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The purpose of this paper is to study the criteria of R & D commercialization capability by using the successful cases from government research institutes.

The data were collected from 272 entrepreneurs and researchers with a structured questionnaire. The data analysis was carried out by the structural equation modeling (SEM).

The research results revealed that there are six criteria for R & D project commercialization capability, these are arranged according to the significance; marketing, technology, finance, non-financial impact, intellectual property, and human resource. Moreover, the evaluator’s roles, both researchers and entrepreneurs, effect the level of criteria for consideration.

This research is derived from samples form voluntary participants from the disclosed lists in the governmental research institutes. Although SEM results provide weight for each R & D commercialization capability that would be the first step for developing the R & D evaluation instrument and the longitudinal will need to investigate.

This study provides the holistic R & D commercialization capability criteria to assist entrepreneurs and researchers when faced with R & D commercialization decision. The criteria was developed from successful innovation cases; hence they enhance decision-making potential, provide a guideline of R & D commercialization evaluation process, speed up the decision-making process, and prevent risk of a resource meltdown and increase innovation exploitation.

The roles of the evaluators are important when considering R & D, hence, to use the same criteria for evaluating the researchers and entrepreneurs seems inappropriate since naturally, the researchers usually lack marketing and financial skills while the entrepreneurs usually lack technological skills and human resources. The answer may be in the form of providing specifics about project requirement, establishment of marketing and financial consultants in research institutes, enhancement of research and development division, or doing the business matching before doing the agreement in order to encourage co-research and increase the technology transfer level.

This paper proposes the holistic criteria in order to decrease the ambiguous subjectivity of fuzzy-expert system and to help with effectively funding R & D and to prevent a resource meltdown.

This purpose of this paper is to synthesize and propose the indicators of knowledge management capability (KMC) in different knowledge management (KM) processes to assess KM effectiveness. It also intends to provide useful indicators for those who are interested in the study of KMC to create effective KM, who can utilize the aforementioned indicators as guidelines in the development of empirical definitions by testing them.

This paper is a literature review research, through which indicators of KMC for KM effectiveness are synthesized, utilizing related documents, literature and other research studies and the characteristics of which are evaluated by the KM experts as specified in qualitative research.

The results of the research suggest two main aspects of KMC for KM effectiveness: first, a resource‐based perspective, which comprises technology, structure and culture; and second, a knowledge‐based perspective, which comprises expertise, learning and information. It is suggested that there are 84 indicators in KMC for KM effectiveness, which can be divided into: 22 indicators on KMC‐knowledge acquisition; 21 indicators on KMC‐knowledge creation; 19 indicators on KMC‐knowledge storage; and 22 indicators on KMC‐knowledge application.

Apparently the existing research concerning KMC does not reveal clear conclusions nor designate indicators of KMC in both aspects: resource‐based perspective and knowledge‐based perspective. The consequence is a lack of direction and precision in developing KMC to achieve its effectiveness. This paper therefore provides clear visions on important aspects of KMC whereby the various indicators of their components need to be developed to enrich the concept and further the development of KM. It also provides future researchers with useful means to assess the KM effectiveness in different KM processes.