Financial consequences of competitive set choice

This study examines the financial consequences of competitive set choice using a sample of 312 hotels in a major metropolitan area in the United States. We document existence of asymmetric competitor monitoring, finding just 55% of monitoring is reciprocal; that is, about half of managers “agree,” by virtue of monitoring one another, on being direct competitors. Monitoring reciprocity is positively associated with performance through average daily rates. With total revenue unchanged, profits are higher through lower occupancy and lower total costs. We examine alternative competitive sets formed using strategic groups- and customer-based approaches, comparing these to actual compsets. We found that performance declines when managers deviate from these alternative sets. Post-hoc analyses provide insight on how overlapping compsets impact rates, occupancy and revenue. Our study is of value to academics and practitioners, providing evidence on the financial impact of competitive monitoring, and insights for managers who choose competitive sets.     

Playing for keeps: local government distortion of depreciation accruals in response to high-stakes public policy-making

Use of discretionary accounting accruals has long been recognized as a vehicle for earnings management in the private sector. More recently, evidence has emerged suggesting that public officials may similarly manipulate discretionary accruals in order to achieve ‘balanced’ operating results. This paper extends the previous literature on the manipulation of depreciation accruals to the realm of high-stakes public policy-making.     

Electronic monitoring at work: The role of attitudes,functions, and perceived control for the acceptance of tracking technologies

Organisations increasingly adopt tracking technologies that allow managers to continuously collect highly detailed records of employee, performance, and health. On the basis of the theory of planned behaviour, we aim to identify attitudes, functions of monitoring, and control perceptions that should strengthen or lower employees' acceptance of these technologies. Our experimental vignette study among 800 respondents in Germany reveals that acceptance is more likely if employees have positive attitudes towards surveillance in general and towards monitoring in private life and if the technologies enhance labour productivity. The tendency to reject the technology increases if it is to be used for monitoring health and performance. The results indicate that these new technologies will not be accepted unconditionally and may be subject to bargaining and conflicts. In the implementation process, human resources departments will have to take account of employee interests and well‐being, which in turn may improve acceptance and performance.     

Directors skill and financial reporting quality

Motivated by theoretical models in economics which show that there is matching between CEO skill and firm size, we introduce a new measure of director skill which is based on the aggregate size of firms on which the director serves as an independent director. We validate our measure by showing that it is positively associated with director experience, financial expertise, industry expertise and managerial experience. We then examine whether our average measure of skill across board members is positively associated with monitoring quality. Controlling for the endogenous relationships between board composition and financial reporting quality, we find a positive association between our board measure for skill and monitoring quality, and we show that directors have a causal impact on monitoring effort and outcomes. Furthermore, consistent with the enhanced monitoring provided by skilled directors, we document a positive association between the level of and changes in our measure and firm value.     

无人机高光谱遥感数据在冬小麦叶面积指数反演中的应用

[目的]无人机高光谱遥感是获取田间尺度作物生长参数的新型手段,如叶面积指数(leaf area index,LAI)的无人机快速观测对作物生长监测具有重要意义。[方法]研究以河北省衡水市冬小麦为研究对象,利用以多旋翼无人机为平台搭载Cubert UHD185成像高光谱传感器获取了冬小麦乳熟期的无人机高光谱影像数据;在无人机飞行的冬小麦试验田,利用LAI 2200进行了同步观测试验。该研究利用植被辐射传输模型PROSAIL模拟小麦冠层反射率数据,进而模拟9种植被指数(RVI,NDVI,EVI2,OSAVI,MSAVI2,TCARI/OSAVI,RENDVI(red edge NDVI),MSI,S2REP)。将模拟的植被指数与LAI进行相关性分析,分别构建LAI反演模型并通过拟合效果选择最优的反演模型。然后基于LAI最优反演模型利用无人机高光谱遥感数据反演冬小麦乳熟期的LAI。最后利用地面实测LAI数据对反演结果进行了验证。[结果]9种植被指数中包含红边波段的RENDVI和S2REP与LAI具有高度相关性,而且消除了在小麦LAI高值区时其他植被指数对LAI饱和的问题。基于RENDVI指数模型模拟的LAI与模型模拟的LAI之间RMSE为0.51,无人机高光谱数据LAI反演结果与地面实测值高度拟合(R2=0.83,RMSE=0.16,NRMSE=10%,n=25,P0.001),因此RENDVI是用于估算LAI的较理想的植被指数。[结论]无人机高光谱是获取小麦LAI的有效手段,该研究为利用无人机高光谱数据监测作物生理生态参数提供了参考。     

Determinants and consequences of timely asset impairments during the financial crisis

U.S. firms recorded an unprecedented number of asset impairments during the recent financial crisis. We investigate the timing of these losses in the context of two competing views on how firms use discretion over asset impairments. The first view posits that firms record impairments to convey private information as part of their commitment to a conditionally conservative reporting strategy. The second view argues that firms use their discretion to report opportunistically by delaying the recording of bad news. Consistent with the first view, we find that firms recorded timelier asset impairments during the financial crisis if they reported more conservatively in the five years preceding the crisis. Further tests show this relation is greater for firms with strong corporate governance, industry‐specialist auditors, and high leverage, indicating the importance of monitoring mechanisms in determining how firms handle the discretion involved in impairment decisions. We also test for the consequences of timely asset impairments during the financial crisis and find that firms reporting conservatively both before and during the crisis were able to acquire more debt financing, and their publicly traded bonds suffered smaller increases in illiquidity. Collectively, our study highlights the role of asset impairments in firms’ accounting choices over time.     

大数据时代计算机远程网络通信技术变革分析

随着科技的发展,大数据时代已经来临,计算机远程通信技术必须与时俱进地发展,才能在大数据时代发挥作用,计算机远程通信经过多年的运营出现了一些问题,在大数据背景下可以利用大数据技术发展计算机远程通信。     

Identification of appropriate risk analysis techniques for railway turnout systems

The construction of railway turnout entails a complex geometry and multi-disciplinary engineering science, which makes it one of the most critical railway infrastructures. As such, these characteristics pose various risks in rail operation. A considerable number of derailment incidents at the turnouts are reported annually worldwide. Not only do these incidents cause operational downtime and financial loss, they also give rise to casualties and sometimes loss of life. One of the fundamental reasons for this may well be the fact that the railway industry pays little attention to the risk elements of railway turnouts. The paper provides an integrated approach of how to deal with the many different risks arising from various sources in railway turnout systems by suitably identifying the multi-disciplinary risk analysis methods for the complex systems. In order to do so, available open literatures are critically analysed by virtue of comparison, industry experiences and deductions. As a result, various qualitative- and quantitative-based risk analysis methods are proposed to fully understand a number of technical phenomena, e.g. ageing, degradation and signalling faults, in a railway turnout system.     

Bringing automated,remote‐sensed,machine learning methods to monitoring crop landscapes at scale

This article provides an overview of how recent advances in machine learning and the availability of data from earth observing satellites can dramatically improve our ability to automatically map croplands over long periods and over large regions. It discusses three applications in the domain of crop monitoring where machine learning (ML) approaches are beginning to show great promise. For each application, it highlights machine learning challenges, proposed approaches, and recent results. The article concludes with discussion of major challenges that need to be addressed before ML approaches will reach their full potential for this problem of great societal relevance.