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인간-생성형 AI 협업이 인간의 노력 정도에 대한 지각과 책임 평가에 미치는 영향: 행위자 또는 관찰자 관점에서

The Effect of Human-Generative AI Collaboration on Perceived Human Effort and Responsibility: Focusing on Actor or Observer Perspectives

김지호|최정현
한국소비자학회 (Korean Society Of Consumer Studies)|2024.02|소비자학연구 (Journal of Consumer Studies)|제35권 제1호|pp. 107-129|DOI http://dx.doi.org/10.35736/JCS.35.1.5

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ChatGPT생성형 AIGenerative AI인간-AI 협업귀인 이론Attribution theoryHuman-AI collaboration노력 귀인Effort attribution
주제분류경제학

초록

본 연구는 생성형 AI를 활용하여 업무를 수행하는 맥락에서 생성형 AI 이용자의 노력 정도와 결과에 대한 책임이 행위자 혹은 관찰자 관점에서 어떻게 지각되는지를 기존 기술인 검색 엔진 비교를 통해 조사하고자 하였다. 이를 위해 2(관점: 행위자 vs. 관찰자) ×2(기술: 생성형 AI vs. 검색 엔진) ×2(결과: 성공 vs. 실패) 집단 간 설계에서 382명을 대상으로 시나리오 기반의 설문조사를 실시한 결과는 다음과 같다. 첫째, 기술 유형과 관점의 상호작용 효과가 나타났는데, 구체적으로 관찰자의 관점에서 생성형 AI 이용자의 노력이 검색 엔진 이용자의 노력보다더 낮게 지각되었으나, 행위자의 관점에서는 기술에 따른 차이가 없었다. 둘째, 행위자의 관점에서 생성형 AI는 검색 엔진에 비해 결과에 대한 이용자의 노력 귀인을 감소시키며, 실패보다는 성공일 때 행위자 노력에 귀인하는 경향이 확인되었으나 이는 한계적 수준에서 허용되었다. 셋째, 관찰자의 관점에서 생성형 AI는 검색 엔진보다 실패의결과에 대한 이용자 노력 귀인을 높이는 것으로 나타났다. 넷째, 관점을 통제했을 때 생성형 AI는 검색 엔진에 비해 성공 결과에 대한 이용자 노력에 귀인을 감소시켰으며, 이용자 노력 귀인이 감소할수록 해당 결과에 대한 이용자 책임이 감소하는 것으로 나타났다. 위 결과를 토대로 이론적 함의 및 실무적 함의를 제시한다.

Abstract

In the context of using generative AI to perform tasks, this study examined how the degree of generative AI user effort and responsibility for outcomes is perceived from an actor or observer perspective compared to a traditional technology, a search engine. We conducted a scenario- based survey of 382 participants in a 2 (Perspective: Actor vs. Observer) ×2 (Technology: Generative AI vs. Search Engine) ×2 (Outcome: Success vs. Failure) between-groups design and found the following: First, there was an interaction effect of technology type and perspective, showing that from an observer's perspective, generative AI users' effort was perceived as lower than search engine users' effort, but from an actor's perspective, there was no difference by technology. Second, from an actor's perspective, generative AI reduces user effort attribution to outcomes compared to search engines, and we find a tendency to attribute more actor effort to success than to failure, both at marginal levels. Third, from an observer's perspective, generative AI increases user effort attribution for failures compared to search engines. Fourth, when controlling for perspective, generative AI reduces the attribution of user effort for a success outcome compared to a search engine, and a reduction in user effort attribution is associated with a reduction in user responsibility for that outcome. Based on these findings, we present theoretical and practical implications.

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