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Feasibility analysis of machine learning for performance-related attributional statements 机器学习用于绩效归因陈述的可行性分析
IF 4.6 3区 管理学 Q1 Business, Management and Accounting Pub Date : 2023-03-01 DOI: 10.1016/j.accinf.2022.100597
Anil Berkin , Walter Aerts , Tom Van Caneghem

We investigate the feasibility of machine learning methods for attributional content and framing analysis in corporate reporting. We test the performance of five widely-used supervised machine learning classifiers (naïve Bayes, logistic regression, support vector machines, random forests, decision trees) in a top-down three-level hierarchical setting to (1) identify performance-related statements; (2) detect attributions in these; and (3) classify the content of the attributional statements. The training set comprises manually coded statements from a corpus of management commentary reports of listed companies. The attributions include both intra- and inter-sentential attributional statements. The results show that for both intra- and inter-sentential attributions, F1-scores of our most accurate classifier (i.e., support vector machines) vary in the range of 76% up to 94%, depending on the identification, detection and classification levels and the content characteristics of attributions. Additionally, we assess the hierarchical performance of classifiers, providing insights into a more holistic classification process for attributional statements. Overall, our results show how machine learning methods may facilitate narrative disclosure analysis by providing a more efficient way to detect and classify performance-related attributional statements. Our findings contribute to the accounting and management literature by providing a basis for implementing machine learning methodologies for research investigating attributional behavior and related impression management.

我们研究了机器学习方法在企业报告中用于归因内容和框架分析的可行性。我们在自上而下的三级层次设置中测试了五个广泛使用的监督机器学习分类器(天真贝叶斯、逻辑回归、支持向量机、随机森林、决策树)的性能,以(1)识别性能相关语句;(2) 检测其中的归因;以及(3)对归因陈述的内容进行分类。该训练集包括来自上市公司管理评论报告语料库的手动编码语句。归因包括句内和句间归因陈述。结果表明,对于句内和句间属性,我们最准确的分类器(即支持向量机)的F1分数在76%到94%之间变化,这取决于属性的识别、检测和分类水平以及属性的内容特征。此外,我们评估了分类器的分层性能,为属性陈述的更全面的分类过程提供了见解。总的来说,我们的研究结果表明,机器学习方法可以通过提供一种更有效的方法来检测和分类与绩效相关的归因陈述,从而促进叙事披露分析。我们的研究结果为实施机器学习方法研究归因行为和相关印象管理提供了基础,从而为会计和管理文献做出了贡献。
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引用次数: 1
How can firms repair their reputations when they discover information technology control material weaknesses? 当企业发现信息技术控制的重大缺陷时,他们如何修复自己的声誉?
IF 4.6 3区 管理学 Q1 Business, Management and Accounting Pub Date : 2023-03-01 DOI: 10.1016/j.accinf.2022.100595
Anna M. Rose , Jacob M. Rose , Kara M. Obermire , Carolyn Strand Norman , Nicole Frydenlund

We examine the effects of information technology material weaknesses on a firm’s reputation by examining how management’s actions before and after disclosure influence investors’ trust in management and perceptions of investment risk. Specifically, we look at the influence of: 1) management taking responsibility for an information technology material weakness, and 2) replacing the CFO with someone with technology expertise. We find that management taking responsibility for a material weakness does not lead to increased trust in management before or after remediation. However, investors perceive more favorable market reactions to remediation when management had previously taken responsibility for the control weakness. Further, we find that replacing the CFO with someone who has technology expertise results in increases in investor trust and improvements in perceptions of investment risk after control weakness remediation. This suggests the importance of sending clear signals to investors that the company is hiring managers with appropriate technology expertise.

我们通过研究管理层在披露前后的行为如何影响投资者对管理层的信任和对投资风险的感知,来研究信息技术实质性弱点对公司声誉的影响。具体而言,我们考察了以下因素的影响:1)管理层对信息技术的重大弱点负责,2)用具有技术专业知识的人取代首席财务官。我们发现,在补救之前或之后,管理层对重大弱点负责并不会增加对管理层的信任。然而,当管理层之前对控制不力负责时,投资者认为市场对补救措施的反应更为有利。此外,我们发现,用具有技术专业知识的人取代首席财务官会增加投资者的信任,并在控制薄弱环节补救后改善对投资风险的感知。这表明向投资者发出明确信号的重要性,即公司正在招聘具有适当技术专业知识的经理。
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引用次数: 0
Enhancing the government accounting information systems using social media information: An application of text mining and machine learning 利用社会媒体信息增强政府会计信息系统:文本挖掘和机器学习的应用
IF 4.6 3区 管理学 Q1 Business, Management and Accounting Pub Date : 2023-03-01 DOI: 10.1016/j.accinf.2022.100600
Huijue Kelly Duan , Miklos A. Vasarhelyi , Mauricio Codesso , Zamil Alzamil

This study demonstrates a way of bringing an innovative data source, social media information, to the government accounting information systems to support accountability to stakeholders and managerial decision-making. Future accounting and auditing processes will heavily rely on multiple forms of exogenous data. As an example of the techniques that could be used to generate this needed information, the study applies text mining techniques and machine learning algorithms to Twitter data. The information is developed as an alternative performance measure for NYC street cleanliness. It utilizes Naïve Bayes, Random Forest, and XGBoost to classify the tweets, illustrates how to use the sampling method to solve the imbalanced class distribution issue, and uses VADER sentiment to derive the public opinion about street cleanliness. This study also extends the research to another social media platform, Facebook, and finds that the incremental value is different between the two social media platforms. This data can then be linked to government accounting information systems to evaluate costs and provide a better understanding of the efficiency and effectiveness of operations.

这项研究展示了一种将创新的数据来源——社交媒体信息——引入政府会计信息系统的方法,以支持对利益相关者的问责和管理决策。未来的会计和审计过程将严重依赖多种形式的外部数据。作为可以用来生成这些所需信息的技术的一个例子,该研究将文本挖掘技术和机器学习算法应用于Twitter数据。该信息是作为纽约市街道清洁度的替代绩效衡量标准而开发的。它利用Naïve Bayes、Random Forest和XGBoost对推文进行分类,说明如何使用抽样方法来解决阶级分布不平衡的问题,并利用VADER情绪来导出公众对街道清洁的看法。本研究还将研究扩展到另一个社交媒体平台Facebook,发现两个社交媒体的增量值不同。然后,这些数据可以与政府会计信息系统联系起来,以评估成本,并更好地了解业务的效率和有效性。
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引用次数: 6
An eye tracking experiment investigating synonymy in conceptual model validation 同义词概念模型验证的眼动追踪实验
IF 4.6 3区 管理学 Q1 Business, Management and Accounting Pub Date : 2022-12-01 DOI: 10.1016/j.accinf.2022.100578
Walter R. Boot , Cheryl L. Dunn , Bachman P. Fulmer , Gregory J. Gerard , Severin V. Grabski

A key advantage of conceptual models is that their quality can be evaluated and validated before beginning the costlier stages of information system development. Few research studies investigate the validation process for such models, particularly regarding multiplicities, even though multiplicity mistakes can be very costly. We investigated the validation of conceptual model multiplicities, varying how closely natural language statements of business rules match the models that purport to represent those rules. Participants in an eye tracking experiment completed validation tasks in which they viewed a statement and an accompanying UML class diagram in which a specified multiplicity was consistent with the statement (valid) or inconsistent with the statement (invalid). We varied whether the focal multiplicity was a minimum or a maximum and varied the class diagram’s semantics and order compared to that of the statement. Logistic regression was used to analyze the relationship between accuracy and the experimental manipulations and controls. The results show that the odds of accuracy in validating class diagrams that used synonyms instead of the exact statement terminology were only 0.46 times the odds of accuracy when the class diagram and statement words matched, showing a costly effect of synonymy. Interestingly, independent of the three levels of relative semantics, the odds of accuracy were 0.48 times when class diagrams were consistent with business rules as they were when class diagrams were inconsistent with business rules. To gain insight into cognition under correct task performance, we conducted additional linear regression analysis on various eye tracking metrics for only the accurate responses. Again, synonymy was observed to be costly, with a cognitive burden of increased integrative transitions between statement and model in the range of 39 to 66%.

概念模型的一个关键优点是,它们的质量可以在开始昂贵的信息系统开发阶段之前进行评估和验证。很少有研究调查这些模型的验证过程,特别是关于多重性,尽管多重性错误可能代价非常高昂。我们研究了概念模型多样性的验证,改变了业务规则的自然语言语句与声称表示这些规则的模型的匹配程度。眼球追踪实验的参与者完成了验证任务,他们在其中查看一个语句和伴随的UML类图,其中指定的多重性与语句一致(有效)或不一致(无效)。我们改变了焦点多重性是最小还是最大,并改变了类图与语句的语义和顺序。采用Logistic回归分析了准确度与实验操作和控制之间的关系。结果表明,当类图和语句词匹配时,使用同义词而不是确切的语句术语验证类图的准确率只有类图和语句词匹配时的0.46倍,这显示了同义词带来的代价高昂的影响。有趣的是,独立于三个相对语义级别,类图与业务规则一致时的准确率是类图与业务规则不一致时的0.48倍。为了深入了解正确任务执行下的认知,我们对各种眼动追踪指标进行了额外的线性回归分析,仅针对准确的反应。再一次,同义被观察到是昂贵的,在陈述和模型之间增加的整合转换的认知负担在39%到66%的范围内。
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引用次数: 1
Estimating the duration of competitive advantage from emerging technology adoption 估算新兴技术采用带来的竞争优势持续时间
IF 4.6 3区 管理学 Q1 Business, Management and Accounting Pub Date : 2022-12-01 DOI: 10.1016/j.accinf.2022.100577
Theophanis C. Stratopoulos , Victor Xiaoqi Wang

This paper proposes a method for estimating the expected duration of competitive advantage from emerging technology adoption for the average adopting firm. The proposed method relies on publicly available data (e.g., web search interest, news articles, book titles, and firm disclosures) and integrates elements from diffusion of innovation theory, hype cycles, and resource-based view of competitive advantage. We validate this method by applying it to two mature technologies, namely ERP and cloud computing, for which we come up with estimates consistent with findings from prior studies. Leveraging our method, researchers and professionals can use readily available data to make their own estimations. Such estimates can inform researchers in answering research questions related to duration of competitive advantage from technology adoption. They can inform professionals in making better business decisions such as forecasting the net present value of an investment in an emerging technology.

本文提出了一种估算新兴技术采用对企业竞争优势预期持续时间的方法。所提出的方法依赖于公开可用的数据(例如,网络搜索兴趣、新闻文章、书名和公司披露),并整合了创新扩散理论、炒作周期和基于资源的竞争优势观等要素。我们通过将其应用于两种成熟的技术,即ERP和云计算来验证该方法,我们提出了与先前研究结果一致的估计。利用我们的方法,研究人员和专业人员可以使用现成的数据来做出他们自己的估计。这样的估计可以告知研究人员回答与技术采用带来的竞争优势持续时间有关的研究问题。它们可以帮助专业人士做出更好的商业决策,比如预测一项新兴技术投资的净现值。
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引用次数: 3
V-Matrix: A wave theory of value creation for big data V-Matrix:大数据价值创造的波浪理论
IF 4.6 3区 管理学 Q1 Business, Management and Accounting Pub Date : 2022-12-01 DOI: 10.1016/j.accinf.2022.100575
Guido L. Geerts , Daniel E. O'Leary

This paper examines the “V-Matrix” and provides a wave theory life cycle model of organizations’ adoption of big data. The V-Matrix is based on the big data five “V’s”: Volume, Velocity, Variety, Veracity, and Value and captures and enumerates the different potential states that an organization can go through as part of its adoption and evolution towards big data. We extend the V-Matrix to a state space approach in order to provide a characterization of the adoption of big data technologies in an organization. We develop and use a wave theory of implementation to accommodate a firm’s movement through the V-Matrix. Accordingly, the V-Matrix provides a life cycle model of organizational use of the different aspects of big data. In addition, the model can help organizations’ plan for decision-making use of big data as they anticipate movement from one state to another, as they add big data capabilities. As part of this analysis, the paper examines some of the issues that occur in the different states, including synergies and other issues associated with co-occurrence of different V’s with each other. Finally, this paper integrates the V-Matrix with other data analytic life cycles and examines some of the implications of those models.

本文考察了“v矩阵”,并提供了组织采用大数据的波动理论生命周期模型。V矩阵基于大数据的五个“V”:体积(Volume)、速度(Velocity)、多样性(Variety)、准确性(Veracity)和价值(Value),并捕获和列举了一个组织在采用和发展大数据的过程中可能经历的不同潜在状态。我们将v矩阵扩展到状态空间方法,以提供组织中采用大数据技术的特征。我们开发并使用了一种实施的波动理论,以适应企业通过v矩阵的运动。因此,v矩阵提供了一个组织使用大数据不同方面的生命周期模型。此外,该模型还可以帮助组织制定大数据的决策计划,因为他们可以预测从一个州到另一个州的移动,因为他们增加了大数据功能。作为分析的一部分,本文考察了发生在不同州的一些问题,包括协同效应和其他与不同V相互共存相关的问题。最后,本文将v矩阵与其他数据分析生命周期相结合,并考察了这些模型的一些含义。
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引用次数: 6
How do the content, format, and tone of Twitter-based corporate disclosure vary depending on earnings performance? 基于twitter的公司信息披露的内容、格式和语气如何随盈利表现而变化?
IF 4.6 3区 管理学 Q1 Business, Management and Accounting Pub Date : 2022-12-01 DOI: 10.1016/j.accinf.2022.100574
Jongkyum Kim , Jee-Hae Lim , Kyunghee Yoon

Using 86,891 tweets, from the official corporate Twitter accounts of 715 unique firms, this study examines whether and how managers strategically attract and distract investors’ attention from corporate news through Twitter. We find that firms with good earnings news use Twitter to post more earnings-related information directly, whereas firms with bad earnings news post more non-earnings-related information on Twitter. We further find that depending on earnings performance firms strategically choose the format of tweets (qualitative or quantitative) and the tone of earnings tweets (positive or negative) to attract investors’ attention to good news or distract investors’ attention from bad news. Our results are robust to difference-in-differences (DID), alternative sample periods, and different variable specifications. Our findings provide empirical evidence for investors and regulators regarding current practices in corporate information on Twitter.

本研究使用来自715家独特公司的官方企业Twitter账户的86,891条推文,研究了管理者是否以及如何通过Twitter战略性地吸引和分散投资者对企业新闻的注意力。我们发现,拥有良好盈利消息的公司使用Twitter直接发布更多与盈利相关的信息,而拥有糟糕盈利消息的公司则在Twitter上发布更多与盈利无关的信息。我们进一步发现,根据盈利表现,公司战略性地选择推文的格式(定性或定量)和推文的语气(积极或消极)来吸引投资者对好消息的注意力或分散投资者对坏消息的注意力。我们的结果对差异中的差异(DID)、可选择的样本周期和不同的变量规格具有鲁棒性。我们的研究结果为投资者和监管机构提供了有关Twitter公司信息当前做法的经验证据。
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引用次数: 1
Development and validation of an improved DeLone-McLean IS success model - application to the evaluation of a tax administration ERP 改进的德龙-麦克莱恩信息系统成功模型的开发和验证-应用于税务管理ERP的评估
IF 4.6 3区 管理学 Q1 Business, Management and Accounting Pub Date : 2022-12-01 DOI: 10.1016/j.accinf.2022.100579
Godwin Banafo Akrong , Shao Yunfei , Ebenezer Owusu

Enterprise resource planning (ERP) is critical to an organization’s success. However, the factors that contribute to the success and usage of these ERP systems have received little attention. This study developed and validation of an improved DeLone-McLean IS success model. Additionally, we examined the factors which influence ERP system usage, employee satisfaction, information quality, service quality, and system quality, as well as the factors that influence the system’s overall success. The proposed model is based on a mixed-methods case study (MM-CS). The results show that the proposed model significantly measures the success of an ERP system. The organizational climate, the information quality, the system quality, and the service quality all have an impact on the usage of an ERP system. The proposed model also shows that the use of an ERP system, training and learning, and the three information (IS) quality constructs are all significant predictors of user satisfaction. The results also indicate that gender and years of ICT use on the path of ERP users have a moderating effect on the relationship between teamwork & support and use.

企业资源规划(ERP)对一个组织的成功至关重要。然而,促成这些ERP系统成功和使用的因素却很少受到关注。本研究开发并验证了改进的DeLone-McLean IS成功模型。此外,我们还研究了影响ERP系统使用、员工满意度、信息质量、服务质量和系统质量的因素,以及影响系统整体成功的因素。该模型基于混合方法案例研究(MM-CS)。结果表明,该模型能够有效地衡量ERP系统的成功与否。组织氛围、信息质量、系统质量和服务质量都会影响ERP系统的使用。该模型还表明,ERP系统的使用、培训和学习以及三种信息(IS)质量结构都是用户满意度的重要预测因素。研究结果还表明,性别和信息通信技术在ERP用户路径上的使用年限对团队合作与员工满意度之间的关系有调节作用;支持和使用。
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引用次数: 9
Stock investment strategy combining earnings power index and machine learning 结合盈利能力指数和机器学习的股票投资策略
IF 4.6 3区 管理学 Q1 Business, Management and Accounting Pub Date : 2022-12-01 DOI: 10.1016/j.accinf.2022.100576
So Young Jun , Dong Sung Kim , Suk Yoon Jung , Sang Gyung Jun , Jong Woo Kim

We propose an intermediate-term stock investment strategy based on fundamental analysis and machine learning. The approach uses predictors from the Earnings Power Index (EPI) as input variables derived from cross-sectional and time-series data from a company’s financial statements. The analytical methods of machine learning allow us to validate the link between financial factors and excess returns directly. We then select stocks for which returns are likely to increase at the time of the next disclosed financial statement. To verify the proposed approach’s usefulness, we use company data listed publicly on the Korean stock market from 2013 to 2019. We examine the profitability of trading strategy based on ten machine-learning techniques by forming long, short, and hedge portfolios with three different measures. As a result, most portfolios, including EPI-related variables, present positive returns regardless of the period. Especially, the neural network of the two layers with sigmoid function presents the best performance for the period of 3 months and 6 months, respectively. Our results show that incorporating machine learning is useful for mid-term stock investment. Further research into the possible convergence of financial statement analysis and machine-learning techniques is warranted.

我们提出了一种基于基本面分析和机器学习的中期股票投资策略。该方法使用来自盈利能力指数(EPI)的预测因子作为输入变量,这些变量来自公司财务报表的横截面和时间序列数据。机器学习的分析方法使我们能够直接验证金融因素与超额回报之间的联系。然后,我们选择在下次披露财务报表时收益可能增加的股票。为了验证该方法的有效性,我们使用了2013年至2019年在韩国股市公开上市的公司数据。我们通过用三种不同的方法形成多头、空头和对冲投资组合,研究了基于十种机器学习技术的交易策略的盈利能力。因此,大多数投资组合,包括与epi相关的变量,无论在哪个时期都呈现正回报。其中,具有s型函数的两层神经网络分别在3个月和6个月时表现最佳。我们的研究结果表明,结合机器学习对中期股票投资是有用的。进一步研究财务报表分析和机器学习技术可能的融合是必要的。
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引用次数: 2
Explainable Artificial Intelligence (XAI) in auditing 可解释的人工智能(XAI)在审计
IF 4.6 3区 管理学 Q1 Business, Management and Accounting Pub Date : 2022-09-01 DOI: 10.1016/j.accinf.2022.100572
Chanyuan (Abigail) Zhang , Soohyun Cho , Miklos Vasarhelyi

Artificial Intelligence (AI) and Machine Learning (ML) are gaining increasing attention regarding their potential applications in auditing. One major challenge of their adoption in auditing is the lack of explainability of their results. As AI/ML matures, so do techniques that can enhance the interpretability of AI, a.k.a., Explainable Artificial Intelligence (XAI). This paper introduces XAI techniques to auditing practitioners and researchers. We discuss how different XAI techniques can be used to meet the requirements of audit documentation and audit evidence standards. Furthermore, we demonstrate popular XAI techniques, especially Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive exPlanations (SHAP), using an auditing task of assessing the risk of material misstatement. This paper contributes to accounting information systems research and practice by introducing XAI techniques to enhance the transparency and interpretability of AI applications applied to auditing tasks.

人工智能(AI)和机器学习(ML)在审计中的潜在应用越来越受到关注。在审计中采用它们的一个主要挑战是其结果缺乏可解释性。随着AI/ML的成熟,可以增强AI可解释性的技术也在成熟,即可解释的人工智能(XAI)。本文向审计从业人员和研究人员介绍了XAI技术。我们将讨论如何使用不同的XAI技术来满足审计文档和审计证据标准的要求。此外,我们展示了流行的XAI技术,特别是局部可解释模型不可知解释(LIME)和Shapley加性解释(SHAP),使用评估重大错报风险的审计任务。本文通过引入人工智能技术来提高人工智能应用于审计任务的透明度和可解释性,为会计信息系统的研究和实践做出了贡献。
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引用次数: 25
期刊
International Journal of Accounting Information Systems
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