A longitudinal study of employee adaptation to organizational change: The role of change-related information and change-related self-efficacy

This study examined the role of information, efficacy, and 3 stressors in predicting adjustment to organizational change. Participants were 589 government employees undergoing an 18-month process of regionalization. To examine if the predictor variables had long-term effects on adjustment, the authors assessed psychological well-being, client engagement, and job satisfaction again at a 2-year follow-up. At Time 1, there was evidence to suggest that information was indirectly related to psychological well-being, client engagement, and job satisfaction, via its positive relationship to efficacy. There also was evidence to suggest that efficacy was related to reduced stress appraisals, thereby heightening client engagement. Last, there was consistent support for the stress-buffering role of Time 1 self-efficacy in the prediction of Time 2 job satisfaction.

Employee reactions to behavioural control under conditions of stress: The moderating role of self-efficacy

Extensive research conducted in the occupational stress literature has failed to provide convincing support for the stress-buffering effects of work control on employee adjustment. Drawing on research conducted in the laboratory context, it was proposed that the stress-buffering effects of work control on employee adjustment would be more marked at high, rather than low, levels of self-efficacy. In a sample of 100 customer service representatives, a significant three-way interaction among role conflict, work control and self-efficacy (measured at Time 1) was observed on (low) depersonalization (measured at Time 2). Consistent with expectations, work control reduced the negative effects of work stress on this outcome measure only for employees who perceived high levels of self-efficacy at work. In addition, there was evidence to suggest that self-efficacy moderated the main effects of work control on job satisfaction and somatic health. These findings are discussed in terms of their theoretical contribution to the job strain model, and also in relation to workplace interventions designed to improve levels of employee adjustment.

Linking client and employee perceptions of the organization: A study of client satisfaction with health care services

Client satisfaction with health care services has usually been researched in terms of socio-demographic and predispositional characteristics associated with the client. The present study included organizational characteristics as predictors of client satisfaction with health care services. Participants in the research were clients and employees of an Australian public-sector health care organization who responded to separate client and employee questionnaires. Hierarchical regression analyses indicated that, after controlling for a number of client characteristics, organizational characteristics, as perceived by employees, accounted for a significant proportion of additional variance in client satisfaction with health care services. Results of the present study provided some support for the proposition that employee perceptions of the working environment should be considered in a more comprehensive understanding of client satisfaction with health care services. Limitations of the study highlight practical difficulties in the assessment of client outcomes and methodological complexities in linking individual and organizational processes.

Integrating customer orientation, employee compensation and performance management: A conceptual framework

Compensation systems are an essential tool to link corporate goals such as customer orientation with individual and organisational performance. While some authors demonstrate the positive effects of incorporating nonfinancial measures into the compensation system empirically, companies have encountered problems after linking pay to customer satisfaction. We argue that reasons for this can be attributed to the measurement of customer satisfaction as well as to the missing link between customer satisfaction and customer retention and profitability in theses cases. Hence, there is a strong need for the development of an holistic reward and performance measurement model enabling an organisation to identify cause-and-effect relationships when linking rewards to nonfinancial performance measures. We present a conceptual framework of a success chain driven reward system that enables organisations to systematically derive a customer-oriented reward strategy. In the context of performance evaluation, we propose to rely on integrated and multidimensional measurement methods.

Creative work environments and employee engagement: Exploring potential links and possibilities

Companies that perform well are often identified as either possessing creative work environments and (or) having high levels of employee engagement. Creative work environments are largely not well defined, although research alludes to contributing factors. On the other hand employee engagement is defined as the multiple emotional, rational and behavioural dimensions of an employee's consistent level of effort, commitment and connection to their job. Some authors including Saks (2006) and Shuck and Wollard (2010) call for more scholarly research to increase our understanding of the drivers of employee engagement and the actions that organisations can take to improve engagement. There are references made in the literature to the existence of a relationship between a creative work environment and engaged employees (Isaksen & Ekvall 2010), but there is a lack of empirical evidence providing support for the direct relationship between the two. This study aims to explore the relationship, addressing the question of how a creative work environment impacts on employee engagement. Exploratory research to investigate this relationship will use a qualitative methodology with semi-structured interviews, field observations and document analysis. Key themes will be analysed at both the individual and team level reflecting the multi-level nature of the constructs.

A multi-criteria analysis approach for ranking and selection of microorganisms for the production of oils for biodiesel production

Oleaginous microorganisms have potential to be used to produce oils as alternative feedstock for biodiesel production. Microalgae (Chlorella protothecoides and Chlorella zofingiensis), yeasts (Cryptococcus albidus and Rhodotorula mucilaginosa), and fungi (Aspergillus oryzae and Mucor plumbeus) were investigated for their ability to produce oil from glucose, xylose and glycerol. Multi-criteria analysis (MCA) using analytic hierarchy process (AHP) and preference ranking organization method for the enrichment of evaluations (PROMETHEE) with graphical analysis for interactive aid (GAIA), was used to rank and select the preferred microorganisms for oil production for biodiesel application. This was based on a number of criteria viz., oil concentration, content, production rate and yield, substrate consumption rate, fatty acids composition, biomass harvesting and nutrient costs. PROMETHEE selected A. oryzae, M. plumbeus and R. mucilaginosa as the most prospective species for oil production. However, further analysis by GAIA Webs identified A. oryzae and M. plumbeus as the best performing microorganisms.

Quality-aware review selection based on product feature taxonomy

As of today, user-generated information such as online reviews has become increasingly significant for customers in decision making process. Meanwhile, as the volume of online reviews proliferates, there is an insistent demand to help the users tackle the information overload problem. In order to extract useful information from overwhelming reviews, considerable work has been proposed such as review summarization and review selection. Particularly, to avoid the redundant information, researchers attempt to select a small set of reviews to represent the entire review corpus by preserving its statistical properties (e.g., opinion distribution). However, one significant drawback of the existing works is that they only measure the utility of the extracted reviews as a whole without considering the quality of each individual review. As a result, the set of chosen reviews may consist of low-quality ones even its statistical property is close to that of the original review corpus, which is not preferred by the users. In this paper, we proposed a review selection method which takes review quality into consideration during the selection process. Specifically, we examine the relationships between product features based upon a domain ontology to capture the review characteristics based on which to select reviews that have good quality and preserve the opinion distribution as well. Our experimental results based on real world review datasets demonstrate that our proposed approach is feasible and able to improve the performance of the review selection effectively.

Genome-wide association study using extreme truncate selection identifies novel genes affecting bone mineral density and fracture risk

Osteoporotic fracture is a major cause of morbidity and mortality worldwide. Low bone mineral density (BMD) is a major predisposing factor to fracture and is known to be highly heritable. Site-, gender-, and age-specific genetic effects on BMD are thought to be significant, but have largely not been considered in the design of genome-wide association studies (GWAS) of BMD to date. We report here a GWAS using a novel study design focusing on women of a specific age (postmenopausal women, age 55-85 years), with either extreme high or low hip BMD (age- and gender-adjusted BMD z-scores of +1.5 to +4.0, n = 1055, or -4.0 to -1.5, n = 900), with replication in cohorts of women drawn from the general population (n = 20,898). The study replicates 21 of 26 known BMD-associated genes. Additionally, we report suggestive association of a further six new genetic associations in or around the genes CLCN7, GALNT3, IBSP, LTBP3, RSPO3, and SOX4, with replication in two independent datasets. A novel mouse model with a loss-of-function mutation in GALNT3 is also reported, which has high bone mass, supporting the involvement of this gene in BMD determination. In addition to identifying further genes associated with BMD, this study confirms the efficiency of extreme-truncate selection designs for quantitative trait association studies. © 2011 Duncan et al.

Channel selection in the short-time modulation domain for distant speech recognition

Automatic speech recognition from multiple distant micro- phones poses significant challenges because of noise and reverberations. The quality of speech acquisition may vary between microphones because of movements of speakers and channel distortions. This paper proposes a channel selection approach for selecting reliable channels based on selection criterion operating in the short-term modulation spectrum domain. The proposed approach quantifies the relative strength of speech from each microphone and speech obtained from beamforming modulations. The new technique is compared experimentally in the real reverb conditions in terms of perceptual evaluation of speech quality (PESQ) measures and word error rate (WER). Overall improvement in recognition rate is observed using delay-sum and superdirective beamformers compared to the case when the channel is selected randomly using circular microphone arrays.

IgE-Associated IGHV Genes from Venom and Peanut Allergic Individuals Lack Mutational Evidence of Antigen Selection

Antigen selection of B cells within the germinal center reaction generally leads to the accumulation of replacement mutations in the complementarity-determining regions (CDRs) of immunoglobulin genes. Studies of mutations in IgE-associated VDJ gene sequences have cast doubt on the role of antigen selection in the evolution of the human IgE response, and it may be that selection for high affinity antibodies is a feature of some but not all allergic diseases. The severity of IgE-mediated anaphylaxis is such that it could result from higher affinity IgE antibodies. We therefore investigated IGHV mutations in IgE-associated sequences derived from ten individuals with a history of anaphylactic reactions to bee or wasp venom or peanut allergens. IgG sequences, which more certainly experience antigen selection, served as a control dataset. A total of 6025 unique IgE and 5396 unique IgG sequences were generated using high throughput 454 pyrosequencing. The proportion of replacement mutations seen in the CDRs of the IgG dataset was significantly higher than that of the IgE dataset, and the IgE sequences showed little evidence of antigen selection. To exclude the possibility that 454 errors had compromised analysis, rigorous filtering of the datasets led to datasets of 90 core IgE sequences and 411 IgG sequences. These sequences were present as both forward and reverse reads, and so were most unlikely to include sequencing errors. The filtered datasets confirmed that antigen selection plays a greater role in the evolution of IgG sequences than of IgE sequences derived from the study participants.

Artificial Neural Network Based Automatic Emergency Landing Site Selection for UAVs and Highly Automated Aircraft

In this report an artificial neural network (ANN) based automated emergency landing site selection system for unmanned aerial vehicle (UAV) and general aviation (GA) is described. The system aims increase safety of UAV operation by emulating pilot decision making in emergency landing scenarios using an ANN to select a safe landing site from available candidates. The strength of an ANN to model complex input relationships makes it a perfect system to handle the multicriteria decision making (MCDM) process of emergency landing site selection. The ANN operates by identifying the more favorable of two landing sites when provided with an input vector derived from both landing site's parameters, the aircraft's current state and wind measurements. The system consists of a feed forward ANN, a pre-processor class which produces ANN input vectors and a class in charge of creating a ranking of landing site candidates using the ANN. The system was successfully implemented in C++ using the FANN C++ library and ROS. Results obtained from ANN training and simulations using randomly generated landing sites by a site detection simulator data verify the feasibility of an ANN based automated emergency landing site selection system.

Influence of road traffic environment and mobile phone distraction on the speed selection behaviour of young drivers

Travel speed is one of the most critical parameters for road safety; the evidence suggests that increased vehicle speed is associated with higher crash risk and injury severity. Both naturalistic and simulator studies have reported that drivers distracted by a mobile phone select a lower driving speed. Speed decrements have been argued to be a risk compensatory behaviour of distracted drivers. Nonetheless, the extent and circumstances of the speed change among distracted drivers are still not known very well. As such, the primary objective of this study was to investigate patterns of speed variation in relation to contextual factors and distraction. Using the CARRS-Q high-fidelity Advanced Driving Simulator, the speed selection behaviour of 32 drivers aged 18-26 years was examined in two phone conditions: baseline (no phone conversation) and handheld phone operation. The simulator driving route contained five different types of road traffic complexities, including one road section with a horizontal S curve, one horizontal S curve with adjacent traffic, one straight segment of suburban road without traffic, one straight segment of suburban road with traffic interactions, and one road segment in a city environment. Speed deviations from the posted speed limit were analysed using Ward’s Hierarchical Clustering method to identify the effects of road traffic environment and cognitive distraction. The speed deviations along curved road sections formed two different clusters for the two phone conditions, implying that distracted drivers adopt a different strategy for selecting driving speed in a complex driving situation. In particular, distracted drivers selected a lower speed while driving along a horizontal curve. The speed deviation along the city road segment and other straight road segments grouped into a different cluster, and the deviations were not significantly different across phone conditions, suggesting a negligible effect of distraction on speed selection along these road sections. Future research should focus on developing a risk compensation model to explain the relationship between road traffic complexity and distraction.

Model selection with misspecified spatial covariance structure

Spatial data analysis has become more and more important in the studies of ecology and economics during the last decade. One focus of spatial data analysis is how to select predictors, variance functions and correlation functions. However, in general, the true covariance function is unknown and the working covariance structure is often misspecified. In this paper, our target is to find a good strategy to identify the best model from the candidate set using model selection criteria. This paper is to evaluate the ability of some information criteria (corrected Akaike information criterion, Bayesian information criterion (BIC) and residual information criterion (RIC)) for choosing the optimal model when the working correlation function, the working variance function and the working mean function are correct or misspecified. Simulations are carried out for small to moderate sample sizes. Four candidate covariance functions (exponential, Gaussian, Matern and rational quadratic) are used in simulation studies. With the summary in simulation results, we find that the misspecified working correlation structure can still capture some spatial correlation information in model fitting. When the sample size is large enough, BIC and RIC perform well even if the the working covariance is misspecified. Moreover, the performance of these information criteria is related to the average level of model fitting which can be indicated by the average adjusted R square ( [GRAPHICS] ), and overall RIC performs well.