Earticle

초록

영어

It is now well established that innovation and knowledge management can improve firm’s performance. Whether innovation leads to firm’s performance, however, what kind of factors affect the innovations and how knowledge is related to innovation remain largely uninvestigated. In this study, we investigate what are the key factors for facilitating innovations and how these factors are formed in the case of Knowledge Intensive Business Services (KIBS) firms. Drawing upon interactivity, we identify service standardization, interactive learning, innovation participation as significant factors that facilitate innovations. We examine the role of interactivity in shaping innovations as a collaborative process between KIBS firms and client firms and its subsequent impact on firm performance. This study also examines the effect of knowledge types (tacit or explicit) on the relationship between interactivity and service innovation. We collected data from 91 IT Service Management firms from a field experiment. Our results provide empirical support for our proposed model. The results show that interactivity may be a significant indicator of innovation within KIBS firms. In the moderating effect, knowledge type does have any direct effect on innovations and only in the case of service innovation. Our intended contribution is to help both managers and academics understand aspects of interactivity and knowledge types in innovation processes. This study also presents suggestions for future research regarding a critical role in service science.

목차

Abstract
 1. Introduction
 2. Literature Review
  2.1 Knowledge Intensive Business Service
  2.2 Interactivity: Dynamic Process
  2.3 Knowledge Type and Its Effect
 3. Research Model and Hypotheses
  3.1 The Effect of Interactivity on Innovation inKIBS firms
  3.2 The effect of knowledge types betweeninteractivity and service innovation
  3.3 Service innovation on firm performance
 4. Research Methodology
  4.1 Sample and Data
  4.2 Operationalization of research variables
 5. Result
  5.1 Data Analysis
  5.2 Measurement model: Reliability and Validity
  5.3 Structural Model Test
 6. Discussion and Conclusion
 7. References

저자

• Yong Jin Kim [ Sogang University ]
• Kichan Nam [ Sogang University ]
• Nam Hee Lee [ Sogang University ]
• Myung Song Yim [ Sogang University ]
• Jae Ki Song [ Texas Tech University/Sogang University ]

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