Based on a review of previous research of organizational learning from experience, this paper aims to point out the notable gaps and unresolved issues in the research area and proposes a “multilevel integrated model of learning from experience”, which could integrate current research findings and serve as the theoretical framework for further investigation.
This paper is a theoretical review.
From the individual, team, organizational and multiple levels, in an order of the outcome of success and failure, this study reviews previous research about organizational learning from experience down to the last detail and points out some of their limitations, including relative fragmented-wise, lack of grope about the underlying motivations, lack of overall framework, etc. Then, this study proposes the “multilevel integrated model of learning from experience”, which provides a systematic and fine-grained framework for studies in this field.
This paper emphasizes that true underlying motivations impelling learning from experience shall be identified and exploration for the antecedents shall be further deepened. Besides, this study figures out that various factors played their parts in the process and outcome of learning from experience through both subjective perception and objective experience. Thus, future research shall distinguish the influence of learning from experience, respectively, into “knowing” and “doing”.
This study is an attempt to review and integrate current research of learning from experience in multiple levels and further differentiates the influences of different experience outcomes (success vs failure). The proposed theoretical model provides clear suggestions of where future research should be directed.
The authors acknowledge financial support from the National Natural Science Foundation of China (No. 71421061, 71121001 and 70625003).
Chen, G., Zhou, Q. and Liu, W. (2017), "Organizational learning from experience: Current status in multilevel perspective, integration model and future direction", Nankai Business Review International, Vol. 8 No. 2, pp. 122-157. https://doi.org/10.1108/NBRI-01-2017-0006Download as .RIS
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