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Considering that when endorsing the same product, virtual influencers with different identity types (self-created, collaborative) can have different impacts on consumers' purchasing behaviors, this paper aims to explore how to maximize the impact effects of the VIs' respective identities. It provides companies with new perspectives on endorsement strategies.

The interaction between VI identity type and post type (informational, storytelling) on purchase intention was analyzed in four experiments (N = 1,007), considering informational and normative social influence as intermediate mechanisms and consumer self-construal as moderators.

The findings show that self-created VI is suited to informational posts and collaborative VI to storytelling posts. This identity-content match effectively triggers the social influence mechanism: informational posts of self-created VI significantly enhance informational social influence. In contrast, storytelling posts of collaborative VI primarily stimulate normative social influence. Consumer self-construal also moderates the process of influencing mechanisms.

Based on social influence theory and matching theory, this paper confirms the existence of an interaction between VI identity types, which influences consumers' purchase intention through informational and normative social influence. This finding fills the research gap in the field of VI endorsement strategy. It also emphasizes the importance of consumer self-construal and contributes new insights into the related field.

Path planning is an important part of three-dimensional (3D) printing data processing technology. This study aims to propose a new path planning method based on a discontinuous grid partition algorithm of point cloud for in situ printing.

Three types of parameters (i.e. structural, process and path interruption parameters) were designed to establish the algorithm model with the path error and the computation amount as the dependent variables. The path error (i.e. boundary error and internal error) was further studied and the influence of each parameter on the path point density was analyzed quantitatively. The feasibility of this method was verified by skin in situ printing experiments.

Path point density was positively correlated with Grid_size and negatively related to other parameters. Point_space, Sparsity and Line_space had greater influence on path point density than Indentation and Grid_size. In skin in situ printing experiment, two layers of orthogonal printing path were generated, and the material was printed accurately in the defect, which proved the feasibility of this method.

This study proposed a new path planning method that converted 3D point cloud data to a printing path directly, providing a new path planning solution for in situ printing. The discontinuous grid partition algorithm achieved controllability of the path planning accuracy and computation amount that could be applied to different processes.

This paper proposes an intelligent fault diagnosis method, which aims to obtain the outstanding fault diagnosis results of the gearbox.

An intelligent fault diagnosis method based on energy entropy-weighted complementary ensemble empirical mode decomposition (EWCEEMD) and support vector machine (SVM) optimized by whale optimization algorithm (WOA) is proposed. The raw signal is first denoised by the wavelet noise reduction method. Then, complementary ensemble empirical mode decomposition (CEEMD) is used to generate several intrinsic mode functions (IMFs). Next, energy entropy is used as an indicator to measure the sensibility of the IMF and converted into a weight coefficient by function. After that, IMFs are linearly weighted to form the reconstruction signal, and several features are extracted from the new signal. Finally, the support vector machine optimized by the whale optimization algorithm (WOA-SVM) model is used for gearbox fault classification using feature vectors.

The fault features extracted by this method have a better clustering effect and clear boundaries under each fault mode than the unimproved method. At the same time, the accuracy of fault diagnosis is greatly improved.

In most studies of fault diagnosis, the sensitivity of IMF has not been appreciated. In this paper, energy entropy is chosen to quantify sensitivity. In addition, high classification accuracy can be achieved by applying WOA-SVM as the final classification model, improving the efficiency of fault diagnosis as well.

This paper aims to analyze the heuristics and cognitive biases described by behavioral finance in the investment decision-making process of Portugal’s housing market.

In a first step, the authors applied an exploratory factor analysis (EFA) to assess the impact of heuristics and cognitive biases on investors’ decision-making. In a second step, the authors run a structural equation model (SEM) diagram path to assess if the sociodemographic characteristics of housing market investors determine the identified heuristics and if the heuristics condition the investors’ investment criteria.

Herd behavior and the heuristics of representativeness, availability and anchoring influence the housing market’s investors’ behavior in their decision-making process. Investors with above-average income show higher levels of overconfidence. Investors showing higher levels of overconfidence also tend to be more sensitive to the house price under analysis for investment. Women tend to show higher levels of the availability and anchoring heuristic. In turn, housing market investors showing higher levels of availability and anchoring heuristic tend to be more sensitive to the price and location of the house under analysis for investment.

The explained variance of the EFA is below 50%, and the root mean square of approximation of the SEM is above the threshold of 0.05. These indicators are evidence of the models’ fragility.

Governments and regulators can better prevent real estate bubbles if they monitor behavioral biases and heuristics of housing investors together with quantitative indicators. Realtors can profit from adapting their marketing strategy and commercial communication to investors of sociodemographic groups more prone to a specific type of heuristics.

To the best of the authors’ knowledge, this is the first study that combines the contributions of behavioral finance with Portugal’s housing investment market and the first study connecting heuristics to investment criteria.

This paper aims to introduce the history and major achievement of the Chinese private enterprise survey (CPES), which is one of the most enduring large-scale nationwide sample surveys in China, providing important micro firm-level data for understanding and studying the development of Chinese enterprises and entrepreneurs over the past 26 years.

The main body of this paper is based on a bibliometric analysis of all literature using CPES until 2017.

This paper discusses problems that users may encounter during data mining. By doing so, it can assist other researchers to get a better understanding of what has been done (e.g. journals, topics, scholars and institutions) and do their research in a more targeted way.

As members of the survey project team, the authors also take a prospect of the future data design and use, as well as offer some suggestions about how to use the CPES data to improve high-quality development and business environment evaluation in China.

This paper is the first to provide an overall picture of academic papers in China and abroad that have used the CPES data.

Digital healthcare manages to grab considerable attention from people and practitioners to avoid severity and provide quick access to healthcare. Entrepreneurs also adopt the digital healthcare segment as an opportunity; nevertheless, their intentions to participate and encourage innovation in this growing sector are unexplored. Drawing upon the social capital theory and health belief model, the study examines the factors that drive entrepreneurship. A novel model is proposed to comprehend entrepreneurial intentions and behavior entrenched in social capital and other encouraging and dissuading perceptive elements with the moderation of trust in digitalization and entrepreneurial efficacy.

The cross-sectional method is used to collect data through a questionnaire from experienced respondents in China. The valid data comprises 280 respondents, analyzed by partial least square structural equation modeling.

Social capital significantly influences monetary attitude, and perceived risk and holds an inconsequential association with perceived usefulness, whereas monetary attitude and perceived usefulness meaningfully explain entrepreneurial activities. Perceived risk has a trivial impact on entrepreneurial intention. Entrepreneurial efficacy and trust in digitalization significantly explain entrepreneurial behavior and moderate the positive relationship between intention and behavior.

The present research proposes a novel research model in the context of entrepreneurship rooted in a digitalized world and offering new correlates. It provides valuable insights by exploring entrepreneurial motivation and deterring factors to get involved in startup activities entrenched in social capital, providing guidelines for policymakers and practitioners to promote entrepreneurship.