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Taking advantage of a unique measure of corporate culture obtained from advanced machine learning algorithms, this study aims to explore how corporate culture strength is influenced by board independence, which is one of the most crucial aspects of the board of directors. Because of their independence from the corporation, outside independent directors are more likely to be unbiased. As a result, board independence is commonly used as a proxy for board quality.

In addition to the standard regression analysis, the authors execute a variety of additional tests, i.e. propensity score matching, an instrumental variable analysis, Lewbel’s (2012) heteroscedastic identification and Oster’s (2019) testing for coefficient stability.

The results show that stronger board independence, measured by a higher proportion of independent directors, is significantly associated with corporate culture. In particular, a rise in board independence by one standard deviation results in an improvement in corporate culture by 32.8%.

Conducting empirical research on corporate culture is incredibly difficult due to the inherent difficulties in recognizing and assessing corporate culture, resulting in a lack of empirical research on corporate culture in the literature. The authors fill this important void in the literature. Exploiting a novel measure of corporate culture based on textual analysis, to the best of the authors’ knowledge, this study is the first to link corporate culture to corporate governance with a specific focus on board independence.

The purpose of this study is to examine the relationship between the productive structure and the size of the informal economy in Latin American countries.

The study employs econometric techniques for panel data covering the period from 2002 to 2017 and considering 17 Latin American countries. The evidence presented is based on the informal economy data generated by Medina and Schneider (2018) who estimate the size of the informal economy using a structural equation model and the share of manufacturing in total employment as a measure of the size of the manufacturing sector. Also, the study addresses the possible endogeneity bias in the relationship studied and makes the conclusions more robust, thus avoiding spurious correlations that weaken the findings.

The results indicate that most industrialized Latin American countries are associated with a smaller size of the informal economy.

The findings have important policy implications, as they suggest that Latin American economies need to switch the structure of the economy toward more sophisticated productive structures if they want to reduce the size of the informal economy. Thus, more efforts should be deployed to policies to diversify and upgrade economies.

The study contributes to the literature on the informal economy by connecting the country’s productive structure and informality. Specifically, the results show that the productive structure of countries is a plausible explanation for the size of the informal economy.

Exploiting a unique measure of hostile takeover exposure principally based on the staggered adoption of state legislations, the authors investigate how external audit quality is influenced by the discipline of the takeover market. External auditors and the takeover market both function as important instruments of external corporate governance.

The authors execute a standard regression analysis and run a variety of robustness checks to minimize endogeneity, namely, propensity score matching (PSM), entropy balancing, an instrumental-variable analysis, Generalized method of moment (GMM) dynamic panel data analysis and Lewbel's (2012) heteroscedastic identification.

The authors’ immense sample spans half a century, encompassing nearly 180,000 observations and 17 takeover-related state legislations, one of the largest samples in the literature in this area. The authors’ results suggest that firms with more takeover exposure are significantly less likely to use Big N auditors. Therefore, a more active takeover market results in poorer external audit quality, corroborating the substitution hypothesis. The discipline of the takeover market substitutes for the necessity for a high-quality external auditor. Specifically, a rise in takeover susceptibility by one standard deviation lowers the probability of using a Big N auditor by 4.29%.

The authors’ study is the first to examine the effect of the takeover over market on audit quality using a novel measure of hostile takeover susceptibility mainly based on the staggered implementation of state legislation. Because the enactment of state legislation is beyond the control of any firm individually, it is plausibly exogenous. The authors’ results therefore probably reflect a causal influence rather than merely a correlation.

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.

The purpose of this paper is to meet Dr Joyce Shaffer, PhD, ABPP, Clinical Associate Professor at the University of Washington.

This case study is presented in two sections: a positive autoethnography written by Joyce Shaffer, followed by her answers to ten questions.

In this positive autoethnography, Shaffer shares her life story and reveals numerous mental health and positive aging recommendations and insights for us to reflect on.

This is a personal narrative, albeit from someone who has been a clinical psychologist and active in the field of aging for many decades.

A pragmatic approach to aging is recommended. According to Shaffer, “those of us who can recognize the beat of the historical drummer can harvest the best of it and learn from the rest of it.”

Positive aging has strong social implications. Shaffer considers that it is not only about maximizing our own physical, mental, emotional and social health but also about maximizing that of others, to make our world a better place for everyone.

Positive aging can be experienced despite adversity. As Shaffer says, “Adversity used for growth and healed by love is the answer.”