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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.

This research navigates the confluence of data analytics, machine learning, and artificial intelligence to revolutionize the management of urban services in smart cities. The study thoroughly investigated with advanced tools to scrutinize key performance indicators integral to the functioning of smart cities, thereby enhancing leadership and decision-making strategies. Our work involves the implementation of various machine learning models such as Logistic Regression, Support Vector Machine, Decision Tree, Naive Bayes, and Artificial Neural Networks (ANN), to the data. Notably, the Support Vector Machine and Bernoulli Naive Bayes models exhibit robust performance with an accuracy rate of 70% precision score. In particular, the study underscores the employment of an ANN model on our existing dataset, optimized using the Adam optimizer. Although the model yields an overall accuracy of 61% and a precision score of 58%, implying correct predictions for the positive class 58% of the time, a comprehensive performance assessment using the Area Under the Receiver Operating Characteristic Curve (AUC-ROC) metrics was necessary. This evaluation results in a score of 0.475 at a threshold of 0.5, indicating that there's room for model enhancement. These models and their performance metrics serve as a key cog in our data analytics pipeline, providing decision-makers and city leaders with actionable insights that can steer urban service management decisions. Through real-time data availability and intuitive visualization dashboards, these leaders can promptly comprehend the current state of their services, pinpoint areas requiring improvement, and make informed decisions to bolster these services. This research illuminates the potential for data analytics, machine learning, and AI to significantly upgrade urban service management in smart cities, fostering sustainable and livable communities. Moreover, our findings contribute valuable knowledge to other cities aiming to adopt similar strategies, thus aiding the continued development of smart cities globally.

This study investigates the direct influence of ambidextrous leadership on employees’ innovation behaviour, the mediating role of innovative self-efficacy and harmonious work passion, and the moderating role of Zhong-Yong thinking.

The authors conducted a series of questionnaire surveys to collect data in three time periods and from multiple sources; 332 supervisor–subordinate matched samples were obtained. The hypothesised relationships were tested using structural equation modelling and ProClin.

Ambidextrous leadership is positively associated with employees’ innovation behaviour, while innovative self-efficacy and harmonious work passion play mediating roles. The analysis further confirms that innovative self-efficacy and harmonious work passion play a chained double-mediating role between ambidextrous leadership and employees’ innovation behaviour, while Zhong-Yong thinking plays moderating roles between ambidextrous leadership and innovative self-efficacy and between ambidextrous leadership and harmonious work passion.

This study demonstrates the influence of ambidextrous leadership on employees’ innovation behaviour, specifically the role of ambidextrous leadership, and extends the relationship’s theoretical foundation. It is also expected to provide inspiration and serve as a reference for local Chinese management.

This study aims to investigate dynamic relations among office property price indices of 10 major cities in China for the years 2005–2021.

Using monthly data, the authors adopt vector error correction modeling and the directed acyclic graph for the characterization of contemporaneous causality among the 10 indices.

The PC algorithm identifies the causal pattern, and the linear non-Gaussian acyclic model algorithm further determines the causal path from which we perform innovation accounting analysis. Sophisticated price dynamics are found in price adjustment processes following price shocks, which are generally dominated by the top tier of cities.

This suggests that policies on office property prices, in the long run, might need to be planned with particular attention paid to the top tier of cities.