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The purpose of this paper is to examine the threshold effect of group size on customer's complaining intention under group service failures.

Based on two main laboratory experiments, through two 10×2 scenario simulation experiments, the authors examine the role of group size in customer's complaining intention.

Results show that: (1) the relationship between group size and customer's complaining intention follows an inverted “U” type trend; (2) evaluation apprehension mediates the relationship between group size and customer's complaining intention; (3) relational distance can change threshold values and (4) relational distance moderates the relationship between group size and customer's complaining intention.

Managers should judge the threshold of group size through experience so as to have a preliminary understanding of customer evaluation concerns and complaint intention. In the face of service failure groups smaller than the threshold range, managers should divided the group into several small groups. For service failure groups larger than the threshold range, the opinion leaders in the group should be given more attention, so as to control the whole group.

This paper is helpful to deeply understand the key role of group size in the process of customer complaints, and also provides decision-making basis for service enterprises to deal with group customer complaints.

There has been little research about the threshold effect of group size on customer's complaining intention. The previous studies on customer's complaining intention focus on its influences on group size, and draw a single common conclusion that the customer's intention to complain will increase with the growing number of groups increases. However, few studies are explored on the threshold of group size. Therefore, this paper will focus on the threshold effect of group size on customer's complaining intention to fill the gap.

This study aims to develop a bimodal nano-silver paste with improved mechanical property and reliability. Silicon carbide (SiC) particles coated with Ag were introduced in nano-silver paste to improve bonding strength between SiC and Ag particles and enhance high-temperature stability of bimodal nano-silver paste. The effect of sintering parameters such as sintering temperature, sintering time and the proportion of SiC particles on mechanical property and reliability of sintered bimodal nano-silver structure were investigated.

Sandwich structures consist of dummy chips and copper substrates with nickel and silver coating bonded by nano-silver paste were designed for shear testing. Shear strength testing was conducted to study the influence of SiC particles proportions on the mechanical property of sintered nano-silver joints. The reliability of the bimodal nano-silver paste was evaluated experimentally by means of shear test for samples subjected to thermal aging test at 150°C and humidity and temperature testing at 85°C and 85 per cent RH, respectively.

Shear strength was enhanced obviously with the increase of sintering temperature and sintering time. The maximum shear strength was achieved for nano-silver paste sintered at 260°C for 10 min. There was a negative correlation between the proportion of SiC particles and shear strength. After thermal aging testing and humidity and temperature testing for 240 h, the shear strength decreased a little. High-temperature stability and high-hydrothermal stability were improved by the addition of SiC particles.

Submicron-scale SiC particles coated with Ag were used as alternative materials to replace part of nano-silver particles to prepare bimodal nano-silver paste due to its high thermal conductivity and excellent mechanical property.

Big data analytics (BDA) and machine learning (ML) can be used to identify the influencing factors of online service supply chains (OSSCs) and can help in the formulation of optimal pricing strategies. This paper analyzes the influencing factors of customer online shopping from the demand-side perspective and formulates optimal pricing strategies from the supply-side perspective.

This paper uses ML and the Stackelberg game approach to discuss OSSC management. ML's feature selection algorithm is used to identify the important influencing factors of 12,330 customers' online shopping intention data using four different classifiers. The Stackelberg game approach is used to analyze the pricing strategies of integrators and suppliers in OSSCs.

First, the feature selection algorithm can improve the efficiency of optimization in big data samples of OSSCs. Second, the level of visualization and the quality of information (page value) will affect the purchase behavior of customers. Finally, the relationship between the optimal pricing and the level of visualization is obtained through the Stackelberg game approach.

This paper reveals the phenomenon of “mystery customers,” and the results of this paper can provide insights and suggestions regarding the decision-making behavior of integrators and suppliers in OSSC management.

Considering customer behavior intention, this paper uses a data-driven method to explore the influencing factors and pricing strategies of OSSCs. The empirical results enrich the existing OSSC management research, proposing that the level of product visualization and information quality plays an important role in OSSCs.

Non-financial corporate debt is one of the important sources of systematic risk in the real economy. Assessing a measure of systematic risk in corporation debt is currently a key challenge. In this regard, we propose a two-tier risk contagion networks model.

Assessing a measure of systematic risk in corporation debt is currently a key challenge. In this regard, we propose a two-tier risk contagion networks model based on four dimensions: concept definition, data structure, risk contagion network construction, and risk measurement indicators construction. We take the Jiangsu bond issuer guarantee network as a sample area.

Taking the Jiangsu bond issuer guarantee network as a sample area, we find that there is a strong correlation between the debts of non-financial corporation in China, and it is easy to become a potential regional systematic risk source. In addition, our empirical research also reveals that external risk exposure and node degree of network are two key indicators when identifying key risk-contagion enterprises.

The main contributions of this study are two-fold. First, this article proposes a two-tier risk contagion networks model to measure systematic risk in non-financial corporation. Second, this article describes the structure of the corporate risk contagion network.

Employing big data analysis tools, this study examines the significance of supply chain integration affecting online financial consumption, analyzes the online financial consumption demand of mobile phone consumers, promotes the optimization of supply chain services with consumers as the focus and proposes full integration of a mobile phone supply chain in terms of product, logistics and marketing, in order to improve the supply and demand relationship between consumers and suppliers; the overall objective is to promote further development of online financial consumption.

In this study, TF-IDF (term frequency–inverse document frequency) and cosine similarity text analysis are used for analyzing online demand for mobile phone products, studying the influence of supply chain services on consumption demand and identifying strategies for promoting overall optimization of the supply chain to meet online financial consumption demands of consumers; the study analyzes online reviews on mobile phone topics from the JingDong (JD) platform and Weibo platform.

Research results show that online demand for mobile phone products is greatly influenced by supply chain links such as product design, logistics transportation and marketing promotion. The consumption demand for different mobile phone products has different emphases, but the differences are not significant. The overall improvement of the supply chain should focus on product research and development, logistics layout optimization and marketing promotion, in order to meet and guide the online financial demand of consumers and improve the effectiveness of supply chain management.

This study only considered data from China's largest online mobile phone sales platform and Weibo text data owing to the data sensitivity involved.

There are few supply chain optimization studies based on online financial consumption reviews from customers. Therefore, this study integrates online consumption trends into a supply chain analysis framework to explore strategies for promoting supply chain optimization according to customer demands, improving the benign interaction of participants in the supply chain and promoting the development of online financial consumption.

This study aims to explore the effect of digitalization on the promotion of enterprise R&D cooperation, and it analyzes the microimpact mechanism and boundary conditions of enterprise digitalization on enterprise R&D cooperation.

Based on survey data sourced from the World Bank Enterprise Surveys of the business environment of Chinese enterprises in 2012, this study applies multiple regression methods to test theoretical hypotheses.

Enterprise digitalization positively affects the breadth and intensity of enterprise R&D cooperation. Employees’ digital literacy plays an intermediary role between enterprise digitalization and enterprise R&D cooperation. The subordinate attributes of enterprises weaken the positive relationship between enterprise digitalization and the breadth and intensity of enterprise R&D cooperation. The shareholding of state-owned enterprises reinforces the positive relationship between digitalization and the intensity of enterprise R&D cooperation. However, such shareholding shows no significant regulatory effect on digitalization and the breadth of enterprise R&D cooperation.

Focusing on the digital transformation of the enterprise, this study discusses its impact mechanism on enterprise R&D cooperation, including the impact on the intensity and breadth of R&D cooperation. The study further examines the regulatory effect of organizational inertia on enterprise digital and R&D cooperation from two aspects: resource rigidity and routine rigidity. It emphasizes the significance of the digital literacy of employees in enterprise digitalization and discusses the micromechanism of enterprise digitalization and enterprise R&D cooperation.

Based on text content analysis using big data, this study aims to explore differences in guest perceptions of peer-to-peer accommodations before and after COVID-19 to provide suggestions for the development of these properties in China postpandemic.

A guest perception dictionary was established by collecting Ctrip customer reviews of peer-to-peer accommodations. After data cleaning, thematic word analysis and semantic association network analysis were used to explore perceptions and thematic differences before and after COVID-19.

This research constructed a multidimensional framework of guest-perceived values for peer-to-peer accommodation in the context of COVID-19. The findings showed that the emphasis on functionality in peer-to-peer accommodation changed; perceived emotional values associated with peer-to-peer stays were more complex; perceived social values decreased, host–guest interactions were reduced and online communication became a stronger trend; tourist preferences for types of experiences changed, and people changed their destination selections; perceived conditional value was reflected in perceived risks, and the perceptions of environmental health, service and physical risks increased.

This research has constructed a multidimensional framework of tourist perceived value on the basis of peer-to-peer accommodation context and epidemic background and has thus shown the changes in tourist perceived value of peer-to-peer accommodation before and after COVID-19.

To the best of authors’ knowledge, this research constitutes the first attempt to explore the perceptual differences for peer-to-peer accommodations before and after COVID-19 based on an extensive data set of online reviews from multiple provinces of China.

Selective electron beam melting (SEBM) is one of the popular powder-bed additive manufacturing (AM) technologies. The purpose of this paper is to develop a simulation strategy for SEBM process to get data which are vital for realistic failure prediction and process parameters control for real complex components.

Focusing on the SEBM process of tantalum, this paper presents a three-dimensional thermo-mechanical modeling strategy based on ABAQUS and its subroutines. The simulation strategy used in this paper is developed for SEBM process of pure tantalum but could be extended to other AM fabrication technologies and other metals without difficulties.

The simulation of multi-track multi-layer SEBM process of tantalum was carried out to predict the temperature field, the molten pool evolution and the residual stress distribution. The key information such as inter-track molten pool overlapping ratio and inter-layer refusion state can be extracted from the obtained molten pool morphologies, which are vital for realistic failure prediction and process parameters control for real components. The authors finally demonstrate the capability of the strategy used by simulating a 2 mm × 2 mm × 10 mm lattice structure with total 200 layers.

The simulation of multi-track multi-layer SEBM process of tantalum was carried out. The key information such as inter-track molten pool overlapping ratio and inter-layer refusion state can be extracted. The authors finally demonstrate the capability of the strategy used by simulating a lattice structure. Not only temperature distribution but also stress evolution are captured. Our simulation strategy is developed for the SEBM process of pure tantalum, but it could be extended to other AM fabrication technologies and other metals without difficulties.

This paper aims to investigate the potential challenges that hinder the effective transfer and sharing of tacit knowledge (knowledge communication [KC]) within a construction supply chain (CSC).

This study identifies six challenges (through literature review) with 15 positive correlations between them. Quantitative methodology is used to validate those challenges and correlations between challenges. First, data are collected through semi-structured e-survey questionnaire. Afterwards, a Frequency and Kruskal–Wallis H test is run for initial validation of identified challenges. A correlation analysis is used to highlight the taxonomic relations between those challenges. Finally, the study establishes the rank order of the first and following challenges.

This study highlights that traditional ways of working with construction organisations are the predominant challenge that hinders effective transferring and sharing of tacit knowledge. The cause of challenges is the fragmented nature of CSC. Also, it brings out the correlation between those challenges. The study draws the conclusion and recommendation to implement KC within a CSC.

The study highlights the challenges that hinder KC in a construction process of a CSC. It establishes that the fragmented nature of the construction sector is not the first challenge that hinders implementation of transferring and sharing of tacit knowledge but somewhat traditional organisation structures and working processes. This is the first paper that investigates and tests the challenges in four dimensions and establishes the rank order of challenges with crucial distinction in a KC approach within a CSC.

The presence of broken clouds leads to frequent fluctuations in direct normal incident solar irradiation as well as diffuse radiation from the sky. This brings a lot of challenge for grid integration of solar power plants. The paper aims to discuss this issue.

A new model is presented to nowcast solar radiation by utilizing hourly global horizontal irradiance (GHI) over a large spatial grid. The spatial distribution of the GHI provides information on the presence of a cloud shadow above a given site. This information is extracted with the help of various data processing techniques. The spatial–temporal data analysis is employed to track the extracted cloud shadow image based on a dynamic model. A Kalman filter is applied for the assimilation of data in the tracking of the extracted shadow over a geographical location.

The proposed model can provide very good forecasting of solar radiation for various time horizons. However, the variation of shadow features between time steps must be included in the dynamic model to forecast accurate GHI values.

In this paper database used is on hourly basis; it can be further improved for the inter-hour level of ground data for more accuracy.

The outcome of this paper would be useful in the field of solar energy application and for weather monitoring purposes.

The forecasted position of the shadow is utilized to prepare and forecast a GHI map for one hour time horizon. Results show that the model can be utilized to forecast solar radiation with accuracy consistent with the contemporary models.