Mechanistic vs. Organic Designs Structure Dimension Mechanistic Organic Stability Change unlikely Change likely Specialization Many specialists Many generalists Formal rules Rigid rules Considerable flexibility Authority Centralized in a few top people Decentralized, diffused throughout the organization Table 12-2
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RISK ASSESSMENT Reliotron III Cathodoluminescence Instrument SMALL ERAMIC JAW CRUSHER HAZARD TYPE Exposure to noise 59-71 (dB9A) X-ray exposure Ethanol (for cleaning microscope optics) DESCRIPTION PERSONS AT RISK FACTOR CONTROLS IN RISK FACTOR PLACE OF RISK WITHOUT CONTROLS WITH CONTROLS IN PLACE IN PLACE HAZARD TO LOWER RISK Prolonged Low level noise. > 2 Hours Low level radiation damage. Risk to pregnant women and cancer patients Current Operator, Person in Charge, Contractors. CONSEQUENCE Negligible hearing loss LIKELIHOOD L4 Unlikely CONSEQUENCE Current B5 Severe injury Operator, LIKELIHOOD Person in L5 Unlikely Charge, Contractors. High Highly Flammable Current Operator. CONSEQUENCE E Negligible LIKELIHOOD L5 Highly unlikely No hearing Protection required Low Lead Glass and sample holder prevents XRay emission Minimum amount and fire blanket Low Acetone Inhalation and skin exposure (during leak testing) Highly flammable Inhalation Irritation to eyes and skin P8 Electrical injury Electrical Hazard Contact resulting in current flowing through the body Current Operator. www.monash.edu/ohs/ CONSEQUENCE B5 Negligible injury LIKELIHOOD L5 Highly Unlikely Low CONSEQUENCE E Negligible LIKELIHOOD L5 Highly unlikely Low Correct storage CONSEQUENCE of acetone in E Negligible spray bottle, LIKELIHOOD not contact L5 with eyes and Highly unlikely skin. Low Current CONSEQUENCE Operation only with with gun Operator, A Major Person in LIKELIHOOD covers on and electronics Charge, L5 Highly cabinets in Contractors. Unlikely place. Risk Assessment Completed By: CONSEQUENCE C5 Negligible injury LIKELIHOOD L4 Unlikely High CONSEQUENCE C5 Negligible injury LIKELIHOOD L4 Unlikely Low CONSEQUENCE E Negligible injury LIKELIHOOD L5 Highly Unlikely Low Signed:
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Slide 14.6 Mechanistic and Organic Organizations  Mechanistic organization  Characterized by a reliance on formal rules and regulations, centralization of decision making, narrowly defined job responsibilities, and a rigid hierarchy of authority  Organic organization  Characterized by low to moderate use of formal rules and regulations, decentralized and shared decision making, broadly defined job responsibilities, and a flexible authority structure with fewer levels in the hierarchy Chapter 14: Designing Organizati 8
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Study Question 1: What are the essentials of organizational design?  Mechanistic Designs – Predictable goals – Centralized authority – Many rules and procedures – Narrow spans of control – Specialized tasks – Few teams and task forces – Formal and impersonal means of coordination  Organic Designs – Adaptable goals – Decentralized authority – Few rules and procedures – Wide spans of control – Shared tasks – Many teams and task forces – Informal and personal means of coordination Management 8/e - Chapter 11 9
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Centralized and Decentralized Authority Organizations that share relatively little authority are centralized. Organizations that share a lot of authority are said to be decentralized. Supervisors who know whether their employer has a centralized or decentralized structure understand how much authority they can expect to have. McGraw-Hill/Irwin 7-7 © 2006 The McGraw-Hill Companies, Inc. All rights rese
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Key Terms accountability authority centralized organization chain of command committee and team authority customer departmentalization decentralized organization delegation departmentalization division divisional structure flat organizational structure functional departmentalization functional structure geographic departmentalization grapevine informal organization international organizational structures intrapreneuring job specialization line authority line department matrix structure organization chart organizational structure process departmentalization product departmentalization © 2009 Pearson Education, Inc.
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If a patch is randomly picked from a test protein, what is the probability (Prandom) to obtain a patch that is at least as good as the top 1 patch in terms of predicting the DNA-binding sites? For each test protein, Prandom is calculated as N/Nall , where Nall is the total number of patches on the protein, N is the number of the patches that have at least as many interface residues as the top 1 patch. The results (Table 2) show that for 9 of the 13 proteins, Prandom is less than 10%. The average Prandom for the 13 protein is 9.8%. This indicates the significance of the predicting method. Obtaining higher coverage by combining multiple top-ranking patches In the evaluation of DNA-binding site prediction methods, there are mainly two measures that researchers would be interested in: coverage (TP/Nint) and accuracy (TP/Npr), where TP is true positive, i.e. the number of residues that are predicted to be interface residues and are actually interface residues, Nint is the total number of interface residues and Npr is the number of residues that are predicted to be interface residues. Coverage shows percentage of the actual interface residues correctly predicted and accuracy is the percentage of the predicted interface residues that are actually interface residues. Fewer top-ranking patches are used More top-ranking patches are used The above section has shown that the top 1 patch can precisely indicate the location of the DNA-binding site on each test protein. However, since a patch has only 6 residues, the predictions solely based on the top 1 patch only have low coverage. We can obtain higher coverage by combining the predictions of multiple top-ranking patches. For example, if only top 1 patch is used to predict DNA-binding sites, the average coverage and accuracy are 23% and 81% for the 13 proteins. When the union of the top 3 patches is used to predict DNA-binding sites, coverage increases to 42%, but accuracy decreases to 72%. Figure 1 shows the tradeoff between coverage and accuracy when multiple top-ranking patches are used. Figure 1 shows the trend that as more top-ranking patches are used, coverage increases but accuracy decreases. If researchers prefer to identify more interface residues at the cost of lower accuracy, then they can choose to use more top-ranking patches to predict DNA-binding sites. The performance will fall at the right side of the curve. On the other hand, if they desire higher accuracy, then they can use fewer patches. Table 2. Predictions by the top 1 patch Unbound Bound Top 1 patch PRandom (%) 4 PDB id 1PDB id TP2 FP3 1iknA,C 1leiA,B 6 0 0.4 1g6nA,B 1zrfA,B 6 0 2.4 1zzkA 1zziA 6 0 2.9 1mml 1ztwA 4 2 4.2 1a2pC 1brnL 6 0 4.5 1ko9A 1m3qA 6 0 4.9 1qc9A 1cl8A,B 4 2 8.0 1lqc 1l1mA,B 6 0 8.7 1qzqA 1rfiB 2 4 9.5 1xx8A 1xyiA 6 0 10.7 1qqiA 1gxpA,B 4 2 19.3 1l3kA 1u1qA 3 3 25.6 2alcA 1f5eP 4 2 26.7 For the proteins in gray shading, the interfaces were predicted using only PSSM. For the others, all seven features were used; 2 TP: number of interface residues in the top 1 patch; 3 FP: number of non-interface residues in the top 1 patch. 4 Prandom: When a patch is
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2) Create Relationships Between Related Tables • In Diagram View, You can drag &drop fields to create one-to-many relationships between Lookup Table (Dimension Table) and Transaction Table (Fact Table) • Although there are other types of relationships, we are only creating One to Many Relationships. • One to Many Relationship means that the Lookup Table (Dimension Table) has a unique list in the first column and the Transaction Table (Fact Table) can have many repeats in its related column. • Relationships allow us to: 1. 2. Drag fields from multiple tables in our PivotTable Field List. Build DAX formulas that can simultaneously work with related tables. • With Relationships, Filters/Criteria flow down from the Lookup Table (Dimension Table) to the Transaction Table (Fact Table) and actually filter the Transaction Table (Fact Table) to cause it to have fewer rows “showing”. This is one aspect of the Columnar Database that contributes to efficient formula calculation. 16
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Probability of Treatment Modality for Orthopedic Specialists Compared to Primary Care Specialists • Orthopedic specialists were -11.6 percentage points less likely to prescribe an opioid than primary care specialists. • Orthopedic specialists were +15.4 percentage points more likely to order physical therapy than primary care specialists. • There was no difference between specialty for the likelihood of prescribing non-opioid analgesics. +15.4 percentage points No difference -11.6 percentage points
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Mechanistic versus Organic Systems Mechanistic systems Highly centralized organizations in which decision-making authority rests with top management. Organic systems Decentralized organizations that push decision making to the lowest levels of the organization in an effort to respond more effectively to environmental change.
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Distributing Authority: Centralization and Decentralization Establishment of a Decision-Making Hierarchy – deciding who will be empowered to make which decisions and who will have authority over others Centralized Organization – organization in which most decision-making authority is held by upper-level management Decentralized Organization – organization in which a great deal of decision-making authority is delegated to levels of management at points below the top Copyright © 2017, 2015, 2013 Pearson Education, Inc. All Rights Reserved
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