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As megaprojects bear extensive and profound social responsibilities throughout the project life cycle, formulating effective measures for improving construction enterprise social responsibility is key to project success. Given the current research is relatively lack of these measures, this study aims to formulate a meta-network framework to improve the megaproject social responsibility behaviour (MSRB) for construction enterprises.

First, this study implements literature review, expert interview and field investigation to identify the construction enterprise MSRB and its influencing factors. Second, this study evaluates the MSRB implementation level of the selected construction enterprises and proposes the above mentioned meta-network framework. Next, this meta-network is configured to reflect the impact of MSRB strategic adjustment. Last but not least, a real-world case study is carried out to validate this framework.

The best MSRB performance is always witnessed from the contractor group, followed by the project client group and the site supervisor group. The outcomes of implementing certain managerial strategies indicate that (1) social responsibility cognition is a critical factor for all the groups; (2) communication mechanism and normative pressure are the critical factors for clients; (3) coercive pressure is a critical factor for supervisors and (4) cultural cognitive pressure is a critical factor for clients and contractors.

The use of the framework in proactive assessment and management of MSRB can lead to effective strategies for construction enterprises to increase the efficiency and quality of projects.

This study, a research project, aims to examine the distinct characteristics of the Fourth Industrial Revolution (4IR), with a focus on construction. Following this examination, the paper presents a field study to evaluate the impact of the 4IR on the construction process.

The first half of this project is dedicated to defining the 4IR by reviewing existing literature. The other half of the project presents a case study to demonstrate the concept of the 4IR and measure the effect of its application. To validate the defined concept of the 4IR, the study focuses on the following: autonomous system for producing drawings and robotics in construction.

The intensive literature review revealed three unequivocal features of the 4IR: defined tasks, undefined tasks and improvement possibilities. The following case study showed that the incorporation of the three 4IR features resulted in improved productivity and efficiency during the construction of the podium for the Lotte World Tower. For example, the macro-based autonomous system achieved 5.52 shop drawings per hour, highlighting the potential impact of independent, autonomous machinery.

The originality of this project stems from its attempt to quantify the effectiveness of applying autonomous technologies to a practical project. While previous works in this field have focused on system development and improvement, this paper presents an autonomous system at work in an actual project, in which junior engineers were able to be entirely replaced. The system was successful in independently creating numerous required shop drawings. The value of this analysis is to generate scientific evidence to evaluate the efficacy of the adoption of 4IR-oriented technologies.

Megaproject knowledge innovation (MKI) is perceived as a critical strategy for engineering value co-creation and industrial chain upgrading. Ascertaining the impact mechanism of MKI is a crucial initial step towards improving management practices. Within the framework of complex systems in megaprojects, factors exhibit intricate interdependencies. However, the current domain of knowledge has either overlooked or oversimplified this relationship and therefore cannot propose pragmatic and efficacious strategies for enhancing MKI. To close this gap, this study develops a Bayesian network (BN) model aiming to investigate the interdependencies among MKI-related factors and their impact on MKI.

First, this study implements literature review, expert interview and field investigation to identify the influencing factor nodes for the network model development. Second, a Bayesian network was constructed by integrating the expert knowledge with Dempster-Shafer theory. Next, a MKI measurement model was established using 253 training samples. Finally, the factor significance and optimal MKI improvement strategies are identified from the sensitivity analysis and probabilistic reasoning within the BNs.

The results indicate that (1) the BN model exhibits significant reliability and holds promotion and application value in formulating MKI management strategies; (2) knowledge sharing, shared vision and leadership are the key influencing factors of MKI; and (3) simultaneously improving institutional pressure, leadership and knowledge sharing is the most optimal strategy to enhance MKI.

This study innovatively introduced the BN method into the domain of MKI management, providing an appropriate approach for modelling complex relationships among factors and investigate nonlinear influences. The developed model raises megaproject stakeholders’ awareness about factors influencing MKI and presents quantified strategies that increase the likelihood of maximising MKI levels. Its ease of generalisability positions it as a promising decision support tool, facilitating the implementation of sustainable MKI practices.

Experienced reviewers in general can produce high-quality product reviews, and thereby get more helpful votes. This paper explores the question that whether the depth and width of the reviewers' experience distribution have effects on the helpfulness of their reviews.

Adopting the restaurant review data from Yelp, the present paper classifies the restaurants in to different categories applying the Word2Vec technique, such as Asian or fast food. By evaluating the number of a user's historical reviews in a specific category, and the evenness of such distribution in different categories, the experience specialty and experience diversity are defined respectively.

The analysis shows that users specialised in a given category can produce more helpful reviews in that category. The users with diverse historical experience, i.e. have posted reviews for many categories, also can produce helpful reviews. In addition, the experience diversity shows a positive moderation effect on the influence of experience specialty. Thus, users with diverse experience while specialized in a particular category are the source of most helpful reviews.

While previous studies mostly consider the raw number of historical reviews as a reviewer's experience, we distinguish such experience by product category and focus on the width and depth of its distribution. The results not only shed lights on the mining of high-quality reviews and reviewers but also provide insights on the management of online review platforms and electronic marketing.

Identifying and predicting the most helpful reviews has been a focal interest in the fields including information management, e-commerce and marketing, etc. Though many factors are found correlated to the helpfulness of reviews, they may suffer endogeneity problems, as normally the data is observed in the same time window. This paper aims to tackle such a problem by examining the predictive power of different factors on the future increment of review helpfulness.

Adopting a longitudinal data of 443 K empirical business reviews from Yelp.com collected at two different time points, six groups of predictors are extracted from the first snapshot of data to predict the helpfulness increment of old and recent reviews, respectively, between the two snapshots.

It is found that these factors in general are with moderate accuracy predicting the helpfulness increment. A different group of features shows quite different predictive power. The reviewer disclosure information is the most significant factor, while the review readability does not significantly improve the accuracy of prediction.

Instead of the total number of helpful votes observed in the same time window with the explanatory variables, this paper focuses on the future increment of helpful votes observed in the following time window. With such a two-wave data set, the endogeneity problem can be avoided and the explanatory factors for review helpfulness can, thus, be further tested in the prediction scenario.

User-generated content (UGC) refers to semantic and behavioral traces created by users on various social media platforms. While several waves of platforms have come and gone, the long-term sustainability of UGC activities has become a critical question that bears significance for theoretical understanding and social media practices.

Based on a large and lengthy dataset of both blogging and microblogging activities of the same set of users, a multistate survival analysis was applied to explore the patterns of users' staying, switching and multiplatforming behaviors, as well as the underlying driving factors.

UGC activities are generally unsustainable in the long run, and natural attrition is the primary reason, rather than competitive switching to new platforms. The availability of leisure time, expected gratification and previous experiences drive users' sustainability.

The authors adopted actual behavioral data from two generations of platforms instead of survey data on users' switching intentions. Four types of users are defined: loyal, switcher, multiplatformer and dropout. As measured by the transitions among the four states, the different sustainability behaviors are thereby studied via an integrated framework. These two originalities bridge gaps in the literature and offer new insights into exploring user sustainability in social media.

This study aims to improve the robot’s performance during interactions with human and uncertain environments.

A joint stiffness model was established according to the molecular current method and the virtual displacement method. The position and stiffness coordination controller and fuzzy adaptive controller of variable stiffness joint are designed, and the principle prototype of variable stiffness joint is built. The position step and trajectory tracking performance of the variable stiffness joint are verified through experiments.

Experimental test shows that the joint stiffness can be quickly adjusted. The accuracy of position and trajectory tracking of the joint increases with higher stiffness and decreases with increasing frequency. The fuzzy adaptive controller performed better than the position and stiffness coordination controller in controlling the position step and trajectory tracking of the variable stiffness joint.

A hybrid flux adjustment mechanism is proposed for the components of variable stiffness robot joints, which reduces the mass of the output end of variable stiffness joints and the speed of joint stiffness adjustment. Aiming at the change of system controller performance caused by the change of joint stiffness, a fuzzy adaptive controller is proposed to improve the position step and trajectory tracking characteristics of variable stiffness joints.

Given the importance of leadership practices and knowledge resources in fostering innovation capabilities of firms, the purpose of this study is to explore the influence of transformational leadership on exploitative and exploratory innovation via mediating role of knowledge management capability. This study also attempts to increase understanding of the appropriate mechanisms for firms to pursue innovation capability by examining the moderating mechanism of competitive intensity.

This study utilized the structural equation modeling and cross-sectional design to test hypotheses in the proposed research model using survey data collected from 351 participants in 120 manufacturing and service firms.

The findings indicate that transformational leadership induces greater effect on exploratory innovation compared to its effect on exploitative innovation. The mediating role of knowledge management capability between transformational leadership and aspects of innovation capability is also supported. Especially, the influences of knowledge management capability on exploratory innovation capability are enhanced and depended on the degree of competitive intensity.

Future research should examine the mediating mechanisms of knowledge acquisition, knowledge sharing and knowledge application to provide deeper insight on the role of specific components of knowledge management capability in linking transformational leadership and innovation capability.

The paper highlights the important role of transformational leadership practices for fostering knowledge management capability and specific aspects of innovation capability under high level of competitive pressure.

The paper is unique in the attempts to provide a prospective solution for firms to pursue and improve innovation based on the meaningful insights on the mediating role of knowledge management capability and moderating effect of competitive intensity in the relationship between transformational leadership and specific dimensions of innovation capability.

Introduction: The rapid growth of high technology has urged many organisations to dynamically look for innovative ways, ideas, testing, and ingenious solution in improving their current product, process, system, and technology. For contemporary business, artificial intelligence (AI)-based people analytics is an instrument currently employed to develop a better prosperous future.

Purpose: The study aims to investigate the usage of AI in human resource management (HRM) practices. It also examines the benefits and challenges of using AI in implementing people analytics in organisations.

Methodology: This chapter contains a systemic review of articles and papers on analytics. The presented qualitative study did a literature review based on the articles published in the last five years and extracted from the Scopus database.

Findings: This chapter indicated that AI-based people analytics is on the verge of changing various aspects of HRM practices better to furnish it for a vibrant, ever-changing workplace. It concludes different usage of AI in people analytics for better managing human resources (HR) at the workplace. Also explored the benefits and challenges of implementing AI in the people analytics domain.

Implications: This chapter will help understand ongoing practices of AI-enabled process benefits and challenges. This insight will help develop a better AI-enabled function for a better decision-making system. The future scope of the study is how to overcome the challenges.

The purpose of this article is to carry out a lightweight design of the joint structure according to the service condition of the joint.

The finite element analysis techniques are used along with the variable density structure topology optimization method, the multi-island genetic algorithm for structural dimension optimization and the fatigue life analysis method.

Utilizing the topology optimization method and size optimization method, the mass of the optimized model for the A100 material model is 9.67 kg. Compared to the pre-optimized model, the mass decreases by 8.23 kg, representing a weight reduction of 46.0% in the optimized model; the fatigue life of the aircraft joint is predicted to be a maximum of 1,460,017 cycles.

The originality of this study is that it provides new design ideas for the lightweight design of aircraft load-bearing structures.