How to Co-operate in Business without Really Trying: A Learning Model of Decentralized Decision Making

1968 ◽  
Vol 76 (4, Part 1) ◽  
pp. 583-600 ◽  
Author(s):  
Richard H. Day ◽  
E. Herbert Tinney
2012 ◽  
Vol 20 (6) ◽  
pp. 1142-1151 ◽  
Author(s):  
Andrea Bernardes ◽  
Greta Cummings ◽  
Yolanda Dora Martinez Évora ◽  
Carmen Silvia Gabriel

OBJECTIVE: This study aims to address difficulties reported by the nursing team during the process of changing the management model in a public hospital in Brazil. METHODS: This qualitative study used thematic content analysis as proposed by Bardin, and data were analyzed using the theoretical framework of Bolman and Deal. RESULTS: The vertical implementation of Participatory Management contradicted its underlying philosophy and thereby negatively influenced employee acceptance of the change. The decentralized structure of the Participatory Management Model was implemented but shared decision-making was only partially utilized. Despite facilitation of the communication process within the unit, more significant difficulties arose from lack of communication inter-unit. Values and principals need to be shared by teams, however, that will happens only if managers restructure accountabilities changing job descriptions of all team members. CONCLUSION: Innovative management models that depart from the premise of decentralized decision-making and increased communication encourage accountability, increased motivation and satisfaction, and contribute to improving the quality of care. The contribution of the study is that it describes the complexity of implementing an innovative management model, examines dissent and intentionally acknowledges the difficulties faced by employees in the organization.


2020 ◽  
Vol 5 (1) ◽  
pp. 60-63
Author(s):  
Eulis Sopia Fardiani ◽  
Yogi Nugraha ◽  
Nadya Putri Saylendra

This study aims to improve the critical thinking skills of students of class XI IPA 2 MAN 2 Karawang on PPKn subjects through the Decision Making learning model. The use of this learning model is one of the efforts to improve critical thinking skills of students of class XI IPA 2 MAN 2 Karawang on PPKn subjects. The research method used in this study is the Classroom Action Research (CAR) method, which is research conducted by teachers in their own class with the aim of improving their performance as teachers, so that student learning outcomes become more improved. The PTK model used is the Kemmis & Mc model. Taggart which consists of planning, implementation, observation, and reflection. The instruments used are test results, observation, and interviews. Learning outcomes tests are used to measure students' success in critical thinking skills in the subject of analyzing cases of threats to ideology, politics, economics, socio-culture, defense and security and strategies to overcome them in the frame of Bhineka Tungga Ika). Observation and interview using observation format and interview format. The results showed that the use of the Decision Making learning model can improve students' critical thinking skills in PPKn subjects. From the pre-action class average value 28.76, it becomes 49.41 in the first cycle, 67.53 in the second cycle, and 91.79 in the third cycle..


2021 ◽  
Vol 336 ◽  
pp. 09004
Author(s):  
Yuxin Wen ◽  
Linyi Wu ◽  
Fengmin Yao

Affected by factors such as cost, the financial constraints faced by the supply chain are becoming more and more severe. This paper constructs a financing and pricing decision-making model for the construction supply chain under capital constraints, and uses Stackelberg game theory to analyze and obtain the best financing and pricing strategy for the construction supply chain under the internal and external financing modes. The study found that when centralized decision-making is adopted, there is a profit distribution model that makes the profits obtained by construction developers and contractors greater than the profits obtained in decentralized decision-making; the internal financing model of the construction supply chain is better than external financing, and can enable the construction supply chain get higher profits.


2017 ◽  
Author(s):  
Joshua Skewes ◽  
Dorthe Døjbak Håkonsson ◽  
Trine Bilde ◽  
Andreas Roepstorff

Collaborative decision making is central to the organization of society. Juries deliberate cases, voters elect government officials, open innovation networks converge on innovative solutions. It is common to think of such groups as decision making entities. But this language is imprecise. Real decision processes do not occur within any group or organization as an abstract entity. Collaborative decision making happens within and between autonomous individuals. This emphasizes the importance of the relationships between individual and social decision-making processes to social organization. Despite a rich body of literature on collaborative decision making we know little about how individuals decide to commit to group decision making in the first place, and how, once joined, they communicate their distributed information for optimal group performance. We introduce a general framework designed to model collaborative decision processes. Our main results are that 1) commitment and gain is enhanced when groups are designed so agents have realistic knowledge about the forgone gains and losses associated with abstaining from the group; and 2) that this effect is accelerated when communication between group members conveys more information about individual preferences. We thus demonstrate that collaborative decision making is done best when it is done by groups that are informationally open.


2021 ◽  
Author(s):  
Yew Kee Wong

Deep learning is a type of machine learning that trains a computer to perform human-like tasks, such as recognizing speech, identifying images or making predictions. Instead of organizing data to run through predefined equations, deep learning sets up basic parameters about the data and trains the computer to learn on its own by recognizing patterns using many layers of processing. This paper aims to illustrate some of the different deep learning algorithms and methods which can be applied to artificial intelligence analysis, as well as the opportunities provided by the application in various decision making domains.


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