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    스마트랩 도입에 따른 시험인증기관의 서비스 품질 지표 도출에 관한 연구 = A Study on the Development of Service Quality Indicators for Testing and Certification Institutions with the Introduction of Smart Laboratories

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    https://www.riss.kr/link?id=A110055934

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    다국어 초록 (Multilingual Abstract) kakao i 다국어 번역

    Testing and certification institutions have recently been striving to transition from traditional labor-intensive testing and certification practices to Smart Laboratory(Smart Lab) operating systems that incorporate automation and artificial intelligence(AI), in line with the broader trend of digital transformation. However, concerns have been raised that conventional evaluation models such as SERVQUAL—representative human-interaction-centered frameworks—are insufficient for adequately reflecting the quality characteristics of such advanced, technology-integrated service environments.
    Considering this limitation, this study aims to systematically derive service quality indicators suitable for testing and certification institutions operating in Smart Lab environments and to identify the relative importance of these quality elements. Brady and Cronin’s hierarchical service quality model was adopted as the theoretical framework, comprising three upper-level criteria—Interaction Quality, Physical Environment Quality, and Outcome Quality—and nine lower-level indicators. The Analytic Hierarchy Process(AHP) was then applied to analyze the relative importance of each indicator using responses from industry professionals, including managers and practitioners in private companies with experience in public procurement and government supply, who possess expertise in recognizing service quality in testing and certification.
    The analysis revealed that Interaction Quality held the highest importance among all upper-level criteria, while AI-based customer interaction, automation level, and system stability emerged as the key lower-level factors. These findings indicate a shift in service quality perception within Smart Lab environments—from traditional technology-focused management toward a more integrated approach that values digital responsiveness and operational stability. This study identifies the structural changes brought about by Smart Lab adoption in service quality management and provides an analytical foundation that enables the integrated consideration of quality factors across the digital environment, moving beyond conventional result-oriented management practices.
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    Testing and certification institutions have recently been striving to transition from traditional labor-intensive testing and certification practices to Smart Laboratory(Smart Lab) operating systems that incorporate automation and artificial intellige...

    Testing and certification institutions have recently been striving to transition from traditional labor-intensive testing and certification practices to Smart Laboratory(Smart Lab) operating systems that incorporate automation and artificial intelligence(AI), in line with the broader trend of digital transformation. However, concerns have been raised that conventional evaluation models such as SERVQUAL—representative human-interaction-centered frameworks—are insufficient for adequately reflecting the quality characteristics of such advanced, technology-integrated service environments.
    Considering this limitation, this study aims to systematically derive service quality indicators suitable for testing and certification institutions operating in Smart Lab environments and to identify the relative importance of these quality elements. Brady and Cronin’s hierarchical service quality model was adopted as the theoretical framework, comprising three upper-level criteria—Interaction Quality, Physical Environment Quality, and Outcome Quality—and nine lower-level indicators. The Analytic Hierarchy Process(AHP) was then applied to analyze the relative importance of each indicator using responses from industry professionals, including managers and practitioners in private companies with experience in public procurement and government supply, who possess expertise in recognizing service quality in testing and certification.
    The analysis revealed that Interaction Quality held the highest importance among all upper-level criteria, while AI-based customer interaction, automation level, and system stability emerged as the key lower-level factors. These findings indicate a shift in service quality perception within Smart Lab environments—from traditional technology-focused management toward a more integrated approach that values digital responsiveness and operational stability. This study identifies the structural changes brought about by Smart Lab adoption in service quality management and provides an analytical foundation that enables the integrated consideration of quality factors across the digital environment, moving beyond conventional result-oriented management practices.

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