This study examines QR code as an on-site tourism information source from the perspective of reliance, sign need, convenience, and credibility. As a whole, QR code receives good comments despite being a relatively new tourism information source. QR code is as good as brochures in meeting the sign need of tourists' on-site search behavior. It is also better than information boards in all the aspects studied. Sign need predicts credibility but not reliance on QR code. Convenience contributes to credibility and reliance of QR code. Reliance on information boards negatively predicts credibility of QR code. QR code can be an efficient information source for the destination marketing organizations. This research contributes to the study of on-site tourism information search behavior and sign need.
Based on the big data analysis technology of small and medium-sized clothing e-commerce enterprises, this paper provides theoretical and practical methods for e-commerce enterprises to apply big data technology. The data sources come from questionnaires and online data grab. Through the Google online questionnaire survey, 252 valid questionnaires were received. Python is used to search valid reviews on Tmall and Internet with some keywords, and a total of 11,864 valid comments were obtained. We find that the reason why men buy clothes is as high as 68% because of the seasonal change, but only 31% of women buy clothes because of the seasonal change. The higher the average monthly income of women, the more attention is paid to whether the style of clothing design is suitable for themselves, and the more attention is paid to the service attitude of e-commerce enterprises. From decision tree analysis, the fit of clothing style is the most important factor affecting the shopping choice of customers. From the data of customer reviews, the most mentioned variables are clothing style, fashion, service attitude and price. The number of reviews and the sentiment analysis score on clothing style are all ranking first. The number of reviews and the sentiment analysis score on clothing fashion are all ranking second. We also proposes corresponding precision marketing strategies based on the results of big data analysis, such as gender recommendation strategies, personalized recommendation strategies, differentiated marketing strategies, enterprise training and clothing design strategies.
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