APPLICATION OF KANO MODEL AND IMPORTANCE - PERFORMANCE ANALYSIS TO IMPROVE SERVICE QUALITY OF CHATBOT (CASE STUDY OF TELKOM INDONESIA)

• Main Author: Kusuma Widyadhana, Carissa
• Format: Theses
• Language: Indonesia
• Subjects: Manajemen umum
• Online Access: https://digilib.itb.ac.id/gdl/view/57184
• Institution: Institut Teknologi Bandung

The progressive advance of technology has brought a lot of changes in human life such as an increase in businesses that are moving from traditional to digital platforms to transact with consumers. Chatbot is one of the most advanced technological developments. Telkom decided to implement technological developments in their customer support by offering an IndiHome Chatbot called Indita. Indita is a chatbot feature of the myIndiHome app that can assist customers. In 2020, Indita became a trending topic on Twitter. The chatbot intended to help users solve their problems, fails to help and frustrates them. This research explains what the user thinks of the Indita and which quality attributes in the customer's view should be enhanced based on the degree of importance, with the aim of improving the performance of the chatbot. In conducting market research, the author reflected on chatbot service quality attribute by Verleye et. al (2019) with total 8 dimensions (functionality, trustworthiness, safety, efficiency, graphical appearance, humanity, empathy, and responsiveness) and 28 attributes that can evaluate the quality of service and make the best recommendation for chatbot performance to be more effective and efficient. In evaluating chatbot service quality, the author uses Kano Model and Importance-Performance Analysis to find out what service attributes must be met and need to be improved in order to increase customer satisfaction. As the sample recommended by Malhotra (2007) for of problem-solving type of research, author get help from 206 Indita users as respondents. This research used quantitative method by distributing questionnaires. From the integration of Kano Model and IPA that has been done previously, 28 attributes have been successfully reduced to 12 attributes that divided into 2 improvement priorities. First priority consists of interpret commands accurately, able to maintain a discussion, read and respond to moods, personalization options, responding immediately, and productivity. While second priority consist of flexible in interpreting knowledge, number of service available in the chatbot, robustness to unexpected input, use of emojis and pictures, create and enjoyable interaction, and convey personality. This research finding can be a guide for companies to make improvements in accordance with the expectations of the customer towards the features provided by the company.