메뉴 건너뛰기
.. 내서재 .. 알림
소속 기관/학교 인증
인증하면 논문, 학술자료 등을  무료로 열람할 수 있어요.
한국대학교, 누리자동차, 시립도서관 등 나의 기관을 확인해보세요
(국내 대학 90% 이상 구독 중)
로그인 회원가입 고객센터 ENG
주제분류

추천
검색

논문 기본 정보

자료유형
학술저널
저자정보
김유규 (한국산업기술대학교) 양우령 (한국산업기술대학교) 김하룡 (안양대학교) 양회창 (장안대학교)
저널정보
국제융합경영학회 The Journals of Economics, Marketing & Management 융합경영연구 제5권 제1호
발행연도
2017.3
수록면
21 - 26 (6page)
DOI
http://doi.org/10.20482/jemm.2017.5.1.21

이용수

표지
📌
연구주제
📖
연구배경
🔬
연구방법
🏆
연구결과
AI에게 요청하기
추천
검색

초록· 키워드

오류제보하기
Purpose - This study extracts performance-reward factors based on the previous studies related to Herzberg’s two-factor theory and performance-reward and proposes a research method to identify how these factors have an influence on task performance directly related to production performance and contextual performance that has an indirect influence. Research Design, Data, and Methodology - This study draws performance-reward factors through Focus Group Interview(FGI), classifies them into economic/uneconomic and direct/indirect factors, draws maintenance/improvement factors and unnecessary ones through IPA, and maximizes the effectiveness of performance-reward factors. Results - It also identifies how performance-reward factors have an influence on internal and external motives based on previous studies, classifies performance-reward factors into task performance and contextual performance and identifies the influence relationship between these, and proposes a research model to identify the roles of equity sensitivity based on equity theory. Conclusion - The findings from this study are expected to lay the groundwork for drawing various methods to reduce the turnover rate of employees and be important resources for reinforcing the competitiveness of businesses by classifying the performance -reward factors that may cause internal and external motives from the small and medium-sized manufacturing perspective and presenting methods to identify if these have an influence on task performance and contextual performance.

목차

등록된 정보가 없습니다.

참고문헌 (17)

참고문헌 신청

함께 읽어보면 좋을 논문

논문 유사도에 따라 DBpia 가 추천하는 논문입니다. 함께 보면 좋을 연관 논문을 확인해보세요!

이 논문의 저자 정보

최근 본 자료

전체보기

댓글(0)

0