생존함수를 활용한 서비스달 시스템 분석 방법론 설계 (Methodology Design for Analyzing Service Delivery Systems using Survival Function)

서지정보

· 발행기관 : 한국경영학회
· 수록지 정보 : 경영학연구 / 46권 / 2호 / 403 ~ 427페이지
· 저자명 : 박근완, 박광태

초록

본 연구는 임상실험 분석에서 주로 사용하는 카플란-마이어 추정치(Kaplan-Meier Estimator)를 재설계하여, 서비스조직의 서비스전달 시스템(service delivery system) 분석에 용이한 방법론을 제시하는 연구이다. 본 연구에서 제시하는방법론은 서비스운영관리(service operations management) 분야의 주된 접근방법, 즉 다차원(multi-dimension) 중심의 통합적인 접근방법으로 서비스속성(service attribute)을 파악하는 것이 아닌, 서비스전달 시스템 상의 서비스접점(service encounter)에서 고객이 지각하는 서비스속성(service attribute)을 분석할 수 있다. 서비스전달 시스템 분석방법론의 타당성 검토를 목적으로 다이어트 한방병원에 적용하여 사례분석을 진행하였다. 본 연구는 제조업에서 주로 적용하는 시스템 접근방법(system approach) 또는 프로세스 모듈화(process modularity) 개념으로 서비스속성을 파악하는 연구라 할 수 있다.

영어초록

We propose a methodology design for quantitatively analyzing service delivery systems of service organizations. The methodology design is developed by applying Kaplan-Meier estimator of survival analysis. We carry out sensitivity analysis through statistical significance test for survival function and hazard function considering different groups. We also consider the characteristics of viability in censored data. In order to test the validity of proposed methodology design of service delivery systems we apply it to Korean Hospital of Oriental Medicine focused on a diet. Case analysis of Korean Hospital is done considering fundamental categorization and content categorization. Fundamental categorization is classified into patient type (new patients and returning patients) and age group (20s, 30s, and 40s) and content categorization is classified into intention to recommend others and revisit Intention. We find as a result that there is a significant difference in the survival function and hazard function for new patients and returning patients. We also show the significant difference among age groups. Survival function of returning patients is lower than that of new patients. Hazard function of returning patients increases rapidly compared to that of new patients as patients move to the end of stages of service delivery systems. Result shows that the survival function of new patients decreases significantly at SE9 (schedule for revisit) and survival function for returning patients decreases significantly at SE8 (medical bill). For Hazard function, returning patients show the greatest increase at SE10 (oriental medicine delivery) and new patients show the greatest increase at SE9 (schedule for revisit). The greatest change in survival function is found in 40s and then the second greatest change is in 20s. The greatest decrease in survival function is shown at SE3 (medical waiting time) for 40s and at SE9 (Medical bill) for 20s. For Hazard function, the greatest increase is seen at SE9 (schedule for revisit) for 40s and at SE10 (oriental medicine delivery) for 20s. For revisit intention of content categorization, survival function graph shows a substantial decrease at SE3 (medical waiting time), SE9 (schedule for revisit), and SE8 (medical bill). For intention to recommend others, there is immense decrease at SE5 (medical treatment) and SE8 (medical bill) for survival rate. We, in this research, focus on process approach not on multi-dimensional service attributes in analyzing service delivery systems. We present a methodology design for quantitative analysis of service delivery systems. Our results allow service organization to evaluate its service level at each service stage and propose strategic guideline for its improvement.

참고자료

· 없음