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The purpose of this paper is to assess the feasibility and comparability of daily self‐report and objective measures of work performance in complex office tasks, and factors affecting the correlation between these measures.

Medical bill auditors provided daily information for 12 weeks through interactive voice response (IVR) on their speed, concentration and accuracy at work, compared to their best job performance.

The paper found that 124 of 142 recruited subjects (87 percent) completed > 50 percent of daily IVR reports. Concentration, speed and accuracy were highly inter‐correlated (R=0.75), and right‐skewed (mean speed=7.7, SD=1.5). Mean adjusted daily productivity rate (MAP) was 34 bills/hour (range 4.7 to 111, SD12.6, 61 percent within‐person variation). Subject‐specific speed – MAP correlation varied from R=−0.20 to +0.75 (mean, 0.28). Health status, years on job, age, IVR completion rate, site, month of study, or total hours worked were not associated with these variations.

This paper provides an unprecedented level of detail in the comparison of self‐reported and objective daily measures of work performance, demonstrates the feasibility of data collection and analysis, and identified significant inconsistencies among workers in the correlation between the two types of measures. Results demonstrated that daily self‐reports cannot be used as a direct surrogate for objective performance measures.

Construction labour productivity is often influenced by variations in work conditions and management effectiveness. It is substantially important to understand the nature and extent to which individual parameters affect productivity. The purpose of this paper is to focus on providing insight on parameters that affect daily job‐site labour productivity by investigating their relative significance and influence on work output.

The methodology is based on the illustration and use of three different data analysis techniques to rank parameters that affect a certain process. These techniques include Fuzzy Subtractive Clustering, Neural Network Modelling and Stepwise Variable Selection Procedure. The first one belongs to inferential statistics, while the other two are artificial intelligence based techniques. The collection of field information, spanning over a time period of ten months, comprised of daily real time observations of job‐site operations, work progress information collected from project managers and supervisors by using customized forms, and daily weather condition recorded through internet sources. Nine parameters are considered in the study presented in this paper. The data on these parameters is examined and their relative influence and contribution in productivity estimates are assessed. The approach was to consider a limited set of parameters relating to daily job‐site productivity. The methodology presented in this paper provides insight on the relative impact of parameters, affecting labour productivity on short term or daily basis. The results based on each of the three methods are analyzed and transformed into a final ranking of parameters.

The three most important parameters are identified in the same order by the fuzzy logic and neural networks methods. Regression analysis, however, provided somewhat different results.

This research investigates the contribution of a set of parameters towards the variations in daily job‐site labour productivity. For practitioners such as site engineers, this is of practical importance for making daily work plans. On the other hand, the structured approach presented to perform significance ranking of parameters relevant to an engineering process, may also be of interest to other researchers and practitioners.

The topic of volatility measurement and estimation is central to financial and more generally time-series econometrics. In this chapter, we begin by surveying models of volatility, both discrete and continuous, and then we summarize some selected empirical findings from the literature. In particular, in the first sections of this chapter, we discuss important developments in volatility models, with focus on time-varying and stochastic volatility as well as nonparametric volatility estimation. The models discussed share the common feature that volatilities are unobserved and belong to the class of missing variables. We then provide empirical evidence on “small” and “large” jumps from the perspective of their contribution to overall realized variation, using high-frequency price return data on 25 stocks in the DOW 30. Our “small” and “large” jump variations are constructed at three truncation levels, using extant methodology of Barndorff-Nielsen and Shephard (2006), Andersen, Bollerslev, and Diebold (2007), and Aït-Sahalia and Jacod (2009a, 2009b, 2009c). Evidence of jumps is found in around 22.8% of the days during the 1993–2000 period, much higher than the corresponding figure of 9.4% during the 2001–2008 period. Although the overall role of jumps is lessening, the role of large jumps has not decreased, and indeed, the relative role of large jumps, as a proportion of overall jumps, has actually increased in the 2000s.

Hospital managers are confronted with decisions that have to account for multiple objectives, which may be in conflict with regard to efficiency and quality of care. In empirical studies occupancy and staffing ratios as well as in‐hospital mortality are frequently used measures for efficiency and quality‐of‐care, respectively. Efficiency and quality measures vary on a daily basis. However, most empirical studies fail to take this variation into account, especially because data of daily staffing levels are lacking. The paper seeks to exploit the notion that staffing levels are planned according to expected occupancy levels, i.e. estimated daily occupancy levels account for unobserved daily staffing levels.

Using administrative data from 2004 for a sample of 62 departments in 33 German hospitals, the relation between daily occupancy levels and in‐hospital mortality count on the department level is analyzed. In an OLS‐framework the paper estimates daily occupancy level for all departments and then uses the predicted occupancy levels in a zero‐inflated Poisson (ZIP) regression framework to explain in‐hospital mortality count.

The results show a potential trade‐off relation between predicted occupancy rates and mortality. More specifically, the paper finds that the trade‐off relation is less pronounced in hospitals with a higher number of available staff per bed.

First, the paper shows evidence for a negative trade‐off between measures of managerial and medical performance on a day‐to‐day basis. Second, interactions between single measures of efficiency are modeled, namely predicted occupancy rate and staff per bed ratios, and policy implications are developed. Third, first empirical results in this respect using German data are presented.

The purpose of this paper is to indicate the existence of certain time series characteristics in daily stock returns of four small Asian (Pacific basin) financial markets. It aims to study efficient capital markets (efficient markets hypothesis (EMH)) as results may infer that there are predictable properties of the time series of prices of traded securities on organized markets in Singapore, Malaysia, Korea and Indonesia.

The paper analyses daily variations in financial market data obtained from the Sandra Ann Morsilli Pacific‐basin Capital Markets Research Center (PACAP).

The weak form efficiency test example examines the wide range of trading rules available to common investors. Some theorists try to convince everyone that the weak form of EMH is acceptable due to the weight of academic opinion. The paper finds that for short‐term (daily) changes, the markets of four of the smaller Pacific‐basin stock markets have predictable properties, which leads to the conclusion that the weak‐form EMH does not hold for these markets.

The study is limited to those firms and exchanges studied and the time period covered.

There have been all too few studies of these small financial markets up to now and there is no other study utilizing these data on the Pacific basin (Asia). The results are unique and original.

The purpose of this paper is to present a methodology for forecasting the availability off-highway trucks used in a fleet for large transport operations of ore and sterile rock from an open pit mine.

This methodology enables the estimation of the number of hours of preventive and corrective maintenance required, which are used to predict truck availability. The authors used historical data for the maintenance strategy based on the hours of operation.

These data are statistically analyzed to obtain the key quantities and statistical models required to project availability and to develop equipment replacement plans.

A methodology for forecasting availability to assets in open pit mining industry was implemented.

The purpose of this paper is to develop a method for predicting the chloride ingress into concrete structures, with an emphasis on the low temperature range where freeze-thaw cycles may cause damage.

The different phenomena that contribute to the rate and amount of transported chlorides into concrete, i.e., heat transfer, moisture transport and chloride diffusion are modeled using a two-dimensional nonlinear time dependent finite element method. In modeling the chloride transport, a modified version of Fick’s second law is used, in which processes of diffusion and convection due to water movement are taken into account. Besides, the effect of freeze-thaw cycles is directly incorporated in the governing equation and linked to temperature variation using a coupling term that is determined in this study. The proposed finite element model and its associated program are capable of handling pertinent material nonlinearities and variable boundary conditions that simulate real exposure situations.

The numerical performance of the model was examined through few examples to investigate its ability to simulate chloride penetration under freeze-thaw cycles and its sensitivity to factors controlling freeze-thaw damage. It was also proved that yearly temperature variation models to be used in service life assessment should take into account its cyclic nature to obtain realistic predictions.

The model proved promising and suitable for chloride penetration in cold climates.

To model the evacuation time from buildings in the event of emergencies based on occupant load, looking specifically at the exit width as a design parameter.

In this study, transient occupant loads from a simulation model based on a recent study of 34 offices in Hong Kong were used to evaluate the probable risk to evacuees in the case of an emergency evacuation. For this analysis the “door carrying capacity” approach was used, together with probability profiles for the occupant loads at certain exit flow rates. This paper investigates the occupant load profiles as well as the yearly and daily occupant load variations of some typical offices in Hong Kong and examines the fire safety implications of the office building designs from the perspective of the risk to evacuees.

The results show that the building occupant load, occupant‐load ratio, total exit width and specific flow rate at the exit significantly affect the risk to evacuees.

The model parameters are not exhaustive and are determined from surveys in Hong Kong.

A useful source of reference in conducting risk assessment for safe egress design of office buildings for those involved in building design, operation and management.

This paper, taking account of exit design, occupant load and its variations using the door carrying capacity approach, presents a simple method to determine the probable risk to evacuees in offices.

Labour productivity plays an important role in the successful delivery of engineering, procurement and construction projects. This paper aims to present a field study that determines the effects of a set of variables on daily and/or short‐term jobsite labour productivity, using artificial neural network model.

The data used in this paper were collected over a period of ten months, directly from the job sites of two building construction projects in Montreal. A neural network model was used to study a number of factors considered to impact labour productivity on daily basis. These included temperature, relative‐humidity, wind speed, precipitation, gang size, crew composition, height of work, type of work and construction method employed. The data were then analyzed to determine the influence of these parameters on site labour productivity.

Among the nine parameters studied, temperature was found to have the most significant impact on productivity, closely followed by the height then by the type of work. Given the range of the collected data available on the variables considered, temperature, humidity and crew composition were found each to have a similar trend, with an optimum value that corresponds to the normalized maximum productivity.

The findings of this paper will provide awareness and better understanding of parameters that impact labour productivity in building construction.

The paper proposes that improvements in the productivity of process technology can be achieved by analyzing and instituting changes in the rules and methods through which work is organized. The implementation of information‐based process technology in a bank’s check processing system provides the context for the study. A simulation approach is used to assess the benefits of introducing alternative workflow scheduling rules after implementation of the new technology. Results from a hypothetical setting, using data from an actual check processing center, demonstrate the cost advantages of introducing a priority‐based scheduling rule. The implications and problems associated with practical implementation are discussed.