Search results

As with many other procurement management concepts, the early contractor involvement (ECI) strategy provides an opportunity to tap on contractors’ specialist knowledge in the design stage, in order to address and forestall problems before moving onto the construction stage. Such practices have been shown to be quite successful, and are also advocated in the lean construction practice. Following this benefit, the purpose of this paper is to examine the relationships between the operations of ECI and the principles of lean construction for productivity enhancement.

Based on the literature review of 11 principles of lean construction and ECI, various relationships were proposed seeking to integrate the two domains. A questionnaire survey was conducted with different stakeholders in Singapore’s construction industry to measure the strength of the relationships. Interviews were also conducted to gain an understanding of the constraints, reluctance, and possible methods for implementing ECI in Singapore with a convergence with lean construction principles.

The results revealed that, when employing lean construction principles as a yardstick, ECI does contribute to elevating the productivity outcomes of some lean construction principles – in particular, for reducing variability, reducing cycle time, minimizing steps, and increasing output flexibility.

It is promising to marry ECI with lean construction, and that the former does appear indeed able to aid in the productivity improvements of a construction project by embracing the latter. However, it requires the building professionals to raise their awareness of both lean and ECI, so that the implementation of ECI can be carried out in a more effective manner.

In the context of the Singapore construction industry’s push for improvements in productivity, this study posits that productivity benefits in particular can be realized through establishing the linkage between ECI and lean construction principles. The lessons from this study can likewise be adopted by other countries.

The purpose of this paper is to examine customers’ perceptions of digital banking (DB), customer experience, satisfaction, loyalty and financial performance (FP) in UK banks.

The research consists of a survey of UK bank customers’ perceptions of the above themes; use of banks’ financial reports to obtain FP ratios; multivariate factor analysis; structural equation modelling; and analysis of variance tests to explore research hypotheses on the relationships among the study factors.

The main factors which determine customer experience in DB are service quality, functional quality, perceived value (PV), employee-customer engagement, perceived usability and perceived risk. There is a significant relationship among customer experience, satisfaction and loyalty, which is related to FP.

This study concentrates on UK bank customers which limits its generalisability to other banks globally. However, the fact that banks typically adopt common standards in bank financial management implies that the findings are potentially robust for global bank management. Replicating the study in banks in other countries will further enhance this robustness.

Some significant effects of customer characteristics on the study factors were observed, which have useful implications for DB, bank marketing services and bank FP.

Unlike previous studies, this study uses both Net Promoter Score and financial ratios as dependent variables, to provide a combined study of the relationships among 14 study factors, with implications for bank marketing and FP.

This paper aims to establish a novel three-way decisions-based grey incidence analysis clustering approach and exploit it to extract information and rules implied in panel data.

Because of taking on the spatiotemporal characteristics, panel data can well-describe and depict the systematic and dynamic of the decision objects. However, it is difficult for traditional panel data analysis methods to efficiently extract information and rules implied in panel data. To effectively deal with panel data clustering problem, according to the spatiotemporal characteristics of panel data, from the three dimensions of absolute amount level, increasing amount level and volatility level, the authors define the conception of the comprehensive distance between decision objects, and then construct a novel grey incidence analysis clustering approach for panel data and study its computing mechanism of threshold value by exploiting the thought and method of three-way decisions; finally, the authors take a case of the clustering problems on the regional high-tech industrialization in China to illustrate the validity and rationality of the proposed model.

The results show that the proposed model can objectively determine the threshold value of clustering and achieve the extraction of information and rules inherent in the data panel.

The novel model proposed in the paper can well-describe and resolve panel data clustering problem and efficiently extract information and rules implied in panel data.

The proposed model can deal with panel data clustering problem and realize the extraction of information and rules inherent in the data panel.

The purpose of this paper is to prove the importance of both cluster leadership and identification on cluster innovation.

The case studies presented in this study involve a cluster by micro-enterprises in Yilan, Taiwan. Data collected during interviews, observations and secondary data provide understanding and practices for the impact of cluster identification on cluster innovation.

This study proved: first, the importance of cluster identification on innovation by representing the need of consensus and collaboration of the members in conducting innovation actions; and second, the cluster identification is influenced by the cluster leadership by showing high satisfaction of the leader, close interaction between the members and high identification with the cluster.

This study predicts the ongoing cluster innovation activities will be achieved due to the transformational leadership and high cluster identification.

This study enriches the factors of cluster innovation accomplishment and proposes the important of cluster identification, which has not been discussed much in the past.

As the core technology of blockchain, various consensus mechanisms have emerged to satisfy the demands of different application scenarios. Since determining the security, scalability and other related performance of the blockchain, how to reach consensus efficiently of consensus mechanism is a critical issue in the blockchain.

The paper opted for a research overview on the blockchain consensus mechanism, including the consensus mechanisms' consensus progress, classification and comparison, which are complemented by documentary analysis.

This survey analyzes solutions for the improvement of consensus mechanisms in blockchain that have been proposed during the last few years and suggests future research directions around consensus mechanisms. First, the authors outline the consensus processes, the advantages and disadvantages of the mainstream consensus mechanisms. Additionally, the consensus mechanisms are subdivided into four types according to their characteristics. Then, the consensus mechanisms are compared and analyzed based on four evaluation criteria. Finally, the authors summarize the representative progress of consensus mechanisms and provide some suggestions on the design of consensus mechanisms to make further advances in this field.

This paper summarizes the future research development of the consensus mechanisms.

This paper aims to investigate the effects of deposition time on the structure and anti-corrosion properties of a micro-arc oxidation (MAO)/Al coating on AZ31B Mg alloy.

The study describes the fabrication of the coating via a combined process of MAO with multi-arc ion plating. The structure, composition and corrosion resistance of the coatings were evaluated using scanning electron microscopy, energy dispersive spectroscopy, X-ray diffraction and electrochemical methods.

The Al-layer is tightly deposited with a good mechanical interlock along the rough interface due to the Al diffusion. However, the Al layer reduces the anti-corrosion of MAO-coated Mg alloy because of structural defects such as droplets and cavities, which act as channels for corrosive media infiltration towards the substrate. Fortunately, the Al layer improves the substrate corrosion resistance owing to its passive behaviour, and the corrosion resistance can be enhanced with increasing deposition time. All results indicate that a buffer layer fabricated through the duplex process improves the interfacial compatibility between the hard coating and soft Mg alloys.

An MAO/Al duplex coating was fabricated via a combined process of MAO and physical vapour deposition. MAO/Al duplex coatings exhibit obviously passive behaviours on AZ31 Mg alloy. The structure and corrosion resistance of MAO/Al coatings were investigated.

The aim of this paper is to implement direct teaching of industrial manipulators using current sensors. The traditional way to implement teaching is either to use a teaching pedant, which is time consuming, or use force sensors, which increases system cost. To overcome these disadvantages, a novel method is explored in the paper by using current sensors installed at joints as torque observers.

The method uses current sensors installed at each joint of a manipulator as torque observers and estimates external forces from differences between joint-driven torque computed based on the values of current sensors and commanded values of motor-driven torque. The joint-driven torque is computed by cancelling out both pre-calibrated gravity and friction resistance (compensation). Also, to make the method robust, the paper presents a strategy to detect unexpected slowly drifts and zero external forces and stop the robot in those situations.

Experimental results demonstrated that compensating the joint torques using both pre-calibrated gravity and friction resistance has performance comparable to a force sensor installed on the end effector of a manipulator. It is possible to implement satisfying direct teaching without using force sensors on 7 degree of freedom manipulators with large mass and friction resistance.

The main contribution of the paper is that the authors cancel out both pre-calibrated gravity and friction resistance to improve the direct teaching using only current sensors; they develop methods to avoid unsafe situations like slow drifts. The method will benefit industrial manipulators, especially those with large mass and friction resistance, to realize flexible and reliable direct teaching.

This study attempts to explore major attributes of food delivery applications in consideration of their impacts upon perceived service quality, satisfaction and usage intention. A multi-group analysis is performed to examine the hypothesized relationships in the structural models for the millennials and non-millennials.

The data was collected from 311 food delivery application users. The structural equation model (SEM) was designed to examine the interrelationships among variables.

The findings indicate that five salient dimensions that include ease of use, facility aesthetics, trustworthiness, value for money and product portfolio have a significant impact on overall service quality. Results further demonstrate that differences in generational difference partially moderate the relationship between each attribute of the food delivery application service quality as well as overall service quality.

The rapid evolution of the Internet and mobile communication has resulted in the proliferation of food delivery applications in China. However, to the best of our knowledge, only a few studies have focused on measuring the key dimension of service quality of food delivery applications.

As digital spaces for team collaboration, virtual worlds bring considerable verisimilitude to technology-mediated social interaction and change the process of traditional team learning. The purpose of this study is to understand how to promote collaborative learning in virtual worlds by leveraging the power of we-intention to participate in virtual worlds. The authors further use the valence–instrumentality–self-efficacy–trust model (VIST) model as a means of understanding the formation of we-intention to participate in virtual worlds, during which behavioral desire serves a bridging role.

The authors tested the research model using the data gathered from 298 users of a prominent form of virtual world, i.e. massively multiplayer online role-playing games. The authors used the structural equation modeling approach and the partial least squares technique for data analysis.

Results show that the four factors of the VIST model (i.e. valence on team goals, instrumentality of contribution, self-efficacy in team tasks and trust in team members) all positively influence we-intention to participate in virtual worlds through behavioral desire for team actions. We-intention to participate in virtual worlds further exerts a stronger positive effect on collaborative learning in virtual worlds, compared with I-intention to participate in virtual worlds.

This work advances the information systems literature by introducing a relevant and important concept, i.e. we-intention, to explain collaborative learning in virtual worlds. This study especially compared the effect of we-intention and I-intention on collaborative learning in virtual worlds. The results of this work also provide practitioners with insights into the role of we-intention in promoting collective actions in virtual worlds.

This paper aims to provide a novel integrated fault detection method for industrial process monitoring.

A novel integrated fault detection method based on the combination of Mallat (MA) algorithm, weight-elimination (WE) algorithm, conjugate gradient (CG) algorithm and multi-dimensional Taylor network (MTN) dynamic model, namely, MA-WE-CG-MTN, is proposed in this paper. First, MA algorithm is taken as data pre-processing. Second, in virtue of approximation ability and low computation complexity owing to the simple structure of MTN, MTN dynamic models are constructed for each frequency band. Furthermore, the CG algorithm is used to discipline the model parameters and the outputs of MTN model of each frequency band are gained. Third, the authors introduce the WE algorithm to cut down the number of middle layer nodes of MTN, reducing the complexity of the network. Finally, the outputs of MTN model for each frequency band are superimposed to achieve outputs of MTN model, and fault detection is proceeded by the residual error generator based on the difference between the output of MTN model and the actual output.

The novel proposed method is used to perform fault detection for industrial process monitoring effectively, such as the Benchmark Simulation Model 1 wastewater treatment process.

The novel proposed method has generality and provides considerably improved performance and effectiveness, which is used to perform fault detection for industrial process monitoring. The proposed method has good robustness, low complexity and easy implementation.