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Selecting the ‘best’ main contractor is a complex decision process for construction clients. It requires a large number of criteria to be simultaneously measured and evaluated. Many of these criteria are related to one another in a complex way and therefore, they very often conflict insofar as improvement in one often results in decline of another(s). Furthermore, as contractors' attributes are expressed in both quantitative and qualitative terms, decision‐makers have to base their judgements on both quantitative data and experiential subjective assessments. In this paper, the evidential reasoning (ER) approach (which is capable of processing both quantitative and qualitative measures) is applied as a means of solving the contractor selection problem (CSP). The process of building a multiple criteria decision model of a hierarchical structure is presented, in which both quantitative and qualitative information is represented in a unified manner. The CSP is then fully investigated using the ER approach. Both the advantages of applying this model in practice and the analysis process itself are discussed.

This paper reports the application of an evidential reasoning (ER) approach, to deal with the evaluation of a contractor, from among different fabrication options of aero‐engine equipment. This is followed by a review of current evaluation practices, which begins by highlighting their limitations and then goes on to justify the use of an evidential reasoning approach. The evaluation model is discussed and techniques for articulating the original evaluation data are also explored. A hypothetical selection problem involving the evaluation of different fabrication options for aero‐engine equipment is then examined using this approach. Given the role of small firms as sub‐contractors, and the difficulties that they face in managing the pricing part of the marketing mix, which is heightened when tendering is involved, this paper has lessons that extend outside of the particular industry that forms the case study. The case study itself draws from the real life experience of actual defence contract assessors.

This study aims to improve the calibration accquracy of the road condition sensor. A road condition sensor is widely used to detect water or ice deposits on the road to assess associated driving risks. Its quantitative calibration is central to the thickness measurement accuracy and conventionally performed using the single fitting method-based calibration method. One existing limitation is that the distribution characteristics of calibration data of different road conditions are diversified, which leads to the fitting error can not be minimized when using the conventional calibration method. Thus, the multiple fitting methods-based calibration method is developed to realize an optimal calibration for the road condition sensor.

A fitting method assignment for the calibration data of different road conditions was introduced to realize an optimal combination for fitting method and calibration data. In the experiments, the calibration methods were tested in the absence of measurement errors, then tested with calibration data, and finally, in the covering thickness measurement.

The comparison results indicate that compared with the conventional calibration method, the multiple fitting methods-based calibration method cuts the fitting error in the quantitative calibration by 13.3% and contributes to reducing the thickness measurement error by 8.11% for different road conditions.

The multiple fitting methods-based calibration method has been successfully applied for quantitative calibration and shown to reduce calibration errors. The comparison between different calibration methods demonstrates the superior performance of the new calibration method.

This paper aims to propose a real-time augmented reality (AR)-based assembly assistance system using a coarse-to-fine marker-less tracking strategy. The system automatically adapts to tracking requirement when the topological structure of the assembly changes after each assembly step.

The prototype system’s process can be divided into two stages: the offline preparation stage and online execution stage. In the offline preparation stage, planning results (assembly sequence, parts position, rotation, etc.) and image features [gradient and oriented FAST and rotated BRIEF (ORB)features] are extracted automatically from the assembly planning process. In the online execution stage, too, image features are extracted and matched with those generated offline to compute the camera pose, and planning results stored in XML files are parsed to generate the assembly instructions for manipulators. In the prototype system, the working range of template matching algorithm, LINE-MOD, is first extended by using depth information; then, a fast and robust marker-less tracker that combines the modified LINE-MOD algorithm and ORB tracker is designed to update the camera pose continuously. Furthermore, to track the camera pose stably, a tracking strategy according to the characteristic of assembly is presented herein.

The tracking accuracy and time of the proposed marker-less tracking approach were evaluated, and the results showed that the tracking method could run at 30 fps and the position and pose tracking accuracy was slightly superior to ARToolKit.

The main contributions of this work are as follows: First, the authors present a coarse-to-fine marker-less tracking method that uses modified state-of-the-art template matching algorithm, LINE-MOD, to find the coarse camera pose. Then, a feature point tracker ORB is activated to calculate the accurate camera pose. The whole tracking pipeline needs, on average, 24.35 ms for each frame, which can satisfy the real-time requirement for AR assembly. On basis of this algorithm, the authors present a generic tracking strategy according to the characteristics of the assembly and develop a generic AR-based assembly assistance platform. Second, the authors present a feature point mismatch-eliminating rule based on the orientation vector. By obtaining stable matching feature points, the proposed system can achieve accurate tracking results. The evaluation of the camera position and pose tracking accuracy result show that the study’s method is slightly superior to ARToolKit markers.

In response to the criticism on the measurement system of self‐assessment against the European Foundation for Quality Management (EFQM) model, this paper reports the development of a more scientific and accurate scoring method. The decision model constructed has focused on the “processes” criterion of the EFQM model and can perform three main tasks: to score the self‐assessment submission document; to identify strengths and areas for improvement; and to simulate different scenarios for improvement planning. The model was implemented and tested using two award simulation documents from an electricity distribution utility and a water‐supply company. The results for one of the companies are reported and analysed in this paper. The analysis of the results has proved the reliability and accuracy of the new model. Using the decision model, two systematic methods were developed to identify strengths and areas for improvement and the findings are reported. The model's ability to link self‐assessment with the strategic‐planning process is also commented upon.

This study aims to explore the relationships among institutional pressures, commitment of resources and returns management. Returns management is regarded as a part of supply chain management. However, the research in returns management has received much less attention. To bridge the gap, this study concerns key concepts from two important schools of thought, i.e. institutional theory and the resource-based view, to build up the research model.

Retailers and maintenance providers in the 3C industry (computers, communication and consumer electronics) in Taiwan were surveyed, and the statistical methods of hierarchical and moderated regression were used to examine the relationships among institutional pressures, commitment of resources and returns management.

Institutional pressures, comprising non-market and market pressures, affect the implementation of returns management (product return practices and product recovery practices). Commitments of resources positively and significantly moderate the relationship between the pressures imposed by non-market and market actors and product return practices and product recovery practices.

This study investigates only the factors that drive returns management. Future research can examine the relationship between the antecedents and consequences of returns management. Furthermore, returns management may become increasingly critical for firms to develop and perform corporate social responsibility (CSR). Therefore, future research can investigate the relationship between CSR practices and returns management.

This research suggests that managers under institutional pressures should continually pay attention to the effects of external factors on returns management. Additionally, the results reveal that a commitment of resources can reinforce the relationship between the pressures imposed by non-market and market actors and the implementation of returns management. Under significant institutional pressures and resource constraints, managers may increase the effectiveness of returns management while attending to the concerns of non-market and market actors.

This study presents a model that considers three major explicative variables: institutional pressures, resources commitment and returns management. It is the first investigation to integrate three streams of literature on institutional theory, the resource-based view and returns management.

Little research has been conducted on the internal factors that drive green product (GP) innovation and how family influence affects firm adoption of GP innovation. This study aims to apply multiple perspectives to bridge this research gap, adopting the resource-based view (RBV) to examine what and how internal factors affect firm adoption of GP innovation, and using the behavioral theory of family firms to investigate whether family influence fosters or hinders firm adoption of GP innovation.

This study used a multichannel approach and adopted content analysis to collect and evaluate data on listed Taiwanese firms and used cross-sectional regression analysis to examine the effect of internal factors and family influence on firm adoption of GP innovation.

The results showed that the internal factors of green capabilities, R&D intensity and firm size significantly and positively affected firm adoption of GP innovation separately. Furthermore, the study found that family influence (ownership and control) significantly and negatively affects firm adoption of GP innovation separately.

This study contributes to the academic research of innovation management, green management and family firms in several aspects, but also has some limitations. This study examined only the relationship between a firm’s internal factors and GP innovation. Future research might test the relationship between a firm’s internal factors and adoption of green process innovation. In addition, such research can explore how integrated internal and external factors influence firm adoption of GP innovation.

From the RBV, the internal factors of green capabilities, R&D intensity and firm size that can exert crucial effects on firm engage in firm’s adoption of GP innovation. This study suggests that top managers in family-influenced businesses should maintain appropriate commitment and support for fostering and facilitating firm GP innovation.

From the RBV, this study examined how internal factors affect firm adoption of GP innovation. Moreover, based on the behavioral theory of family firms, this study further examined how family influence (ownership and control) affects firm adoption of GP innovation. This paper extended both perspectives to examine green issues.

From the RBV, this study examined how internal factors affect firms’ GP innovation. Moreover, based on institutional theory, this study further examines how a family firm moderates the relationship between a firm’s internal factors and GP innovation. The paper extended both perspectives to probe further the green issues.

The purpose of this article is to address ways in which customer satisfaction could be improved.

In this article it is shown how the analytic hierarchy process (AHP) can be of use in making a choice between a number of alternative quality improvement projects. A case study performed in a large information technology oriented company illustrates that the model can play an important role in reaching a consensus about the ranking of quality management projects.

Results of the case study suggest that the AHP can strongly contribute to a more effective way of communicating and transforming knowledge between experts from different disciplines during quality improvement projects.

Based on this case study, it can be concluded that by means of the AHP model a rather simple method of making a decision regarding pilot projects related to quality improvement is available to companies. One final advantage of the AHP approach as experienced in the case study relates to the possibility to explore inconsistent judgements.

The aim of this paper is to introduce a new technique for convection visualization. This is similar to Bejan's heatlines and is even an exact match to Landau and Lifshitz's energy streamlines for two‐dimensional geometries.

The work benefits from a combination of numerical and analytical tools to show that, in two‐dimensional space, heatlines and energy streamlines are effectively the same. More importantly, the energy flux vectors are tracing both of them accurately; as verified for some cases of free and forced convection problems in this paper.

The new technique is easier to implement compared to the existing counterparts which are available in the literature. More specifically, the advantage of this new technique is that, contrary to heatlines and energy streamlines, it does not require further numerical analysis in addition to solving momentum and energy equations.

Energy flux vectors offer higher resolution compared to existing visualization tools.

For a long time, women have not been judged by the same standards as men when trying to enter a trade or profession reserved traditionally for men or even after becoming part of it, while progressing. This paper explores the concept of career progression of female staff in an academic institute. It seeks to answer the following research questions: What barriers are faced by women in career progression? What are the differences in perceptions among female sub‐groups regarding these barriers? What policies and improvement programs can be provided to promote women's career progression? What is the relationship between women's career progression and justice?

According to the research objectives, the best way to collect the required data was a questionnaire. In this research, three different questionnaires were used. Also, to rank women's career progression barriers, the TOPSIS technique was used.

The authors found that there is a significant relationship between career progression barriers with interactional and distributive justice. Our ranking results showed that lack of organisational support and job restrictions are the main barriers to women's career progression. Also, flexible working time options is the main support program for eliminating of these barriers.

The major research limitations of this study were the failure to collect and analyse data that would yield a quantitative assessment of organisational justice, and the distribution of questionnaires among women at work.

This is the first study of its nature conducted among female Iranian university staff to focus on barriers to women's career progression. The paper also offers practical guidance that can be used by management and women employees to facilitate career progression.