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With numerous and ambiguous sets of information and often conflicting requirements, construction management is a complex process involving much uncertainty. Decision makers may be challenged with satisfying multiple criteria using vague information. Fuzzy multi-criteria decision-making (FMCDM) provides an innovative approach for addressing complex problems featuring diverse decision makers’ interests, conflicting objectives and numerous but uncertain bits of information. FMCDM has therefore been widely applied in construction management. With the increase in information complexity, extensions of fuzzy set (FS) theory have been generated and adopted to improve its capacity to address this complexity. Examples include hesitant FSs (HFSs), intuitionistic FSs (IFSs) and type-2 FSs (T2FSs). This chapter introduces commonly used FMCDM methods, examines their applications in construction management and discusses trends in future research and application. The chapter first introduces the MCDM process as well as FS theory and its three main extensions, namely, HFSs, IFSs and T2FSs. The chapter then explores the linkage between FS theory and its extensions and MCDM approaches. In total, 17 FMCDM methods are reviewed and two FMCDM methods (i.e. T2FS-TOPSIS and T2FS-PROMETHEE) are further improved based on the literature. These 19 FMCDM methods with their corresponding applications in construction management are discussed in a systematic manner. This review and development of FS theory and its extensions should help both researchers and practitioners better understand and handle information uncertainty in complex decision problems.

Social shopping website (SSW) introduce the social side into the shopping process, thus making “window” shopping or browsing more interesting for customers. The purpose of this paper is to investigate customer online browsing experience and its antecedents (i.e. information quality and social interaction) and consequences (i.e. urge to buy impulsively and continuous browsing intention) in the context of SSW.

A survey questionnaire was distributed to visitors of online SSW to collect data, and partial least squares technology was used to test the research model.

The results of this study reveal that three types of web browsing, namely, utilitarian browsing, hedonic browsing and social browsing, take place in a SSW. The unique factors of SSW, namely, the quality of user generated contents and social interaction are critical for facilitating customers’ browsing experiences. Furthermore, the findings reveal that hedonic browsing experience is found to be the most salient factor influencing customers’ urge to buy impulsively and continuance intention.

The findings suggest that practitioners, such as designers and managers of SSW should give special attention to the benefits of browsing activity to convert web browsers into impulse purchasers and increase customers’ loyalty. Moreover, they should focus on improving the quality of user generated content and pay more attention to support and encourage social interaction to enhance browsing experiences on a SSW.

Existing studies about browsing behavior mostly focus on traditional online e-commerce website. This study represents the first step toward understanding browsing activity on SSW. Moreover, prior studies mainly focused on utilitarian and hedonic browsing experience; however, there is a lack of research on social browsing experience. The current study attempts to fill this research gap.

Advances in material agency driven by artificial intelligence (AI) have facilitated breakthroughs in material adaptivity enabling smart objects to autonomously provide individualized smart services, which makes smart objects act as social actors embedded in the real world. However, little is known about how material adaptivity fosters the infusion use of smart objects to maximize the value of smart services in customers' lives. This study examines the underlying mechanism of material adaptivity (task and social adaptivity) on AI infusion use, drawing on the theoretical lens of social embeddedness.

This study adopted partial least squares structural equation modeling (PLS-SEM), mediating tests, path comparison tests and polynomial modeling to analyze the proposed research model and hypotheses.

The results supported the proposed research model and hypotheses, except for the hypothesis of the comparative effects on infusion use. Besides, the results of mediating tests suggested the different roles of social embeddedness in the impacts of task and social adaptivity on infusion use. The post hoc analysis based on polynomial modeling provided a possible explanation for the unsupported hypothesis, suggesting the nonlinear differences in the underlying influencing mechanisms of instrumental and relational embeddedness on infusion use.

The formation mechanisms of AI infusion use based on material adaptivity and social embeddedness help to develop the business strategies that enable smart objects as social actors to exert a key role in users' daily lives, in turn realizing the social and economic value of AI.

This study advances the theoretical research on material adaptivity, updates the information system (IS) research on infusion use and identifies the bridging role of social embeddedness of smart objects as agentic social actors in the AI context.

The intense competitive and dynamic environment in mobile social-media market forces service providers to introduce incremental technological changes to achieve sustainable competitive advantage. The purpose of this paper is to investigate what and how the user attitude to change influences members' behavioral support for incremental technological change in mobile social media service.

Using the tripartite model of attitude, this study examines the influence of the cognitive aspect (empowerment with change), affective aspect (arousal with change) and behavioral aspect (habit to change) of attitude toward change on members' behavioral support for incremental technological change. Drawing on the commitment to change theory, we assessed the underlying mechanism by which attitudes toward change influences behavioral support for incremental technological change through the two components of commitment to change (i.e. affective and normative commitment to change). We tested the model using structural equation modeling on the data collected from the popular mobile social media services in China.

Our results indicate that the effect of empowerment with change, arousal with change and habit to change varies with different dimensions of commitment to change and significant influence of commitment to change on members' behavioral support for incremental technological change.

The findings of this study contribute to better insights for services providers for implementing incremental technological change strategies.

This study contributes to the theory of incremental technological changes by empirical examination of the impacts of users' attitudes toward change on members' behavioral support for incremental technological change in mobile social media. The paper extends the commitment to change theory with the discussion of the mediating effect of commitment to change in the continuing members' behavioral support for incremental technological change in mobile social media.

This study aims to apply the affective events theory and psychological contract theory to investigate how job satisfaction and psychological safety mediate the effect of the behavioral integrity of supervisors on the organizational commitment of employees.

A questionnaire survey was conducted using purposive sampling. In total, 500 questionnaire copies were distributed, and 453 responses were collected, of which 441 were valid (valid response rate = 88.2%).

The behavioral integrity of supervisors has a direct negative effect on organizational commitment but significant positive effects on job satisfaction and psychological safety, and job satisfaction and psychological safety have significant positive effects on organizational commitment. Job satisfaction and psychological safety have significant mediating effects on the association between the behavioral integrity of supervisors and the organizational commitment of employees.

Leaders and top management should “practice what they preach,” integrate honesty into organizational culture through training and establish a code of conduct to ensure that employees uphold their commitments. Companies should establish appropriate disciplinary systems and norms related to work and other aspects of organizational culture; they should also establish fair, just and open assessment systems to minimize the gap between their employees’ actual and expected earnings.

This study is the first to simultaneously consider the mediating effects of job satisfaction and psychological safety on the association between behavioral integrity and organizational commitment.

Generative conversational artificial intelligence (AI) demonstrates powerful conversational skills for general tasks but requires customization for specific tasks. The quality of a custom generative conversational AI highly depends on users’ guidance, which has not been studied by previous research. This study uses social exchange theory to examine how generative conversational AI’s cognitive and emotional conversational skills affect users’ guidance through different types of user engagement, and how these effects are moderated by users’ relationship norm orientation.

Based on data collected from 589 actual users using a two-wave survey, this study employed partial least squares structural equation modeling to analyze the proposed hypotheses. Additional analyses were performed to test the robustness of our research model and results.

The results reveal that cognitive conversational skills (i.e. tailored and creative responses) positively affected cognitive and emotional engagement. However, understanding emotion influenced cognitive engagement but not emotional engagement, and empathic concern influenced emotional engagement but not cognitive engagement. In addition, cognitive and emotional engagement positively affected users’ guidance. Further, relationship norm orientation moderated some of these effects such that the impact of user engagement on user guidance was stronger for communal-oriented users than for exchange-oriented users.

First, drawing on social exchange theory, this study empirically examined the drivers of users’ guidance in the context of generative conversational AI, which may enrich the user guidance literature. Second, this study revealed the moderating role of relationship norm orientation in influencing the effect of user engagement on users’ guidance. The findings will deepen our understanding of users’ guidance. Third, the findings provide practical guidelines for designing generative conversational AI from a general AI to a custom AI.

Navigating in off-road environments is a huge challenge for autonomous vehicles, due to the safety requirement, the effects of noises and non-holonomic constraints of vehicle. This paper aims to describe a path planning method based on fuzzy support vector machine (FSVM) and general regression neural network (GRNN) that is able to provide a solution path for the autonomous vehicle navigating in the off-road environments.

The authors decompose the path planning problem into three steps. In the first step, A* algorithm is applied to obtain the positive and negative samples. In the second step, the authors use a learning approach based on radial basis function kernel FSVM to maximize the safety margin for driving, and the fuzzy membership is designed based on GRNN which can help to resolve the problem that the traditional path planning method is easily influenced by noises or outliers. In the third step, the Bezier interpolation algorithm is used to smooth the path. The simulations are designed to verify the parameters of the path planning algorithm.

The method is implemented on autonomous vehicle and verified against many outdoor scenes. Road test indicates that the proposed method can produce a flexible, smooth and safe path with good anti-jamming performance.

This paper applied a new path planning method based on GRNN-FSVM for autonomous vehicle navigating in off-road environments. GRNN-FSVM can reduce the effects of outliers and maximize the safety margin for driving, the generated path is smooth and safe, while satisfying the constraint of vehicle kinematic.

Sufficient sample data are the necessary condition to ensure high reliability; however, there are relatively poor fatigue test data in the engineering, which affects fatigue life's prediction accuracy. Based on this, this research intends to analyze the fatigue data with small sample characteristics, and then realize the life assessment under different stress levels.

Firstly, the Bootstrap method and the principle of fatigue life percentile consistency are used to realize sample aggregation and information fusion. Secondly, the classical outlier detection algorithm (DBSCAN) is used to check the sample data. Then, based on the stress field intensity method, the influence of the non-uniform stress field near the notch root on the fatigue life is analyzed, and the calculation methods of the fatigue damage zone radius and the weighting function are revised. Finally, combined with Weibull distribution, a framework for assessing multiaxial low-cycle fatigue life has been developed.

The experimental data of Q355(D) material verified the model and compared it with the Yao’s stress field intensity method. The results show that the predictions of the model put forward in this research are all located within the double dispersion zone, with better prediction accuracies than the Yao’s stress field intensity method.

Aiming at the fatigue test data with small sample characteristics, this research has presented a new method of notch fatigue analysis based on the stress field intensity method, which is combined with the Weibull distribution to construct a low-cycle fatigue life analysis framework, to promote the development of multiaxial fatigue from experimental studies to practical engineering applications.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

In a typical additive manufacturing (AM) system, it is critical to make a trade-off between the resolution and the build area for applications in which varied dimensions, feature sizes and accuracies are desired. Conventional solutions to this challenge are based on curing of multiple areas with a single high resolution and stitching them to form a large layer. However, because of the lack of the capability in adjusting resolution dynamically, such stitching approaches will elongate the build time greatly in some cases. To address the challenge without sacrificing the build speed, this paper aims to design and develop a novel AM system with dynamic resolution control capability.

A laser projector is adopted in a vat photopolymerization system. The laser projection system has unique properties, including focus-free operation and capability to produce dynamic mask image irrespective of any surface (flat or curved). By translating the projector along the building direction, the pixel size can be adjusted dynamically within a certain range. Consequently, the build area and resolution could be tuned dynamically in the hardware testbed. Besides, a layered depth image (LDI) algorithm is used to construct mask images with varied resolutions. The curing characteristics under various resolution settings are quantified, and accordingly, a process planning approach for fabricating models with dynamically controlled resolutions is developed.

A laser projection-based stereolithography (SL) system could tune resolution dynamically during the building process. Such a dynamic resolution control approach completely addresses the build size-resolution dilemma in vat photopolymerization AM processes without sacrificing the build speed. Through fabricating layers with changing resolutions instead of a single resolution, various build areas and feature sizes could be produced precisely, with optimized build speed.

A focus-free laser projector is investigated and adopted in a SL system for the first time. The material curing characteristics with changing focal length and therefore changing light intensities are explored. The related digital mask image planning and process control methods are developed. In digital mask image planning, it is the first attempt to adopt the LDI algorithm, to identify proper resolution settings for fabricating a sliced layer precisely and quickly. In the process of characterizing material curing properties, parametric dependence of curing properties on focal length has been unveiled. This research contributes to the advancement of AM by addressing the historical dilemma of the resolution and build size, and optimizing the build speed meanwhile.