management and decision sciences from various theorists; and, analyzes the evolution of managerial decision making from scientific management to the complicated forecasting models used today. The objectives of the Breadth component were four-fold: (a) to examine the theories of applied management and decision sciences as interpreted by the research of Ducker (1974), Harrison (1975), and others as listed in the reference section; (b) to analyze the historic evolution of decision making from scientific management to modern applications of operations research; (c) to examine the decision making process, with a particular emphasis on the importance of values and management judgment; and (d) to describe, assess, and evaluate various decision evaluation tools including matrix analysis, influence diagrams, payoff matrices, sensitivity analysis, decision tree, probabilistic forecasting, and multi-attribute utility analysis. To accomplish these goals, a paper is provided that addresses each of the foregoing in turn, followed by a summary of the research and important findings in the conclusion to the paper.
Depth Abstract
This component evaluates the usefulness of various tools formed to enhance decision making in management, particularly in terms of their applicability to decision making in university circles as well as critically assess recent research which addresses the application of diverse decision methodologies. The objectives of the Depth component were four-fold: (a) to explore and assess recent research-based knowledge concerning the role of quantitative models and tools in higher education decision making; (b) to describe the prevalent models currently used in most universities and judge their relative merits; (c) to evaluate the significance of each of the decision-making methods explained in the breadth component for higher education administration; and (d) to describe the management fads which have evolved through university administration and critically analyze why each had failed. To achieve these goals, an annotated bibliography of 15 relevant peer-reviewed journal articles addressing the application of various decision methodologies to higher education administration is provided followed by a paper that corresponds to the evolution of management techniques in higher education with the evolution of methods used in businesses described in the Breadth component is provided, followed by a summary of the research and important findings in the conclusion to the paper.
Application Abstract
The final component uses the pertinent decision making tools identified and discussed in the Breadth and Depth components to” (a) examine the decision making process used by Zomba University Isoka Campus in launching a new program and apply the decision science theories that were learned and demonstrated in the preceding components; and (b) to develop a decision model for analyzing the feasibility of a new program at Zomba University Isoka Campus and make recommendations to the university’s director. To achieve these goals, this component uses the decision-making models analyzed and discussed in the Breadth and Depth components together with additional relevant materials to develop an analysis of the decision to launch a specified new program. Following the assessment of the implications of the new program, this component proposes a decision-making methodology to the university’s director for use as a protocol for evaluating other programs that will be launched in the future.
Applied Management and Decision Sciences
Breadth Component
The first part, the breadth component, will examine theories of applied management and decision sciences from various theorists; analyze the evolution of managerial decision making from scientific management to the complicated forecasting models used today. Second part, the depth component will evaluate the usefulness of various tools formed to enhance decision making in management, particularly in terms of their applicability to decision making in university circles as well as critically assess recent research which addresses the application of diverse decision methodologies. The third part, application component will utilize the pertinent decision making tools to assess the feasibility of a new program at Zomba University Isoka Campus.
PART 1: The Breadth Component
A.
Objectives. The objectives of this part are four-fold as follows: (a) to examine the theories of applied management and decision sciences as interpreted by the research of Ducker (1974), Harrison (1975), and others as listed in the reference section; (b) to analyze the historic evolution of decision making from scientific management to modern applications of operations research; (c) to examine the decision making process, with a particular emphasis on the importance of values and management judgment; and (d) to describe, assess, and evaluate various decision evaluation tools including matrix analysis, influence diagrams, payoff matrices, sensitivity analysis, decision tree, probabilistic forecasting, and multi-attribute utility analysis.
B.
Learning Resources. The materials to be reviewed and interpreted in this part include, but are not limited to, the resources listed in the reference page.
C.
Criteria for Evaluation. In a paper of approximately 30 pages, first, I will discuss the components of a good decision, as articulated by aforementioned theorists. Second, I will trace the historic evolution of management thought, compare each approach, and critique the implications of each on the management decision making process. Third, I will describe the steps in decision making process, focusing on the importance of values and management judgment. Finally, I will describe various decision evaluation tools and evaluate each tool in terms of its strengths and weaknesses, its relationships to other tools, and its incorporation into management judgment.
Review of the Relevant Literature
Theories of applied management and decision sciences as interpreted by the research of Drucker (1954, 1974, 1985), Newman (1971), Harrison (1975), and others.
Peter F. Drucker. One of the leading “gurus” of management theory, the late-great Peter F. Drucker has received accolades from all over the world for his insightful analyses of how businesses work in the real world and how theoretical constructs can be applied to them in meaningful ways. According to Boudreaux (2005), “Peter F. Drucker died on November 11, 2005 at the age of 95 at his home in Claremont, California. Often called the father of management and the world’s most influential business thinker, his work influenced businesses and organizations large and small. Notable corporate giants, including Jack Welch and Bill Gates, acknowledge Drucker’s profound influence” (p. 18). Drucker (1974; p. 400) reports that motivation represents one of the fundamental responsibilities of management. In addition, Drucker is almost fanatical about using reliable information upon which to base informed decision, including the demographics of the targeted population of an organization of whatever type it might be. For instance, according to Drucker (1985), “It is sheer folly to disregard demographics. The basic assumption for our time must be that populations are inherently unstable and subject to sudden sharp changes — and that they are the first environmental factor that a decision maker, whether businessman or politician, analyzes and thinks through” (p. 90). These insights suggest that some decision-making tools are better suited for a given analysis than others, but selecting the correct variables for use in the decision-making process is equally important as well.
Although much has changed in terms of computer-based applications that can be used to facilitate the decision-making process, the underlying tenets of his advice remain salient today. In this regard, Drucker (1985) advises that his so-called “Business X-Ray” can provide decision makers with a useful analysis of their organizations as well as its products and services, its markets, as well as the technologies upon which it relies to remain competitive. For instance, Drucker (1985) notes that, “The Business X-Ray is a tool for decision making. It enables us, indeed forces us, to allocate resources to results in the existing business. But it also makes it possible for us to determine how much is needed to create the business of tomorrow and its new products, new services, and new markets. It enables us to turn innovative intentions into innovative performance” (p. 155).
In order to achieve this optimal level of organizational performance, though, means that management must assume a leading role in making its own products and/or services obsolete instead of waiting for its competition to do so. In this regard, Drucker advises, “The business must be managed so as to perceive in the new an opportunity rather than a threat. It must be managed to work today on the products, services, processes, and technologies that will make a different tomorrow” (1985, p. 155). Drucker’s philosophy of management reached its mature form in his book, Management: Tasks, Responsibilities, Practices (1974). According to Witzel (2003), in this book, Drucker “begins with the notion that managers should move away from the idea of managing processes and instead seek to manage for results. He emphasizes the central importance of the manager to the business enterprise; the manager’s function is that of a catalyst, pulling together the otherwise static resources of production and making them active” (p. 76).
The purpose of all decisions is to compel an action of some type and it is Drucker’s view that it is the decision maker who is the driving force behind organizational change. In this regard, Witzel cites Drucker’s work and states, “Quite literally, it is the manager who breathes life into the enterprise and makes it function. It was possible that in the future workers would become redundant, having been replaced by automation; but machines could never replace the spark of life provided by management” (p. 76). As automation increasingly assumes the more mundane and routine aspects of work of all types, Drucker was visionary in his assessment of how decisions would be made in the years to come. “In the future,” said Drucker, “it was possible that all employment would be managerial in nature, and we would then have progressed from a society of labor to a society of management” (Witzel, p. 76). The first tasks of the manager, then, are to coordinate an organization’s resources and provide a viable framework in which they can be used to produce goods and services effectively and efficiently. The second set of tasks concern guidance and control. In Drucker’s view, this role is almost entirely proactive: “Economic forces set limits to what a manager can do. They create opportunities for management’s action. But they do not by themselves dictate what a business is or what it does” (Drucker, 1974, p. 88).
In a well-known statement, Drucker added that assigns to managers the primary role not only for creating the enterprise but also for creating its markets: “There is only one valid definition of business purpose: to create a customer. Markets are not created by God, nature or economic forces, but by the people who manage a business. The want a business satisfies may have been felt by customers & #8230;but it remained a potential want until business people converted it into effective action. Only then are there customers and a market” (Drucker, 1974, p. 89).
Drucker established that management is legitimate field of endeavor that had not existed previously. It is neither an art nor a science, but a profession, akin to medicine or law, and one that is even more complicated. What is management? As he said again and again, it is about people: “Management’s task is to make people capable of joint performance, to make their strengths effective and weaknesses irrelevant.” Management defines and communicates common goals and values, by creating the right organizational structure and providing the training and development that employees need to respond effectively to change. Unlike the doctor or lawyer, the manager functions only through others, and in fact, through many others. Management is not simply getting other people to perform, but getting many others to perform in a joint, orchestrated process. This is why it is more complicated. The only real tools the manager has are communication, organizational design, and training and development (Boudreaux, p. 19).
According to East (1997), Drucker characterized decisions as falling into “generic,” “new generic,'” and “exceptional decisions” categories; East uses a comparable approach and classifies them into routine, problem-solving, and innovative categories which are described further in Table __ below as they apply to academic institutions.
Table
Topological Structure of Decisions
Type of Decision
Description
Routine Decisions
Routine, or generic, decisions are those decisions that carry the unit forward in daily operation. These decisions are characterized as high-volume, low-impact types of decisions. The dilemmas are fairly straightforward and the consequences of the action are reversible – for example, adding an additional class to the course schedule, purchasing routine supplies, and signing add cards.
Problem-solving Decisions
Problem-solving, or “new” generic, decisions involve repetitive circumstances that require unique solutions. Because problem-solving dilemmas are repetitive, operational procedures often are established within organizations to facilitate solutions to “new” generic problems. For example, the tenure/promotion of a faculty member is a repetitive event that requires a relatively unique solution. Although the procedures and criteria used to solve these dilemmas are fairly well established, the administrator must interpret these criteria and apply them to a specific faculty member.
Innovative Decisions
Innovative, or exceptional, decisions represent significant paradigm shifts within the organization. Seldom are there precedents for these decisions, and the referent information is often subject to extraneous or strategic misrepresentation. Many curricular decisions may be classified as innovative, that is, adding a new doctoral program in health sciences, terminating a major program of study, or revising the general education core curriculum. These decisions usually involve significant risks for the decision maker; however, the potential benefits of controlled change often outweigh these risks. Innovative decisions normally involve a protracted period of information gathering, and often simulate the characteristic process of consensus building.
Source: East, 1997, p. 40.
Frederick W. Taylor. Taylor was the originator of Scientific Management at the turn of the 20th century, a period in which enormous industrialization would result in the ubiquitous adoption of assembly-line techniques production in the United States. His “task-management” approach greatly expanded management’s role in the performance of traditional tasks and also grafted itself onto the new emerging tasks. Traditional rules of thumb transmitted orally from worker to worker were to be replaced by rational methods communicated to employees by managers using written instructions and precise recordkeeping. By contrast, Performance Management has fully emerged in the U.S. during a time of relative deindustrialization and a corresponding development of an economy based on services and information. The decline — or rather, the expatriation — of the steel and manufacturing industries, once almost synonymous with the U.S. economy, has cut a rusty gash across the countryside. Since the early 1970s, U.S. firms of all sorts have sought less expensive labor pools and less restrictive regulation in Latin America and the Asian Pacific rim. While the industrially-based economy has declined in the U.S., another has arisen, that of the “service” or “information economy.” Performance Management has become the organizational theory of the new information economy, and in this role it has relied heavily upon a school of management known as “information processing and decision-making” or IPDM.
As its name suggests, IPDM theorists focus on the gathering, storage, and transmission of information, and, more importantly, on the decision-making processes that depend upon it, processes that include identifying problem situations, developing possible solutions, choosing a course of action, and evaluating past decisions. Writing in 1975, E. Frank Harrison sketches this genealogy of IPDM: “Decision theory as an academic discipline is still relatively young. It is only since the Second World War that operations research, statistical analysis, and computer programming have imparted a ‘scientific’ aura to the process of choice and only within the last ten or fifteen years that the behavioral sciences — sociology, psychology and social psychology — have begun to contribute to the body of knowledge comprising decision theory” (Harrison, 1975, p. 5).
During mid-20th century, IPDM was introduced and eventually became influential in organizational theory by the 1970s; although its scientific basis make this concept an extension of Scientific Management rather than a drastic departure, theorists who subscribe to IPDM tenets have made important contributions to the displacement of Taylorism and to the generalization of organizational performance. In particular, by analyzing the recordkeeping and planning activities of managers, they have helped theorize the performance of management itself (McKenzie, 2001). According to one analyst, “Throughout the history of IPDM, its theorists have sought to justify their approach in terms of the rapid and fundamental changes occurring in the U.S. economy. They have focused primarily upon the decline of blue-collar employment and the increasing importance of white-collar jobs, and significantly, they have attributed these changes in large part to technological developments” (McKenzie, 2001, p. 74).
According to McKenzie, “IPDM seeks to empower workers at even the lowest organizational levels. and, like the sociotechnical systems approach, it conceives of human performance as intimately connected to technology. The significance of IPDM lies in the relation it draws between the diffusion of decision-making and the introduction of a technical system capable of totally transforming management” (p. 74). One of the key features of IPDM that makes it an attractive model for decision-makers in higher educational institutions is its ability to include input from a wide range of stakeholders in a participatory fashion that helps to identify unknowns that might otherwise be overlooked. In this regard, McKenzie concudes that, “In investigating the performance of management, IPDM has generalized performance to include both physical and cognitive activities, thereby greatly expanding its field of reference. At the same time, its theorists have folded back information processing and decision making over the entire field of organizational performance” (p. 75).
Analysis of the historic evolution of decision making from scientific management to modern applications of operations research.
On the one hand, Frederick W. Taylor was an early influential writer on management (1890) who developed a theory of scientific management in which supervisors were taught to break jobs down into individual activities that could be easily taught to unskilled workers. On the other hand, Drucker’s work was unique in that he demonstrated how management evolved as a distinct discipline, simultaneous with and related to the development of new large business and not-for-profit organizations that were not centralized, that were not “command and control,” and that were not made up of employees performing unskilled activities (Boudreaux, p. 19).
According to Witzel, “On managers, therefore, fall the twin tasks of pulling together the labor and resources to create production, and of creating markets in which the resulting products can be sold. Through both of these tasks managers must strive to add value, creating something that is greater than the sum of the resources put in” (p. 76). In this regard, though, Drucker departs from scientific management, which stresses the most efficient use of resources; instead, he emphasizes a creative environment in which managers use resources in the most effective way in order to achieve the goals of the enterprise. This combination of catalyst and proactive control comes very close to a direct identification of the enterprise with its managers. Drucker does not go so far as to say that the managers are the enterprise, but he repeatedly stresses their paramount role; earlier, he had commented that: “The enterprise can decide, act and behave only as its managers do-by itself the enterprise has no effective existence” (1954, p. 7).
Examination of the decision-making process, with a particular emphasis on the importance of values and management judgment.
It is reasonable to posit that everyone uses a different process in formulating a reasoned decision based on a given set of circumstances, and, not surprisingly, the decision-making process itself has been categorized into an endless parade of different approaches in the literature as well. The ecological approach to decision making is familiar to business strategists and economists. The use of ecology as a metaphor goes back at least as far as the late 1950s, when the economist Charles Lindblom described how public managers “muddle through” most of their day-to-day decisions. The most consistent proponent of an ecological approach to the decision-making process has been Henry Mintzberg; in numerous journal articles and texts, this authority has emphasized that business strategy and management are not the predictable, streamlined processes that many managers would desire, but are rather “emergent,” based on the exigencies of politics, conflicting motivations, and imperfect perceptions. The Mintzberg organizational vision provides for both deliberate and emergent strategy. In this regard, Mintzberg describes this managerial combination as being similar to craftsmanship in that the preferred outcome is shaped both by the design of the craftsperson and by day-to-day requirements that affect every manager at every level in every type of organization (Davenport & Prusak, 1997).
According to Parnell, Carraher and Holt (2002), “Participative decision making has been found to increase organizational effectiveness, improve relationships between managers and subordinates, increase creativity and productivity, increase company loyalty, and reduce absenteeism and turnover” (p. 161). As a matter of fact, a growing number of managers have maintained that participative decision making is the “right” or ethical approach to leadership; however, it should be noted that there remains a lack of consensus concerning the universal effectiveness of participative decision making in many organizations today (Parnell et al.).
It appears to increase job performance and satisfaction in some situations but not in others. Some top managers have adopted and then abandoned this practice for various reasons, concluding the participation is not for their organizations. Research has examined possible reasons for the discontinuation of participative management: a lack of commitment or interest by management and employees, failure to properly implement the processes, and a lack of fit between the organization and the participative management processes (Parnell et al.).
According to Fairweather (2002) besides hiring new faculty members, the principal expression of academic values concerning faculty work are related to promotion and tenure decisions. This author emphasizes that, “It is here rather than in institutional rhetoric that the faculty seek clues about the value of different aspects of their work. It is here that productivity is most meaningfully defined and evaluated. Yet promotion and tenure decisions are both individual and private in nature. These characteristics make it difficult to identify the cumulative effects of individual decisions within an institution, much less identify patterns across types of institutions and disciplines” (Fairweather, p. 26). Based on his analysis of decision-making patterns in institutions of higher education, Fairweather maintains that all aspects of faculty work — particularly teaching and research — can be equally (or somewhat equally) addressed by the work of each faculty member: This tenet asserts that each faculty member is expected to be (and can be) the complete faculty member — simultaneously productive in both teaching and research” (Fairweather, p. 26).
Description, assessment, and evaluation of various decision evaluation tools including matrix analysis, influence diagrams, payoff matrices, sensitivity analysis, decision tree, probabilistic forecasting, and multi-attribute utility analysis.
Matrix analysis. This approach is a task analysis method that can be used to identify and depict relationships between and among concepts. The result of a matrix analysis is the identification of all the possible paired relationships among the concepts (Jonassen, Tessmer & Hannum, 1999). The form of a matrix has been widely used in many types of analyses including statistical and financial analysis. A matrix organized by rows and columns is also the basis for spreadsheet software. During instruction students often see matrices in the form of tables or charts to convey content. In science classes relationships among types of plants can be shown in a matrix as can relationships among types of animals. Characteristics of different architectural forms or types of music can be shown in a matrix. To some extent matrix analysis follows from this prior work using matrices in education.
The specific background of matrix analysis is the work of Evans, Homme, and Glaser (1962) who used matrix analysis as a task analysis method to identify the content and instructional sequence when developing programmed instruction. They classified all verbal subject matter into two types of statements: rule statements and example statements. Rule statements contain statements that define general content. Thus, a rule statement could be a statement of a concept, a principle, or a rule. Example statements are specific instances of the general statements. The example statements concretely illustrate the rule statements. Example statements also form the basis for student practice using the rules. This classification of content into rules and examples follows from the RULEG system that was popular in programmed instruction. The term RULEG is shorthand for the combination of rules and examples. In this system of programmed instruction, the text would first present a statement of a rule to be learned followed by an example of the rule to clarify its application. The matrix analysis was used to specify in advance the rules and examples that were to be used.
Matrix analysis followed from the belief of Evans, Homme, and Glaser (1962) that the highest form of subject matter knowledge an expert possesses is the knowledge of how to relate subject matter concepts. Experts know many concepts but, more importantly, they also know how concepts are related to other concepts. Thus, an expert’s knowledge is characterized as consisting of items of content and relationships among content items. The purpose of instruction is to teach both the specific content items and the relationships among the content. In order to do this, the instructional designer must use matrix analysis to identify the content and all the relationships among the content before developing the instructional materials. Matrix analysis explores all possible relationships between each item of content and all other items of content.
Still working within the framework of programmed instruction, Thomas, Davies, Openshaw, and Bird (1963) extended the matrix analysis method by specifying procedures to construct and interpret a matrix. They indicated that the relationships between concepts could take several different forms. Later Hartley (1972) indicated that a matrix could have many different operators to describe the relationships between concepts, not just association and discrimination.
Because matrix analysis is based on programmed instruction and programmed instruction is firmly grounded in behavioral psychology, matrix analysis shares this background in behavioral psychology. The emphasis is on specifying instructional outcomes in advance in specific terms. Associations and discriminations describe relationships among content items. Although rooted in behavioral psychology, matrix analysis is not exclusively tied to behaviorism. Some aspects of cognitive psychology begin to appear in matrix analysis. In fact, matrix analysis entails much the same kind of thinking as conceptual graph analysis and other concept mapping methods. These include dealing with internal representations of knowledge, trying to extract how experts organize their knowledge, and structuring knowledge as nodes of content and relationships among the nodes. These ideas are more consistent with cognitive psychology but are a part of matrix analysis.
Matrix analysis consists of three analytical processes.
1. Identifying the concepts of the task
2. Specifying the operators to explain the relationships among all the concepts
3. Constructing a relational matrix of these concepts
In the first step of matrix analysis the instructional designer begins by identifying all the concepts they know that are part of the general topic of the instruction. This is continued by reviewing materials on the task to identify additional concepts. Then the instructional designer interviews subject matter experts to tap their knowledge and identify even more content to include. As a result of this process, the instructional designer would identify all the items of content to include in the instruction. Once all the possible content is determined, the attention shifts to the relationships among the content items.
Matrix analysis is an orderly process that follows the ten steps shown in Table __ below.
Table
Matrix Analysis Process
Step
Description
1. Specify task criterion behavior.
First an instructional designer must specify exactly what a student must do following instruction to demonstrate task mastery. This requires constructing a detailed terminal objective.
2. Brainstorm major task concepts of the criterion behavior.
Construct a preliminary list of important concepts to the subject matter by drawing on knowledge of the subject. This should be a free-form exercise accomplished without use of any reference materials like books, notes, or the World Wide Web. If the person doing the matrix analysis has no, or very little, knowledge of the content, then he or she might consult a subject matter expert for the brainstorming.
3. Determine if matrix analysis can be used.
Matrix analysis is useful in situations that have many task-related concepts of rules that underlie task performance. If this underlying knowledge is not there, then matrix analysis is not a useful task analysis method in that circumstance.
4. Complete a list of task concepts.
Using the initial brainstorming of concepts as a starting point, list all additional concepts related to the task by using texts, notes, other training courses, the World Wide Web, and subject matter experts. For ease of use, decision-makers might record each concept on a separate card or use software to create a separate place for each item.
5. Organize and order the task concepts.
Determine the order in which the concepts will be entered into the matrix. There are several different approaches to accomplishing this. The concepts could be arranged from most simple to most complex. They could be arranged according to chronology with concepts that occurred first appearing first. The concepts could be arranged according to some spatial relationship from near to far.
6. Arrange all concepts into a matrix.
In this step the concepts are arranged along the first row of a matrix based on the order established in step 5. Next the same concepts are entered in the first column in this same order. This matrix will form the basis for comparing each concept with every other concept (see example in Table ____ below).
7. Choose a relational operator to compare concepts.
This is done by examining every cell in the matrix and asking how the concept pairs are related. These relational operators will be used to describe what students must master in order to understand fully the content.
8. Describe the conceptual relationship represented by each cell.
This can be done by starting with the concept contained in row 1 of the matrix and comparing it with the concept in each column. Note the concept in row 1 is the same as the concept in column 1 because this is how the matrix was constructed. When the decision-maker encounters this relation of a concept with itself, just enter a definition of the concept in that cell. These definitions would appear along the diagonal of the matrix. Decision-makers should work along row 1 describing the relationship between this concept and the concept contained in column 2, then the concept contained in column 3, and so forth until each cell of the matrix is completed.
9. Review the matrix.
Check to see if all important concepts have been included and all relationships have been expressed adequately. This is a good time to give the matrix to another subject matter expert to check for accuracy. During this review, decision-makers may find concepts to add, concepts to delete from the matrix, concepts to combine, or concepts to split into two new concepts. Any of these adjustments can be made as they are necessary.
10. Decide if another matrix is necessary.
When reviewing the completed matrix you may decide that the matrix is too general in the description of content or in the relationships. Decision-makers may note additional relationships among items of content, or may identify a need for other relational operators to describe the relationships. The solution in such cases may be to conduct another matrix analysis using much the same content but with a different degree of specificity and with different operators
Source: Jonassen et al., p. 217-218.
Table
Concept Matrix General Form
Relational Operator
Concept 1
Concept 2
Concept 3
Concept 4
Concept 1
Concept 1
Concept 1
Concept 1
1 to 1
2 to 1
3 to 1
4 to 1
1 to 2
2 to 2
3 to 2
4 to 2
1 to 3
2 to 3
3 to 3
4 to 3
1 to 4
2 to 4
3 to 4
4 to 4
Source: Jonassen et al., p. 218.
Influence Diagrams. According to Mellers, Schwartz and Cooke (1998), “Influence diagrams are used for making decisions. They contain value nodes, decision nodes, and Bayesian belief networks. Influence diagrams also provide power and visual simplicity” (p. 447). Properly constructed, influence diagrams provide a framework that allows (a) integrating diverse forms of expertise and (b) assessing the importance of different facts (Morgan, Fischhoff, Bostrom & Atman, 2002). Influence diagrams should summarize the relevant expert knowledge rather than the views of a single individual. Influence diagrams were developed by decision analysts as a convenient way to summarize information about uncertain decision situations, allowing effective communication between experts and decision makers and the conduct of information-related analyses (Morgan et al.). An influence diagram is a directed graph, with arrows or “influences” connecting related “nodes.” Simple influence diagrams contain nodes of two kinds: ovals, which represent uncertain circumstances or “states of the world,” and rectangles, which represent choices made by a decision maker. An arrow between two nodes means that the node at the arrow’s tail exerts some “influence” on the node at the arrow’s head; more formally, knowing the value of the variable at the tail node helps one to predict the value of the variable at the head node. For example, an influence diagram of the weather might include an arrow from an oval representing sunshine to an oval representing air temperature, because sunshine is a factor that influences air temperature (and knowing how sunny it is helps in predicting the temperature). An useful way to illustrate the concepts of influence diagrams is with an illustration as shown in Figure __ and ____ below.
Figure ____. Representative Influence Diagram.
Source: Lee & Bradshaw, 2005.
A simple influence diagram is shown in Figure __ below.
Figure __. Representative Influence Diagram.
Source: http://upload.wikimedia.org
According to Jonassen (2004), “Influence diagrams graphically represent the structure of problems as well as depict the interaction of factors that predict an outcome. Influence diagrams represent mechanistic models or causality” (p. 68). By capturing the formalisms that underlie influence diagrams, it is possible to include both causal and noncausal (or indirectly causal) influences in the decision-making algorithm. In this regard, Morgan and his associates emphasize that, “Being able to include both causal and noncausal relationships allows influence diagrams to accommodate whatever information is available. Indeed, when properly constructed, an influence diagram can be converted into a decision tree, a standard tool in the field of decision analysis (p. 38). As Jonassen (2004) also points out, “Integration or explication of causal factors involved in the problem provides learners with clues about the nature of the problem as well as the conceptual understanding about the nature of the system being troubleshot. They should be included in representations of the problem” (p. 70).
Payoff Matrices. In some situations of uncertainty, the payoff matrix, or payoff table is a useful device for helping decision makers reach choices. The matrix is useful, not in the sense of assuring that correct decisions will be made, or that all marketing problems under conditions of uncertainty can be solved through the payoff table. Rather, this device permits the decision maker to array and assess alternative courses of action in a logical manner. It thereby facilitates the choice process. The logic behind a payoff table is relatively simple. The decision maker is faced with a number of choices called acts (strategies or policies) (Lazer, 1971). Decision-makers are free to choose any of them. (an act, may be the decision to stock a certain number of items in inventory or to select a specific marketing program.) After the decision-maker selects a given act, a certain event or “state of nature” will occur in the real world. For example, the actual number of units demanded is a “state of nature.” The decision maker controls the acts but exerts little or no control over the “state of nature” that will exist during some future period of time. To help arrive at a decision under conditions of risk, the decision maker may sort out the acts, events, payoffs, and probabilities of event in matrix form. The payoffs used in conjunction with the probability of each state’s occurring are helpful in reaching expected value figures for alternatives (Lazer, 1971).
The president of the XYZ Corporation seeks a consultant’s advice on the following proposition. XYZ Corporation is attempting to determine what quantity of inventory to purchase in order to yield the greatest expected monetary value to the company. It is faced with a situation in which it can only purchase units in the following quantities: 1,000,000 units, 2,000,000 units, or 3,000,000 units. Moreover, the president informs the consultant that the total cost per unit is $1.00 and the selling price will be $3.00.
The corporation is faced with the usual dilemma that confronts purchasing agents. It must place its order without any assurance that the quantity of materials ordered will actually be used or sold. The president is not sure what the demand for the product will be. As a result of research reports and analysis, he feels that the probability is .2 that the level of demand will be 1,000,000 units, .5 that it will be 2,000,000, and .3 that it will be 3,000,000 units. The president also has good reason to feel that actual demand will only occur in units of either 1,000,000, 2,000,000 or 3,000,000 items. Given this information, the president wants to select the best course of action; to this end, the payoff matrix shown in Table __ below could be used.
Table
Illustrative Payoff Matrix
Events (amounts demanded)
1,000,000
2,000,000
3,000,000
Probability
1,000,000
$2,000,000
$1,000,000
$0
.2
2,000,000
$2,000,000
$4,000,000
$3,000,000
.5
3,000,000
$2,000,000
$4,000,000
$6,000,000
.3
Source: Lazer, 1971, p. 218
The columns in Table __ correspond to the particular acts; in this case, purchase of 1,000,000, 2,000,000, or 3,000,000 units. The rows correspond to the particular events, the demand for 1,000,000, 2,000,000, or 3,000,000 units. The payoff for the combination of every act and event can be calculated and inserted in the matrix (Lazer, 1971).
Management can control the acts, the decision to buy 1,000,000, 2,000,000, or 3,000,000 units. However, management has limited control, if any, over the events. The actual demand for 1,000,000, 2,000,000 or 3,000,000 units will depend on factors outside the company to a large extent, although these purchases can be influenced by advertising, selling, and other aspects of the marketing program. (Lazer, 1971).
Table ____ above shows that if 2,000,000 units are stocked and 1,000,000 are actually demanded, the payoff to the company will be $1,000,000. If 2,000,000 units are stocked and 3,000,000 are demanded, the payoff will be $4,000,000, and so on. Uncertainty arises from the fact that the marketing manager does not know what the actual demand will be. If he knew that the demand would actually be 3,000,000 units, then obviously he would stock 3,000,000 and realize a $6,000,000 profit. A choice must be made, however, among the courses of action before knowing what the demand will actually be. Management, therefore, must gamble. (Lazer, 1971, p. 219).
Usually management does have some insights into what demand is likely to be. In our illustration, the president reported that he felt the probability was .2 that the level of demand would be 1,000,000 units, .5 that it would be 2,000,000 units, and .3 that it would be 3,000,000 units. These figures were determined based on previous experience, empirical observations, sales forecasts, analysis of research reports, judgment, and even intuition or professional “hunches.” Such probability evaluations are, of course, subjective and not precise; however, such techniques represent the decision maker’s best estimate, and are also valuable techniques in determining the best decision alternatives when complete information is unavailable (Lazer, 1971).
It is recognized that management often reaches decisions without stating such probabilities explicitly. There are obvious difficulties in expressing probabilities in specific numbers. When decisions are made without following this process, probabilities have actually been implied or assumed. It is more desirable to have an explicit statement of what management feels the probability is of an event occurring, thus bringing probabilities more directly into decisions (Lazer, 1971). This example indicates that through the use of payoff tables, it may be possible to formalize the reasoning that permits making some decisions under conditions of risk. The effort involves the following procedure:
1. Developing a matrix outlining all the possible acts (decision alternatives) and all the possible events (“states of nature”).
2. Determining a definite payoff (or cost) for the outcome of each of the acts for each possible event.
3. Determining the probability or weight of every possible event’s occurring.
4. Computing for every possible act a weighted average of the values attached to that act.
5. Selecting the act with the highest weighted average value (Lazer, 1971, p. 220).
Sensitivity Analysis. The goal of sensitivity analyses is to determine which variables (or parameters) have the greatest impact on the project’s outcome. According to Dayananda, Irons, Harrison, Herbohn and Rowland (2002), “In this process, individual forecasted variables are progressively stepped through their pessimistic, most likely and optimistic levels, to determine which variables cause the largest shifts in the project’s net present value” (p. 133). For instance, management may wish to know whether optimistic or pessimistic unit sales prices have greater impacts than optimistic and pessimistic values of sales growth rates. There are numerous ways to analyse projects for risk. One of these is to evaluate the project under various scenarios in which selected variables are stepped through their pessimistic, most likely and optimistic values. In this analysis, only one variable at a time is changed. The resulting set of net present values for the project will show management which variables have material impact on the financial outcome. Management can then decide either to invest time and effort in establishing more reliable forecasts for these variables, or to abandon the project because of excessive risk. Table __ below provides a description and definitions of the terminology used in this decision-making approach.
Table
Sensitivity Analyses Terminology Definitions
Term
Definition/Description
Sensitivity analysis:
Generally speaking, this is the process of analyzing risky projects by estimating a net present value for each of the pessimistic, most likely and optimistic values for each variable under consideration. Only one variable at a time is analyzed, and all other variables are held at their most likely value 1 whilst this one variable is analyzed. The process is designed to set apart those variables which have material impacts on the project’s estimated net present value. The term ‘sensitivity analysis’ here encompasses other common, similar terms such as ‘scenario analysis’ and ‘what-if analysis’.
Sensitive variables:
These are variables which return wide ranges of estimated net present values, or which return negative net present values, and hence are those most likely to receive management’s attention. Since all variables are forecast variables, all will have some impact on the project’s estimated net present value. Those which have the largest relative impacts are known as the sensitive variables.
Optimistic, most likely and pessimistic values:
These are estimated values at three identified points along the range of possible forecast values for the variables under consideration. The terms, ‘optimistic’ and ‘pessimistic’ are used in the context of impact on net cash flows and the positive wealth of the firm.
Best case, base case and worst case results:
These are the names of each individual net present value estimate when the optimistic, most likely and pessimistic forecast value for an individual variable is used in calculation. The terms are sometimes used to define the three individual special cases when all the optimistic, all the most likely, or all the pessimistic values are used in calculation. That meaning is not used in this book, as the term ‘sensitivity analysis’ has been restricted to the analysis of individual variables one at a time. The term ‘results’ here encompasses other common, like terms such as ‘scenario’, ‘outcome’, ‘output’ and ‘solution’.
Source: Dayananda et al., pp. 134-135.
Like the matrix analysis techniques described above, sensitivity analyses also proceed in a step-wise fashion as follows:
1. Calculate the project’s net present value using the most likely value estimated for each variable.
2. Select from the set of uncertain variables those which management feels may have an important bearing on predicted project performance.
3. Forecast pessimistic, most likely and optimistic values for each of these variables over the life of the project.
4. Recalculate the project’s net present value for each of the three levels of each variable. While each particular variable is stepped through each of its three values, all other variables are held at their most likely values.
5. Calculate the change in net present value for the pessimistic to optimistic range of each variable.
6. Identify the sensitive variables (Dayananda et al.).
These authors conclude that, “The choice of variables should be based on mature and experienced judgment combined with a knowledge of the sensitivity analysis process. The choice ought to be made by management in conjunction with the project analyst” (Dayananda et al., p. 138). A representative graphic view of such a sensitivity analysis is provided in Figure __ below.
Figure __. Representative Sensitivity Analysis Graph.
Source: http://www.ald.co.il/products/images/dl_fig06.gif
Decision Tree. Another useful and related concept for analyzing decision alternatives in a logical manner is the decision tree. Similar to a payoff matrix, the decision tree is a convenient way of lining up alternative choices, specifying the probabilities of events occurring, and determining the expected value for each alternative. Again the decision maker is perceived of as being involved in a game against nature. He chooses an act, then nature determines an event, and as a result of this, a payoff occurs. By using the decision tree, it is possible to determine on a logical base which course of action the decision maker feels he should pursue in fairly complex situations. Since there are computer programs for decision trees, the decision maker’s information capabilities are increased.
Three aspects of a decision tree may be distinguished:
1. The whole tree represents the total decision space, all of the alternatives, or the whole set of experiments.
2. The paths are made up of several branches, with each path representing one of the possible sequences of outcomes. The weights on the paths correspond to the probabilities.
3. The branches refer to the line segments, with each line segment being a branch.
The decision tree starts from an initial point, and the branches issuing from it are the first stage of a tree. The branches emanating from the end points of the branches at the first stage represent the second stage, and so on. The points from which new branches are issued are called the branch points, and each branch point corresponds to a unique sequence of events. The sum of the weights of branches issuing from any branch point is one (the alternatives are collectively exhausted) (Lazer, 1971). A representative decision tree is illustrated in Figure ____ below.
Figure ____. Representative Decision Tree
Source: http://www.time-management-guide.com/images/decision-tree.gif
Probabilistic Forecasting. According to Doswell and Brooks (1999), “In meteorological forecasting, the categorical forecast is one that has only two probabilities: zero and unity (or 0 and 100%). Thus, even what we call a categorical forecast can be thought of in terms of two different probabilities; such a forecast can be called dichotomous. On the other hand, the conventional interpretation of a probabilistic forecast is one with more than two probability categories; such a forecast can be called polychotomous, to distinguish it from dichotomous forecasts. Forecasting dichotomously implies a constant certainty: 100%. The forecaster is implying that he or she is 100% certain that an event will (or will not) occur in the forecast area during the forecast period.
There are at least three different sorts of probability forecasts you might be called upon to make: 1) point probabilities, 2) area probabilities, and 3) probability contours. The first two are simply probability numbers. Probability of precipitation (PoP) forecasts are the most familiar probability forecasts, are generally associated with average point probabilities (which implies a relationship to area probability and area coverage). Some might interpret a probabilistic forecast as a hedge, and that is not an unreasonable position, from at least some viewpoints. However, what we are concerned with regarding “hedging” in verification is a tendency to depart from a forecaster’s best judgment in a misguided effort to improve verification scores. (Doswell & Brooks, 1999). Although typically used for meteorological forecasting purposes, probabilistic forecasting has also been used to predict shifts in population demographics as well (Wilson & Bell, 2007).
Multi-Attribute Utility Analysis. This model takes into account not only the potential loss in predictive accuracy of individual job performance, but key strategic business variables at the group and organizational levels as well (Aguinis, 2004). A multi-attribute utility analysis provides for the inclusion of key stakeholders in the decision-making process, in addition to human resources staff members, which will likely contribute to the credibility of the resulting findings (Aguinis). A further illustration of calculation in value decisions can be seen in Walker and Smith’s (1995) review of build-operate-transfer (BOT) schemes which are mechanisms for securing private finance for public infrastructure projects such as power stations and road bridges. These authors recommend the technique of multi-attribute utility analysis (MAUA, although the technique is referred to as MAUT when analysis is replaced by testing) to evaluate BOT proposals. The analysts, when using this method, identify the criteria against which the proposals will be judged and give each a priority score on a scale of 1 to 20. These figures are then transformed into rationalized priority ratings (RPR) by calculating the rating of each criterion as a percentage of the total ratings. The proposals are then analyzed in turn by comparing them against each of the evaluation criteria (Fisher, 1998).
A proposal that performs well against a criterion will score highly on a scale of 10 to 110 whilst a low performing proposal will score poorly. Once all these assessments are made, utilities are calculated by multiplying proposals’ scores for effectiveness on each criterion by the RPR. The products of all these calculations are summed, giving an overall utility score for each proposal. The scores can then be used to put the proposals in order of rank. All other things being equal, and the analysts being content with the method, the highest ranking proposal ought to be the best. MAUA gives arithmetical flesh to the logical bones of formal decision making as shown in Figure __ below.
Figure __. The Rational Model of Decision Making
Source: Fisher, p. 30
In MAUA evaluations arguments between competing values and preferences become transmuted into sensitivity analyses in which the calculations are adjusted to see the impact that changes in the RPRs and the proposal scores would have on the bottom-line rankings of the different proposals. (Fisher, 1998, p. 31).
Conclusion
This component examined theories of applied management and decision sciences from various theorists and provided an analysis of the evolution of managerial decision making from scientific management to the complicated forecasting models used today. Taken together, it is clear that there are a number of approaches, tools and techniques available to assist the decision maker in formulating an informed decision. Moreover, some types of decision making require a different set of tools and techniques than others, but the adage, “garbage in, garbage out,” though, certainly comes into play in virtually all of these techniques with the quality of the decision that results being largely based on the quality of the input and the appropriateness of the variables selected for their use. The research showed that scientific management and performance management emerged in the 20th century and became refined over the years as ways to help managers better understand what their organizations needed to accomplish their goals.
Breadth References
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PART 2: The Depth Component
A.
Objectives. The objectives of this part are four-fold as follows: (a) to explore and assess recent research-based knowledge concerning the role of quantitative models and tools in higher education decision making; (b) to describe the prevalent models currently used in most universities and judge their relative merits; (c) to evaluate the significance of each of the decision-making methods explained in the breadth component for higher education administration; and (d) to describe the management fads which have evolved through university administration and critically analyze why each had failed.
B.
Learning Resources. The materials to be reviewed and interpreted in this part include, but are not limited to, the resources listed at the reference page.
C.
Criteria for Evaluation. For annotated bibliography, a critical analysis of 15 relevant peer-reviewed journal articles addressing the application of various decision methodologies to higher education administration. Then, a paper of approximately 25 pages corresponding to the evolution of management techniques in higher education with the evolution of methods used in businesses described in the breadth component will be presented.
Annotated Depth References
Bovens, M. & Zouridis, S. (2002). From street-level to system-level bureaucracies: How information and communication technology is transforming administrative discretion and constitutional control. Public Administration Review, 62(2), 174.
More efficient ways of making decisions that affect the lives of young people does not necessarily translate into better ways. These authors provides an insightful analysis of what can happen when an institution of higher education relegates is decision-making
processes for determining which students should be entitled to grants and financial aid assistance from a human-based approach to one that is completely automated. Citing the case of the Netherlands where the responsibility for such decisions had traditionally been the responsibility of the Ministry of Education, Culture and Science, the authors report that in the past, there was a personal relationship between students and decision makers
that allowed for a fine-tuned and informed approach to the provision of aid including what extent and for what periods of time such aid should be provided. By the 1960s,
though, the introduction of computer-based decision-making systems altered this relationship in fundamental ways, removing some of the discretionary authority previously enjoyed by the Ministry and placing it wholly within the purview of automated systems. Further erosions of the individual discretionary authority previously enjoyed by decision makers concerning scholarship applicants took place in the 1970s
through the 1990s as increasingly sophisticated and powerful computer systems with data being inputted directly by the scholarship candidates and students themselves, eliminating even more of the humans involved in the decision-making process and making discretionary decisions on the part of the Ministry all but impossible with the only decisions available to the decision maker being whether to accept or reject the decision provided by the computer. Not surprisingly, decision makers and students alike resented this approach but the trend towards increasing automation of the decision-making process has inexorably continued unabated and the authors suggest the Ministry’s function in the future will be concerned more about optimizing information processes rather than improving the ability of the organization to provide services to colleges, universities and students alike. The authors conclude that the value of such information system in the decision-making processes must be viewed in the context that humans still must play the final role and not be replaced in their entirety by automation.
Bryan, G.A. & Whipple, T.W. (1995). Tuition elasticity of the demand for higher education among current students: A pricing model. Journal of Higher Education, 66(5), 560-562.
Because resources are by definition scarce, establishing tuition rates for higher educational institutions represents a vitally important decision for college and university administrators seeking to overcome diminished allocations from external sources and rising educational and facility costs. These authors emphasize the need for models that can provide decision makers in higher educational settings with the ability to make these types of decisions in informed and timely ways. In response to these needs, institutions of higher education have attempted to become more proficient in their use of tuition pricing
as a positioning device by taking into account, among other variables, the effects of students’ ability to pay, institutional student aid, and expenditure plans on enrollment
rates; to date, though, there has been a dearth of research concerning a tuition pricing
model that is based on students’ willingness to pay. These authors suggest that this type of model would be a valuable tool to assist college decision makers in establishing tuition rates. To this end, the authors used a sensitivity analysis to determine the tuition parameters for the student tuition range that would provide the optimum impact on enrollments and the needs of the institution. Through the use of surveys of 150 students at three colleges, the authors established the lower and upper tuition bounds using a random sample of current completed first-choice trade-off procedures that gauged the students’
switching behavior to another college at increasing rates of tuition to identify optimum tuition levels at all three institutions and suggest there are only marginal returns above these optimal levels that would preclude their use.
Chen, X.P. & Li, S. (2005). Cross-national differences in cooperative decision-making in mixed-motive business contexts: The mediating effect of vertical and horizontal individualism. Journal of International Business Studies, 36(6), 622-623.
The authors are professors at the Chinese Academy of Sciences and University of Washington, respectively. In this study, they emphasize that there remains a dearth of timely studies concerning to extent to which people in different cultures differ in their
decision-making in mixed-motive situations where individual interest may be in conflict
with the collective interest; moreover, there is even less known concerning the respective decision-making styles used by members of different cultures. A comparison of the decision-making styles of educators in China and Australia determined that the institutional culture of the country tends to influence the manner in which decisions were made, with collectivist cultures such as China emphasizing the use of formal and informal sanctioning systems to guide student behavior while highly individualistic cultures such as that in Australia tend to focus on the individual students in terms of reaching goals or self-achievement. In individualistic cultures, educators tend to encourage students to challenge themselves and to achieve their potential. The authors did find, though, that even in highly collectivist cultures such as China’s, Chinese made less cooperative decisions in mixed-motive business situations than did their Australian counterparts. The authors attribute these findings to the increasingly globalized environment in which both Chinese and Australians are making decisions today with some cultural transmission taking place between the two cultures in ways that influence decision makers in both countries.
Desjardins, S.L., Ahlburg, D.A. & Mccall, B.P. (2006). An integrated model of application, admission enrollment, and financial aid. Journal of Higher Education, 77(3), 381-382.
Authors are professors at the University of Michigan, University of Colorado and the University of Minnesota, respectively, and report that many institutions of higher learning have been faced with economic difficulties in recent years that have required programs to be discontinued and tuition rates to be increased. Over the past decade or so, tuition rates have increased at roughly twice the rate of inflation and the authors emphasize that many students are becoming increasingly selective in their choice of colleges and universities based on their career goals as well as their ability to pay for their education. Although various modeling strategies have been used to some good effect in the past in improving the understanding of student college choice, the models tend to assume the independence of the application, admission, and enrollment stages as well as the impact of the availability of financial aid assistance. In an effort to develop a superior model upon which student college choice can be discerned, the authors used a random utility model to make some informed estimates of various factors such as the extent to which a given school provides a student with superior outcomes in terms of program offerings, the costs of tuition, and the availability of financial aid in the decision-making process. Based on their analysis, the authors conclude that student enrollment decisions are not only affected by the level of expected aid, but are also sensitive to deviations of actual return on the students’ investment in time and resources, but suggest that the expectation of receiving financial aid represent one of the more powerful influences in East, W.B. (1997). Decision-making strategies in educational organization. JOPERD — the
Journal of Physical Education, Recreation & Dance, 68(4), 39-40.
The author is a professor at a midwestern university in the United States who presents a valuable overview of common decision making techniques used in universities and explains what types of decisions are required on an ongoing basis. The author emphasizes that although decision making in any type of organization is a complex and complicated process, it is particularly so in academic settings because of the number of stakeholders with divergent goals and priorities involved. Decision making in higher educational settings, the author suggests, requires a mindset that recognizes that cooperation will be required in order to achieve the goals that are identified by whatever decision-making approach is used, and recommends that certain so-called “tribal rituals”
that are routinely followed that are specific to the college or university be taken into account when seeking assistance or allies in this regard. Although the action required by any decision will likely be achieved through such assistance from others, the author also emphasizes that ultimately, the responsibility for decision making typically rests with just one individual. Likening the decision-making to both an art and a science, the author describes how some academicians are able to achieve excellent results using a strictly intuitive based approach to their decision making while others may struggle through the process attempting to take every possible factor into account in their analysis.
Truly, practice does make perfect, especially when it comes to making decisions the author notes, and past experiences and the results obtained by a given decision-making approach can provide invaluable feedback for future efforts. Whatever approach is used, the author emphasizes the need for those making decisions in academic settings to both understand and appreciate the context in which decisions are made because only then can meaningful decisions be made and successfully implemented.
Eckel, P.D. (2002). Decision rules used in academic program closure: Where the rubber meets the road. Journal of Higher Education, 73(2), 237-238.
The costs associated with the provisions of a comprehensive program of instruction at the higher educational level involve some serious choices and trade-offs, with some observers suggesting that it is no longer feasible or cost effective to provide the wide-
ranging curricular offerings that have characterized many larger institutions in the past.
The decision to discontinue a given curricular offering, though, is a tough decision in higher educational institutions since such changes are emotionally charged, faculty members may be terminated and have their careers interrupted and such discontinuances may jeopardize the institutions’ core values and change its institutional identities in undesirable ways. The decision to discontinue curricular offerings, then, differs from other types of decisions that are made in higher educational settings such as what new programs should be offered or revisions to endowment investment plan. When programs
are being considering for discontinuance, the process can affect the entire university community and result in an inordinate amount of stress and emotional turmoil among faculty members who are affected either directly or even indirectly because “they may be next.” In those cases where such program closures are inevitable, the author emphasizes the need to establish relevant criteria by which to assess which offerings should be considered for discontinuance, and suggests that at least 10 such criteria should be included in the decision-making process as follows: (1) history, development, and expectations of the program; (2) external demand for the program; (3) internal demand for the program; (4) quality of program inputs and processes; (5) quality of program outcomes; (6) size, scope, and productivity of the program; (7) revenue and other resources generated by the program; (8) costs and other expenses associated with the program; (9) impact, justification, and overall essentiality of the program; and (10)
opportunity analysis of the program (p. 54). The decision rules he suggests are economic
(demand, revenue, and costs), quality, and centrality. The author concludes that all or part of the foregoing criteria should also be used in evaluating programs for reinstatement.
Enderlin-Lampe, S. (1997). Shared decision making in schools: Effect on teacher efficacy.
Education, 118(1), 150.
In response to the call for reform of the educational process in the United States, the author cites the increasing use of shared decision making in schools at all levels to help bring as many educators’ talents and insights to bear on a problem as possible. There are a number of advantages to the use of the shared decision making model that transcend the basic decision-making approach as well, with a number of studies finding other positive payoffs including an improved sense of educator empowerment, a heightened sense of efficacy, and improved motivation. Unlike many of the management “fads” that have been criticized as being fashionable but ineffective, shared decision making is a methodology rather than a true reform initiative. Despite the known advantages of a shared decision making methodology in academic settings, the author also notes that some educators may be reluctant to participate in the process or lack the requisite knowledge, skills or expertise needed to help arrive at a sound decision. Nevertheless, the author suggests that notwithstanding these limitations, the shared decision making model can provide schools of all types and at all levels with an improved decision-making model that provides educators with a real voice in how their institutions will be run and what goals and visions should be in place to help guide all stakeholders in achieving them.
Fairweather, J.S. (2002). The mythologies of faculty productivity: Implications for institutional policy and decision making. Journal of Higher Education, 73(1), 26-27.
The author is a professor of education, Educational Administration, Michigan State
University. In this study, the author uses a quantitative model to assess faculty productivity along a continuum of three domains (i.e., the number of refereed publications published during the previous two years; student classroom contact hours per semester; and, student ratings of faculty teaching). Although the quantitative model was forced to ignore some important qualitative considerations in its analysis, the author developed some interesting findings through this approach. Educators were deemed highly effective in teaching or research if the following conditions were found: their publication of refereed publications exceeded the median level for the relevant program area and institutional type; any faculty member who performed above the relevant median in student classroom contact hour production was regarded as a highly productive teacher. The author also found that about half of the faculty members analyzed were shown to be highly productive, with women scoring slightly lower than their male counterparts in these terms. The findings of the study were deemed less relevant for the purposes of the instant investigation than the use of the quantitative model employed by the author that was shown to produce some robust results; however, even this researcher emphasized the inability of the model to capture some important qualitative considerations that should also be taken into account.
Fenstermaker, W.C. (1996, October). The ethical dimension of superintendent decision making.
School Administrator, 53(9), 16.
a study of ethical decision-making practices among university superintendents from 1972
compared to the types of choices being made at the time of writing. Comparing the results of 1,725 responses from the 1972 study with 1,341 responses to the current survey, the author identified a less than one percent change in the educational leaders’ responses over time (47.3% versus 48.1%, respectively), clearly indicating that little had changed in the intervening years. Some of the more salient findings to emerge from this study included the fact that educational leaders with schools that were larger (more than 20,000 students) tended to make more ethical choices. In addition, the more salary the superintendents received, the more ethical the decisions being made were shown to be. Interestingly, both studies indicated a slightly higher ethical decision-
making style among those educational administrators with fewer years of service. Despite these findings, the majority of ethical decision making was reported by the respondents as being based on actual experience rather than intuitive approaches. Overall, though, the majority of educational administrators were shown to either be unaware of the ethical standards required for their decision making or to simply disregard them. The author recommends that although the majority of higher education administrators undergo a lengthy period of preparation in graduate school, more attention should be paid to ethical decision making that is required on a daily basis upon graduation. The author includes several recommendations to help guide the decision-making process at the higher educational level, but emphasizes that foremost among these is the need to make the well-being of students the fundamental value of all decision-making and actions.
Fife, J.D. (2003). Management fads in higher education: Where they come from, what they do, why they fail. Journal of Higher Education, 74(4), 469-470.
In this review, Fife cites a number of contributions made by Birnbaum to the analysis of innovations in management systems in academia, he is empathic that Birnbaum is biased in his use of the peer-reviewed literature to make his case. Because there is typically a large amount of attention given to new management “fads” in the scholarly literature,
Fife suggests that Birnbaum is simply picking and choosing those authors that support his negative views of these systems while excluding the rest. Indeed, Fife goes so far as to say that Birnbaum’s personal biases against these popular management systems may have prevented some institutions of higher education from realizing the benefits that may accrue from their judicious use through this stratagem of berating without thoroughly explaining. Moreover, not only does Birnbaum tend to ignore authors who disagree with his personal biases in his analyses, Fife argues that the evolution of innovation is
described in the scholarly literature in fundamentally differently ways that the discussion presented by Birnbaum. The life cycle of management fads presented by Birnbaum is predictable enough, Fife notes, because any change initiative follows much the same pattern, with the need for such change being external to an organization, the need for overzealous advocacy of the approach in order to garner sufficient attention and resources to secure its adoption, and a period in which new skills and knowledge much be developed following its adoption. It is not surprising that many of these management systems are not as effective in producing the desired results immediately following their adoption, though, and Fife maintains that many organizations, including those of higher education, tend to give up during this period without giving the management system a chance to work to its full capacity. Organizations that are able to weather this transitional period have been able to realize many of the benefits that the proponents of these management systems believe are possible elsewhere, with the overriding factor being an adaptation of the management system to the organization’s culture or a shift in the organizational culture to meet the change initiative.
Kezar, a. (2005). Consequences of radical change in governance: A grounded theory approach.
Journal of Higher Education, 76(6), 634-635.
Faculty and administrators at colleges and universities are increasingly being called upon to respond to a wide range of challenges today, including what innovations in technology are best suited for their needs as well as their students, developing curricular offerings that are relevant and timely for diverse and changing populations, competition, financial stress, and globalization among others. In many cases, though, decision-making
mechanisms at these academic institutions are ill-suited to deal with these increasingly complex questions and problems. Moreover, traditional perceptions of academic governance have become subject to intense scrutiny based on a number of constraints such as being slow, inefficient, and unresponsive to the external environment in which the college or university operates. The author cites the results of a nation-wide study that was conducted during the 1990s that showed there none of the groups on college and university campuses felt that decision-making processes were operating effectively to emphasize the need for new processes today. Yet other studies have found that governance in higher educational institutions was completely ineffective and inefficient based on its inherent structure and the outdated processes that were used. The author concludes that the debate over whether decision-making processes in higher educational settings should be improved is over with the only remaining issues being what forms should take the place of existing decision-making techniques.
-. (2008). Understanding leadership strategies for addressing the politics of diversity. Journal of Higher Education, 79(4), 406-407.
The author cites the enormous range of issues confronting leaders in higher education today and notes the conflicting interests and desires that disparate stakeholders bring to bear on the decisions that must be made on a daily basis in all colleges and universities.
The author cites the paucity of timely studies that have examined the types of political situations that are faced by university presidents in their efforts to balance these disparate interests, and points out that change initiatives result from the top down, as in the case of the university president or other leadership, or such initiatives can spawn from the bottom up from faculty and staff to ultimately become integrated into the overall institutional agenda. Whatever the source of the decision that results in such initiatives, though,
Kezar emphasizes that the university’s top leadership will ultimately play the most important role in effecting meaningful change because others do not possess the authority needed to accomplish these fundamental alterations in a college or university’s vision and goals. Reflecting the adage that “it is lonely at the top,” the author also notes that besides these overriding responsibilities, university presidents typically must also participate in decisions concerning transforming the curriculum, assessing progress, and evaluating and creating accountability. It is important, therefore, for top leaders in higher educational setting to engage in a coalition building approach to decision making that fosters a sense of shared responsibility and provides the bottom-up support that will be needed to make effecting change possible and successful.
Kezar, a. & Eckel, P.D. (2004). Meeting today’s governance challenges: A synthesis of the literature and examination of a future agenda for scholarship. Journal of Higher
Education, 75(4), 371-372.
Governance in academic settings refers in general to those processes used to formulate policy making and decision making at the macro level within the higher educational institution. Traditionally, such governance responsibilities have included participation in scholarship matters on state boards, board of trustees, faculty senates, and even student government bodies. Governance, then, represents a multifaceted aspect of the operation of a college or university that involves a wide range of bodies and processes that involves different decision-making functions. Some of the entities involved in the process such as faculty senates will make decision concerning curriculum development and offerings while boards of trustees will be concerned with formulating timely decisions about budgetary issues. The authors suggest that some of the more important issues to take into account in understanding academic governance are the organizational structures that exist
(i.e., lines of authority, roles, procedures, and bodies responsible for decision making).
The authors describe various decision-making approaches including centralized vs. decentralized, authoritative, hierarchical, bureaucratic and other approaches that are typically used for various purposes in higher educational settings and conclude that in recent years, decision making in higher educational settings has shifted from being mostly centralized to one that is increasingly decentralized in nature in ways that have diffused authority structures, with the majority of authority for the decision-making process being divided between trustees, administration, and faculty.
Lueddeke, G.R. (1999). Toward a constructivist framework for guiding change and innovation in higher education. Journal of Higher Education, 70(3), 235-237.
This author cites the impact of incessant change on institutions of higher education and emphasizes the need for new decision-making approaches that are not based on strictly executive fiat or a centralist management strategy. The author also suggests that a participatory decision-making process would be more appropriate in many cases because it would provide all stakeholders with the opportunity to feel that they are part of a meaningful process that places a high value on their experience or ability to identify potential pitfalls that might otherwise be overlooked. Although the mission and goals of higher education are fundamentally different from those in the business world, there are some external forces at work that are compelling many colleges and universities to adopt a more pragmatic approach to their decision-making processes that contribute to their bottom-line. The author recommends the use of various modeling techniques that can facilitate the otherwise-complicated decision-making process to help institutions of higher education identify opportunities for implementing change initiatives that improve their financial performance while maintaining their ability to deliver high-quality educational services. Beyond the traditional stakeholders that need to be included in such
participatory decision-making processes are the other staff members, students and professional associations that have frequently been left out of the loop in the past.
Mclendon, M.K., Heller, D.E. & Young, S.P. (2005). State postsecondary policy innovation:
Politics, competition, and the interstate migration of policy ideas. Journal of Higher
Education, 76(4), 363-364.
The authors are professors at Pennsylvania State University and Vanderbilt University
and cite Birnbaum’s (2000) scathing criticisms of so-called “management fads” in higher
education wherein he tracks the adoption and eventual abandonment of various management approaches that are considered fashionable but which are ultimately short-
lived and indicates that, in the final analysis, the value of any such innovative management approach must be viewed over the long-term. The authors then provide an analysis of the relative effectiveness of centralized decision-making techniques vs. decentralized ones in academic settings, and provide a useful overview of the historical evolution of these different techniques over the past 5 decades or so. The authors note that the decision-making process in academe was shifted to a centralized configuration in the mid-20th century based on the perceived benefits of improving the decentralized approach which was characterized by poorly informed, inadequately coordinated which would be replaced a centralized approach that would offer improved knowledgeable planning, flexibility, adaptation, and policy development. The authors also note that centralized decision-making approaches in higher education provided the opportunity to enlist the assistance of those with the requisite technical knowledge required to make informed decisions on highly complex issues in a fashion that would assist the leadership in formulating timely and effective decisions. Based on their comprehensive analysis of the effectiveness of this approach over the past 10 years in promoting improved governance and accountability in higher educational institutions, the authors determined that states with highly centralized decision making apparatuses tended to model the way for neighboring states which were inclined to follow their lead in innovation in financing and governance issues.
Depth Component Paper: Evolution of Management Techniques in Higher Education
As noted above, this segment of the Depth Component is comprised of a paper of approximately 25 pages that correspond to the evolution of management techniques in higher education with the evolution of methods used in businesses described in the breadth component as reviewed and analyzed below. A summary of the research and important findings are presented in the conclusion.
Review and Analysis
Exploration and assessment of recent research-based knowledge concerning the role of quantitative models and tools in higher education decision making.
The need for effective decision-making in higher educational settings has never been greater. As Kezar and Eckel (2004) emphasize, “The intense environmental demands on higher education place great responsibility and strain on institutional leaders to make wise decisions in a timely manner” (p. 371). According to a study by Eckel (2002), “The most common portrayal of organizational decision making is one of limited rational choice, where decision makers identify alternatives, explore consequences, and make choices based upon a set of decision rules that differentiate consequences” (p. 237). In institutions of higher education as in other organizational settings, then, decisions are required in order to achieve specific goals, a process that is frequently wrought with balancing a wide range of variables, not all of which may be quantifiable and appropriate for use in a quantitative decision-making model. The bottom line for decision makers, then, relates to the basic issues of “What acceptable actions are possible? What future consequences might follow each alternative? How valuable are the various consequences? What decision rules are used to select among alternatives?” (Eckel, 2002, p. 238).
According to Cheng (1993), the most significant operational problems affecting institutions of higher education include resource allocation, financial planning, budgeting, formation of student project groups, scheduling and classroom allocation, student registration, tuition and fee structure determination, and doctoral submission rates, with the most important problem being a paucity of communication between operations research analysts and educational administrators. When an academic institution’s sustainability depends on its ability to evaluate the environment in which it functions and competes for students, quantitative models can provide useful insights into future trends based on current statistical data. In fact, as Atran and Medin (2004) emphasize, “Quantitative models can be applied to issues of environmental cognition and management that are central to cultural survival” (p. 395). Quantitative decision-making models can help illuminate the reasons for certain undergraduates seeking to attend colleges or universities near or distant from their homes, or provide generalizations concerning the population of young people who attend schools near major universities in industrialized countries, for example (Altran & Medin).
Likewise, in a study examining the anticipated monetary value of a student to an institution of higher education, Hoverstad, Sylvester and Voss (2001) introduces a model for estimating the amount of revenue that a typical student will bring to an institution of higher education. The model uses event history analysis to analyze the length of time a typical student will remain enrolled at the college or university, and then uses this estimate in combination with tuition numbers to determine relevant revenue estimates (Hoverstad et al.). The model uses an event history analysis approach in order to analyze the length of time a typical student will remain enrolled, and then uses this estimate in combination with tuition numbers to arrive at revenue estimates (Hoverstad et al.).
Description of the prevalent models currently used in most universities and their relative merits.
Whatever decision-making approach is used and for whatever purpose it is applied, Eckel (2002) suggests that in higher educational settings, “Institutional decision makers use criteria that lead to action” (p. 237). Decisions that lead to action, though, require a thoughtful assessment of what decision-making approach will provide the best results given the fact that information will always be flawed, unavailable or otherwise faulty, but it is all that is available for decision making. Therefore, identifying what decision-making methodology is best suited for a given decision dilemma represents the first step in achieving action. As Enderlin-Lampe (1997) emphasizes, “It is not only a question of who makes the school decisions, but also what methodology is used. Certainly, a knowledge base in decision making, training, willingness, ability to take risks, and experience are all necessary to assure sound decision making” (p. 151).
One successful strategic planning process that enjoyed success at a large community college linked the analytic inputs of research with the authority and intuition of leaders. According to Goho and Webb (2003), the key factors that were responsible for the success of the management process at this large community college included a collegial and organized structure, detailed project management plans, and confidence in the environmental scan. Schroeder (1973) categorizes the applications of management science to higher education into the following categories:
1. Planning, programming, and budgeting systems;
2. Management information systems;
3. Resource allocation models;
4. Models for student planning;
5. Faculty staffing models;
6. Optimization models.
This author distinguishes the last three categories as being “mathematical models”; however, Hopkins and Massy (1981) suggest that this is not exactly precise because the same term can apply to the third category and some aspects of the first two.
Other prevalent models being used in colleges and universities include powerful computer-based applications that have been developed in recent years to facilitate the use of quantitative decision-making models. For example, Byrne (2003) reports that, “Quantitative models play a key role in modern engineering practice. In many engineering domains, the space of design possibilities is too large to allow empirical assessment of it all” (p. 2).
As with many of the other decision-making models described herein, the effectiveness and utility of quantitative models in facilitating the decision-making process directly relate to the selection of relevant variables and how they are interpreted by the decision maker. There may be, and likely will be, some degree of “guesstimation” or intuition involved in this regard, as Byrne points out, though, the introduction of computer-based quantitative models for decision making have eliminated some of the guesswork and have provided decision makers with a more fine-tuned approach that eliminates or overcomes some of the problems that have been attributed to non-computer-based applications in the past. For example, Byrne concludes that, “Some winnowing of the space is accomplished through guidelines and intuition, but truly novel designs generally fall outside the scope of such techniques.
Thus, in many engineering areas, design guidance and evaluation rely on quantitative modeling, and modeling practices have become codified enough that software tools to support such modeling are widely available. In fact, use of such tools is so standardized that thousands of undergraduate engineering students are trained on them each year” (emphasis added) (p. 2).
According to East (1997), every educational organization is unique and will have its own specialized set of rites and rituals, and these “tribal customs” are extremely powerful and important to the effectiveness of the decision-making process; some of the more generalizable rituals are:
1. Personal Rites – Seniority, pecking order, and faculty cliques, tenured vs. nontenured, faculty vs. staff.
2. Social Rites – Luncheons, socials, TGIF, “play time,” holiday functions.
3. Process Rites – Teaching schedules, course loads, release time, office and lab space.
4. Recognition Rites – “Attaboys,” informal thank yous, formal recognition for service and performance.
5. Personal Rituals – Identifying the key performers; the heroes, spies, gossips, high priest/priestess, storytellers, and so on.
Although the foregoing “tribal rituals” are not necessarily difficult to discern, especially over time, decision makers ignore them at their peril. Indeed, according to East, “Decision makers who ignore or underestimate the power of prevailing organizational rituals, symbols, and cultural rites will meet with extraordinary resistance from various constituents within the organization” (p. 40). Because change is extremely difficult to effect in any type of organization and the need for change will be inevitable, these are important yet common sense considerations to take into account when forging strategic alliances to help achieve a goal that results from whatever decision-making process was used.
Evaluation of the significance of each of the decision-making methods explained in the breadth component for higher education administration.
As noted in the Breadth component above, there are a number of conventional decision-making methods that are available for use by institutions of higher education, some of which may be more suitable for certain purposes and applications than others. Because there are a number of bodies involved in the administration of a typical college or university, these decision-making needs will range the entire gamut from allocation of funding to curricular offerings and everything in between. Therefore, an evaluation of the respective advantages and disadvantages of each of these methods as they pertain to higher educational settings is in order and this analysis is provided below.
Matrix analysis. A study by Harter and England (2002) reports on the success of the use of a matrix analysis approach by the planning office of a large urban university. This university produced an induced course load matrix (ICLM) analysis to support the university’s plans for undergraduate enrollment growth at its three campuses. The ICLM tables, based on the complete course histories of the 1993 entering cohort, summarize the program and course selections of a cohort of students as they progressed through their studies (Harter & England). The authors also note that the assignment of some students with double-majors required an arbitrary assignment to one category or another, most students are able to be clearly classified in one main program of study.
A representative ICLM table is shown in Table __ below.
Table
Sample ICLM Table — Distribution of Courses by Program of Study
Program
Department 1
Department 2
Department 3
All Departments
60%
20%
20%
40%
50%
10%
15%
20%
65%
All Programs
38%
30%
32%
Although the analysis was technically challenging, Harter and England report that the results that were obtained from this exercise proved to be valuable in several ways. In particular, the results of the use of the matrix analysis by this university demonstrate how program enrollments create instructional demands across academic divisions and illustrate the manner in which some departments play an important part in service teaching at the university. Because the course load analysis involves detailed quantitative data, senior administrators were consulted during the initial planning of the project, and care was taken to present the results clearly and succinctly. Ultimately, the results were well received and have since been incorporated into several planning exercises (Harter & England).
Influence diagrams. According to Naidu (2003), “Model facilitated learning advocates learning with models as an instructional approach to introduce learners to a new domain or problem situation and to promote learning simpler procedures and associated concepts. Causal loop diagrams (also called causal influence diagrams) are quite good at providing a representation of an entire system” (p. 19). In higher educational settings, such influence diagrams can be employed in many situations in order to facilitate gaining a better grasp of a problem scenario, as well as promoting knowledge elicitation and the assessment of understanding by decision makers (Naidu). Suggesting that many academic leaders are forced to make decisions in environments that are heavy on information but lean on effective decision-making techniques, Tucker and Codding (2002) recommend the use of spreadsheets and computer models to help provide the critical insights that are needed for decision making today. According to these authors, “Managers today are asked to make fast, effective decisions of economic consequence to their firms. The problems they face are challenging and beyond routine, requiring creative but analytically sound approaches and solutions that can be explained in simple terms” (p. 110).
Although sorting through the flood of information that is available to the decision maker is much like trying to drink from a fire hose, influence diagrams can help sort out the wheat from the chaff in ways that help distill the importance factors from the irrelevant. In this regard, the Tuck School of Business at Dartmouth and the University of Pennsylvania’s Wharton School both provide executive programs in modeling for decision making. In this regard, Tucker and Codding note that, “These programs teach managers how to design, build, and test effective spreadsheet models and use influence diagrams for problem structuring and prototyping for model development” (p. 111).
Payoff matrices. According to Kriesberg (1973), most real-world situations are much more complex than the constructs used in simple payoff matrices; however, payoff matrices are deemed useful in some cases because of their ability to reveal how individual, reasonable calculations can result in different outcomes. There are some constraints to the use of payoff matrices that may restrict their applicability and utility for decision makers in higher educational settings. For instance, payoff matrices to not capture the “real” uncertainties that confront the decision maker; however, as Goodin and Klinglemann point out, “It is precisely these ‘real’ uncertainties that structure — that is, act as structural constraints upon — both the choices that are made and the outcomes that obtain in the ‘real world.’ The question must remain open therefore as to whether [these] perspectives adequately reflect a central aspect of the structure which they were expressly intended to encapsulate” (p. 435). In addition, Goodin and Klinglemann (1998) emphasize that payoff matrices “radically understate the degree of uncertainty that actually faces policy-makers” (p. 434).
Sensitivity analysis. This technique is commonly used to determine how different values of an independent variable will tend to impact a particular dependent variable under a given set of assumptions. This technique is used within specific boundaries that will depend on one or more input variables. Sensitivity analysis is similar to many of the other decision-making techniques described herein based on its ability to develop a set of “what-if” scenarios that can be used to predict the outcome of a decision if a situation turns out to be different compared to the key prediction(s) (Sensitivity analysis, 2009).
Decision trees. In operations research, specifically in decision analysis, a decision tree is a decision support tool that uses a graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility. A decision tree is used to identify the strategy most likely to reach a goal. Another use of trees is as a descriptive means for calculating conditional probabilities. Decision analysis (DA) is the discipline comprising the philosophy, theory, methodology, and professional practice necessary to address important decisions in a formal manner. Decision trees represent a type of abstract model that employs cause and effect logic in order to provide a description of the behavior of a given system (Decision tree, 2009).
The introduction of sophisticated and increasingly powerful computer-based applications that provide decision makers with the various strategies that can be used to reach a given goal has been one of the driving forces behind the restructuring of many academic institutions in recent years. In this regard, Bovens and Zouridis (2002) emphasize that, “Instead of noisy, disorganized decision-making factories populated by fickle officials, many of these are fast becoming quiet information refineries, in which nearly all decisions are pre-programmed by algorithms and digital decision trees. Today, a more true-to-life vision of the term ‘bureaucracy’ would be a room filled with softly humming servers, dotted here and there with a system manager behind a screen” (p. 174).
In data mining and machine learning settings, decision trees are predictive models in that they codify observations concerning a given issue through to conclusions concerning its target value. Some of the other and more descriptive terms in use for decision tree models include (a) classification tree (discrete outcome) or (b) regression tree (continuous outcome). In these tree structures, the resulting “leaves” of the tree are used to represent classifications and the “branches” are used to stand for conjunctions of features that result in such classifications (Decision trees, 2009). The machine learning technique for inducing a decision tree from data is called decision tree learning, or (colloquially) decision trees. Data mining has been defined as the nontrivial extraction of implicit, previously unknown, and potentially useful information from data and the science of extracting useful information from large data sets or databases. Data mining involves sorting through large amounts of data and picking out relevant information. As a broad subfield of artificial intelligence, machine learning is concerned with the design and development of algorithms and techniques that allow computers to learn. At a general level, there are two types of learning: inductive, and deductive. In decision theory and decision analysis, a decision tree is a graph or model of decisions and their possible consequences, including chance event outcomes, resource costs, and utility (Decision tree, 2009).
Probabilistic forecasting. Probabilistic forecasting is a technique that is generally used for weather forecasting but has also been applied to various academic settings in the past as well, but apparently to a far lesser extent than most of the other decision-making models reviewed herein. The technique depends on different methods to establish an event occurrence/magnitude probability. This approach differs significantly from a model that provides definite information concerning the occurrence/magnitude (or not) of the same event, technique used in deterministic forecasting. Both techniques attempt to provide decision makers with accurate prediction concerning future events; however, information on the uncertainty of the prediction is only represented in the probabilistic forecast. The probability information is typically obtained by employing a number of numerical model runs, with slightly varying initial conditions; in these cases, the technique is usually referred to as ensemble forecasting or an ensemble prediction system (EPS) (Toth & Kalnay, 1997).
Multi-attribute utility analysis. A study by Lewis and Kallsen (1993) reports on the development and use of a multiattribute evaluation model for making resource reallocation decisions in a large college of education. According to these authors, “As an evaluation and decision making framework, MAU analysis is especially appropriate for assisting decision makers in higher education” (p. 3). Notwithstanding the dated nature of this study, the authors present a number of criteria with measurable attributes, procedures for use, and software templates that remain relevant, together with data from a recent cycle of reviews to illustrate their concepts (Lewis & Kallsen).
Final estimates on weighted utility values for each of the program reallocation requests (largely targeted on faculty line items) are ranked and illustrated for the decision maker. Eight step-by-step processes in multi-attribute evaluation techniques are discussed; as well as several criteria used (i.e., quality of outcomes, centrality to mission, program demand, uniqueness of program, and cost-effectiveness), along with their related measurable attributes. The study examined specific data concerning the performance of 17 identified measurable attributes found within five criteria that were collected from a 2-year study of 16 alternative reallocation requests. The study showed how these 16 alternatives were ranked from the most effective and highest valued option, having a utility value of 81, to the lowest valued request, having a utility value of 16, respectively (Lewis & Kallsen).
Description of the management fads which have evolved through university administration and critically analysis of why each had failed.
Among the various management systems and processes introduced in the mid-20th century was the Planning, Programming, and Budgeting System (PPBS) approach which was originally developed by Rand Corporation to be used by the Department of Defense; however, in subsequent years, PPBS was embraced by a number of other types of organizations, including educational institutions, by the early 1960s (Birnbaum, 2000b). Other recent are management systems and processes that have emerged include Business Process Reengineering (BPR), and Benchmarking but there are far more than these two well-known examples. In fact, during the period from 1950 to 1990, business management researchers have identified at least 25 different management systems and processes that have been cited as the ideal solution to various organizations’ performance management needs, some of which have been adopted by institutions of higher education (Birnbaum, 2000b).
This unending stream of management approaches has been met with skepticism by some observers while others have readily embraced the newest and slickest package to come down the pipes. Unfortunately, these techniques are not “one-size-fits-all” approaches, nor are they adopted without a significant investment of time, resources and ongoing fine-tuning. Even in the best case scenarios, though, most of these management systems rarely provide the return on investment that their proponents tout, and it is little wonder that so much skepticism concerning the latest management fad abounds among many management theorists. For instance, according to Birnbaum (2000b), “As many managers will attest, the result has been a dazzling array of what are often perceived as management fads — fads that frequently become discredited soon after they have been widely propagated” (p. 3). Some authorities suggest that the adoption of fashionable management techniques may be due to a lack of vision and overall goals among educators today. In this regard, Enderlin-Lampe (1997) emphasizes that, “We just keep trying ‘flavor of the month’ innovations in a desperate search for a solution. This lack of systemic reflection is fundamental to long-term improvement [and results] from not having any real incentive to change from the status quo” (p. 150).
To help overcome this administrative and academic inertia, Enderlin-Lampe suggests that there are five basic areas that are critical to the process that must be taken into account when seeking to effect substantive improvement initiatives in educational settings: (a) vision and core values, (b) the schools’ strengths and weaknesses, (c) priorities and strategies for change, (d) goals, and (e) needed skills and resources. According to this author, “Before undertaking another reform, we need to know what we want from schooling and systemically reflect on the process for change” (Enderlin-Lampe, p. 151). Therefore, even the most effective management approach will fail to “deliver the goods” unless an academic institution understands what it wants to achieve and what resources are available to help it accomplish these goals. In other words, it is impossible to get from point a to point b unless the higher educational institutions identities what point b should be.
Indeed, unless even the best management system is integrated in a careful and holistic fashion with an enormous amount of ongoing top-down support, most of these methods stand little chance of success over the long-term. The situation becomes even more complicated, though, when these business-related management systems are adopted wholesale by institutions of higher education whose missions and goals are fundamentally different from their counterparts in the business world. Despite these constraints, Birnbaum (2000b) points out that, “The development and advocacy of new management approaches in both nonacademic and academic management continues, and at an increasing pace. In the business sector these new ideas are often presented as universally applicable quick-fix solutions — along with the obligatory and explicit caution that their recommendations are not quick fixes and will require substantial management understanding and commitment” (p. 4).
This is not to say, of course, that all management systems are flawed or none of them are appropriate for institutions of higher education; it is to say, though, that colleges and universities run a very real risk of jeopardizing their investment in these management systems without a careful review of what they are capable of doing for their organizations and whether the latest “management fad” is all that it is chalked up to be. When the term “fad” is applied to management systems such as BRP or benchmarking, it is with good reason in the case of institutions of higher education. While a business might well benefit in some ways or even enjoy truly spectacular success using something like ISO9000 to help it better manage its personnel and policies in ways that achieve its organizational goals, these techniques are not necessarily suitable for academic settings when they are adopted without giving careful consideration to what is involved in their use. In this regard, Birnbaum emphasizes that, “Many of these management innovations, when adopted by higher education, also exhibit the characteristics that define them as fads; they are usually borrowed from other settings, applied without full consideration of their limitations, presented either as complex or deceptively simple, rely on jargon, and emphasize rational decision making” (p. 4). Here again, this is not to suggest that all management systems are fads or that they are worthless; in fact, Birnbaum (2000b) points out that some techniques (the author cites fund accounting as a good example) may become adopted by a sufficient number of organizations to make their use standard and accepted. By sharp contrast, though, management fads remain outside the organization system and are used sporadically by different types of organizations which realize different levels of success through their use (Birnbaum, 2000b).
Although fads fail, the paradigm supporting them remains. As Kuhn pointed out, failure of a paradigm to solve problems does not by itself negate the paradigm; it merely suggests to its adherents that the puzzle has not yet been solved and that further work is necessary. For example, after acknowledging that TQM/CQI has been ignored or rejected by most potential users, advocates still point to a small number of limited but presumably successful programs to claim that the system does work; it just isn’t being implemented properly. It is typical to deny the failure of fads by arguing that others have used it successfully, that it takes time to overcome past practices, and that results will be achieved in the future (Birnbaum, 2000b).
Because of these compelling arguments, it is virtually impossible to develop sufficient information to the contrary that will convince adherents to a given “fad” that it is not effective, and it is therefore likewise virtually impossible to disprove a fad “works.” In his book, Management Fads in Higher Education: Where They Come From, What They Do, Why They Fail, Birnbaum (2000a), identifies seven management systems that he maintains satisfy the definition of “management fad”; these are: Planning, Programming, Budgeting Systems (PPBS); Management by Objectives (MBO); Strategic Planning; Zero-Based Budgeting (ZBB); Benchmarking; Total Quality Management (TQM) and Business Process Reengineering. Although Birnbaum (2000a) does not provide a summary of these business management systems per se, Fife (2003) presents an overview of these seven management systems in his review of Birnbaum’s text as follows:
Table
Summary of Seven Management Systems Classified as Management Fads by Birnbuam (2000a)
Management Fad
Summary/Description
Planning, Programming, Budgeting Systems (PPBS)
PPBS was concerned with creating a more rational approach at the beginning or input stage of a process by linking planning based on program priorities with budgeting. It was seen as a solution to the historically based, incremental budgeting systems (called Ur-Management by the author) that rarely questioned the appropriateness of a budget and had as a basic assumption that more money would solve any problem. As Hopkins and Massy (1981) emphasize, “In 1973, Schroeder reported that, there were “no successful ongoing applications of a comprehensive PPBS” in, higher education. To our knowledge none have occurred since” (p. 7).
Management by Objectives (MBO)
MBO attempted to bring some control to an organization’s output or what an organization should actually be doing by identifying and holding people accountable for specific objectives or outcomes.
Strategic Planning
Strategic planning came into existence because existing planning techniques primarily focused internally on an organization’s priorities and did not take into consideration how external events might also affect an organization.
Zero-Based Budgeting (ZBB)
The underlying assumption of ZBB was that both PPBS and MBO blindly accepted an organization’s existing priorities. ZBB required that budget priorities and assumptions be continually reexamined and just ified in order to qualify for future funding.
Benchmarking
This management system evolved as a more sophisticated form of MBO. While MBO was focused on existing organizational objectives, benchmarking assumed that an organization could be improve by studying and adopting the processes of another organization that was doing something better than anyone else.
Total Quality Management (TQM)
TQM attempted to bring together, as a total system, the concepts that related to the inputs of an organization (i.e., planning and budgeting); how an organization functioned or its processes (i.e., the major focus of benchmarking); and the outcomes of an organization (i.e., the basis of MBO). Fundamental to TQM was the assumption that if inputs, processes, and outcomes were seen as an interrelated and interdependent system, an organization would better understand the cause and effects of the various parts of organization’s processes and be able to make changes that would help an organization better achieve its vision and mission (i.e., create focused continuous improvement).
Business Process Reengineering (BPR)
BPR was also an extension of MBO and benchmarking, and was the result of the frustration people have with the slowness of change. Reengineering assumed that once reorganization or the implementation of new systems was determined as necessary in order to eliminate internal resistance, it was critical to make rapid changes in personnel, organization, and processes
Source: Fife, 2003, p. 470 unless otherwise indicated
From Birnbaum’s (2000a) perspective, these seven management innovations have been inappropriately adopted at one time or another by higher education institutions whose faculty members go on to invariably learn that these management systems are simply incongruent with their organizational culture and, following the expenditure of much time, effort and scarce resources, are ultimately abandoned.
Furthermore, Birnbaum (2000a, b) also provides a “life cycle” framework to explain the manner in which management fads are initially developed in the business world to be subsequently replaced with the hottest new “fad” to come along. During the decline of such fads, administrators and educators in higher educational institutions become aware of them and seek to take advantage of what the latest fad has to offer their schools. Not surprisingly, the majority of these initiatives eventually also fail and are abandoned by institutions of higher education (Birnbaum, 2000a). According to Fife (2003), Birnbaum (2000a) “determines the genesis, adoption, and rejection of these innovations through a review of the literature. For each new management innovation there is a flurry of literature proclaiming its benefits, some examination of its actual successes, and lastly, usually with great glee, the failure and rejection of the innovation by some organizations” (p. 470).
Finally, Birnbaum (2000a) concludes that through this cycle of literature it is possible to identify the birth and death of each innovation and, therefore, establish the appropriateness of labeling each management innovation a fad (Fife, 2003). Birnbaum has helped focus on their advantages, disadvantages, and the common features of both. Two of the most significant issues that have been identified are that when innovations are incongruent with the values of an organizational culture and when there is a lack of training to implement these innovations so that they are congruent with these values, the implementation of innovation will always fail (Fife). In this regard, Lueddeke (1999) reports that while there is a growing recognition that the decision-making framework used by many institutions of higher education are affected to a large extent by various external factors, there are also a number of sound reasons that support focusing on the building and management of institutional culture(s) that are congruent with a participatory decision-making approach, including the following:
1. A healthy culture can promote identification (who we are),
2. Legitimation (why we need to do);
3. Communication (with whom we talk);
4. Coordination (with whom we work); and,
5. Development (what are the dominant perspectives and tasks) (Lueddeke, 1999, p. 236).
In recent years, there has also been a growing recognition that organizational leaders in higher education play vital roles in institutional processes, that institutions have goals, that individuals can specify their preferences, that chains of cause and effect lead individuals and organizations to take certain actions in order to effectuate outcomes they consider desirable, that problems are solved by decisions, and that decision making is a primary occupation of organizational participants (Lueddeke).
A generic framework that would provide decision-makers in higher education with this ability would encourage collegial and collaborative (as opposed to strictly authoritarian or managerial) decision making, emphasizing linkages and relationships, not structures; process is active, authentic, social, and collaborative, because it occurs during the process of development and involves a team of participants who cooperate to make decisions. As Lueddeke emphasizes, “Team-oriented leadership assumes that differences exist among people; it searches actively and affirmatively for them and seeks to bring them to light” (p. 236).
The importance of participatory decision-making models in higher educational settings is also noted by Noble (1998) who suggests that many decision makers at the upper echelons of academia may simply be embracing the latest management fad so as to avoid “getting left behind.” In this regard, Noble reports, “It is no accident, then, that the high-tech transformation of higher education is being initiated and implemented from the top down, either without any student and faculty involvement in the decision-making or despite it” (p. 38).
Notwithstanding any changes that have taken place since this study was compiled (1998), Noble cites some classic examples of how changes initiatives at various colleges and universities were implemented without any feedback from staff, faculty, students or professional associations. For example, Noble notes that, “At UCLA the administration launched their Initiative during the summer when many faculty are away and there was little possibility of faculty oversight or governance; faculty were thus kept in the dark about the new web requirement until the last moment. UCLA administrators also went ahead with their initiative, which is funded by a new compulsory student fee, despite the formal student recommendation against it” (p. 39).
Likewise, this author cites the initiatives of the York administration on the deployment of computer technology in education which were launched without faculty oversight and deliberation, much less any active student involvement in the process (Noble). In sum, Noble concludes that, “What is driving this headlong rush to implement new technology with so little regard for the pedagogical and economic costs, and at the risk of student and faculty alienation and opposition? A short answer might be the fear of getting left behind, the incessant pressures of ‘progress.’ But there is more to it. For the universities are not simply undergoing a technological transformation. Beneath that change, and camouflaged by it, lies another: the commercialization of higher education. For here as elsewhere technology is but a vehicle and a disarming disguise” (p. 40). Indeed, these points are made throughout the relevant literature:
1. Technological innovations can facilitate the decision-making process in various ways, but there is the danger of losing the human element in the process if automation is allowed to assume too much of the process in ways that detract from the ability of educators and administrators alike to provide the individualized level of services needed by students in all disciplines today.
2. There must be a high level of congruence between the organizational culture and whatever management approach is adopted in order for it to have half a chance of success, and a college or university must accept the fact that there are no “quick fixes” involved when adopting a new management approach and time must be allowed for inevitable problems to be resolved and for the system to become effective.
Conclusion
The research showed that decision makers in higher educational settings typically use many of the same decision-making techniques as their counterparts in the business world, but with some important differences noted as well. The decision making techniques reviewed in the Breadth component were shown to be appropriate for certain situations although there is no “cookbook” approach available to the decision-making process. Such decision-making models have assumed new relevance and importance in recent years as educational administrators are increasingly confronted with a widening range of decisions than span the continuum of educational problems from routine decisions, such as the purchase of supplies, to more innovative decisions as described by Drucker such as whether curricular offerings should be continued, or reinstated if removed from a previous curriculum. Such decision-making settings are frequently convoluted and are typically complex, but most of them in higher educational settings have serious implications for the decision maker, the constituent, and the organization alike. In resolving most decisions, educational leaders must first determine who should be responsible for making the decision before they can determine which decision-making approach is best suited for a given purpose. Educational leaders frequently must become involved in decision-making triads in which faculty members, staff and students will all seek to resolve peer dilemmas in appropriate ways. In those cases where the decision is deemed to be more appropriately made by administrators themselves, they should attempt to collect as much relevant information as possible concerning the issues at hand, develop a prioritization of these issues as they relate to the decision dilemma, and formulate an appropriate decision using one of the methodologies described herein. The choice of what decision making apparatus will provide the “most bang for the decision buck” though will ultimately be a highly subjective choice with some decision makers preferring to base such decisions on past experience in an intuitive fashion while others will seek to maximize the value of the decision by codifying all of the relevant variables in a matrix format or an influence diagram that can help illustrate the eventualities that will result from a series of “what-if” type scenarios. In the final analysis, whatever decision-making methodology is used to improve the quality of the decisions reached, it is important to take into account the unique values and qualities of the educational institution itself while maintaining a focus on garnering strategic alliances and building coalitions of support to help effectuate the decision reached.
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PART 3: The Application Component
A.
Objectives. The objectives of the Application Component are two-fold as follows: (a) to examine the decision making process used by Zomba University Isoka Campus in launching a new program and apply the decision science theories that were learned and demonstrated in the preceding components; and (b) to develop a decision model for analyzing the feasibility of a new program at Zomba University Isoka Campus and make recommendations to the university’s director.
B.
Learning Resources. The materials to be reviewed and interpreted in this part include, but are not limited to, the resources listed at the reference page.
C.
Criteria for Evaluation. In the paper of approximately 20 pages, I will first explain the process used by Zomba University Isoka Campus in launching their new program (the background of the university is provided at Appendix a). Using the model analyzed from the Breadth and Depth components with additional relevant materials, an analysis of the decision to launch the new program will be provided. After assessing implications of the new program, I will propose the methodology to the university’s director for use as a protocol for evaluating other programs that will be launched in the future.
Review and Analysis
Examination of the decision making process used by Zomba University Isoka Campus in launching a new program and apply the decision science theories that will be learned and demonstrated in the preceding components.
The process used by Zomba University to launch the new program in question was based in large part on the increasing availability of innovations in technology that allowed the institution to expand its offerings to nontraditional students in ways that were not possible in the past. In this groundbreaking endeavor, there were a number of factors to take into account because in many ways, there were no precedents to follow or relevant guidelines available to provide a set of best practices. What emerged from the decisions that resulted from this need were focused on the provision of high quality educational services with the interests of the students involved foremost among the relevant issues.
In 1998, Zomba University Isoka Campus was established in the Isoka province of northern Zambia. Its mandate is to increase and disperse higher educational opportunities for Zambia students in rural area. By integrating two-way communication videoconference technology and processes with its traditional instructor led classroom delivery system, Zomba University Isoka Campus is able to offer Zambia students undergraduate and graduate level degrees through this approach. While the university’s strategy has succeeded in providing the university with a large increase in its student population, it has also presented the university with additional challenges.
First, the dramatic growth in student enrolment from 100 in 2003 to 8,056 in 2007 is not accompanied by a corresponding increase in infrastructure. This naturally resulted in overcrowded lecture halls and other facilities. Under such conditions the teaching and learning process is bound to be very ineffective.
Second, staff recruitment is far less than the growth in student enrolment so the staff-student ratio is high at the Zomba University Isoka Campus. This also rendered teaching and the supervision of student research very difficult.
Third, Laboratory equipment is grossly insufficient for the number of students enrolled for such courses. This either resulted in students shifting to other faculties or ineffectiveness in the teaching and learning process.
The last most important is that the programs offered by the university do not correspond to the academic needs of the tens of thousands of students who left secondary school every year. This is because new programs offered each year which should be informed by an objective decision capability is inherently dominated by administrator’s intuition or guess. These factors affected both the motivation and possibilities of the students and therefore resulted in low academic performance and capacity.
The curricula offered each year do not correspond to the demand of the expanding private sector, market forces and the increasing tendency of the government towards retrenchment and down sizing of the public service manpower. The number of unemployed graduates is growing in the society. This is mostly due to the fact that the skills acquired in the university are highly inadequate for the requirements of the labor market. The general picture of the university community is that if a demoralized and de-motivated academic and non-academic staff. The situation is highly compounded by the following problems:
1. Lack of an Active Strategic Plan: The University does not have a current Strategic Plan. Although the university has a clear vision and mission statements (attachment 1), they do not transform these statements into Strategies and Time Bound Objectives; i.e., development of strategic plans. Factors such as rapidly changing system priorities, changing university leadership, severe budget reductions, and economic/demographic challenges, have all impeded university planning efforts.
2. Lack of a Planning Process: The university constituents indicated that the planning initiatives of the past several years seem to be centered upon top level university planning. There is no strategic planning process, planning is uneven, there is a lack of horizontal and vertical coordination among planning groups, planning is not specifically tied back to university priorities and mission, few department heads lead their staffs in strategic planning activities, input is not gathered from all levels of the university, and there is no staff development on how to conduct and organize planning. When planning is selective, not broad-based or systematic, the university’s leadership risks overlooking critical and relevant needs. Parts of the institution, even basic ones, may be left behind. Priorities and resource allocations set under these conditions may not accurately match the university needs or best interests.
3. Lack of a Resource Allocation Process Linked to Planning: Most university constituents indicated that they did not believe that there was any linkage between planning and resource allocation. The budget process is top-down and departments give little input on decisions, there is no explanation of resource allocation decisions, there is a lack of goal clarity which makes linkage to resources difficult, faculty have a difficult time understanding or accepting resource allocation decisions, the budget situation rather than programmatic need appears to dictate personnel decisions, and resource allocation information is not readily available. The university budget model is not shared with or explained to campus constituents on a regular basis, and it does not clearly demonstrate linkages to planning efforts.
4. Lack of Effective Communication: Campus constituents were generally dissatisfied with communications regarding planning, resource allocation, and assessment activities. This is not surprising, for so far the creation of a planning process has concentrated on the upper leadership of the university. Constituents felt that not all administrators communicate or explain planning and resource allocation decisions to unit personnel; the top-level administration does not consistently communicate its actions to departmental chairpersons and staff members; employees do not always understand the relevance of planning efforts to their individual units; most unit heads do not engage in strategic planning; and reciprocal communication needs to be improved. It is evident that personnel cannot be very engaged in the strategic planning process, and that communication, including engagement, requires improvement.
Taken together, it is clear that a number of constraints and problems were subsequently identified following the launch of the distance learning initiative. This is not necessarily surprising or unusual, though, given the circumstances described above wherein there were few relevant guidelines available for the implementation of the program and its administration. Nevertheless, some of the lessons learned from this experience provide some valuable insights for future efforts of this type as well as for other types of decision-making needs.
Table __ below provides an overview of the decision-making methods described in the Breadth and Depth components, as well as their respective strengths and weaknesses for institutions of higher education.
Table
Respective Strengths and Weaknesses of the Decision-Making Methodologies
Decision-Making Methodology
Strengths for Institutions of Higher Education
Weaknesses for Institutions of Higher Education
Matrix analysis
This analytical method is a planning tool that highlights objectives, evaluations, and resources along a continuum of three domains: (a) knowledge, (b) performance and (c) attitudes. Educators who are already familiar with matrix analyses will be able to begin translating their current course outlines into the matrix format following some general guidance (Alvir, 1975).
Matrix analyses are labor-intensive and require a significant amount of training in order to be used effectively (Alvir, 1975).
Influence diagrams
Used in combination with various software applications, influence diagrams can be a useful tool in courseware development; the mechanism can also be used easily by instructor and student because of its user-friendly interface, and it transforms the course content to a scalable content object reference mode (Chang et al., 2004).
Requires software and hardware that might not be readily available.
Payoff matrices
Payoff matrices show alternatives on the rows, contingent events on the columns, and payoffs in the cells from each alternative given the occurrence or nonoccurrence of the contingent event (Nagel & Mills, 1993). Payoff matrices can also identify what alternatives are most feasible and attractive (Kriesberg, 1973).
Complicated and frequently provides less valid results (Nagel & Mills, 1993). The primary difficulty in the use of payoff matrices is whether the options defined even by multi-dimensional, multi-choice payoff matrices bear sufficient resemblance to the “real” choices of “real” policy-makers for the structure of the matrix to be regarded as an analogue for the structure of the system under study (Goodin & Klinglemann, 1998).
Sensitivity analysis
Sensitivity analysis is very useful when attempting to determine the impact the actual outcome of a particular variable will have if it differs from what was previously assumed. By creating a given set of scenarios, the analyst can determine how changes in one variable(s) will impact the target variable (Sensitivity analysis, 2009).
The accounting-based values used in the sensitivity analysis model must be estimated very carefully to obtain realistic results (Bryan & Whipple, 1995). Models must be ‘fit for purpose’ and deliver the outputs needed. In addition, models for decision-making in higher educational settings must be driven by business needs rather than the availability of data. Where the required data is not immediately available then specific exercises may be required to collect that data. Failing that, assumptions may have to be made which will then comprise factors of uncertainty to be considered when undertaking a sensitivity analysis (Prowle & Morgan, 2005).
Decision trees
Decision trees represent a combination of arrow diagrams and payoff matrices by showing a set of decision forks, probability forks, and other paths leading to a set of payoffs (Nagel & Mills, 1993).
Complicated and frequently provides less valid results (Nagel & Mills, 1993).
Probabilistic forecasting
A probabilistic forecasting is used primarily for meteorological applications, nevertheless, a probabilistic forecasting model was to Australia to generate 2002-based population forecasts for Queensland and the rest of Australia in ways that might be of benefit to colleges and universities seeking to identify future demographic shifts in their target populations (Wilson & Beli, 2007).
A number of constraints are associated with this technique that may prevent its widespread adoption by statistical offices in other countries (Wilson & Beli, 2007). Likewise, Kok, Vogelezang and Schmeits (1999) emphasize that, “The interpretation and use of probabilistic forecasts is a notoriously difficult subject for the untrained mind” (p. 37).
Multi-attribute utility analysis
Multi-attribute utility analysis (MAUA) has emerged as a powerful tool for materials selection and evaluation. An operations research technique, MAUA has been used in a wide range of disciplines, of which materials science and engineering is one of the more recent. Utility analysis affords a rational method of materials selection which avoids many of the fundamental logical difficulties of many widely used alternative approaches (Roth, Fields & Clark, 1999). The multidimensional nature of program and college goals as well as the number of stakeholders involved in reallocation decisions in higher education requires the unique methods and procedures that multi-attribute utility analyses provide (Lewis & Kallsen, 1993). The multi-attribute utility analytical approach can be supported by an increasing number of powerful and sophisticated software programs designed for this purpose (Bryson & Anderson, 2000).
MAUA has traditionally been used in selection problems only in which there is certainty regarding the attribute levels of the alternatives (Roth, Fields & Clark, 1999).
Sources: As indicated.
Development of a decision model for analyzing the feasibility of a new program at Zomba University Isoka Campus and recommendations for the university’s director.
Today, overall educational levels in Zambia are increasing but many rural development policies remain overly cautious (Grant, 2007). It is clear, though, that distance learning initiatives that can take maximum advantage of the Internet-based resources that are available hold enormous promise for providing increased access to higher educational services compared to traditional brick-and-mortar institutional settings.
The research shows that there are a wide range of decision-making methodologies available that hold some value for the educational decision maker, but some are clearly better suited for some applications than others. Payoff matrices and sensitivity analyses, for example, could provide some useful “what-if” scenarios concerning the benefits and disadvantages associated with various approaches to a decision dilemma, while decision trees could be used to illustrate the various permutations that would result from a given set of alternatives, some of which might not be readily apparent otherwise.
One of the more important criterion against which decision-making models were judged was their ability to include the views and insights available from all stakeholders who would be affected by a given decision, and these are particularly relevant in academic settings where proposals for innovation potentially represent some fundamental changes in the status quo. For instance, Kotelnikov (2001) makes the timely point thatt, “Rapid innovation requires an effective innovation process” (p. 1). A number of collaborative approaches to innovation have been developed and used with mixed results over the years, and these steps are described further in Table __ below.
Table
Steps Involved in Effecting Innovation in Organizational Settings
Approach
Description
Holistic Approach
Innovation represents the primary source of competitive advantage for companies of all types in all types of industries, and is vital to competitiveness and firm performance. From a holistic perspective, the innovation process is comprised of a number of components, including new product development, strategy innovation, creative approaches to decision making, idea management, suggestion systems, and so forth. All of these constituent elements remain essential to some extent throughout the process; however, applying them in a piecemeal fashion will, not surprisingly, result in piecemeal outcomes. As a result, savvy managers will seek to integrate these different components in meaningful ways that can help a company achieve its innovative goals and improve its profitability. Consequently, achieving systemic innovation requires a holistic approach.
Publicly Defined Innovation Process
Organizations that are able to learn from their successes and failures will be better situated to take advantage of new opportunities in the marketplace and this lesson applies to achieving successful innovation processes as well. Just as many people are uncertain about what they think about a given topic until they express their opinions in public and receive feedback from others, it is difficult for organizations to innovate without guidelines to help them through the process. Publicly defined innovation processes, like mission statement and business plans, serve as roadmaps that can help organizations better define and implement their innovative processes.
The Traditional Phase-Gate Model
This is the oldest and remains the most common approach to innovation in the world today. The phase-gate model to innovation divides the innovation process into a series of sequential steps, with so-called “gates” that must be successfully passed through in order to achieve the next phase. In ideal situations, these phases and the criteria that define their success are established well in advance (by so-called “gatekeepers”), but this is not always the case of course; however, this approach includes a “design freeze” feature that provides a stable target for the reminder of the innovation process.
Radical vs. Incremental Innovation
When the level of uncertainty is low, incremental innovation projects, typically use orderly phase-gate process. By contrast, radical innovation projects employ the flexible model when there are high levels of uncertainty.
Source: Kotelnikov, 2001 at pp. 3-4.
Given the importance of identifying a decision-making model that provides opportunities for participation by all affected stakeholders when innovations are introduced, selecting the appropriate model requires a review of the available decision-making methodologies for their appropriateness and utility in providing the decision maker with what needs to be known when it is needed. Although it is reasonable to posit that there are as many decision-making styles as there are decision makers, some authorities have categorized these decision-making styles in general ways as follows.
Table ____.
Comparison of the Impact of Authoritarian, Participative and Delegative styles of Decision Making.
Decision-Making Style
Description of Impact
Authoritarian
Much direction and little collaboration, step-by-step specification of what must be done and accurately controlling the task performance.
Participative
Little direction and much collaboration; joint decision-making with employees and direct support in task execution.
Delegative
Little direction and little collaboration, leaving decisions about tasks as well as the responsibility for these decisions to employees.
Source: Volberda, 1999, p. 170.
Finally, the Breadth and Depth components clearly demonstrated the need for a close congruence between the organizational culture in place at an institution of higher education and whatever management approach was used. This was determined to be an especially important point when considering adopting new management approaches (termed “fads” in many cases) that hold the promise of delivering the goods but which require a considerable amount of time and effort to achieve. To this end, in it possible to change the organizational culture to more closely align it with the desired management approach, although here again, such efforts require an investment of time and resources and cannot be expected to produce substantive results overnight. Some of the more viable methods that have been described for changing organizational cultures are described in Table __ below.
Table
Common Methods used to Align Organization Cultures with Management Techniques.
Functions
Authoritarian climate
Democratic climate
Laissez-faire climate
Policy determination
By group discussions and decision, assisted by the leader.
Freedom for group or individual decision without leader participation.
Task activities
Techniques and steps dictated by the leader one at a time.
Perspective gained during first discussion, steps to goal sketched by group, technical advice provided by the leader in the form of multiple alternatives from which group makes choices.
Materials supplied by leader; leader also indicates information is available if needed. Leader not involved in work discussions.
Division of labor and companions
Dictated by the leader.
Members chose own work companions and group decided division of labor.
No participation by leader.
Praise and criticism by leader
Personal by leader; aloof from group participation; friendly or impersonal, not hostile.
Objective or fact-minded; a regular group member, but without doing a lot of the actual work.
Very infrequent; only when questioned; no participation in course of the work.
Source: Miner, 2002, p. 42.
Taken together, though, of the available decision-making methodologies, it would appear that the multi-attribute utility analysis provides the most robust results for decision-making purposes for many educational leaders today assuming that the appropriate and most relevant factors are used because of its ability to incorporate disparate views and insights from a number of perspectives. To the extent that these variables are on-point, though, is likely the extent to which the resulting decision permutations will provide viable alternatives for the decision maker. Because there is a need to include feedback from the affected stakeholders in many decisions required by educational leaders, it is important to employ a decision-making methodology that can incorporate these views and perspectives as well. In this regard, Manz, Bastion and Hostanger (1991) note that participative decision-making by organizational leaders has been conceptualized along a number of lines that involve the participation of employees in different ways, including the following:
1. As an obligation to work in the best interest of the group;
2. As ego involvement;
3. As a managerial style;
4. As a legally mandated approach in which employees can influence decisions;
5. As elected employee representatives in management meetings;
6. As a form of delegation; and,
7. As a mechanism for power sharing (Manz et al., p. 276).
In this regard, as noted in the Breadth component, there are some definite advantages that accrue to the use of the multi-attribute utility analytical approach including the fact that it is a more participatory decision-making methodology that allows for the inclusion of key stakeholders in the decision-making process, including both faculty and students, which will likely contribute to the credibility of the resulting decision. Furthermore, there are an increasing number of powerful and relatively inexpensive software applications that can be used to facilitate and support the multi-attribute utility analytical approach. In this regard, Bryson and Anderson (2000) report that “Sophisticated multi-attribute utility analysis tools [can be used] to make resource-allocation decisions among or across policy options with which to pursue the mission, vision, and goals” (p. 143).
Because the University has a computer network that could be used for this purpose as well as a fiber optic backbone that connects all academic buildings including the library and the two residence halls, the use of the multi-attribute utility analysis could be facilitated through the use of an Intranet that would provide a forum in which all affected stakeholders could weigh in on the issues at their convenience. This approach would provide an effective way to quantify the respective views of faculty, staff and students alike and would provide the benefits of participatory decision making that were shown to be important considerations in higher educational settings.
Decision makers using the multi-attribute utility method should first determine who is responsible for making the decision, of course, with the next steps involving the identification of the criteria against which various alternatives will be measured. The responsible decision maker then assigns each a priority score on a scale of 1 to 20 to each of these various alternatives. These figures are subsequently transformed into what are termed rationalized priority ratings (RPR) through a calculation of the rating of each criterion as a percentage of the total ratings. The alternatives are then analyzed in turn by comparing them against each of the evaluation criteria (Fisher, 1998).
A decision alternative that performs well against a criterion will score highly on a scale of 10 to 100 while a low performing alternative will likewise score poorly. Once all these assessments are made, utilities are calculated by multiplying proposals’ scores for effectiveness on each criterion by the rationalized priority ratings described above. The resulting products of all these calculations are summed, thereby providing the decision maker with an overall utility score for each decision alternative. The scores can then be used to place the alternatives in their order of rank according to these findins. All other things being equal, and the decision maker being satisfied with the decision-making methodology, the highest ranking alternative should represent the best one available. This authority emphasizes that, “Multi-attribute utility analyses give arithmetical flesh to the logical bones of formal decision making” (Fisher, p. 31).
This recommendation, though, should not be construed to mean that every time “situation a” is encountered that “decision-making methodology b” should be used because every decision dilemma will involve unique variables that may not be conducive to the multi-attribute utility analytical approach; it is to say, though, that the MAUA approach provides the decision maker with some valuable insight concerning potential outcomes and allows for a participatory decision-making approach that is not available with many of the other decision-making methodologies reviewed herein.
Finally, whatever decision-making methodology is used, the foremost issue that should remain paramount is the need to make the well-being of students the fundamental value of all decision-making and resulting actions.
Summary and Conclusion
The research was consistent in demonstrating that there are a wide range of decision-making models, as well as several permutations of each of these techniques, available for decision makers confronted with decision dilemmas that require a thoughtful and informed approach to identify superior alternatives. The research was also consistent in emphasizing that there is no “one-size-fits-all” approach to decision making that can be used in all situations and settings to achieve the best possible outcomes. What did emerge from the review of the relevant literature, though, was the need to approach the decision-making process in a step-wise fashion, first identifying who should make the decision and then moving on to subsequent steps such as which decision-making model is best suited for a given set of circumstances and what factors should be included — and excluded — from the analysis. Some decision-making methodologies provided decision makers with graphic perspectives of the possible outcomes while others rely on numeric-based approaches.
A common theme that ran through all of these techniques, though, was that even the most structured decision-making approach typically involved a number of subjective assessments on the part of the decision maker that would inevitably affect the quality of the decision that resulted. In a perfect world, decision makers would have access to absolutely reliable information when they needed it, but alas, this is not the case and decision makers at all levels are forced to rely on what is known and frequently make some informed guesses concerning the relative importance of the variables involved. The research also showed that decision making can be improved, though, with practice and many effective decision makers use past experience to help them determine what decision making approach is best suited for a given problem without having to resort to a formal analysis or quantitative model.
Based on the foregoing considerations and factors, it is reasonable to conclude that even armed with a smorgasbord of decision-making alternatives, many educational leaders will continue to rely on what works best for them, whether this is an intuition-based approach or one that involves the use of various computer-based expert systems to help them identify superior alternatives in an imperfect world. Although many of the decision-making models reviewed herein required a consideration amount of effort to learn and use, many authorities suggest that the payoff is worth this investment and that the multi-attribute utility approach can provide academic leaders in particular with some valuable benefits for their decision-making needs.
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Appendix a
Background of Zomba University Isoka Campus
Zomba University Isoka Campus is striving to be upgraded as an autonomous state university specializing in both social science and technology in order to produce higher qualified and internationally standardized graduates. The university is also aiming to construct novel body of knowledge by seeming out partnership and to establish academic network domestically and internationally so as to achieve the state of leading academic excellence. In addition, the university will serve as an academic resource for guiding societies and communities and for building up Zambia people’s awareness to perceive the needs of changing of thinking process, attitudes and working system for effective development of the nation. Furthermore, the university will also train its graduates to pursue changes in global societies, be able to utilize knowledge and technology wisely and appropriately and be flexible for any changes. Importantly, students have to well-equipped with good morals also.
Zomba University Isoka Campus as a highly recognized and standardized institute is committed to widening access to higher education and create equal educational opportunities for students particularly in the northern part of Zambia. The curriculum is divided into two branches: one is social science studies; the other is science and technology in accordance with the needs of society and the country. Moreover, the university participates in many aspects of community services and has significant aim as follows:
1. Producing graduates. Zomba University Isoka Campus has the main continual mission to develop human resources at all levels with the hope that the human resource development is crucial factor for the sustainable growth of the country and helps move aside from economic stagnation. For these reasons, the university focuses on educating students to be internationally well-trained and well-qualifies for all types of national and international work. Also, to produce both undergraduates and graduates, it is conducts with partnership and establishment of network with prestigious universities locally and internationally in order to upgrade lecturers’ potentials and academic standard. Besides, the university has to adjust itself as a dynamic university with diversities of objectives to develop national manpower at all level continually. The aims are also to develop students’ skill of work and local wisdom career. Students as national workforce must be equipped with the awareness of human being and being good members of Zambia and global societies. Simultaneously, the prospect of higher education must have diversities to cover those who aiming for and being in labor market.
2. Research. Zomba University Isoka Campus intends to support and develop all kinds of academic research, especially in applied studies, to enhance social development and national economic growth. For example, study of modern technology to improve manufacturing systems relying more on technology than man power or raw materials, study of the sufficient ways to depend on natural resources, restore nature and preserve the environment, study of the management of the public health and the list goes on. Moreover, Zomba University Isoka Campus will focus more on the parallel between fundamental and applied research. In so doing,
Zomba University Isoka Campus attempts to integrate several related primary studies as much as possible to create more advanced level of study. The outcome of applied research will not only be practical in university’s classes but also will indicate the national ability to rely on our knowledge in the process of developing the country. Zomba University Isoka Campus also plans to conduct this project by initiating the partnership or networking with other researches in both domestic and international universities to become world-class university.
3. Academic services. Zomba University Isoka Campus will contribute to the society the variety of academic services. Zomba University Isoka Campus, in some cases, to co-operate with the public organizations that have financial support, for example, public company limited and international industry. The university aims to collaborate with the public institutions by offering them academic services, such as the public testing center in various fields. This support will be held in partnership and networking systems in order to promote the university’s reputation as well as to receive public acceptance.
4. Art and Cultural Conservation. Zomba University Isoka Campus realizes that art and cultural heritage will become more and more important in the future. In the globalization world, Zomba
University Isoka Campus believes that the Zambia art and cultural awareness bring sustainable development of the nation in the context of cultural assimilation and social domination. The concept of cultural conservation is not simply limited to the national art and culture, but should extend to the true awareness and the pride of being Zambia. This realization helps maintain the cultural identity and enhances the feeling of love and awareness to improve the society. We expect that all the university’s members take pride of being Zambia.
Organizational structure: Degrees Available
a. Bachelor’s Degree
1. Bachelor of Science (B.S)
2. Bachelor of Business Administration (B.B.A.)
3. Bachelor of Accounting (B.Acc.)
4. Bachelor of Communication Arts (B.Com.)
5. Bachelor of Arts (B.A.)
6. Bachelor of Laws (LL.B.)
7. Bachelor of Engineering (B.Eng.)
8. Bachelor of Nursing Science (B.N.S.)
9. Bachelor of Public Health (B.P.H.)
b. Graduate School
1. Master of Science (M.S)
2. Master of Business Administration (M.B.A.)
3. Master of Arts (M.A.)
4. Master of Public Health (M.P.H.)
5. Master of Education (M.Ed.)
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