| Summary: | 碩士 === 淡江大學 === 資訊管理學系碩士班 === 102 === With the development of information technology, information technology not only improves human’s living environments and the quality of life but also increases the productivity of industries. Self-service technology can efficiently to enable customers to acquire proper services. Meanwhile, information technology helps businesses to provide customers with quality services using existing resources and make the right and effective service decisions. Accordingly, businesses have to not only understand customer needs but also pay attention to customer emotions when they make service decisions. Affective computing is an important approach to recognize human’s emotions that information technology has high abilities of revealing and recognizing emotions to businesses to make effective service decisions. The service industry plays an important role in economic activities in Taiwan. Businesses need to create innovative valuable services to customers by understanding customer emotions within service encounters. Consequently, in order to dealing with customer problems, businesses should apply affective computing to service decision processes to recognizing customer emotions and deliver suitable services to customers.
This study aims to build a service decision system by adopting affective computing, artificial neural networks and decision trees based on the concept of service interaction design. This study uses service recovery as a case to test the service decision performance of the service decision system. Affective computing is to recognize customer emotions when customers encounter service failures. The customer emotions can be analyzed and clustered to positive and negative emotions via artificial neural networks. Then, this study tries to design effective service decision rules based on C4.5 algorithm of decision trees. This study builds the service decision system by using Matlab R2013a tool and defines four scenarios of service failures. Three experiments are conducted to evaluate the service decision system. Experiment 1 is to build an effective approach to classify customer emotions and to explore the proper analysis structure of artificial neural networks. Experiment 2 is to build suitable decision rules for the service decision system by using decision trees and surveying real data through 110 internet subjects. Experiment 3 is to evaluate the performance of the service decision system that 35 subjects are invited to experience the service decision system and respond to the service failures based on the experiment results of experiment 1 and experiment 2.
The experiment results show that the service decision system is built by combining the concept of service interaction design and affective computing which can have the high performance of customer recognition. The customer recognition rate is about 72.9%, the accuracy rate of service decision for customers is about 73.57 and the customer satisfaction rate for the service recovery is about 87.5%. Hence, according to the result findings, customer emotions can be a clue to enable businesses to make the right service decisions and create innovative service experiences for customers within service interactions. Meanwhile, in order to increase customer satisfaction and loyalty, businesses can effectively understand customer needs and requirements to deliver suitable services when customers encounter service failures. This study only focuses on recognizing customers’ speech to understand their emotions and use the service recovery as a case. Researchers continuously pay attention to the applications of combining face recognizing with action recognizing to cluster more emotion types. Besides, further research can elaborate the proper applications of service fields based on the idea of pervasive computing.
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