Understanding Maintenance Decisions: How to Support Acquisition of Capital Assets

The relation between R&D accounting treatment and the risk of the firm: Evidence from the Italian market

FINANCIAL REPORTING ◽

10.3280/fr2014-001002 ◽

2014 ◽

pp. 33-54 ◽

Cited By ~ 1

Author(s):

Riccardo Cimini ◽

Alessandro Gaetano ◽

Alessandra Pagani

Keyword(s):

Earnings Management ◽

Fiscal Year ◽

Investment Strategies ◽

Capital Assets ◽

Italian Market ◽

D Investment ◽

Firm Evidence

In this paper, we investigate the relation between the different accounting treatments of R&D expenditures and the risk of the entity in order to identify under which treatment insiders are more likely to carry out earnings management. By analysing the R&D investment strategies of a sample of 137 listed Italian entities that complied with the requirements of IAS 38 during fiscal year 2009, following Lantz and Sahut (2005), we calculate several indexes that show the preferences of insiders to account R&D expenditures as costs or capital assets, and we study the relation of such preferences with the risk of the entity, which we measure with the unlevered beta. We hypothesize that the entities, which considered the R&D investments as costs, are the riskiest ones due to the higher probability that insiders carried out earnings management. Our results confirm such hypothesis. This paper could have implications for academics and standard setters that could learn that behind accounting discretion, insiders could opportunistically behave against outsiders.

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La valutazione dei tangible capital assets delle amministrazioni pubbliche. criteri di valutazione e implicazioni sui risultati bi bilancio

SSRN Electronic Journal ◽

10.2139/ssrn.2078990 ◽

2012 ◽

Cited By ~ 1

Author(s):

Francesco Capalbo

Keyword(s):

Capital Assets

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Exploiting Data Analytics and Deep Learning Systems to Support Pavement Maintenance Decisions

Applied Sciences ◽

10.3390/app11062458 ◽

2021 ◽

Vol 11 (6) ◽

pp. 2458

Author(s):

Ronald Roberts ◽

Laura Inzerillo ◽

Gaetano Di Mino

Keyword(s):

Deep Learning ◽

Management Practices ◽

Prediction Models ◽

Road Networks ◽

Pavement Management ◽

Efficient Operation ◽

Pavement Maintenance ◽

Goods And Services ◽

Maintenance Decisions ◽

Computational Systems

Road networks are critical infrastructures within any region and it is imperative to maintain their conditions for safe and effective movement of goods and services. Road Management, therefore, plays a key role to ensure consistent efficient operation. However, significant resources are required to perform necessary maintenance activities to achieve and maintain high levels of service. Pavement maintenance can typically be very expensive and decisions are needed concerning planning and prioritizing interventions. Data are key towards enabling adequate maintenance planning but in many instances, there is limited available information especially in small or under-resourced urban road authorities. This study develops a roadmap to help these authorities by using flexible data analysis and deep learning computational systems to highlight important factors within road networks, which are used to construct models that can help predict future intervention timelines. A case study in Palermo, Italy was successfully developed to demonstrate how the techniques could be applied to perform appropriate feature selection and prediction models based on limited data sources. The workflow provides a pathway towards more effective pavement maintenance management practices using techniques that can be readily adapted based on different environments. This takes another step towards automating these practices within the pavement management system.

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