Compensation, Corporate governance, Performance, Pay dispersion, G30, G34, J33, L22,

No abstract

In this .stud.v, M Y ident(fj1 (I ,f~~ndunicntul uttrihute oj the organizational structure of' the j i m~i i e intensity of' interdivisional trunsuctiorz relatedness unu' ~~o n i p l e n i r r i t u i~i t~~~~h i c~k contributes to earnings management. We drmv jkoni the theorcticul ccotiomics literature thut demonstrates thut intrqfirni cdliisiori is morc likc1.y in hirrurchicul und cwnplex organizationul striic~irrc. We p s i t thut intrufi:rm cwllusion toward a c'ommori oi-~ycirii~mtion~I goal is more prewlent in highly reluted organizational .str~uc~turc hccaiisc the economic wdfurc of economic agents is highly intri.dcpPricIcrit. Consistent with our hypothesis, we find that eurnings nianagmment i.r positiivly us.soc%~tcd Mith orgunizational relutedness. Wc also jind that, ,fiw ,firms with high organizutional relatedness, those Mith a high proportion of' outside directors und high institutional equity oMwo:ship h u \~) 1 r . s~ pronounced curnings munugement. Collectiwl.y, our result sug~yrsts an interaction hctll'een corporate governunce structure und or~ycirii3NtioriNI i-clatednc,ss in uflkcting eurnings quulity. agement." .lournu/ of Awounting Re.scwr.c~lr 34( I ): 45-05.

We investigate going private transactions in Australia between 1988 and 1991. Approximately ten percent of all takeovers during this period are instances of going private. In contrast to studies of similar transactions in the United States, we find no direct evidence to support a free cash flow explanation for going private, although going private is frequently preceded by the threat of a takeover offer. However, the free cash flow explanation for going private may not be applicable in Pacific Basin countries where exchange‐traded investment activity is in relatively high growth sectors and foreign ownership accounts for a large part of those investment sectors where managerial abuse of free cash flow has been alleged.

Melanoma skin cancer has been a serious threat due to its high fatality. For this reason, early detection and treatments are given more attention as countermeasures. In recent years, skin cancer detection has been utilizing artificial intelligence techniques, specifically deep convolutional neural network. However, the performance of the convolutional neural network is highly vulnerable to different data constraints, such as the quality and quantity of the data.Therefore, this study explores the synthetization of training data using different data augmentation methods. The work presented in this paper utilizes four different categories of data augmentation methods, which include geometrical transformation, noise addition, colour transformation, and image mix. Multiple layers data augmentation approach is also explored.Dataset expansion strategies and optimized dataset expansion scale are determined to improve the performance of the skin cancer classification. The core findings in our study revealed that single-layer augmentation has better performance than multiple layers augmentation approaches, where region of interest (ROI) image mix has the highest effectiveness compared to other methods. In addition, the best dataset expansion strategy is random ROI image mix. Finally, the optimized dataset expansion is determined at 300%, which yielded the best overall test accuracy at 82.9%, 4.6% improvement compared to unprocessed raw dataset.