided dataset, the authors present details of how to employ LSA in a research study by replicating the mechanics used in an LSA study conducted by Hutchison, Plummer, and George (2018b). Their intent is to present thorough guidance on data selection, the analysis platform, and the necessary steps needed to conduct LSA research. This article also briefly compares LSA with other topic modeling methodologies, presents several accounting research opportunities where LSA could be utilized, and outlines LSA’s limitations. Data Availability: Data are available from the public sources cited in the text; sample dataset is available for download, see footnote 5.
{"title":"Application of Latent Semantic Analysis in Accounting Research","authors":"P. Hutchison, Benjamin T. George, Binod Guragai","doi":"10.2308/isys-2022-013","DOIUrl":"https://doi.org/10.2308/isys-2022-013","url":null,"abstract":"\u0000 The purpose of this study is to review a text topic modeling methodology, latent semantic analysis (LSA), and provide researchers with the requisite knowledge to allow them to learn and implement their own accounting research study using LSA. The authors first provide a brief literature review of prior business and accounting research studies that have utilized the LSA methodology. Using a provided dataset, the authors present details of how to employ LSA in a research study by replicating the mechanics used in an LSA study conducted by Hutchison, Plummer, and George (2018b). Their intent is to present thorough guidance on data selection, the analysis platform, and the necessary steps needed to conduct LSA research. This article also briefly compares LSA with other topic modeling methodologies, presents several accounting research opportunities where LSA could be utilized, and outlines LSA’s limitations.\u0000 Data Availability: Data are available from the public sources cited in the text; sample dataset is available for download, see footnote 5.","PeriodicalId":42112,"journal":{"name":"African Journal of Information Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77164404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The 2008 financial crisis highlighted the significant, vertical pay disparity between chief executive officers (CEOs) and all other employees. Following equity theory, prior research finds negative associations between vertical pay disparity and job satisfaction and performance (i.e., more errors). We build on this research to investigate if vertical pay disparity between the CEO and employees/other executives is a previously unidentified determinant of data security breaches (DSBs). Results suggest that firms with large, vertical pay disparities are more likely to be breached, are associated with more DSBs, and are more likely to be associated with internal DSBs. We also find some evidence of remediation through narrowing the pay disparity between CEOs and employees/other executives after the DSB. Our findings contribute to the growing accounting information systems (AIS) cybersecurity literature, extend equity theory to a new context, and should be of interest to AIS governance researchers and stakeholders.
{"title":"Firm-Level Pay Disparity and Breach Risk","authors":"C. He, Tianpei Li, R. Pinsker","doi":"10.2308/isys-2021-040","DOIUrl":"https://doi.org/10.2308/isys-2021-040","url":null,"abstract":"\u0000 The 2008 financial crisis highlighted the significant, vertical pay disparity between chief executive officers (CEOs) and all other employees. Following equity theory, prior research finds negative associations between vertical pay disparity and job satisfaction and performance (i.e., more errors). We build on this research to investigate if vertical pay disparity between the CEO and employees/other executives is a previously unidentified determinant of data security breaches (DSBs). Results suggest that firms with large, vertical pay disparities are more likely to be breached, are associated with more DSBs, and are more likely to be associated with internal DSBs. We also find some evidence of remediation through narrowing the pay disparity between CEOs and employees/other executives after the DSB. Our findings contribute to the growing accounting information systems (AIS) cybersecurity literature, extend equity theory to a new context, and should be of interest to AIS governance researchers and stakeholders.","PeriodicalId":42112,"journal":{"name":"African Journal of Information Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89860823","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Comparing an entity’s financial indicators with those of similar organizations can provide a better understanding of its operational and financial health. This study describes the design and implementation of a prototype multilabel classification method to categorize nonprofit organizations (NPOs) using the textual content of their mission statements to enable beneficial comparisons. Positive unlabeled learning was used to improve the classification performance of partially labeled data. Naive Bayes, Gradient Boosting, Random Forest, and Support Vector Machine (SVM) algorithms were applied to determine the most effective method for classifying NPOs. The SVM model performed best in identifying “Housing and Shelter” organizations. The SVM classifier identified organizations that were not previously classified as “Housing and Shelter” but provided housing and shelter services as a part of their programs and activities. The new classification method can help donors, grant providers, and researchers to identify similar nonprofit organizations at the operational level.
{"title":"Designing a Classifying System for Nonprofit Organizations Using Textual Contents from the Mission Statement","authors":"Heejae Lee, Xinxin Wang, Richard B. Dull","doi":"10.2308/isys-2021-033","DOIUrl":"https://doi.org/10.2308/isys-2021-033","url":null,"abstract":"\u0000 Comparing an entity’s financial indicators with those of similar organizations can provide a better understanding of its operational and financial health. This study describes the design and implementation of a prototype multilabel classification method to categorize nonprofit organizations (NPOs) using the textual content of their mission statements to enable beneficial comparisons. Positive unlabeled learning was used to improve the classification performance of partially labeled data. Naive Bayes, Gradient Boosting, Random Forest, and Support Vector Machine (SVM) algorithms were applied to determine the most effective method for classifying NPOs. The SVM model performed best in identifying “Housing and Shelter” organizations. The SVM classifier identified organizations that were not previously classified as “Housing and Shelter” but provided housing and shelter services as a part of their programs and activities. The new classification method can help donors, grant providers, and researchers to identify similar nonprofit organizations at the operational level.","PeriodicalId":42112,"journal":{"name":"African Journal of Information Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2023-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78284461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Despite the growing interest in blockchain (BC) technology, recent research shows mixed perceptions due to its risks and returns. We utilize event study methodology to examine overall market reactions and to delineate how specific contexts associated with BC technology influence the market reactions. Using 149 BC technology adoptions announced between January 2015 and December 2019, we find positive abnormal returns, reflecting investors’ optimistic perception towards BC technology adoption. Specifically, we find that abnormal returns from BC technology are higher when used in a transformative strategic role and offered by leading vendors, suggesting that investors view this adoption as a value-enhancing activity. Our results are robust to the use of an alternative sample and different methods of measuring variables. Overall, our study provides insights for academics and practitioners to better understand the market value of BC technology adoptions.
{"title":"The Impact of Blockchain Technology Adoption Announcements on Firm’s Market Value","authors":"A. Y. Jeong, Jee-Hae Lim","doi":"10.2308/isys-2021-038","DOIUrl":"https://doi.org/10.2308/isys-2021-038","url":null,"abstract":"Despite the growing interest in blockchain (BC) technology, recent research shows mixed perceptions due to its risks and returns. We utilize event study methodology to examine overall market reactions and to delineate how specific contexts associated with BC technology influence the market reactions. Using 149 BC technology adoptions announced between January 2015 and December 2019, we find positive abnormal returns, reflecting investors’ optimistic perception towards BC technology adoption. Specifically, we find that abnormal returns from BC technology are higher when used in a transformative strategic role and offered by leading vendors, suggesting that investors view this adoption as a value-enhancing activity. Our results are robust to the use of an alternative sample and different methods of measuring variables. Overall, our study provides insights for academics and practitioners to better understand the market value of BC technology adoptions.","PeriodicalId":42112,"journal":{"name":"African Journal of Information Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2022-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87632454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper presents the Python code and illustrative crisis management data from Twitter. The code includes Twitter data collection and three machine learning algorithms that are readily usable. Three machine learning algorithms generate sentiment measures, extract topics from the tweets and, compare the similarity of topics across time. The code and the illustrative data will be accessible to researchers that are interested in using Twitter data to analyze a wide range of public perceptions and responses such as StockTwits activity; firm events such as the announcement of investment decisions or security breaches; public movements such as #earthday; and significant global events such as the invasion of Ukraine. A better understanding of the code and datasets will enable researchers in this field to engage in more extensive studies that fully utilize this rich data source to capture public perceptions.
{"title":"Python Code and Illustrative Crisis Management Data from Twitter","authors":"Y. Wang, T. Wang","doi":"10.2308/isys-2022-011","DOIUrl":"https://doi.org/10.2308/isys-2022-011","url":null,"abstract":"This paper presents the Python code and illustrative crisis management data from Twitter. The code includes Twitter data collection and three machine learning algorithms that are readily usable. Three machine learning algorithms generate sentiment measures, extract topics from the tweets and, compare the similarity of topics across time. The code and the illustrative data will be accessible to researchers that are interested in using Twitter data to analyze a wide range of public perceptions and responses such as StockTwits activity; firm events such as the announcement of investment decisions or security breaches; public movements such as #earthday; and significant global events such as the invasion of Ukraine. A better understanding of the code and datasets will enable researchers in this field to engage in more extensive studies that fully utilize this rich data source to capture public perceptions.","PeriodicalId":42112,"journal":{"name":"African Journal of Information Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2022-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79793959","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-10DOI: 10.24167/SISFORMA.V8I1.3025
S. Supangat, Mohd Zainuri Saringat, G. Kusnanto, A. Andrianto
Colleges are an optional final stage of formal education. However, with time, the Management Section finds the fact that the student churn rate in the university scope becomes a problem. The purpose of this research is to predict whether the student will be churn or loyal in the future, the data will be taken from 2014. The analytical technique used in this research process is the Fuzzy method of C-Means. At a university, the variable that will be tested is the Length of students who are currently attending higher education at the University or campus, when the last student pays enrollment, the last payment period, and the student's total payment. The data used as many as 100 datasets were collected in this research, starting from 2014 to 2020. Of the 100 Datasets of informatics engineering, students gained 92% of loyal students, and 8% of students predicted to churn.
{"title":"Churn Prediction on Higher Education Data with Fuzzy Logic Algorithm","authors":"S. Supangat, Mohd Zainuri Saringat, G. Kusnanto, A. Andrianto","doi":"10.24167/SISFORMA.V8I1.3025","DOIUrl":"https://doi.org/10.24167/SISFORMA.V8I1.3025","url":null,"abstract":"Colleges are an optional final stage of formal education. However, with time, the Management Section finds the fact that the student churn rate in the university scope becomes a problem. The purpose of this research is to predict whether the student will be churn or loyal in the future, the data will be taken from 2014. The analytical technique used in this research process is the Fuzzy method of C-Means. At a university, the variable that will be tested is the Length of students who are currently attending higher education at the University or campus, when the last student pays enrollment, the last payment period, and the student's total payment. The data used as many as 100 datasets were collected in this research, starting from 2014 to 2020. Of the 100 Datasets of informatics engineering, students gained 92% of loyal students, and 8% of students predicted to churn.","PeriodicalId":42112,"journal":{"name":"African Journal of Information Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78937044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-10DOI: 10.24167/SISFORMA.V8I1.2916
Bagus Kristanto Putra
The internet is not a strange thing in people's lives, now with the internet, it is easier for people to communicate between regions and between countries. Internet, which stands for inter and networking, is a collection of computer networks and various types that communicate with each other using a communication standard. The method used in this literature search is the systematic literature review (SLR) method. By using this method, researchers can carry out systematic reviews and journal identification in which each process follows the steps that have been determined. Based on the results of the review search for the article above, the researcher can conclude: (1) there are so many articles about the characteristics of good social media, and affect many factors so that researchers find many new problems that are interesting to research. (2) Some articles state that the main characteristics that must be owned are attractive and customization so that it can affect the brand equity of a brand.
{"title":"Marketing Characteristics Through Social Media That Are Able To Produce Brand Equity And Consumer Response Through Consumer Interest","authors":"Bagus Kristanto Putra","doi":"10.24167/SISFORMA.V8I1.2916","DOIUrl":"https://doi.org/10.24167/SISFORMA.V8I1.2916","url":null,"abstract":"The internet is not a strange thing in people's lives, now with the internet, it is easier for people to communicate between regions and between countries. Internet, which stands for inter and networking, is a collection of computer networks and various types that communicate with each other using a communication standard. The method used in this literature search is the systematic literature review (SLR) method. By using this method, researchers can carry out systematic reviews and journal identification in which each process follows the steps that have been determined. Based on the results of the review search for the article above, the researcher can conclude: (1) there are so many articles about the characteristics of good social media, and affect many factors so that researchers find many new problems that are interesting to research. (2) Some articles state that the main characteristics that must be owned are attractive and customization so that it can affect the brand equity of a brand.","PeriodicalId":42112,"journal":{"name":"African Journal of Information Systems","volume":null,"pages":null},"PeriodicalIF":0.7,"publicationDate":"2021-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88129730","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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