Purpose
This study aims to investigate the impact of both physical and personality-related anthropomorphic features of an artificial intelligence service robot on the cognitive and affective appraisals and acceptance of consumers during service delivery.
Design/methodology/approach
The proposed hypotheses that investigate the effects of service robots’ physical appearance on the emphasis consumers place on each evaluation criteria they use in determining their willingness to accept the use of service robots in service delivery and the moderating role of sense of humor are tested by conducting two studies using scenario-based experiments.
Findings
The results show that humanlike appearance leads to higher performance expectancy, mascot-like appearance generates higher positive emotions and machine-like appearance results in higher effort expectancy. The effects of humanlike and mascot-like appearances on consumer acceptance are moderated by the sense of humor of service robots. However, the sense of humor effect is attenuated with a machine-like appearance owing to the lack of anthropomorphism.
Practical implications
This study provides crucial insights for hospitality managers who plan to use service robots in service delivery. The findings highlight the key roles of appearance type and sense of humor of service robots in influencing the appraisals and acceptance of consumers regarding the use of service robots in service delivery.
Originality/value
This study focuses on comparing the effects of traditional and mascot-like appearances of service robots on consumer appraisals and identifies sense of humor as a cute anthropomorphized personality trait of service robots.
The early warning of financial risk is to identify and analyze existing financial risk factors, determine the possibility and severity of occurring risks, and provide scientific basis for risk prevention and management. The fragility of financial system and the destructiveness of financial crisis make it extremely important to build a good financial risk early-warning mechanism. The main idea of the K-means clustering algorithm is to gradually optimize clustering results and constantly redistribute target dataset to each clustering center to obtain optimal solution; its biggest advantage lies in its simplicity, speed, and objectivity, being widely used in many research fields such as data processing, image recognition, market analysis, and risk evaluation. On the basis of summarizing and analyzing previous research works, this paper expounded the current research status and significance of financial risk early-warning, elaborated the development background, current status and future challenges of the K-means clustering algorithm, introduced the related works of similarity measure and item clustering, proposed a financial risk indicator system based on the K-means clustering algorithm, performed indicator selection and data processing, constructed a financial risk early-warning model based on the K-means clustering algorithm, conducted the classification of financial risk types and optimization of financial risk control, and finally carried out an empirical experiments and its result analysis. The study results show that the K-means clustering method can effectively avoid the subjective negative impact caused by artificial division thresholds, continuously optimize the prediction process of financial risk and redistribute target dataset to each cluster center for obtaining optimized solution, so the algorithm can more accurately and objectively distinguish the state interval of different financial risks, determine risk occurrence possibility and its severity, and provide a scientific basis for risk prevention and management. The study results of this paper provide a reference for further researches on financial risk early-warning based on K-means clustering algorithm.
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