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This paper aims to identify consumers' health information consultation patterns by analyzing information sources to better understand consumers' health information needs and behavior in the context of multisource health information.

Haodaifu Online, an online health consultation (OHC) website in China, was used as a research data source, and 20,000 consultation cases were collected from the website with Python. After screening and cleaning, 1,601 consultation cases were included in this study. A content analysis-based mixed-methods research approach was applied to analyze these cases.

The results indicate that with the participation of OHC, there are 15 patterns of consumer health information consultation. Besides OHC, health information sources reported by consumers included medical institutions family/friends and the Internet. Consumers consult on a wide range of health issues including surgical conditions obstetrical and gynecological conditions and other 20 subjects. Consumers have multiple information needs when using OHC: getting prescriptions, diagnosing diseases, making appointments, understanding illnesses, confirming diagnoses and reviewing costs. Through further analysis it was found that consumers’ health information consultation patterns were also significantly different in health issues and health information needs.

This study broadens one’s understanding of consumer health information behavior, which contributes to the field of health information behavior, and also provides insight for OHC stakeholders to improve their services.

Using WeChat moments as an example, this article explores the impact of user role conflict on privacy concerns, social media fatigue and the three dimensions of discontinuous usage intention: control activities, short breaks and suspend usage intentions. Moreover, the moderating function of self-esteem in this process is examined.

The conceptual model includes role conflict, privacy concerns, social media fatigue, discontinuous usage intention and self-esteem. Three hundred and thirty-one questionnaires were collected using an online survey, and the data were analyzed with structural equation and hierarchical regression modeling.

The results show that (1) role conflict positively affects privacy concerns and social media fatigue; (2) privacy concerns also positively affect social media fatigue; (3) privacy concerns positively affect control activities intentions, although their impact on short breaks and suspend usage intentions is not significant, whereas social media fatigue significantly influences control activities, short breaks and suspend usage intentions; and (4) self-esteem negatively moderates the influence of role conflict on privacy concerns.

A key limitation of this research is that it is designed for WeChat. Therefore, the question of whether other social media platforms face role conflict or discontinuous usage problems should be explored in the future.

The article is interesting in that it focuses on the discontinuous usage of social media and identifies factors that contribute to the discontinuous usage of social media. The findings make some theoretical contributions to, and have practical implications for, research into social media usage.

Previous studies have considered customers' psychological responses to intelligent retail technology adoption, but have not considered how technology integration systems could promote the relationship between retailers and consumers. Based on the Stimulus–Organism–Response framework, this paper proposes a customer engagement model in a fully intelligent retail environment. The concept of the quality of intelligent experience is constructed from the perspective of customer experience, and the effect of the mechanism of smart retail on the customer engagement relationship is discussed.

Using two surveys, this study analyzes 201 (in study 1) and 321 (in study 2) questionnaires by using structural equation model in partial least square software.

The analysis shows that the human–machine interaction, intelligent systems and the product content of the quality intelligent experience significantly impact customer engagement on smart retail.

This research was designed for general retail products, without distinguishing between different product types. Thus, it did not consider the moderating effect of product types.

The findings enrich the intelligent retail technology field and provide operable guidance to help smart retailers improve customer relations.

This paper proposes a customer engagement model to describe how technology integration systems promote the relationship between retailers and consumers.

Based on the cue utilization theory and congruity theory, the purpose of this paper is to investigate the mediating effects of perceived quality on the relationships between category characteristics and purchase intention for private labels.

To examine the research hypotheses, the authors conducted a questionnaire survey on 703 adult consumers in China.

The results show that perceived quality fully mediates the relationships between category complexity, risk importance, category quality variation, product signatureness and purchase intention. In addition, consumers’ knowledge moderates the relationship between perceived quality and purchase intention. The implications and future research directions are discussed in this study.

The results show that the category complexity is positively related to consumers’ perceived quality. Although opposite to the conclusions in prior research, the findings are consistent with the unique phenomenon in China, that is, to label the name and location of the contract manufacturers. The authors investigate the moderating role of consumer knowledge, which will provide meaningful guidance for the Chinese retailing market.

Marketers recognize that the internet is crucial in the lives of consumers; thus, they invest money on online advertisements. Using multiple online media primarily influences the message acceptance of consumers. The synergistic effect of online multimedia relies on form, content, and sources of information, and time. The paper aims to discuss these issues.

A model that reflects the influence of the characteristics of online multimedia on message response through message acceptance is established based on theories of information persuasion, encoding variability, and multiple-source assumption. Based on a survey of 411 online media users, the study applies partial least-squares regression to test the research model.

The results show that variety of forms, complementary of contents, diversity of sources, and time interval influence message response via message strength. Complementary of contents and diversity of sources affect message response via perceived credibility. Synergy type moderates the relationship between variety of forms and perceived credibility and between diversity of sources and perceived credibility.

The current study mainly tests the effect of these characteristics on message response and the moderating effect of synergy type. Future research can examine the effect of these characteristics on information seeking and consumption behavior and the moderating effect of the cognitive mode of consumers.

This study provides insight into the characteristics of synergy and contributes to the literature on integrated marketing communication. The results provide guidance for practitioners to effectively plan online multimedia practices.

This study explored the influence of the characteristics of online media synergy on message response through message acceptance. The study also discussed the moderating effect of the type of online multimedia synergy.

The purpose of this paper is to examine the synergistic effects of online multimedia by categorizing it into online broadcast media (OBM) and online interactive media (OIM).

The authors used an online experiment method to manipulate the online message stimuli level (online media synergy and online single media repetition).

The results revealed that participants exposed to message stimuli of online media synergy reported greater source credibility, cognitive responses (brand credibility and positive thoughts about the brand), attitude toward the brand and purchase intention. In online multimedia, source credibility influences attitude toward the brand through brand credibility and positive thoughts about the brand; in online single media repetition, source credibility influences attitude toward the brand through only brand credibility.

In addition, the relationship between online media synergy and marketing outcomes might be moderated by consumers’ goals and thought patterns, and future research could further explore the moderating effects of these variables.

This study contributes to media synergy research, assists marketing planners in their understanding of the importance of online media synergy and serves as a reference for marketing planners considering an integrated online marketing plan.

The current study investigated how the synergy of OBM and OIM influences message persuasiveness for consumers (cognitive responses, attitude toward the brand and purchase intention).

We investigate the long‐term dependency behavior of Asian foreign exchange markets by using rescaled range analysis. Emerging markets in Korea, Taiwan, India, and Thailand, show evidences of long memory in the exchange rate return series, while the exchange rate return persistence is not found in more developed and mature markets in Japan, Australia, Hong Kong, and Singapore. Our results suggest that the return‐generating processes and presence of long memory depends on the degree of market development. In addition, the findings suggest that Asian financial crisis affects long‐term dependences of Korean won and Thai baht in which their economies and currency were hard hit by the crisis.

The purpose of this paper is to present a comprehensive self‐adaptive genetic algorithm (GA) based on fuzzy mechanism, aiming to improve both the optimizing capability and the convergence speed.

Many key factors that affect the performance of GAs are identified and analyzed, and their influences on the optimizing capability and the convergence speed are further elaborated, which prove to be very difficult to be described with explicit mathematical formulas. Therefore, a set of fuzzy rules are used to model these complicated relationships, in order to effectively guide the online self‐adaptive adjustments, such as changing the crossover and mutation probabilities, and thus to improve the optimizing capability and convergence speed.

Simulation results illustrates that, compared with a normal GA and another self‐adaptive GA based on explicit mathematical modeling of the key factors, the new GA is more advanced in terms of the optimizing capability and the convergence speed.

This paper develops a fuzzy‐rule‐based approach to describe the relationships between multiple GA parameters and online states, and the approach is useful in the design of a comprehensive self‐adaptive GA.

The purpose of this paper is to detect wound infection by electronic nose (Enose) and to improve the performance of Enose.

Mice are used as experimental subjects. Orthogonal signal correction (OSC) is applied to preprocess the response of Enose. Radical basis function (RBF) network is used for discrimination, and the parameters in RBF are optimized by particle swarm optimization.

OSC is very suitable for eliminating interference and improving the performance of Enose in wound infection detection.

Further research is required to sample wound infection dataset of human beings and to demonstrate that the Enose with proper algorithms can be used to detect wound infection.

In this paper, Enose is used to detect wound infection, and OSC is used to improve the performance of the Enose. This widens the application area of Enose and OSC.

The innovative concept paves the way for the application of Enose.

The purpose of the paper is to propose a new optimization algorithm to realize a synchronous optimization of sensor array and classifier, to improve the performance of E-nose in the detection of wound infection. When an electronic nose (E-nose) is used to detect the wound infection, sensor array’s optimization and parameters’ setting of classifier have a strong impact on the classification accuracy.

An enhanced quantum-behaved particle swarm optimization based on genetic algorithm, genetic quantum-behaved particle swarm optimization (G-QPSO), is proposed to realize a synchronous optimization of sensor array and classifier. The importance-factor (I-F) method is used to weight the sensors of E-nose by its degree of importance in classification. Both radical basis function network and support vector machine are used for classification.

The classification accuracy of E-nose is the highest when the weighting coefficients of the I-F method and classifier’s parameters are optimized by G-QPSO. All results make it clear that the proposed method is an ideal optimization method of E-nose in the detection of wound infection.

To make the proposed optimization method more effective, the key point of further research is to enhance the classifier of E-nose.

In this paper, E-nose is used to distinguish the class of wound infection; meanwhile, G-QPSO is used to realize a synchronous optimization of sensor array and classifier of E-nose. These are all important for E-nose to realize its clinical application in wound monitoring.

The innovative concept improves the performance of E-nose in wound monitoring and paves the way for the clinical detection of E-nose.