Exploring Age Differences in Motivations for and Acceptance of Chatbot Communication in a Customer Service Context This qualitative interview study explores age differences in perceptions of chatbot communication in a customer service context. Socioemotional selectivity theory and research into technology acceptance suggest that older adults may differ from younger adults in motivations to use chatbots, and in perceived complexity and security of this chatbot communication. The indepth interviews with older adults (54xe2x80x9381 years; N  7) and younger adults (19xe2x80x9330 years; N  7) revealed that both groups were aligned in their prime motivation: They used chatbots to get their (simple) customer queries answered in a fast and convenient manner. However, they seemed to differ in their need for additional human contact. In both age groups, there were participants for whom it was easy to communicate with chatbots, and the two groups were united in their frustrations when the chatbot did not understand and answer their queries. They were aligned as well in the difficulty they experienced in assessing the security of the chatbot. The two age groups may differ in the factors that contribute to perceived ease of use and perceived security. Directions for future research and implications for the implementation of chatbots for customer service are discussed.

Improving Conversations: Lessons Learnt from Manual Analysis of Chatbot Dialogues Analysing and improving chatbot dialogues xe2x80x93 socalled chatbot xe2x80x98trainingxe2x80x99 xe2x80x93 is key to the successful implementation and maintenance of chatbots for customer service. Nevertheless, the details of this practice and what service providers may learn from the analysis of such dialogues is not investigated in current research on chatbots. As a first step towards bridging this gap in existing knowledge, we present a study of the qualitative analysis of chatbots dialogues in the context of the customer service department of a large telecom provider. In total 406 dialogues, randomly sampled from all chatbot dialogues during a fourweek period, were included in the analysis. The analysis concerned the chatbotxe2x80x99s ability to resolve customersxe2x80x99 requests, the quality in the chatbot dialogues, and suggestions for improvements of the chatbot knowledge base generated through the analysis. The findings shed light on characteristics of successful and unsuccessful chatbot dialogues and the kind of improvements that may be derived from such analysis. On the basis of the findings we summarize implications for theory and practice, and suggest future research.