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Surface integrity optimization in milling of aluminum 1100 : effects of supercritical CO₂ + MQL and emulsion cooling with various milling strategies
Full text linkVorbereitung deutscher Notaufnahmen auf Ereignisse mit chemischen, biologischen, radiologischen und nuklearen (CBRN) Gefahrstoffen
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Lehrwerkstätten als Orte der interdisziplinären Kollaboration. Eine Umbruchphase in der qualitativen Methodenlehre gestalten
Full text linkProcess development and microstructural evolution of laser powder bed fusion processed novel β Ti–42 Nb and Ti–20Nb–6Ta alloys
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The Influence of Chatbot Characteristics on Customer Decision-Making across Different Scenarios
No full textWhile chatbots are being increasingly integrated into areas of digital commerce, customer support, and marketing, their impact on consumer decision making across shopping contexts is yet to be fully explored. This study investigates the impact of various chatbot characteristics including, communication style, tone, and design, on user satisfaction, confidence in decisions, and purchasing behavior across various shopping scenarios.
Employing a within-subject experimental procedure, 20 participants engaged with four distinct chatbots with differing anthropomorphism vs. non-anthropomorphism (human-like vs. non-human-like) and shopping task types (emotional vs. functional).
Findings indicates that human-like chatbots improve customer satisfaction and decision confidence, even when functional shopping was induced as the task with prioritized efficiency. However, excessive humanization, including overuse of emojis and informal tone, was also unsettling. Non-human-like chatbots were perceived as efficient but lacked engagement. These findings indicate that chatbot design should balance engagement and efficiency based on user expectations and task complexity.
This paper builds upon existing literature and employs the Meta-UTAUT framework to understand how prior user experiences can predict chatbot acceptance. The insights offer practical insights for businesses optimizing chatbot interactions, highlighting the need for adaptable, user-centered design. Limitations regarding sample size and experimental constraints are discussed, along with recommendations for future research on diverse user demographics and advanced chatbot functionalities