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The railway signal equipment failure diagnosis is a vital element to keep the railway system operating safely. One of the most difficulties in signal equipment failure diagnosis is the uncertainty of causality between the consequence and cause for the accident. The traditional method to solve this problem is based on Bayesian Network, which needs a rigid and independent assumption basis and prior probability knowledge but ignoring the semantic relationship in causality analysis. This paper aims to perform the uncertainty of causality in signal equipment failure diagnosis through a new way that emphasis on mining semantic relationships.

This study proposes a deterministic failure diagnosis (DFD) model based on the question answering system to implement railway signal equipment failure diagnosis. It includes the failure diagnosis module and deterministic diagnosis module. In the failure diagnosis module, this paper exploits the question answering system to recognise the cause of failure consequences. The question answering is composed of multi-layer neural networks, which extracts the position and part of speech features of text data from lower layers and acquires contextual features and interactive features of text data by Bi-LSTM and Match-LSTM, respectively, from high layers, subsequently generates the candidate failure cause set by proposed the enhanced boundary unit. In the second module, this study ranks the candidate failure cause set in the semantic matching mechanism (SMM), choosing the top 1st semantic matching degree as the deterministic failure causative factor.

Experiments on real data set railway maintenance signal equipment show that the proposed DFD model can implement the deterministic diagnosis of railway signal equipment failure. Comparing massive existing methods, the model achieves the state of art in the natural understanding semantic of railway signal equipment diagnosis domain.

It is the first time to use a question answering system executing signal equipment failure diagnoses, which makes failure diagnosis more intelligent than before. The EMU enables the DFD model to understand the natural semantic in long sequence contexture. Then, the SMM makes the DFD model acquire the certainty failure cause in the failure diagnosis of railway signal equipment.

Mobile social apps permeate every facet of daily life through the pervasive use of smartphones. Customer retention with mobile social apps has become extremely important for app-related companies. The purpose of this paper is to explore why experienced users in mobile social apps (e.g. WeChat) are likely to continue using the app.

This study proposed a conceptual model to identify key determinants of continuance intention of WeChat users and highlight the effects of individual experience. Data were collected from WeChat users, which is one of the most popular mobile social apps in China. The study employed partial least squares regression to test the research model based on a survey of 295 valid responses.

Results showed that trust, which was driven by user satisfaction and perceived critical mass, played a critical role in influencing the continuance intention of WeChat users. Moreover, tie strength exerted a negative moderating effect on the relationship between trust and continuance intention. Specifically, tie strength and perceived critical mass had strong impacts on the continuance intention of low-experience users. In addition, the effect of frequency was closely associated with the continuance intention of high-experience users.

This study addressed the issue of mobile social app continuance intention by providing an innovative means to explore the key antecedents of user continuance intention from the experience perspective. The findings not only prove that trust plays a central role in influencing the continuance intention of experienced users but also reveal that the determinants of continuance intention vary among users with different experience. The results provide insights into the key antecedents of experienced WeChat user continuance intention and contribute to the literature on mobile social apps and individual differences.

The results provide suggestions for mobile social app practitioners to effectively plan mobile social app retention practices and to set up appropriate incentive mechanisms for retaining users with different experiences.

Although abundant studies have focused on the adoption of media users, few studies have investigated the post-adoption behavior of experienced users in the context of mobile social apps. This study revealed the key determinants of the continuance intention of WeChat users and pinpointed the different impacts of these antecedents on users with different levels of experience. It also provides useful guidelines for practitioners to effectively retain users with different levels of experience.

This article seeks to understand how live-streaming technology (i.e. interactivity and effective use of live-streaming shopping’s information presentation tool) impacts consumers’ credibility perception regarding live streamers.

The authors empirically examined their hypotheses with data (n = 405) collected from a survey of consumers who engage in live-streaming shopping.

The results demonstrate that vicarious learning strategies (both coactive and independent) can shape consumers’ benefit perceptions (i.e. virtual presence and psychological proximity), and further have a positive effect on consumers’ personal value (i.e. perceived live streamer credibility). Furthermore, the consumers’ perception of the live streamers’ credibility positively affects their purchase intention and ultimately influences their purchase behavior.

Building on the vicarious learning theory and means-end chain (MEC) model, this study investigates the mechanism of the IT features of live-streaming shopping in reducing consumers’ uncertainty about live streamers. This study reveals the value of vicarious learning experiences in reducing consumers’ uncertainty and further enhancing their purchase behavior.

The purpose of this paper is to establish a stylized model to solve the pricing strategy, resource allocation and consumer surplus problems of multichannel healthcare services.

This paper considers a two-stage decision model with different levels of consumers’ knowledge. Faced with physical problems, knowledgeable consumers can solve their problems by seeking online healthcare channels, while unknowledgeable consumers need to make a two-stage decision to try to solve their problems.

The effective diagnosis rate and the proportion of knowledgeable consumers positively impact the optimal pricing in online and offline channels. In addition, a higher proportion of knowledgeable consumers does not result in higher demand in the online and offline channels. Moreover, if service providers lower their prices a small amount, they will lose some profit, but the consumer surplus will be higher, which will encourage more consumers to access healthcare services.

Knowledge levels are simplified into two categories. Also, the authors assume the resources of online and offline healthcare services are comparable.

This paper incorporates the knowledge level and misdiagnosis rate into the model framework to study the most effective pricing strategy for multichannel healthcare services.

The purpose of this paper is to empirically test a research model that incorporated antecedents of praise feedback behaviour (fear of confrontation and incentive for reducing nuisance costs), praise feedback behaviour (deliberatively praise feedback, casual praise feedback, and true compliment feedback) and consequences (trust and repurchase intention).

A structural equation model was employed to test the relationships of the research model using survey data collected from 398 Taobao consumers.

The results showed that fear of confrontation and incentive for reducing nuisance costs had a significant positive influence on deliberatively praise feedback and true compliment feedback, respectively, and both antecedents had a significant positive influence on casual praise feedback of consumers. It also showed that trust was influenced negatively by deliberatively praise feedback, and positively by casual praise feedback and true compliment feedback. Meanwhile, deliberatively praise feedback and true compliment feedback were found to have negative and positive influences on repurchase intention, respectively.

This research was a pilot study to identify a three-dimension conceptualization of praise feedback behaviour from the perspective of customer satisfaction, and to understand positive review bias from the perspective of input processes.