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Capital project delivery, such as the delivery of transportation networks and industrial facilities, often suffers losses due to overly aggressive planning. Planners often are overly optimistic about the chance of success while underestimating risks. The purpose of this paper is to examine the hypothesis that these biases are from the difficulties most decision makers face when interpreting probabilistic information.

Three behavioral experiments were conducted to test the theoretical fitness of the paradigms proposed by the description–experience gap literature, namely, the sampling errors effect, the recency effect and statistical information format. College students were recruited to participate in a series of estimating tasks. And their estimating results were compared given different levels of information completeness.

It was found that the existing paradigms could predict risk decision making in the risk-averse estimating scenarios where test subjects were required to give a relatively conservative estimate, but they seemed to be less effective in predicting decisions in the risk-seeking estimating scenario, where test subjects were asked to give a relatively aggressive estimate.

Based on these findings, an integrative model is proposed to explain the observations pertaining to aggressive planning in capital projects. Two dimensions are deemed to be relevant: including risk-taking intentions, and an information uncertainty continuum that ranges from an implicit experience-based information representation to an explicit description-based information representation.

The ubiquity of smartphones has changed how people communicate, work and entertain. In view of conservation of resources theory and the positive spillover effect, this study explores the effect of non-work-related instant messaging (IM) in the workplace on daily task performance.

The authors use the experience sampling method to collect day-level data from 75 employees over a period of 10 workdays. Multilevel path analysis is used to test the hypotheses.

Non-work-related IM exerts a significant negative indirect effect on daily task performance through diminished cognitive engagement. This negative indirect effect disappears when social support is high, thereby showing the function of social support as a neutralizer of the detriment of non-work-related IM on daily task performance.

The findings suggest that organizations can neutralize the harm of non-work-related IM in the workplace by promoting social support perceived by employees.

This study advances the technology and management literature by developing and testing a balanced perspective on the ambivalent effect of workplace smartphone use that considers social and cognitive resource implications.

This study aimed two objectives: The first objective was to explore carriers' intentions to use cargo electric vehicles (EVs) and the factors influencing these intentions in last-mile delivery (LMD). The other objective was to provide recommendations for policymakers and manufacturers to promote and customize cargo EVs to meet the requirements of carriers in the LMD sector.

In this study the authors constructed a research framework that adjusted and extended the original technology acceptance model (TAM). The proposed model combines eight psychological factors, including attitude, perceived usefulness, perceived ease of use, perceived risk, public engagement, face consciousness, financial incentive policy and carrier intention, in which four factors, namely attitude, perceived usefulness, perceived ease of use and intention, were obtained from the original TAM and the four remaining factors, namely perceived risk, public engagement, face consciousness and financial incentive policy, were added.

The results showed that psychological factors such as attitude, perceived ease of use, perceived risk, public engagement and face consciousness might affect carriers' intentions to use electric cargo vehicles in LMD service in Vietnamese cities. These results agree with the previous studies and confirm that attitude, perceived ease of use, perceived risk, public engagement and face consciousness might be important for shaping intention to use electric cargo vehicles in emerging markets.

This study has several limitations, first, the data were collected in Vietnam, a country with weak academic contributions. Therefore, these findings might not be generalizable to other areas. The authors expect to apply the same research framework to other countries to explore the similarities and differences across the countries. Second, the authors conducted the surveys in three cities; except for Hanoi, the other two cities are not really large markets in the LMD service sector. Third, the authors ignored the relationship between the demographic characteristics and electric cargo vehicles. Further studies should address this gap.

Based on the findings, manufacturers should ensure the high-quality performance of electric cargo vehicles in terms of extending driving range and shortening recharging time. Policymakers should develop the roadmaps for electric cargo vehicles, starting from switching from conventional cargo motorcycles to electric motorcycles. In addition, developing public charging infrastructure should be prioritized, which is the fundamental basis for operating electric cargo vehicles. Finally, manufacturers should research and develop a product that would improve the reputation of carriers because carriers with higher face consciousness are more likely to pursue brand-name and high-priced products in order to enhance their reputation.

This study contributes to the literature in two aspects: First, the authors investigated intentions to use electric cargo vehicles in LMD service, which is rare from other studies, and they further identified the psychological determinants of carriers' intentions. Second, the findings increase the knowledge of carriers' intentions and suggest implications for policymakers and manufacturers to promote the adoption of electric cargo vehicles in last-mile deliver service.

The purpose of this research is to explore the mechanisms of knowledge management (KM) for innovation and provide an approach for enterprises to leverage KM activities into continuous innovation.

By reviewing the literature from multidisciplinary fields, the concepts of knowledge, KM and innovation are investigated. The physical, human and technological perspectives of KM are distinguished with the identification of two core activities for innovation: knowledge creation and knowledge usage. Then an essential requirement for continuous innovation – an internalization phase – is defined. The systems thinking and human‐centered perspectives are adopted for providing a comprehensive understanding about the mechanisms of KM for innovation.

A networking process of continuous innovation based on KM is proposed by incorporating the phase of internalization. Three sources of organizational knowledge assets in innovation are identified. Then, based on the two core activities of innovation, a meta‐model and a macro process of KM are proposed to model the mechanisms of KM for continuous innovation. Then, in order to operationalize the KM mechanisms, a hierarchical model with four layers is constructed by integrating three sources of knowledge assets, the meta‐model and the macro process into the process of continuous innovation.

According to the lessons learned about KM practices in previous research, the three perspectives of KM should collaborate with one another for successful implementation of KM projects for innovation; the networking process of innovation provides a new way to integrate KM process in innovation; the hierarchical model provides a suitable architecture to implement systems of KM for innovation.

The meta‐model and macro process of KM explain how the next generation of KM can help the value creation and support the continuous innovation from the systems thinking perspective. The hierarchical model illustrates the complicated knowledge dynamics in the process of continuous innovation.

This paper aims to describe how a novel biomimetic chewing robot was designed, including its motion, force, control and mechanical designs, and shows some initial experiments about motion tracking.

According to the biomechanics, the authors modeled the muscles of mastication in six linkages and the temporomandibular joint in higher kinematic pairs of point contact. As a result, the chewing robot was represented in a redundantly actuated parallel mechanism. With reference to literature data on the biological system, the authors specified the motion and force requirements for the robot via inverse kinematics and force analysis. A prototype of the robot was built, which has a position control system and is driven by six linear actuators. Experiments were conducted to show the capability of the robot in reproducing the human chewing motion.

A chewing robot was successfully modeled and developed, which is able to simulate the motion of human mastication in a biologically faithful way.

The chewing robot as a scientific instrument can be used to test dental materials and evaluate food textural properties of chewing.

Two higher kinematic pairs of point contact are proposed to simulate the two temporomandibular joints. The mechanism of the novel chewing robot is the first of this kind, which has two higher kinematic pairs of point contact and is a redundantly actuated spatial parallel mechanism.

The purpose of this paper is to propose a novel target automatic recognition method for unmanned aerial vehicle (UAV), which is based on backpropagation – artificial neural network (BP-ANN) algorithm, with the objective of optimizing the structure of backpropagation network, to increase the efficiency and decrease the recognition time. A hardware-in-the-loop system for UAV target automatic recognition is also developed.

The hybrid model of BP-ANN structure is established for aircraft automatic target recognition. This proposed method identifies controller parameters and reduces the computational complexity. Approaching speed of the network is faster and recognition accuracy is higher. This kind of network combines or better fuses the advantages of backpropagation artificial neural algorithm and Hu moment. with advantages of two networks and improves the speed and accuracy of identification. Finally, a hardware-in-the-loop system for UAV target automatic recognition is also developed.

The double hidden level backpropagation artificial neural can easily increase the speed of recognition process and get a good performance for recognition accuracy.

The proposed backpropagation artificial neural algorithm can be ANN easily applied to practice and can help the design of the aircraft automatic target recognition system. The standard backpropagation algorithm has some obvious drawbacks, namely, converging slowly and falling into the local minimum point easily. In this paper, an improved algorithm based on the standard backpropagation algorithm is constructed to make the aircraft target recognition more practicable.

A double hidden levels backpropagation artificial neural algorithm is presented for automatic target recognition system of UAV.

The applications of multi-agent systems (MASs) are considered to be among the most promising paradigms for detailed investigations and reliable problem-solving methods, and MAS applications make it possible for researchers and practitioners to better understand complex systems. Although a number of prior studies have been conducted to address complex issues that arise from construction projects, few studies have summarised the applications and discussed the capacity of MASs from the perspective of construction management. To fill the gap, this paper provides a comprehensive literature review of MAS applications from the perspective of construction management.

Web of Science and Scopus are the most commonly used international databases in conducting the literature reviews. A total of 86 relevant papers published in SCI-Expanded, SSCI and Ei Compendex journals related to the application of MASs from the perspective of construction management are selected to be analysed and discussed in this paper.

Based on the 86 collected publications, the utilisations of MASs to support the management of the supply chain and the improvement of project performance are identified from the perspective of construction management, the characteristics and barriers of current MAS applications are analysed, a framework for developing agent-based models to address complex problems is proposed, and future research directions of MAS applications are discussed.

This review can serve as a useful reference for scholars to enhance their understanding of the current research and guide future research on MASs. The proposed framework can help build agent-based models to address complex problems in construction management.

This study assessed the key construction technology trends and tracked the impact of these developments on labor influencing factors in an effort to alleviate the limitations posed by skilled labor scarcity in offsite construction (OSC).

The study followed systematic literature review to acquisition initial data sets. The data retrieved form identified databases was then subjected to scientometric analysis. In next stage, disruptive factors were identified to produce a conceptual framework followed by its practical application for an OSC firm.

This study identified seven construction technologies with a potential to disrupt OSC in future. The study also developed a conceptual framework as a benchmark to help establish further frameworks for policy interns in OSC.

The review conducted only takes into account the literature from 2009 to March 2021. Any prior to the date and unpublished studies were not included in study.

A proper utilization of conceptual framework can contribute to technological adoption significantly lowering the need of labor in OSC industry. Moreover, the established discussion on understanding the purpose of deploying latest technology may reduce additional costs, management, time and allocation of labor resources.

This study provides a mechanism to intern OSC policymakers to impartially incorporate the latest technology in OSC.

This study fulfilled an identified need to study latest technological trends in OSC by utilizing bibliometric tools with SLR. The disruptive categories and factors highlighted by the study were not identified in a systematic manner in previous studies, which could further enhance the implementation of latest construction technology in OSC.

The objective of this research study is to formulate and develop a novel optimization model that enables planners of modular construction to minimize the total transportation and storage costs of prefabricated modules in modular construction projects.

The model is developed by identifying relevant decision variables, formulating an objective function capable of minimizing the total transportation and storage costs and modelling relevant constraints. The model is implemented by providing all relevant planner-specified data and performing the model optimization computations using mixed-integer programming to generate the optimal solution.

A case study of hybrid modular construction of a healthcare facility is used to evaluate the model performance and demonstrate its capabilities in minimizing the total transportation and onsite storage costs of building prefabricated modules.

The model can be most effective in optimizing transportation for prefabricated modules with rectangular shapes and might be less effective for modules with irregular shapes. Further research is needed to consider the shape of onsite storage area and its module arrangement.

The developed model supports construction planners in improving the cost effectiveness of modular construction projects by optimizing the transportation of prefabricated modules from factories to construction sites.

The original contributions of this research is selecting an optimal module truck assignment from a feasible set of trucks, identifying an optimal delivery day of each module as well as its location and orientation on the assigned truck and complying with relevant constraints including the non-overlap of modules on each truck, shipment weight distribution and aerodynamic drag reduction.

Industrialized construction (IC) has been recognized as a game-changing approach in Hong Kong (HK). However, the increasing risks of disruptions in IC supply chains (SCs) raise SC vulnerability levels, prompting attention to developing supply chain resilience (SCR). Since SCR is only attainable through overcoming critical supply chain vulnerabilities (CSCV) with enhanced SC capabilities, this study first aimed to determine the most CSCV of ICSCs by addressing this current research gap and practical need.

Drawing on SCV factors identified from a precursor literature review, an empirical study of IC in HK was conducted using a questionnaire survey and interviews with industry experts. Focussed significance analysis of the data collected through questionnaire survey enabled the selection of 26 CSCV as appropriate to IC. Next, factor analysis was conducted, enabling the grouping of these CSCV under five components. The results were verified and reinforced by interview findings.

The results revealed 26 CSCV pertinent to resilient ICSCs in HK with five underlying components: economic, technological, procedural, organizational and production-based vulnerabilities. Loss of skilled labour is the most critical vulnerability, whereas organizational SCV is the most critical component identified.

Findings of this study would motivate IC project professionals to appreciate and address the CSCV in the context of five components and thereby develop adequate specific capabilities to successfully withstand these CSCV. This should trigger future studies to map CSCV with appropriate capabilities in developing an envisaged powerful assessment model for evaluating the SCR in IC in HK.