Benefits of all work and no play: The relationship between neuroticism and performance as a function of resource allocation

The authors evaluate a model suggesting that the performance of highly neurotic individuals, relative to their stable counterparts, is more strongly influenced by factors relating to the allocation of attentional resources. First, an air traffic control simulation was used to examine the interaction between effort intensity and scores on the Anxiety subscale of Eysenck Personality Profiler Neuroticism in the prediction of task performance. Overall effort intensity enhanced performance for highly anxious individuals more so than for individuals with low anxiety. Second, a longitudinal field study was used to examine the interaction between office busyness and Eysenck Personality Inventory Neuroticism in the prediction of telesales performance. Changes in office busyness were associated with greater performance improvements for highly neurotic individuals compared with less neurotic individuals. These studies suggest that highly neurotic individuals outperform their stable counterparts in a busy work environment or if they are expending a high level of effort.

Self-efficacy and task performance: An investigation from two levels of analysis and specificity

This research adopts a resource allocation theoretical framework to generate predictions regarding the relationship between self-efficacy and task performance from two levels of analysis and specificity. Participants were given multiple trials of practice on an air traffic control task. Measures of task-specific self-efficacy and performance were taken at repeated intervals. The authors used multilevel analysis to demonstrate dynamic main effects, dynamic mediation and dynamic moderation. As predicted, the positive effects of overall task specific self-efficacy and general self-efficacy on task performance strengthened throughout practice. In line with these dynamic main effects, the effect of general self-efficacy was mediated by overall task specific self-efficacy; however this pattern emerged over time. Finally, changes in task specific self-efficacy were negatively associated with changes in performance at the within-person level; however this effect only emerged towards the end of practice for individuals with high levels of overall task specific self-efficacy. These novel findings emphasise the importance of conceptualising self-efficacy within a multi-level and multi-specificity framework and make a significant contribution to understanding the way this construct relates to task performance.

A multilevel analysis of BIS sensitivity and negative affect during skill acquisition

This Study examined the muldlevel reladonships among negadve affect Behavioural Inhibidon System (BIS) Sensidvit)' and performance. It also invesdgated whether the reladonship among these variables changed across pracdce. Pardcipants performed muldple trials of a simulated air traffic control task, A single measure of BIS was taken before pracdce, while negadve affect and performance were measured at repeated intervals. As expected, negadve affect was detrimental to performance at both a between-person and withinperson level, BIS was also found to be detrimental to performance. Contrary' to expectadons, the reladonship between BIS and performance was not mediated by overall levels of negadve affect. As predicted, the effects of overall levels of negadve affect and BIS strengthened across pracdce as pardcipants gained task knowledge and skill. The findings of this study are interpreted using resource allocadon theor}' and the implicadons for skiU acquisidon discussed.

The moderating effects of goal orientations on reactions to goal-performance discrepancies

Due to the growing popularity of goal setting programs within organisations, an understanding of the mechanisms underlying the dynamic regulation of performance is paramount (Williams, Donovan, & Dodge, 2000). Goals serve as standards or referents by which behaviour is directed and evaluated. Whilst their importance is well established in the existing literature (e.g. Locke & Latham, 1990), more recent research has highlighted the potential importance of goal-performance discrepancies. Moreover, the relationship between goal-performance discrepancies and outcomes such as self-efficacy and personal goals appears to vary between people (Schmidt & Chambers, 2002). Of interest in the current study was how these relationships were impacted by goal orientation. Ninety-seven participants completed 30 two-minute trials of an Air Traffic Control task. Task specific goal orientation was measured prior to commencement of the task and measures of self-efficacy and personal task goals were taken at each trial to assess the within-person relationships between goal performance discrepancies and each of these dependant variables, as well as the moderating effects of goal orientations on these relationships. Analysis supported the existence of a positive relationship between goal-performance discrepancies and outcome variables, with performance-approach and –avoidance orientations significantly moderating these associations. Implications and future directions are discussed.

Formal analysis of human-computer interaction using model checking

Experiments with simulators allow psychologists to better understand the causes of human errors and build models of cognitive processes to be used in human reliability assessment (HRA). This paper investigates an approach to task failure analysis based on patterns of behaviour, by contrast to more traditional event-based approaches. It considers, as a case study, a formal model of an air traffic control (ATC) system which incorporates controller behaviour. The cognitive model is formalised in the CSP process algebra. Patterns of behaviour are expressed as temporal logic properties. Then a model-checking technique is used to verify whether the decomposition of the operator's behaviour into patterns is sound and complete with respect to the cognitive model. The decomposition is shown to be incomplete and a new behavioural pattern is identified, which appears to have been overlooked in the analysis of the data provided by the experiments with the simulator. This illustrates how formal analysis of operator models can yield fresh insights into how failures may arise in interactive systems.

Personality and goal orientation as predictors of performance in a learning context

The current study aimed to investigate and provide furthering evidence of individual differences as determinants of task performance. This research focused on the effects of the personality traits Openness to Experience and Neuroticism, and two goal orientation traits. Learning Orientation and Avoid Orientation, on task performance. The hypotheses addressed the predictability of the traits, the differential effects of personality and goal orientation traits, and the mediating effects of goal orientation on the relationship between personality and performance. The results were based on questionnaire responses completed by a sample of 103 students. Scores on a computerised Air Traffic Control (ATC) decision-making task were used as a measure of task performance. Learning Orientation was found to be a significant predictor of performance, whilst the effect of Neuroticism was 'approaching' significance. Results indicated strong support for the differential relationship between personality traits and corresponding goal orientation traits. The mediating relationship between Openness to Experience, Learning Orientation and performance was also found to be 'approaching' significance. Results were indicative of the influences of personality and goal orientation on consequent performance outcomes. Implications were discussed, as well as suggestions for possible future directions in research assessing the predictabilit)' of individual differences in learning contexts.

Goal orientation, ability and task performance: A moderated analysis

Achievement goal orientation represents an individual's general approach to an achievement situation, and has important implications for how individuals react to novel, challenging tasks. However, theorists such as Yeo and Neal (2004) have suggested that the effects of goal orientation may emerge over time. Bell and Kozlowski (2002) have further argued that these effects may be moderated by individual ability. The current study tested the dynamic effects of a new 2x2 model of goal orientation (mastery/performance x approach/avoidance) on performance on a simulated air traffic control (ATC) task, as moderated by dynamic spatial ability. One hundred and one first-year participants completed a self-report goal orientation measure and computerbased dynamic spatial ability test and performed 30 trials of an ATC task. Hypotheses were tested using a two-level hierarchical linear model. Mastery-approach orientation was positively related to task performance, although no interaction with ability was observed. Performance-avoidance orientation was negatively related to task performance; this association was weaker at high levels of ability. Theoretical and practical implications will be discussed.

Antecedents of task-specific goal orientation: An investigation of task practice, personality and global goal orientation

Goal orientation, a mental framework for understanding how individuals approach learning and achievement situations, has emerged as an important topic in organisational psychology. This study investigated the effects of task practice, personality (openness to experience and neuroticism), and global goal orientation (predisposition to adopt a certain response pattern across all domains) on participants’ task-specific goal orientation (response pattern adopted for a specific task). One hundred and three participants performed an air traffic control task and their task-specific goal orientation was measured prior to each of a total of thirty trials. Results revealed an effect of task practice such that individuals’ task-specific learning orientation decreased over time while their task-specific prove orientation increased over time. The results also showed that individuals’ personality can influence their task-specific goal orientation and further, that this relationship can be mediated by global goal orientation. Specifically, the positive relationship between openness to experience and task-specific prove orientation was mediated by global prove orientation. Similarly, the positive relationship between neuroticism and task-specific avoid orientation was mediated by global avoid orientation. Theoretical and practical implications of these findings are considered.

Efficient Multi-hop Broadcast Data Dissemination in IoT and Smart Cities

Internet of Things (IoT) can be defined as a “network of networks” composed by billions of uniquely identified physical Smart Objects (SO), organized in an Internet-like structure. Smart Objects can be items equipped with sensors, consumer devices (e.g., smartphones, tablets, or wearable devices), and enterprise assets that are connected both to the Internet and to each others. The birth of the IoT, with its communications paradigms, can be considered as an enabling factor for the creation of the so-called Smart Cities. A Smart City uses Information and Communication Technologies (ICT) to enhance quality, performance and interactivity of urban services, ranging from traffic management and pollution monitoring to government services and energy management. This thesis is focused on multi-hop data dissemination within IoT and Smart Cities scenarios. The proposed multi-hop techniques, mostly based on probabilistic forwarding, have been used for different purposes: from the improvement of the performance of unicast protocols for Wireless Sensor Networks (WSNs) to the efficient data dissemination within Vehicular Ad-hoc NETworks (VANETs).

Exploring innovation in policy-making within central government:the case of the UK's Highways Agency

The first and main contribution of this article is its access to the decision-making processes which drive innovation in policy-making within central government. The article will present a detailed case history of how the innovation came about and conclude by highlighting analytic possibilities for future research. The policy in focus is the UK’s Traffic Management Act 2004, which passed responsibility for managing incidents on major roads from the police to the Highways Agency (HA), and has been interpreted as a world first in traffic management. The article tracks the Traffic Management Act 2004 from problem identification to a preliminary evaluation. It is then suggested that future research could explain organizational change more theoretically. By taking a longitudinal and multi-level approach, the research falls into a processual account of organizational change. The second contribution of the article is to highlight two novel ways in which this approach is being applied to policy-making, through an institutional processualist research programme on public management reform and empirical investigations using complex systems to explain policy change.

Quality of Service System Approximation in IP Networks

This paper is sponsored by the Ministry of Education and Research of the Republic of Bulgaria in the framework of project No 105 “Multimedia Packet Switching Networks Planning with Quality of Service and Traffic Management”.

Freeway travel time estimation and prediction using dynamic neural networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. ^ This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.^

Application of intervention analysis to evaluate the impacts of special events on freeways

In China in particular, large, planned special events (e.g., the Olympic Games, etc.) are viewed as great opportunities for economic development. Large numbers of visitors from other countries and provinces may be expected to attend such events, bringing in significant tourism dollars. However, as a direct result of such events, the transportation system is likely to face great challenges as travel demand increases beyond its original design capacity. Special events in central business districts (CBD) in particular will further exacerbate traffic congestion on surrounding freeway segments near event locations. To manage the transportation system, it is necessary to plan and prepare for such special events, which requires prediction of traffic conditions during the events. This dissertation presents a set of novel prototype models to forecast traffic volumes along freeway segments during special events. Almost all research to date has focused solely on traffic management techniques under special event conditions. These studies, at most, provided a qualitative analysis and there was a lack of an easy-to-implement method for quantitative analyses. This dissertation presents a systematic approach, based separately on univariate time series model with intervention analysis and multivariate time series model with intervention analysis for forecasting traffic volumes on freeway segments near an event location. A case study was carried out, which involved analyzing and modelling the historical time series data collected from loop-detector traffic monitoring stations on the Second and Third Ring Roads near Beijing Workers Stadium. The proposed time series models, with expected intervention, are found to provide reasonably accurate forecasts of traffic pattern changes efficiently. They may be used to support transportation planning and management for special events.

Freeway Travel Time Estimation and Prediction Using Dynamic Neural Networks

Providing transportation system operators and travelers with accurate travel time information allows them to make more informed decisions, yielding benefits for individual travelers and for the entire transportation system. Most existing advanced traveler information systems (ATIS) and advanced traffic management systems (ATMS) use instantaneous travel time values estimated based on the current measurements, assuming that traffic conditions remain constant in the near future. For more effective applications, it has been proposed that ATIS and ATMS should use travel times predicted for short-term future conditions rather than instantaneous travel times measured or estimated for current conditions. This dissertation research investigates short-term freeway travel time prediction using Dynamic Neural Networks (DNN) based on traffic detector data collected by radar traffic detectors installed along a freeway corridor. DNN comprises a class of neural networks that are particularly suitable for predicting variables like travel time, but has not been adequately investigated for this purpose. Before this investigation, it was necessary to identifying methods for data imputation to account for missing data usually encountered when collecting data using traffic detectors. It was also necessary to identify a method to estimate the travel time on the freeway corridor based on data collected using point traffic detectors. A new travel time estimation method referred to as the Piecewise Constant Acceleration Based (PCAB) method was developed and compared with other methods reported in the literatures. The results show that one of the simple travel time estimation methods (the average speed method) can work as well as the PCAB method, and both of them out-perform other methods. This study also compared the travel time prediction performance of three different DNN topologies with different memory setups. The results show that one DNN topology (the time-delay neural networks) out-performs the other two DNN topologies for the investigated prediction problem. This topology also performs slightly better than the simple multilayer perceptron (MLP) neural network topology that has been used in a number of previous studies for travel time prediction.