• 한국IT서비스학회지
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• 제14권3호
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• pp.163-182
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• 2015

The spread of mobile commerce due to popularization of smartphones not only broke down the boundary between online and offline, but also changed the consumer life, and hence brought change of commerce paradigm that creates new demand. The success of innovative information technology such as mobile shopping can be defined as an individual accepting the technology and continuously using it, but the studies on the usage behavior after the acceptance have been done in very restrictively, despite its importance. In this study, in order to empirically investigate the factors that influence the continuance in mobile shopping usage experience and its causal relationship, Social Cognitive Theory and Habit Theory were applied to IS Continuance Model, and the extended IS Continuance Model was suggested and proved. As a result, the usefulness, enjoyment, and self-efficacy perceived in usage experience significantly influence satisfaction, and usefulness, self-efficacy, and satisfaction influence habit. Also, usefulness, self-efficacy, satisfaction, and habit significantly influence continuance intention. This study provides a valuable asset in providing an opportunity to understand the usage behavior of mobile shopping service users after the acceptance, and furthermore proving directionality in improving customer loyalty.

• 한국컴퓨터정보학회논문지
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• 제23권4호
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• pp.107-114
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• 2018

Recently, various wearable personal devices such as a smart watch have been developed and these personal devices are being miniaturized. The user desires to receive new services from personal devices as well as services that have been received from personal computers, anytime and anywhere. However, miniaturization of devices involves constraints on resources such as limited input and output and insufficient power. In order to solve these resource constraints, this paper proposes a resource collaboration system which provides a service by composing sharable resources in the resource sharing environment like IoT. the paper also propose a method to infer and recommend user-customized resources among various sharable resources. For this purpose, the paper defines an ontology for resource inference. This paper also classifies users behavior types based on a user model and then uses them for resource recommendation. The paper implements the proposed method as a prototype system on a personal device with limited resources developed for resource collaboration and shows the effectiveness of the proposed method by evaluating user satisfaction.