Bershca: bringing chatbot into hotel industry in Indonesia

Adopting technology could give competitive advantage and positively impact the hotel’s profitability, thus hotels should keep up with the latest hotel technologies. An important part in the hotel services is the customer service. A problem with the human-to-human customer services today is a long time in answering customers query. On the other hand, nowadays customers need easy and effective services. Thus, a chatbot is required to answer consumers' issues automatically which leads to higher customer satisfaction and a growing profit. Because of the need and there is still an absence of chatbot for hotel industry in Indonesia, this study is conducted. The chatbot for hotel industry in Indonesia, named Bershca, has been successfully developed using artificial intelligence markup language (AIML) to construct the knowledge. Google Flutter is used for the system’s front-end, while Python is used for the back-end of the system. As a text-preprocessing method, Nazief-Adriani Algorithm is implemented in the system’s back-end. The system is evaluated using technology acceptance model (TAM). As a result, 85.7% of the respondents believe that using chatbot would enhance their job performance and 84.33% of the respondents believe that using the technology would be free of effort

Conversation Mining for Customer Service Chatbots

More and more companies are using chatbots in customer service. The large number of chatbots and their interactions with customers produce a huge amount of data, which is useful to track the usage and performance of the chatbot. However, many established performance metrics (e.g., intent scores, conversa-tions per day) could be considered too intuitive to be helpful and are either at a very high level or at the level of single question-answer pairs. Our research aims to address this challenge by presenting a novel approach and system for conver-sation analysis of customer service chatbots. More specifically, we extend estab-lished metrics and concepts with ideas from process mining since every conver-sation with customer service chatbots can be interpreted as a sequence of discrete steps. This paper presents the methodological foundations for our approach, which we call conversation mining, and demonstrates its potential with first in-sights into our prototype. Ultimately, we aim to draw the attention of chatbot researchers and practitioners to the value of conversation data by describing a novel approach for automatically processing and analyzing at a process level

Can we Help the Bots? Towards an Evaluation of their Performance and the Creation of Human Enhanced Artifact for Emotions De-escalation

We propose a hybrid intelligence socio-technical artifact that identifies a threshold where the chatbot requires human intervention in order to continue to perform at an appropriate level to achieve the pre-defined objective of the system. We leverage the Yield Shift Theory of Satisfaction, the Intervention Theory and the Nudge Theory to develop meta requirements and design principles for this system. We discuss the first iteration of implementation and evaluation of the artifact components

Towards Designing a Conversation Mining System for Customer Service Chatbots

Chatbots are increasingly used to provide customer service. However, despite technological advances, customer service chatbots frequently reach their limits in customer interactions. This is not immediately apparent to both chatbot operators (e.g., customer service managers) and chatbot developers because analyzing conversational data is difficult and labor-intensive. To address this problem, our ongoing design science research project aims to develop a conversation mining system for the automated analysis of customer-chatbot conversations. Based on the exploration of large dataset (N= 91,678 conversations) and six interviews with industry experts, we developed the backend of the system. Specifically, we identified and operationalized important criteria for evalu-ating conversations. Our next step will be the evaluation with industry experts. Ultimately, we aim to contribute to research and practice by providing design knowledge for conversation mining systems that leverage the treasure trove of data from customer-chatbot conversations to generate valuable insights for managers and developers

Leveraging the Potential of Conversational Agents: Quality Criteria for the Continuous Evaluation and Improvement

Contemporary organizations are increasingly adopting conversational agents (CAs) as intelligent and natural language-based solutions for providing services and information. CAs promote new forms of personalization, speed, cost-effectiveness, and automation. However, despite their hype in research and practice, organizations fail to sustain CAs in operations. They struggle to leverage CAs’ potential because they lack knowledge on how to evaluate and improve the quality of CAs throughout their lifecycle. We build on this research gap by conducting a design science research (DSR) project, aggregating insights from the literature and practice to derive a validated set of quality criteria for CAs. Our study contributes to CA research and guides practitioners by providing a blueprint to structure the evaluation of CAs to discover areas for systematic improvement

ChatrEx: Designing explainable chatbot interfaces for enhancing usefulness, transparency, and trust

When breakdowns occur during a human-chatbot conversation, the lack of transparency and the “black-box” nature of task-oriented chatbots can make it difficult for end users to understand what went wrong and why. Inspired by recent HCI research on explainable AI solutions, we explored the design space of explainable chatbot interfaces through ChatrEx. We followed the iterative design and prototyping approach and designed two novel in-application chatbot interfaces (ChatrEx-VINC and ChatrEx-VST) that provide visual example-based step-by-step explanations about the underlying working of a chatbot during a breakdown. ChatrEx-VINC provides visual example-based step-by-step explanations in-context of the chat window whereas ChatrEx-VST provides explanations as a visual tour overlaid on the application interface. Our formative study with 11 participants elicited informal user feedback to help us iterate on our design ideas at each of the design and ideation phases and we implemented our final designs as web-based interactive chatbots for complex spreadsheet tasks. We conducted an observational study with 14 participants to compare our designs with current state-of-the-art chatbot interfaces and assessed their strengths and weaknesses. We found that visual explanations in both ChatrEx-VINC and ChatrEx-VST enhanced users’ understanding of the reasons for a conversational breakdown and improved users\u27 perceptions of usefulness, transparency, and trust. We identify several opportunities for future HCI research to exploit explainable chatbot interfaces and better support human-chatbot interaction