Analyzing the dynamics between organizational culture and change : a case study of China Central Television (CCTV) in transition

The Thesis sets out to analyze CCTV's transition from 1979-2003 with a special focus on its most influential reform entitled Producer Responsibility System (PRS). In order to present a real picture of CCTV's organizational culture, this research uses multiple research methods to synthesize valuable contributions from two schools of organizational culture theory driven by different research orientations. Data collection methods include a6 months' ethnographic research project inside CCTV. The research has two main research findings. First, following the introduction of PRS, the reform process has been uneven. A split has emerged at CCTV between an 'inner' and an 'outer' management circles, with very different organizational cultures and responses to organizational change. Second, the research identifies four logics which have shaped CCTV's organizational culture: Party logic, Commercial logic, Professional logic and Social and ethnic logic. CCTV's transition has been defined by a complex interaction and negotiation between these four logics. This thesis summarizes CCTV's organizational change from 1979-2003 into three stages, from a 'frozen' status to 'change by exception' and then to 'incremental change'. Analysis of the relationship between these four logics suggests that to achieve a real transition from Party mouthpiece to modem media enterprise, CCTV needs to achieve a new 'paradigm change'. The key to the success of this 'paradigm change' will be a systematic reconstruction of CCTV's organizational culture based on the central objective of building media professionalism. The single case study places some limits on the generalizability of the findings but other Chinese media businesses share a similar economic, historical and cultural context. The problems at CCTV can thus be seen to be representative general issues of the Chinese media industry in transition

Adaptive Federated Learning via Entropy Approach

Federated Learning (FL) has recently emerged as a popular framework, which allows resource-constrained discrete clients to cooperatively learn the global model under the orchestration of a central server while storing privacy-sensitive data locally. However, due to the difference in equipment and data divergence of heterogeneous clients, there will be parameter deviation between local models, resulting in a slow convergence rate and a reduction of the accuracy of the global model. The current FL algorithms use the static client learning strategy pervasively and can not adapt to the dynamic training parameters of different clients. In this paper, by considering the deviation between different local model parameters, we propose an adaptive learning rate scheme for each client based on entropy theory to alleviate the deviation between heterogeneous clients and achieve fast convergence of the global model. It's difficult to design the optimal dynamic learning rate for each client as the local information of other clients is unknown, especially during the local training epochs without communications between local clients and the central server. To enable a decentralized learning rate design for each client, we first introduce mean-field schemes to estimate the terms related to other clients' local model parameters. Then the decentralized adaptive learning rate for each client is obtained in closed form by constructing the Hamilton equation. Moreover, we prove that there exist fixed point solutions for the mean-field estimators, and an algorithm is proposed to obtain them. Finally, extensive experimental results on real datasets show that our algorithm can effectively eliminate the deviation between local model parameters compared to other recent FL algorithms.Comment: 7 pages, 4 figure