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The study aims to examine the effects of ulterior motives in peer and expert supplementary online hotel reviews on consumers' perceived deception, dissatisfaction, and its downstream effects on altruistic response and repurchase intentions. The research also examines the moderating role of hotel attribute performance on perceived deception and its consequents.

This study used convenient non-probability sampling and collected data from 448 inbound tourists in China. It used partial least square structural equation modeling technique and SmartPLS 3.0 for analyzing the main and moderating effects of the variables.

The ulterior motives in peer and expert supplementary reviews significantly affect perceived deception, further leading to consumers' dissatisfaction and engagement in altruistic response. Noticeably, consumers' dissatisfaction is positively associated with repeat purchase intentions. Hotel attribute performance significantly moderates the relationship between the ulterior motives in supplementary reviews and consumers' perceived deception.

The study examines the key issue in online hotel reviews using the expectancy disconfirmation theory and identifies consumers' altruistic behavior because of their dissatisfaction, contributing to ethics and consumer behavior literature. Moreover, the research offers prolific implications for hotel and travel websites and hoteliers in the study context.

Driven by the development in sensing techniques and information and communications technology, and their applications in the manufacturing system, data-driven quality control methods play an essential role in the quality improvement of assembly products. This paper aims to review the development of data-driven modeling methods for process monitoring and fault diagnosis in multi-station assembly systems. Furthermore, the authors discuss the applications of the methods proposed and present suggestions for future studies in data mining for quality control in product assembly.

This paper provides an outline of data-driven process monitoring and fault diagnosis methods for reduction in variation. The development of statistical process monitoring techniques and diagnosis methods, such as pattern matching, estimation-based analysis and artificial intelligence-based diagnostics, is introduced.

A classification structure for data-driven process control techniques and the limitations of their applications in multi-station assembly processes are discussed. From the perspective of the engineering requirements of real, dynamic, nonlinear and uncertain assembly systems, future trends in sensing system location, data mining and data fusion techniques for variation reduction are suggested.

This paper reveals the development of process monitoring and fault diagnosis techniques, and their applications in variation reduction in multi-station assembly.

Measurement of diminishing or divergent cross section dispersion in a panel plays an important role in the assessment of convergence or divergence over time in key economic indicators. Econometric methods, known as weak σ-convergence tests, have recently been developed (Kong, Phillips, & Sul, 2019) to evaluate such trends in dispersion in panel data using simple linear trend regressions. To achieve generality in applications, these tests rely on heteroskedastic and autocorrelation consistent (HAC) variance estimates. The present chapter examines the behavior of these convergence tests when heteroskedastic and autocorrelation robust (HAR) variance estimates using fixed-b methods are employed instead of HAC estimates. Asymptotic theory for both HAC and HAR convergence tests is derived and numerical simulations are used to assess performance in null (no convergence) and alternative (convergence) cases. While the use of HAR statistics tends to reduce size distortion, as has been found in earlier analytic and numerical research, use of HAR estimates in nonparametric standardization leads to significant power differences asymptotically, which are reflected in finite sample performance in numerical exercises. The explanation is that weak σ-convergence tests rely on intentionally misspecified linear trend regression formulations of unknown trend decay functions that model convergence behavior rather than regressions with correctly specified trend decay functions. Some new results on the use of HAR inference with trending regressors are derived and an empirical application to assess diminishing variation in US State unemployment rates is included.

This paper aims to investigate the differential effects of vertical attributes and horizontal attributes on visit intention under proximal and distal sensory imagery in travel live streams.

This research used a multimethod approach with four studies. Three designed experiments were first employed to prove casual relations of the hypothesized relations. Then, a structural model provided a new sample of the framework.

The results suggest that visit intention is higher when vertical (vs horizontal) attributes are associated with proximal (vs distal) sensory imagery. Physical presence mediates the interaction effects between attribute type and sensory imagery on visit intention.

The finding offers suggestions for multilateral information providers' capability of real-time advertising, seller-focused technology development and proactive relationship management with potential consumers.

Previous study is less sufficient to describe consumers' traveling interactivities in live-streaming media, where streamers are capable of modifying attribute-based messages and sensory modalities. Rather than focusing on imagery as a comprehensive modality or visual-dominated imagery, this study examines the interaction effects between attribute type and sensory imagery on visit intention. Drawing on reason-based choice and distance-on-distance theories, the finding enriches the evaluation of the effectiveness of live-streaming marketing across varying sensory interactions.

The purpose of this paper is to describe a visual try-in evaluation framework for the template-guided modelling of a nasal prosthesis.

For patients with nasal defects, there is no self-information that can be used for the fabrication of the nasal prosthesis. Based on model retrieval from a database, the template-guided model construction method can ensure successful building of the nasal prosthesis. The deviation measurement between the two mid-planes of pre-operative and visual post-operative patient’s face allowed a virtual try-in approach in the symmetry evaluation of the prosthetic rehabilitation. The test of fit between the prosthesis model and the surrounding tissue data also provided an evaluation of whether the nasal prosthesis fit the patient’s appearance well before operation.

A case study confirmed that this visual try-in evaluation framework has potential to design the desired nasal prosthesis for daily clinical practice.

This technique facilitates modelling of nasal prostheses while helping to predict the effect before the prosthesis is manufactured.

This visual try-in evaluation framework has great potential for use in clinical applications because of its advantages on the aesthetic evaluation of the prosthetic rehabilitation.

New asymptotic approximations are established for the Wald and t statistics in the presence of unknown but strong autocorrelation. The asymptotic theory extends the usual fixed-smoothing asymptotics under weak dependence to allow for near-unit-root and weak-unit-root processes. As the locality parameter that characterizes the neighborhood of the autoregressive root increases from zero to infinity, the new fixed-smoothing asymptotic distribution changes smoothly from the unit-root fixed-smoothing asymptotics to the usual fixed-smoothing asymptotics under weak dependence. Simulations show that the new approximation is more accurate than the usual fixed-smoothing approximation.

The purpose of this paper is to explore and examine the different word-of-mouth (WOM) acceptance and diffusion in social brand communities according to the level of self-monitoring.

A web-based experimental design was used. The design consisted of three-mixed design of 2 (type of social networking sites) × 2 (type of online brand communities) × 2 (self-monitoring). ANOVA analysis was conducted.

Findings indicate that the differences in acceptance and diffusion of WOM according to online brand community type, and there was a significant three-way interaction effect. Specifically, people who have high propensity to self-monitor showed greater WOM acceptance in a consumer-driven community in either type of social networking sites while people who have low propensity to self-monitor showed greater WOM diffusion in a consumer-driven community only in interest-based social networking sites.

An important implication is that the social networking sites where brand communities can be placed should be chosen with the full consideration of different desires consumers have in terms of their level of self-monitoring to increase WOM effects.

This paper proposes the self-monitoring tendency as the key factor that predicts WOM effects with revealing the optimal combination of types of social networking sites and online brand communities that is most preferable for consumers with different self-monitoring level.

Complicated workpiece, such as an engine block, has special rough locating datum features (i.e. six independent datum features) due to its complex structure. This locating datum error cannot be handled by current variation propagation model based on differential motion vectors. To extend variation prediction fields, this paper aims to solve the unaddressed variation sources to modify current model for multistage machining processes.

To overcome the limitation of current variation propagation model based on differential motion vectors caused by the unaddressed variation sources, this paper will extend the current model by handling the unaddressed datum-induced variation and its corresponding fixture variation.

The measurement results of the rear face with respect to the rough datum W and the pan face with respect to the hole Q by coordinate measuring machine (CMM) are −0.006 mm and 0.031 mm. The variation results for rear face and pan face predicted by the modified model are −0.009 mm and 0.025 mm, respectively. The discrepancy of model prediction and measurement is very small.

This paper modifies the variation propagation model based on differential motion vectors by solving the unaddressed variation sources, which can extend the variation prediction fields for some complicated workpiece and is useful in the future work for many fields, such as process monitoring, fault diagnosis, quality-assured setup planning and process-oriented tolerancing.

With the advance of the Silk Road Initiative proposed by China, it has been a focus of China government to develop strategic emerging industries. The development of strategic emerging industries needs the support of competitive intelligence on many aspects such as strategical planning, policy making, industrial structure adjustment, and technology innovation. However, so far there are few studies toward the competitive intelligence systems for strategic emerging industries. In this article, we focus on a number of issues related to the competitive intelligence for strategic emerging industries in China. First, we conduct a strengths, weaknesses, opportunities, and threats analysis on the situations of strategic emerging industries in China, based on which the necessity of building a competitive intelligence (CI) service system for strategic emerging industries is discussed. Next, the authors present a framework of a CI service system for strategic emerging industries in China. The principles, components, working process, and product forms are deeply described. The CI service system proposed in this article consists of a cooperation network platform, three layered organizations, and three systems, which integrates organizations, information, people, network, and service platforms into an ecosystem to offer competitive intelligence supports for government, industry, and enterprises. Finally, the authors discuss a case study of the proposed CI service system for the new energy automobile industry.

This paper aims to carry out assembly variation analysis for mechanisms with compliant joints by considering deformations induced by manufactured deviations. Such an analysis procedure extends the application area of direct linearization method (DLM) to compliant mechanisms and also illustrates the dimensional interaction within multi-loop compliant structures.

By applying DLM to both geometrical equations and Lagrange’s equations of the second kind, an analytical deviation modeling method for mechanisms with compliant joints are proposed and further used for statistical assembly variation analysis. The precision of this method is verified by comparing it with finite element simulation and traditional DLM.

A new modeling method is proposed to represent kinematic relationships between joint deformations and parts/components deviations. Based on a case evaluation, the computational efficiency is improved greatly while the modeling accuracy is maintained at more than 94% rate comparing with the benchmark finite element simulation.

The Equilibrium Equations of Incremental Forces derived from Lagrange’s equations are proposed to quantitatively represent the relationships between manufactured deviations and assembly deformations. The present method extends the application area of DLM to compliant structures, such as automobile suspension systems and some Micro-Electro-Mechanical-Systems.