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The purpose of this paper is to present a framework for intellectual capital (IC) disclosure in cross-cultural E-tourism which includes identifying key success factors, as well as selecting indicators in operational level.

The method described in this paper involves IC process model initiating from strategy formulation, following key success factors analyst by using a detailed interpretative structural model (ISM) instrument, and the indicators selection employing Delphi approach. The paper explicates methodological decisions associated with cross-cultural E-tourism context: selecting the appropriate specialists panel; the approach to data collection; and selecting the measurable indicators.

This paper proposes three critical components of the framework: key success factors that based on an organization's strategies and contexts; indicators that measure and monitor the value of IC; an IC model that can help cross-cultural E-tourism enterprise improve management effectively.

This paper extends the methodology developed previously to examine IC information in tourism enterprise. The paper introduces an alternative methodological paradigm to the study of IC measurement framework by employing the ISM. This exploratory study employs IC process model to build up cross-cultural E-tourism IC model, which is a start point for the value and performance research of this kind of enterprise.

This paper aims to provide a framework of the supervised hyperspectral classification, to study the traditional flowchart of hyperspectral image (HIS) analysis and processing. HSI technology has been proposed for many years, and the applications of this technology were promoted by technical advancements.

First, the properties and current situation of hyperspectral technology are summarized. Then, this paper introduces a series of common classification approaches. In addition, a comparison of different classification approaches on real hyperspectral data is conducted. Finally, this survey presents a discussion on the classification results and points out the classification development tendency.

The core of this survey is to review of the state of the art of the classification for hyperspectral images, to study the performance and efficiency of certain implementation measures and to point out the challenges still exist.

The study categorized the supervised classification for hyperspectral images, demonstrated the comparisons among these methods and pointed out the challenges that still exist.

The purpose of this paper is to study some evolving mechanisms for producing weighted networks, as well as to analyze the statistical properties of the networks.

A simple one‐parameter evolution model of weighted networks is proposed, in which the topological growth combines with the variation of weights. Based on weight‐driven dynamics, the model can generate scale‐free distributions of the degree, node strength and edge weight, as confirmed in many real networks.

The exponent of the edge weight can be widely tuned. The unique parameter p controls the edge weight dynamical growth. The authors also obtain the non‐trivial weighted clustering coefficient and the weighted average to the nearest neighbors' degree.

Accessibility and availability of data are the main limitations which apply to the figures.

The new evolving networks method may be beneficial for understanding real networks.

The paper proposes a new approach of explaining the evolving mechanisms of the real networks.

In this research, the authors aim to investigate the impact of product display quantity on consumers' online purchase intention through the diminished pain of payment, in light of signaling theory and mental accounting theory. Additionally, the authors test the role of price in this psychological mechanism.

The authors use a combination of online studies and field experiments to test our predictions. Four online studies are conducted to test the main effect of product display quantity on purchase intention (Studies 1A and 1B), the mediating effect of the diminished pain of payment (Study 2), and the moderating role of price (Study 3). Two field experiments are conducted to strengthen the robustness of our findings.

The findings suggest that product display quantity has a significantly positive impact on purchase intention, mediated by the diminished pain of payment, and this effect is moderated by price.

This is the first study to investigate the effect of product display quantity on online purchase intention. This research advances the discussion of product display quantity, which provides retailers and consumers with a convenient way to communicate with each other and leads to a more relaxed purchase experience.

The increasing adoption of digital technologies such as social media have changed the way consumers share knowledge about products and services among each other. The aim of this paper is to test what factors drive customers to share knowledge about products and services on social media pages.

A quantitative survey design was employed for this study. Empirical data were drawn from 358 consumers in Italy, using a purposive sampling technique. The hypothesised relationships were tested using ordinary least squares regression modelling.

The results of this study reveal that the usage frequency of online reviews (UFORs), social bonds (SBs), subjective happiness (SH) and reciprocity positively impact on customer knowledge sharing (CKS). By contrast, the perceived usefulness of online reviews (PUORs), helping others, customer susceptibility to interpersonal influence (CSII) and informational (INFO) do not impact CKS.

To the best of the authors' knowledge, this study is amongst the first to empirically test the antecedents of knowledge-sharing behaviours about products and services on online social media. The present work offers relevant implications for theory. First, the work enriches the customer knowledge management (CKM) theory by providing empirical evidence on factors leading to the higher sharing of knowledge amongst customers. Second, the work adds to the literature on social media, demonstrating the individual determinants on knowledge-sharing behaviours about products and services in online communities. Practically speaking, this paper identifies some key elements driving CKS in social media conversations. Thus, building upon the findings of this study, the authors provide some guidelines for social media managers and retailers for promoting CKS on social media pages.

The purpose of this paper is to simplify the computation of parameter estimation in the grey linear regression model and solve the problem that the development coefficient could not be computed in some sequence data, such as short‐term traffic flow.

Starting from the limitation that can be identified in the equation and analyzing the range using the method to estimate parameters, this paper researches the modelling mechanism and the other forms which are equivalent with the original form. At the same time, this paper gives an estimation method and gets the relationship in various forms and the relationship between the model and GM(1,1) model.

For the grey linear regression model, there exists a new method of parameter identification and three other forms as follows: the original form, the Whitenization equation and the connotation form.

The method of parameter identification exposed in the paper expanded the scope of the application of the grey linear regression model, and it can be used to model and forecast the urban road short‐time traffic flow.

This paper has solved some complicated problems such as the parameter estimation computation in the grey linear regression model. In addition, three kinds of representation forms of the model and its relationship between the model and GM(1,1) have also been presented. Finally, its application of the model in a short‐term traffic flow prediction has shown its superiority.

The purpose of this study is to investigate the deposition of SS–Al transitional wall using the wire arc directed energy deposition (WA-DED) process with a Cu interlayer. This study also aims to analyse the metallographic properties of the SS–Cu and Al–Cu interfaces and their mechanical properties.

The study used transitional deposition of SS–Al material over each other by incorporating Cu as interlayer between the two. The scanning electron microscope analysis, energy dispersive X-ray analysis, X-ray diffractometer analysis, tensile testing and micro-hardness measurement were performed to investigate the interface characteristics and mechanical properties of the SS–Al transitional wall.

The study discovered that the WA-DED process with a Cu interlayer worked well for the deposition of SS–Al transitional walls. The formation of solid solutions of Fe–Cu and Fe–Si was observed at the SS–Cu interface rather than intermetallic compounds (IMCs), according to the metallographic analysis. On the other hand, three different IMCs were formed at the Al–Cu interface, namely, Al–Cu, Al₂Cu and Al₄Cu₉. The study also observed the formation of a lamellar structure of Al and Al₂Cu at the hypereutectic phase. The mechanical testing revealed that the Al–Cu interface failed without significant deformation, i.e. < 4.73%, indicating the brittleness of the interface.

The study identified the formation of HCP–Fe at the SS–Cu interface, which has not been previously reported in additive manufacturing literature. Furthermore, the study observed the formation of a lamellar structure of Al and Al2Cu phase at the hypereutectic phase, which has not been previously reported in SS–Al transitional wall deposition.