{"id":18898,"date":"2026-05-03T22:17:39","date_gmt":"2026-05-03T22:17:39","guid":{"rendered":"https:\/\/finteqc.ca\/?post_type=finteqc_paper&#038;p=18898"},"modified":"2026-05-03T22:19:44","modified_gmt":"2026-05-03T22:19:44","slug":"15956","status":"publish","type":"finteqc_paper","link":"https:\/\/finteqc.ca\/index.php\/papers\/15956\/","title":{"rendered":"When the Bot Stays Cold: How Non-Empathic Insurance Chatbots Shape Trust, Disclosure and User Experiences"},"content":{"rendered":"<p>This qualitative study examines how consumers experience a non-empathic chatbot during a simulated auto-insurance quote. Ten participants completed the interaction and then participated in a Retrospective Think-Aloud Protocol (RTAP) interview to articulate interpretations and emotional reactions at key moments. Interviews were analyzed using an inductive approach, and findings were triangulated with facial-expression data. Results converge around three aggregated dimensions (1) Trust, disclosure and reassurance in a non-empathic context, where requests for sensi-tive information were scrutinized through privacy risk, perceived opacity, and need for justification, and where the chatbot\u2019s mechanical tone could reduce willingness to disclose; (2) Humanization and emotional disconnect, where the absence of ac-knowledgment after emotionally salient disclosures was often perceived as cold or missing, while anthropomorphic cues (photo\/emojis) produced polarized reactions (helpful vs. suspicious\/inauthentic); and (3) Efficiency vs. emotional resonance di-lemma, where participants prioritized speed and clarity, but still valued brief\/task-linked cues and explanatory statements that improved comfort and perceived pro-fessionalism without slowing the process. These findings show how emotion-neutral chatbot design shapes trust, disclosure comfort\/privacy perceptions and UX in a high-involvement\/low-touch\/data-intensive insurance service context. Meth-odologically, it shows how triangulating facial-expression data with qualitative feedback strengthens and nuances interpretive validity by revealing convergence and divergence across sources.<\/p>\n","protected":false},"featured_media":0,"template":"","class_list":["post-18898","finteqc_paper","type-finteqc_paper","status-publish","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/finteqc.ca\/index.php\/wp-json\/wp\/v2\/finteqc_paper\/18898","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/finteqc.ca\/index.php\/wp-json\/wp\/v2\/finteqc_paper"}],"about":[{"href":"https:\/\/finteqc.ca\/index.php\/wp-json\/wp\/v2\/types\/finteqc_paper"}],"wp:attachment":[{"href":"https:\/\/finteqc.ca\/index.php\/wp-json\/wp\/v2\/media?parent=18898"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}