Do human resource practices enhance organizational commitment in SMEs with low employee satisfaction?

This paper considers a large matched employee–employer data set to estimate a model of organizational commitment. In particular, it focuses on the role of firm size and management formality to explain organizational commitment in British small and medium-sized enterprises (SMEs) with high and low levels of employee satisfaction. It is shown that size ‘in itself’ can explain differences in organizational commitment, and that organizational commitment tends to be higher in organizations with high employee satisfaction compared with organizations of similar size with low employee satisfaction. Crucially, the results suggest that formal human resource (HR) practices can be used as important tools to increase commitment and thus, potentially, effort and performance within underperforming SMEs with low employee satisfaction. However, formal HR practices commonly used by large firms may be unnecessary in SMEs which benefit from high employee satisfaction and positive employment relations within a context of informality.

Success, satisfaction and scrutiny: the business benefits of involving residents

This document summarises work to develop a compelling business case for landlord investment in resident involvement. Its key argument is involvement not only assists in improving satisfaction and service delivery, but also provides value for money.

Examining Chinese Tourists’ Nature-based Tourism Participantion Behavior: Incorporating Environmental Concern into a Constraint-Negotiation Modela

Finding the balance between economic development and preservation of the natural environment is a challenging yet important task. This is a particularly pressing issue in the case of China, as it is the largest and fastest-growing market for tourism. The purpose of this research is to examine Chinese tourists’ participation in nature-based, tourism activities by incorporating tourists’ environmental concern, measured by a revised New Environmental Paradigm (NEP) scale, into a tourism constraint-negotiation model. The responses of 409 Chinese tourists show environmental concern will positively affect tourists’ motivation, which, in turn, will affect their negotiation strategy and ultimately their participation behavior. The theoretical and managerial implications of this study are discussed in the context of the tourism literature.

A Fuzzy-Wavelet Neural Network Model for the Detection of Meat Spoilage using an Electronic Nose

Food product safety is one of the most promising areas for the application of electronic noses. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). This paper proposes a fuzzy-wavelet neural network model which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modeling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results indicated that the proposed modeling scheme could be considered as a valuable detection methodology in food microbiology

A Rapid Detection of Meat Spoilage using an Electronic Nose and Fuzzy-Wavelet systems

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid detection of meat spoilage microorganisms during aerobic or modified atmosphere storage, an electronic nose with the aid of fuzzy wavelet network has been considered in this research. The proposed model incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results against neural networks and neurofuzzy systems indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology

Student Satisfaction Negates Pedagogic Rights, Theirs and Ours!

This article outlines how the potential for students to be co-participants, via a critical education, risks being further co-opted through the marketization of higher education by constructing students as consumers with power over academics to make judgments on pedagogic quality through student satisfaction ratings. We start by outlining the relevant components of marketization processes, and their associated practices of financialization and managerialism that have developed in response to the “legitimation crisis” in HE and argue that these have profoundly altered the university landscape with a significant impact on our working practices. Student engagement is increasingly being appropriated as a quantifiable measurement of “student satisfaction”, which then profoundly alters the teaching and learning experience with different understandings of what acquiring knowledge requires and what it feels like. We draw on our experience of working in the post 1992 sector to describe how we are increasingly working under conditions of “reified exchange” and how this affects our relationships with students, other academics and management, eroding our pedagogic rights and theirs in the process. Specifically, we conclude that marketization is likely to further reduce the institutional space and opportunities for both lecturers and students to exercise their “pedagogic rights” to personal enhancement, social inclusion and civic participation through education.

Neuro-Fuzzy based Identification of Meat Spoilage using an Electronic Nose

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). An adaptive fuzzy logic system model that utilizes a prototype defuzzification scheme has been developed to classify beef samples in their respective quality class and to predict their associated microbiological population directly from volatile compounds fingerprints. Results confirmed the superiority of the adopted methodology and indicated that volatile information in combination with an efficient choice of a modeling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage

Application of adaptive neuro-fuzzy inference for wind power short-term forecasting

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. In this paper, an adaptive neuro-fuzzy inference approach is proposed for short-term wind power forecasting. Results from a real-world case study are presented. A thorough comparison is carried out, taking into account the results obtained with other approaches. Numerical results are presented and conclusions are duly drawn. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Increase of the delivered energy probability in DES using a fuzzy probabilistic modeling

The paper proposes a methodology to increase the probability of delivering power to any load point by identifying new investments in distribution energy systems. The proposed methodology is based on statistical failure and repair data of distribution components and it uses a fuzzy-probabilistic modeling for the components outage parameters. The fuzzy membership functions of the outage parameters of each component are based on statistical records. A mixed integer nonlinear programming optimization model is developed in order to identify the adequate investments in distribution energy system components which allow increasing the probability of delivering power to any customer in the distribution system at the minimum possible cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 180 bus distribution network.

Hybrid fuzzy Monte Carlo technique for reliability assessment in transmission power systems

This paper proposes a new methodology to reduce the probability of occurring states that cause load curtailment, while minimizing the involved costs to achieve that reduction. The methodology is supported by a hybrid method based on Fuzzy Set and Monte Carlo Simulation to catch both randomness and fuzziness of component outage parameters of transmission power system. The novelty of this research work consists in proposing two fundamentals approaches: 1) a global steady approach which deals with building the model of a faulted transmission power system aiming at minimizing the unavailability corresponding to each faulted component in transmission power system. This, results in the minimal global cost investment for the faulted components in a system states sample of the transmission network; 2) a dynamic iterative approach that checks individually the investment’s effect on the transmission network. A case study using the Reliability Test System (RTS) 1996 IEEE 24 Buses is presented to illustrate in detail the application of the proposed methodology.

Logic programming and fuzzy Monte Carlo for distribution network reconfiguration

This paper present a methodology to choose the distribution networks reconfiguration that presents the lower power losses. The proposed methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modeling for system component outage parameters. The proposed hybrid method using fuzzy sets and Monte Carlo simulation based on the fuzzyprobabilistic models allows catching both randomness and fuzziness of component outage parameters. A logic programming algorithm is applied, once obtained the system states by Monte Carlo Simulation, to get all possible reconfigurations for each system state. To evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation an AC load flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 115 buses distribution network.

Power systems reliability calculation based on fuzzy data mining

This paper presents a methodology supported on the data base knowledge discovery process (KDD), in order to find out the failure probability of electrical equipments’, which belong to a real electrical high voltage network. Data Mining (DM) techniques are used to discover a set of outcome failure probability and, therefore, to extract knowledge concerning to the unavailability of the electrical equipments such us power transformers and high-voltages power lines. The framework includes several steps, following the analysis of the real data base, the pre-processing data, the application of DM algorithms, and finally, the interpretation of the discovered knowledge. To validate the proposed methodology, a case study which includes real databases is used. This data have a heavy uncertainty due to climate conditions for this reason it was used fuzzy logic to determine the set of the electrical components failure probabilities in order to reestablish the service. The results reflect an interesting potential of this approach and encourage further research on the topic.

Organisational influence on volunteer satisfaction and attitudes towards HRM practices: the case of hospital volunteers

We examine volunteer satisfaction with HRM practices, namely recruitment, training and reward in NPOs and attitudes regarding the appropriateness of these practices. The participants in this study are 76 volunteers affiliated with four different NPOs, who work in hospitals and have direct contact with patients and their families. Analysing aggregate results we show that volunteers are more satisfied with training, and consider the training strategies to be very appropriate. After identifying differences between organisations we discover that in some organisations volunteers are satisfied with rewards but they have negative attitudes regarding the appropriateness of the recognition strategies. We also identify the volunteers who are the most and the least satisfied.

Learner’s satisfaction within a breast imaging eLearning course for radiographers

Background: An asynchronous eLearning system was developed for radiographers in order to promote a better knowledge about senology and mammography. Objectives: to assess the learners’ satisfaction. Methods: Target population included radiographers and radiogr aphy students, in order to assess eLearning satisfaction according to different experience levels in breast imaging. Satisfaction was measured through a questionnaire developed especially for eLearning systems, using a seven - point Likert scale. Main topics related are content, interface, personalization and learning community. Results: Overall, 85% of learners were satisfied with the course and 87,5% considered that the course is successful. Main areas that were evaluated by most learners in a positive way were interface and content (between six and seven - point); on the other hand, learning community presented a wider distribution of answers . Conclusions: The course provides an overall high degree of learner satisfaction, thus providing more effective knowle dge gain on breast imaging for radiographers.