scholarly journals On the Determinants of Organizational Forgetting

2011 ◽  
Vol 3 (3) ◽  
pp. 100-123 ◽  
Author(s):  
Guy David ◽  
Tanguy Brachet

Studies of organizational learning and forgetting identify potential channels through which the firm's production experience is lost. These channels have differing implications for efficient resource allocation within the firm, but their relative importance has been ignored to date. We develop a framework for distinguishing the contributions of labor turnover and human capital depreciation to organizational forgetting. We apply our framework to a novel dataset of ambulance companies and their workforce. We find evidence of organizational forgetting, which results from skill decay and turnover effects. The latter has twice the magnitude of the former. (JEL D23, D83, J24, J63)

2009 ◽  
Vol E92-B (2) ◽  
pp. 533-543 ◽  
Author(s):  
Jae Soong LEE ◽  
Jae Young LEE ◽  
Soobin LEE ◽  
Hwang Soo LEE

2020 ◽  
Vol 13 (5) ◽  
pp. 1008-1019
Author(s):  
N. Vijayaraj ◽  
T. Senthil Murugan

Background: Number of resource allocation and bidding schemes had been enormously arrived for on demand supply scheme of cloud services. But accessing and presenting the Cloud services depending on the reputation would not produce fair result in cloud computing. Since the cloud users not only looking for the efficient services but in major they look towards the cost. So here there is a way of introducing the bidding option system that includes efficient user centric behavior analysis model to render the cloud services and resource allocation with low cost. Objective: The allocation of resources is not flexible and dynamic for the users in the recent days. This gave me the key idea and generated as a problem statement for my proposed work. Methods: An online auction framework that ensures multi bidding mechanism which utilizes user centric behavioral analysis to produce the efficient and reliable usage of cloud resources according to the user choice. Results: we implement Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis. Thus the algorithm is implemented and system is designed in such a way to provide better allocation of cloud resources which ensures bidding and user behavior. Conclusion: Thus the algorithm Efficient Resource Allocation using Multi Bidding Model with User Centric Behavior Analysis is implemented & system is designed in such a way to provide better allocation of cloud resources which ensures bidding, user behavior. The user bid data is trained accordingly such that to produce efficient resource utilization. Further the work can be taken towards data analytics and prediction of user behavior while allocating the cloud resources.


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