User willingness is a crucial element in the sales talk process that affects the achievement of the salesperson’s or sales system’s objectives. Despite the importance of user willingness, to the best of our knowledge, no previous study has addressed the development of automated sales talk dialogue systems that explicitly consider user willingness. A major barrier is the lack of sales talk datasets with reliable user willingness data. Thus, in this study, we developed a user willingness–aware sales talk collection by leveraging the ecological validity concept, which is discussed in the field of human–computer interaction. Our approach focused on three types of user willingness essential in real sales interactions. We created a dialogue environment that closely resembles real-world scenarios to elicit natural user willingness, with participants evaluating their willingness at the utterance level from multiple perspectives. We analyzed the collected data to gain insights into practical user willingness–aware sales talk strategies. In addition, as a practical application of the constructed dataset, we developed and evaluated a sales dialogue system aimed at enhancing the user’s intent to purchase.

Generating and evaluating character-like utterances automatically is essential for applications ranging from character simulation to creative-writing support. Existing approaches primarily focus on basic aspects of character‐likeness, such as script-fidelity knowledge and conversational ability. However, achieving a higher level of character‐likeness in utterance generation and evaluation requires consideration of the character’s identity, which deeply reflects the character’s inner self. To bridge this gap, we identified a set of identity-centric character-likeness elements. First, we listed 27 elements covering various aspects of identity, drawing on psychology and identity theory. Then, to clarify the features of each element, we collected utterances annotated with these elements from a commercial smartphone game and analyzed them based on user evaluations regarding character-likeness and charm. Our analysis reveals part of element-wise effects on character‐likeness and charm. These findings enable developers to design practical and interpretable element-feature-aware generation methods and evaluation metrics for character-like utterances.