The emergence of machine learning (ML) and blockchain (BC) technology has greatly enriched the functions and services of healthcare, giving birth to the new field of “smart healthcare.” This study aims to review the application of ML and BC technology in the smart medical industry by Web of Science (WOS) using bibliometric visualization. Through our research, we identify the countries with the greatest output, the major research subjects, funding funds, and the research hotspots in this field. We also find out the key themes and future research areas in application of ML and BC technology in healthcare area. We reveal the different aspects of research under the two technologies and how they relate to each other around five themes.
This study explored the relationship between occupational health risk perception and job satisfaction. Based on the job demand-resources model and resource conservation theory, eight hypotheses were proposed in this study. In a survey of 237 production line workers and managers, we found that perceived occupational health risks significantly negatively affected job satisfaction. Both work stress and organizational commitment mediate the relationships between perceived occupational health risks and job satisfaction. We also examined whether safety culture could weaken the negative impact of perceived occupational health risks on job satisfaction. However, the results of our study did not support this hypothesis. This study not only helped managers to realize the hazards of occupational health risks, but also encouraged employees to actively participate in safety construction and pay attention to their own health. In addition, we also put forward some targeted intervention measures to reduce the negative impact of perceived occupational health risks on job satisfaction. Therefore, this study had certain practical implications.
With the rapid development of e-commerce technology, cross-channel consumption has become the mainstream mode of contemporary consumers. However, there are several problems of cross-channel consumption such as inconsistency of online and offline channel information and service, disfluency of channel switching which have brought adverse effects on user experience. The question arises here as to what factors influence user experience and how to build a scientific and effective evaluation index system. Different from previous studies based on sellers, this paper used grounded theory to analyze and summarize the evaluation index system of user experience under cross-channel consumption from the perspective of consumers. We summarized and refined four first level indexes which are “online platform attribute, offline entity attribute, channel switching attribute, and individual demand” and 13 second level indexes which are “platform operation, platform information, platform service, platform promotion, product quality, service quality, environment quality, channel consistency, channel switching cost, channel switching fluency, psychological expectation, personal interests and individual needs.” Then, we used BP neural network to build the evaluation model and trained and simulated the performance of the sample. The results show that the evaluation model has a good generalization ability and can effectively evaluate user experience under cross-channel consumption. Finally, implications and limitations are also discussed. This study helps to enrich the theoretical research on user experience and consumer behavior. It also provides targeted basis for in-depth analysis of cross-channel consumption behavior, establishment of user experience evaluation index system, and improving user experience and multichannel management of physical stores.
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