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Krull (2004) finds that US multinational corporations (MNCs) increase amounts designated as permanently reinvested earnings (PRE) to maximize reported after-tax earnings and meet earnings targets. We extend this research by examining the relationship between executive equity compensation and the opportunistic use of PRE by US MNCs, and the market reaction to earnings management using PRE designations. Firms use equity compensation to incentivize executives to strive for maximum shareholder wealth. One unintended consequence is that executives may engage in earnings management activities to increase their equity compensation. In this study, we examine whether the equity incentives of management are associated with an increased use of PRE. We predict and find strong evidence that the changes in PRE are positively associated with the portion of top managers' compensation that is tied to stock performance. In addition, we find this relationship to be strongest for firms that meet or beat forecasts, but only with the use of PRE to inflate income, suggesting that equity compensation incentivizes managers to opportunistically use PRE, especially to meet analyst forecasts.

Further, we provide evidence that investors react negatively to beating analysts' forecasts with the use of PRE, suggesting that investors find this behavior opportunistic and not fully convincing. This chapter makes an important contribution to what we know about the joint effects of tax policy, generally accepted accounting principles, and incentive compensation on the earnings reporting process.

In this chapter we analyze how the designation of foreign earnings as “permanently reinvested” outside the US (PRE) is related to subsequent firm growth and market returns. Prior research suggests that firms that hold excess cash in foreign markets to avoid the US corporate income tax experience lower growth, since such “trapped” cash is inefficiently invested. However, foreign earnings can be inefficiently invested in forms other than cash. We hypothesize and find that as the ratio of PRE to total assets increases, firms' growth rates decline. Our results suggest that trapped earnings, and not just trapped cash, are associated with lower growth. Because PRE have also been associated with earnings management in the literature, we further analyze the association between the use of PRE to meet or beat earnings targets and subsequent growth, observing a significant and persistent negative association. Finally, we note that the market discount for PRE, and especially for the use of PRE to manage earnings, appears to be relatively small. Our results provide support for FASB's stated plans to increase disclosure requirements surrounding the tax accrual.

The purpose of this study is to present a methodology for enhancing the quality and usefulness of online reviews for prospective customers to investigate how this contemporary form of instrumental support can be facilitated to strengthen customer-to-customer support.

This study develops an analytics framework with applications of machine learning models using customer review data from Amazon.com. Linear regression is commonly used for review helpfulness and sales prediction. In this study, Random Forest model is applied because of its strong performance and reliability. To advance the methodology, a custom script in Python is created to generate Partial Dependence Plots for intensive exploration of the dependency interpretations of review helpfulness and sales. The authors also apply K-Means to cluster reviewers and use the results to generate reviewer qualification scores and collective reviewer scores, which are incorporated into the review facilitation process.

The authors find the average helpfulness ratio of the reviewer as the most important determinant of reviewer qualification. The collective reviewer qualification for a product created based on reviewers’ characteristics is found important to customers’ purchase intentions and can be used as a metric for product comparison.

The findings of this study suggest that service improvement efforts can be performed by developing software applications to monitor reviewer qualifications dynamically, bestowing a badge to top quality reviewers, redesigning review sorting interfaces and displaying the consumer rating distribution on the product page, resulting in improved information reliability and consumer trust.

This study adds to the research on customer-to-customer support in the service literature. As customer reviews perform as a contemporary form of instrumental support, the authors validate the determinants of review helpfulness and perform an intensive exploration of its dependency interpretation. Reviewer qualification and the collective reviewer qualification scores are generated as new predictors and incorporated into the helpfulness-based review facilitation services.

“Smart devices think you're “too lazy” to opt out of privacy defaults” was the headline of a recent news report indicating that individuals might be too lazy to stop disclosing their private information and therefore to protect their information privacy. In current privacy research, privacy concerns and self-disclosure are central constructs regarding protecting privacy. One might assume that being concerned about protecting privacy would lead individuals to disclose less personal information. However, past research has shown that individuals continue to disclose personal information despite high privacy concerns, which is commonly referred to as the privacy paradox. This study introduces laziness as a personality trait in the privacy context, asking to what degree individual laziness influences privacy issues.

After conceptualizing, defining and operationalizing laziness, the authors analyzed information collected in a longitudinal empirical study and evaluated the results through structural equation modeling.

The findings show that the privacy paradox holds true, yet the level of laziness influences it. In particular, the privacy paradox applies to very lazy individuals but not to less lazy individuals.

With these results one can better explain the privacy paradox and self-disclosure behavior.

The state might want to introduce laws that not only bring organizations to handle information in a private manner but also make it as easy as possible for individuals to protect their privacy.

Based on a literature review, a clear research gap has been identified, filled by this research study.

Paying to view others' answers is a new mode for question and answer (Q&A) platforms. The purpose is to build a model to explore the determinants of the number of listeners and further explore certain meaningful characteristics of the model in the context of different types of questions and answerers.

The authors develop an empirical model and use real panel data to test the hypothesis. Specifically, cues from the answerer and from the question elicit the listener's trust in the answerer (including direct and indirect trust) and perceived value in the question (including intrinsic and extrinsic attributes), respectively.

The authors find that cues from answerers (experience for paid Q&As and popularity for free Q&As) and questions (length, sentence structure, value and number of likes) all have positive effects on the number of listeners. The impact of answerer authentication is more significant than the popularity of free Q&As. Moreover, the length of the question matters only for subjective questions, while sentence structure matters only for objective questions. In addition, the answerer's own attributes and the behavior and feedback of others have greater impacts when the answerer is below average in popularity.

The authors summarize the unique features of the mode of paying to view others' answers in contrast with the traditional mode of paid Q&As. In addition, the authors focus on the characteristics of the question (including the subjectivity and the sentence structure of the question), a topic which has not been studied previously. Our research provides a reference for exploring user behavior patterns. The practical implications for knowledge platforms are also concretely described.

Web users rely heavily on online content make decisions without assessing the veracity of the content. The online content comprising text, image, video or audio may be tampered with to influence public opinion. Since the consumers of online information (misinformation) tend to trust the content when the image(s) supplement the text, image manipulation software is increasingly being used to forge the images. To address the crucial problem of image manipulation, this study focusses on developing a deep-learning-based image forgery detection framework.

The proposed deep-learning-based framework aims to detect images forged using copy-move and splicing techniques. The image transformation technique aids the identification of relevant features for the network to train effectively. After that, the pre-trained customized convolutional neural network is used to train on the public benchmark datasets, and the performance is evaluated on the test dataset using various parameters.

The comparative analysis of image transformation techniques and experiments conducted on benchmark datasets from a variety of socio-cultural domains establishes the effectiveness and viability of the proposed framework. These findings affirm the potential applicability of proposed framework in real-time image forgery detection.

This study bears implications for several important aspects of research on image forgery detection. First this research adds to recent discussion on feature extraction and learning for image forgery detection. While prior research on image forgery detection, hand-crafted the features, the proposed solution contributes to stream of literature that automatically learns the features and classify the images. Second, this research contributes to ongoing effort in curtailing the spread of misinformation using images. The extant literature on spread of misinformation has prominently focussed on textual data shared over social media platforms. The study addresses the call for greater emphasis on the development of robust image transformation techniques.

This study carries important practical implications for various domains such as forensic sciences, media and journalism where image data is increasingly being used to make inferences. The integration of image forgery detection tools can be helpful in determining the credibility of the article or post before it is shared over the Internet. The content shared over the Internet by the users has become an important component of news reporting. The framework proposed in this paper can be further extended and trained on more annotated real-world data so as to function as a tool for fact-checkers.

In the current scenario wherein most of the image forgery detection studies attempt to assess whether the image is real or forged in an offline mode, it is crucial to identify any trending or potential forged image as early as possible. By learning from historical data, the proposed framework can aid in early prediction of forged images to detect the newly emerging forged images even before they occur. In summary, the proposed framework has a potential to mitigate physical spreading and psychological impact of forged images on social media.

This study focusses on copy-move and splicing techniques while integrating transfer learning concepts to classify forged images with high accuracy. The synergistic use of hitherto little explored image transformation techniques and customized convolutional neural network helps design a robust image forgery detection framework. Experiments and findings establish that the proposed framework accurately classifies forged images, thus mitigating the negative socio-cultural spread of misinformation.