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Organizations make many informed decisions such as increasing production capacity, improving human capital, entering a new market etc. This paper shows that executives take either of the two major types of decisions: programmed (structured) and nonprogrammed (unstructured) decisions. While the programmed decisions are for perfectly stable situations, the nonprogrammed decisions are for the real world situation surrounded by uncertainties, risks and ambiguities. For an optima value creation, this paper is succinct that a robust decision theory and analysis serve as a precursor. The environment of decision-making keeps changing and it takes decision-making for organizations to change proportionately to these environmental changes if they must survive. The decision-maker uses probability values to convert uncertainties and risks into perfect knowledge poles so as to make informed decisions. Models are veritable decision making tools and are deterministic and probabilistic (or stochastic) for programmed and nonprogrammed decisions respectively. Real-world value optimization in this paper centres on decisions under pure uncertainty and risky situations generating model fits for an optima value creation. Finally, the optima value creation models under the uncertainty and risk are suggested and organizations advised to use professional decision theorists and analysts as the need arise.

Almost all human endeavours involve decisions, and consequently, success in business and industry is the sole preserve of the quality of decision-making. This quality of decision-making and the technical progress amongst societies are inextricable and diametrically symbiotic. Theorizing about decision-making is almost the same as theorizing about human endeavours. Concomitantly, a Hegelian approach about decision-making discourse is same as Hegelian approach with human endeavour discourses. However, decision theory and analysis are not quite as all-embracing as it focuses only on some aspects of human endeavours. In particular, it focuses on how the decision-maker (or executive) uses his/her freedom among the myriad of decision choices [

Decision-making is not an endless process but a discontinuous one. In the history of almost any endeavours, there are periods of decision-making being crafted and other periods in which most of the decision-making implementation takes place. Decision theory and analysis try to throw some light, in various ways, on the former type of period (i.e. the period of crafting decision-making) [

As organizations responds to diverse environmental changes, the decision-making process involves the clarification of objectives, the specification of problems and the search for implementation of solutions. Thus, the organization is seen as an information-processing network with numerous decision points. An understanding of how decisions are made helps in understanding behaviour in organizations with mechanisms by which conflict is resolved and choices are made [

The understanding of the operation of decision theory and analysis in shaping behavior in organizations and society’s decision process is very crucial. Also, the critical synthesis of theoretical and conceptual foundations of decision theory and analysis considering their differences and common grounds for decision approaches in a wide range of corporate and public spheres is invaluable. The act of decision making, whether under certainty, uncertainty, risk and ambiguity is founded on both subjective and objective realms of human behavior. Thus, the key theoretical issues of decisions under certainty, uncertainty and risky conditions dealing with deterministic and probabilistic (stochastic) outcomes together are very necessary a precursor for optimal value creation in organizations and business. The theoretical views from the foundational theories as they relate to the factors that influence organizational abilities for decision-making based on modeling, alternative choices, and past occurrences in the decision environment are brought to bear in this study. The in-depth synthesis of these theoretical issues directed to management theory and organizations with respect to why, what, how and when issues of planned (structured) and unplanned (unstructured) decisions for optimal value creation are also explained in this study.

Finally, decision-making for optimal value creation in organizations requires answers to questions, such as; what are the key issues and decision variables interacting that should be considered? What are the situational and environmental variables that form the domain of decision-making for optimal value creation in organizations? How can organizations formulate direct functions (or models) for optimal value creation of their resources? These above posted questions represent the knowledge gaps calling for this research study. Since the real world issues are masterminds of uncertainty and risk challenging executives and managers decision-making, decision analysis for optima value creation are detailed in this study for probable decision solutions. Lastly, the study is arranged into five sections with the first foregoing section highlighting the study’s overview. The second section discusses the problems of the study while the third section deals with theoretical framework and reviews. The fourth section discusses methodology and analysis, with the fifth section closing the study with discussions, conclusion, and recommendations.

Organizations are living organisms existing as bio-corporate ecological units in the global business ecosystem. The business ecosystems economy is controlled by many factors such as population size, industrial activities, agriculture, government policies, culture, educational system, infrastructure and etc. The process of need satisfaction in the business ecosystem constrains people and government to engage in various socioeconomic activities for production of goods and services. Policies and guidelines of the central and state governments facilitate the integration, coordination and control of all activities with the main objective of optimizing (maximizing or minimizing) socioeconomic growth and development. Suffice it to say that, the entity of the economy, at every point in time, is influenced by certain forces to have competitive role in order to maximize its productivity for its sustenance, continuity, growth and survival [

where; n = total period in consideration, in this case, n = 1 year (12 months); t = (0, 1, 2, ××× n),

This is a general form of decision-objective function for optimizing productivity. Decision analysis and theory is the heart of it as it assesses all combinations of variables and criteria for optimization. In any business ecosystem, this productivity is affected by many ecological and environmental factors that are of quantitative and qualitative characteristics. Thus, the development of models using the data of these factors augmented with a detailed survey analysis is the decision theory and analysis which is largely informed by the logic of rationality and the mathematical/statistical theory of probability.

Decision-making in business and organization is not an easy task and hardly follow any consistent procedure. where decisions are to be made abound. Changing times and environment evolve situations which business and industrial problems are becoming more and more complex [

Organizations today face major business crises and human-accelerated environmental changes worldwide. Arguably, there is a critical need for evidence based information to guide business policy. Most times, business decisions are taken with certainty and/or uncertainty and are pivotal to the future survival of the organization. In most cases, the managers who take these decisions will not know whether they have the right choices. They have to take these decisions against the backdrop of uncertainty or risk [

1) How do people make decisions under certainty and uncertainty and what do they consider desirable or the subjective element in their decision process?

2) What objective and subjective probabilities do decision making assign to the occurrences of different desirable outcomes in business and organizations?

3) What are the best methods of rationalization and designing decision models to inform effective decision process?

This study’s intention is to provide answers to the manner, style and what situational variables influences in decision-making. Many a managers would wish organizations to operate a perfectly stable environment which they can easily make informed decisions. This is a perfect and utopian business ecosystem with a fixed organizational environment. The real world however presents a different business ecosystem which change is continuous and the only constant with challenges that dreads the corporate existence of business and organizations requiring managers and executives to make hard and right-fast decisions. This study explored decision-making situations and the corresponding strategies to clone a fit and congruence in decision-making and decision-objective function for organizational superior performance (optima value creation).

Seminal research pointed that, “a theory is an organized body of concepts and principles intended to explain a particular phenomenon” [

The theoretical framework for this study is shown below in

Decision theory and analysis is the combination of descriptive and prescriptive business modelling approach to classify the degree of knowledge. The degree of knowledge is usually classified in order from ignorance-uncer- tainty-risk-certainty. The complete knowledge (or certainty) is on the far right and complete ignorance is on the far left. Between the two are risk and uncertainty. Decision theory and analysis provides an analytical and systematic approach to depict the expected result of a situation, when alternative managerial actions and outcomes are compared [

risk and ambiguity. These are situations in which a decision is made, an event occurs, another decision is made, another event occurs, and so on.

In a perfect world, organizations would have all the information necessary for making decisions. In reality, however, some things are unknowable, thus; some decisions will fail to solve the problem or attain the desired outcome [

Every decision situation can be organized on a scale according the availability of information and possibility of failure. These four positions on the scale are certainty, risk, uncertainty, and ambiguity. Whereas programmed or structured decisions can be made in situations involving certainty, many decisions that managers’ deal with everyday involve at least some degree of uncertainty and require nonprogrammed or unstructured decisions making. From the foregoing, it can be generalized that; decision theory is a continuum and the central part of decision science with programmed decisions and nonprogrammed decisions at the continuum extreme ends for effective decision problem solutions.

It is desirable to know the details about resources (such as managers, employees, equipment, finance and etc.) that are required to carry out policies of the organization and at the same time keeping in mind the social and ecological environments in which the organization functions. Knowledge of such factors will help in modifying the initial set of decision-makers’ objectives. The decision environment is the situational framework within which the decision is taken [

Certainty―This is the situation where the outcome of a specified system of decision can be predetermined with exactness or certainty. It is a determinate situation which each action will lead to only one or same outcome. In this type of decision situation, the decision maker knows without doubt the outcome of every alternative courses of action because all the information the decision maker needs is fully available [

Risk―This means that a decision has clear-cut goals and that good information is available, but the future outcomes associated with each alternative are subject to chance. However, enough information is available to allow the probability of a successful outcome for each alternative to be estimated [

Uncertainty―Managers know which goals they wish to achieve, but the information about alternatives and future events in incomplete. Managers may have to make assumptions from which to forge the decision even though it will be wrong if the assumptions are incorrect. Some of the questions concerning decision maker under uncertainty include: Will the product catch up with the market? Will the new branch of business be successful? [

Ambiguity and Conflict―Ambiguity is by far the most difficult decision situation. Ambiguity means that the goals to be achieved or the problem to be solved is unclear, alternatives are difficult to define, and information about outcomes is unavailable [

The approach managers use to make decisions usually falls into one of the three types: the classical model, the administrative model or the political model. These models and their various characteristics descriptions and differences are as shown in

The end-point of decision theory and analysis is model-building as it is both quantitative, quantitative and mixed-method (i.e. quantitative-qualitative). With respect to organizations objectives, alternative strategies and decision-making environment, there are basic theoretical constructs that are premises in decision theory for decision model-building to aid effective management decision-making. Through modeling, the assumed real world is abstracted from the real situation by concentrating on the dominant variables that control the behavior of the real system. The model expresses in an amenable manner the mathematical functions that represent the behavior of the assumed real world. In other words, effective model must be representative of reality that is being investigated and have major impact on the decision [

1) Validity―How the model will represent the critical aspects of the system or problem under study;

2) Usability―Whether the model can be used for the specific purposes;

3) Value―Attain value expectation of the user.

The conditional characteristics and dimensions of decision theory models are classified into eight different but interacting variables. These are: function, structure, dimensionality, and degree of certainty, time reference, and degree of generality, degree of closure, and degree of quantification. However, this study discusses models

Classical Model | Administrative Model | Political Model |
---|---|---|

Clear-cut problem and goals | Vague problems and goals. | Pluralistic, conflicting goals |

Condition of certainty | Condition of uncertainty | Uncertainty/ambiguity conditions |

Full information about alternatives and their outcomes, | Limited information about alternatives and their outcomes | Inconsistent viewpoints; ambiguous information. |

Rational choice by individual for maximizing outcomes. | Satisficing choice for resolving problems using intuition | Bargaining and discussion among coalition members. |

based on degree of certainty and are basically two types: the deterministic models and probabilistic or stochastic models.

Deterministic Models―If all the parameters, constants and functional relationship are assumed to be known with certainty when the decision is made, the model is said to be deterministic. Thus, in such a case where the outcome associated with a particular course of action is known, i.e. for a specific set of input values, there is a uniquely determined output which represents the solution of the model under conditions of certainty. The results of this model assume a single value. Examples of this model include linear programming models, input-output models, and activity models, Transportation and assignment models, etc. [

Probabilistic (Stochastic) Models―These are models in which at least one parameter or decision variable is a random variable. Since at least one decision variable is random, the independent variable(s), will also be random. This means consequences or payoff due to certain changes in the independent variable cannot be predicted with certainty. However, it is possible to predict a pattern of values of both the variables by their probability distribution. Stochastic programming models and Bayesian models are examples of probabilistic models [

The objective of decision making takes priority in the decision making process (or decision theory). Thus, it is very important to define clearly and explicitly the objectives involved in the decision process. Managers are expected to be rational (i.e. able to choose worthwhile in the light of cost-benefit analysis), and are required to possess acceptable value systems necessary for effective decision making [

Most decision problems in real life are probabilistic (or stochastic) in nature due to changing environment that makes information incomplete, scarce and uncontrollable to the decision-maker. Thus, their models contains at least one parameter or decision variable that is a random variable. Since at least one decision variable is random, the independent variable(s), will also be random. That means consequences or payoff due to certain changes in the independent variable cannot be predicted with certainty. This requires a critical search for best-fit decisions centers on optima plan of action.

Alternative Plans of Actions (Strategies)―The problem arises only when there are several courses of action available for a solution. An exhaustive list of courses of action can be prepared in the process of going through problem formulation. Courses of action that are not feasible with respect to the objectives and resources may be ruled out [

Decision Payoff―A numerical value (outcome) resulting from each possible combination of alternatives and states of nature is called payoff. The payoff values are always conditional because of unknown states of nature. Payoff is measured within a specified period (e.g. after one year). This period is sometimes called the decision horizon and the tabular arrangement of these conditional outcome (payoff) values is known as payoff matrix [

Selection of Desired Alternative―Once feasible alternatives are developed, one must be selected. The decision choice is the selection of the most promising of several alternative courses of action. The best alternative is one in which the solution best fits the overall goals and values of the organization and achieves the desired results using the fewest resources. Executives try to select the choice with least risk and uncertainty. Choosing among alternatives also depends on manager’s personal factors and willingness to accept risk and uncertainty. Risk propensity is the willingness to undertake risk with the opportunity of gaining increased payoff. Payoff is always shown in a table form known as the payoff matrix, as shown in

Implementation of Decision Solution (Alternative)―This involves the use of managerial, administrative, and persuasive abilities to ensure that the chosen alternative is carried out. The decision-maker not only has to identify good decision alternative to select but also to select the alternatives that are capable of being implemented. It is important to ensure that any decision implemented is continuously reviewed and updated in the light of changing environment. The behavioral aspects of change are exceedingly important to the successful implementation of decision. Studies show that when employees see that managers follow up on their decisions by tracking implementation success, they are more committed to positive actions [

Evaluation and Feedback―In the evaluation stage of the decision process, decision makers gather information that tells them how well the decision was implemented and whether it was effective in achieving its goals. Feedback is important because decision making is not a continuous and never-ending process [

The methodology of this study considers existing works of various seminal researchers and theoretical foundations of decisions-making using probabilities as a measure of risk and uncertainty. The models developed with their respective analyses are carefully examined toward value creation optimization. Globally, decisions are most often taken under conditions of uncertainty and risk to arrive at optima value creation in organizations [

Uncertainty is the fact of life and business, whilst probability is the guide for a “good” life and successful busi-

States of Nature | Probability | Courses of Action (Alternatives) | ||
---|---|---|---|---|

S_{1} | S_{2} | S_{n} | ||

N_{1 } N_{2 } . . N_{m} | P_{1} P_{2} . . P_{m} | P_{11 } P_{21}_{ } . . P_{m1} | P_{12 } P_{22}_{ } . . P_{m2} | P_{1n} P_{2n} . . P_{mn} |

ness. The concept of probability occupies an important place in the decision making process, whether the problem is one faced in business, in government, in the social sciences, or just in one's own everyday personal life. In very few decisions making situations is perfect information that all the needed facts are available, as most decisions are made in the face of uncertainty [

Probability is derived from the verb to probe meaning to “find out” what is not too easily accessible or understandable. The word “proof” has the same origin that provides necessary details to understand what is claimed to be true. Probabilistic models are viewed as similar to that of a game; actions are based on expected outcomes. The centre of interest moves from the deterministic to probabilistic models using subjective statistical techniques for estimation, testing and predictions. In probabilistic modelling, risk means uncertainty for which the probability distribution is known. Therefore risk assessment means a study to determine the outcomes of decisions along with their probabilities [

Clearly, the more information the decision maker has, the better the decision will be. Treating decisions as if they were gambles is the basis of decision theory. This means that we have to trade off the value of a certain outcome against its probability. To operate according to the canons of decision theory, we must compute the value of a certain outcome and its probabilities; hence, determining the consequences of our choices. The origin of decision theory is derived from economics by using the utility function of payoffs. It suggests that decisions be made by computing the utility and probability, the ranges of options, and also prescribes strategies for good decisions. Probability assessment is nothing more than the quantification of uncertainty. In other words, quantification of uncertainty allows for the communication of uncertainty between persons. There can be uncertainties regarding events, states of the world, beliefs and so on. Probability is the tool for both communicating uncertainty and managing it.

Lastly, as succinctly positioned by [

1) Decision making under pure uncertainty;

2) Decision making under risk;

3) Decision making by buying information.

In decision making under pure uncertainty, the decision maker has absolutely no knowledge, not even about the likelihood of occurrence for any state of nature. In such situations, the decision maker’s behaviour is purely based on his/her attitude toward the unknown [

1) Optimism (Maximax or Minimim) criterion;

2) Pessimism (Maximin or Minimax) criterion;

3) Equal probabilities (Laplace) criterion;

4) Coefficient of optimism (Hurwiez) criterion;

5) Regret (Salvage) criterion.

Optimism (Maximum and Minimum) Criterion―The decision-maker ensures that the opportunity to miss the largest possible profit (maximax) or the lowest possible cost (minimin). This is called the “best of best” criterion [

Pessimism (Maximin or Minimax) Criterion―The decision-maker requires in this theoretical criterion to earn no less (or pay no more) than some specified amount. This is achieved by selection of the alternative that represents the maximum of the minima (or minimum of the maxima in case of loss) payoffs in case of profits. It is called the “best of worst” criterion [

Equal Probabilities (Laplace) Criterion―This is a situation that the probabilities of the states of nature are not known, so, an assumption is generated that all the states of nature will have equal probability of occurrence (that is assigning equal probability to each state of nature). Given that states of nature are mutually exclusive and collectively exclusive, the probability of each of the state must be 1/(number of states of nature). This theoretical criterion is known as the theory of insufficient reason, because except in few cases, some information about the likelihood of occurrence of states of nature is available.

Coefficient of Optimism (Hurwiecz) Criterion―This criterion is based on the theoretical premise that a rational decision-maker should be neither optimistic nor pessimistic and, therefore, must display a mixture of both. Hurwiecz in this theory, introduced a coefficient of optimism (denoted by α) to measure the decision-makers degree of optimism. This coefficient takes values between 0 and 1, where; 0 represents complete pessimistic attitude about the future, and 1 a complete optimistic attitude about the future. Thus, α is a coefficient of optimism, then (1?α) will be the coefficient of pessimism. Hurwiecz suggested that the decision maker must select an alternative that maximizes, thus;

In other words, Hurwiecz theory is based on the weighted average of the best and worst payoffs of each action and is calculated thus: Weighted payoff = α × worst payoff + (1 ? α) × best payoff.

The Regret (Savage) Criterion―This is the opportunity loss decision criterion or minimax regret decision criterion because the decision-maker regrets the fact that he/she has adopted a wrong course of action (or alternative) resulting in an opportunity loss of payoff. Thus, he/she always intend to minimize this regret.

Decision-making under risk is a probabilistic decision situation in which more than one state of nature exists and the decision-maker has sufficient information to assign probability values to the likely occurrence of each of the states. Knowing the probability distribution of the states of nature, the best decision is to select that course of action which has the largest expected payoff value. The expected (average) payoff of an alternative is the sum of all possible payoffs of that alternative, weighted by the probabilities of the occurrence of those payoffs. Some of the most widely used criteria for evaluating various courses of action (alternatives) under risk are [

1) Expected Monetary Value (EMV);

2) Expected Opportunity Loss (EOL);

3) Expected Value of Perfect Information (EVPI);

4) Posterior Probabilities and Bayesian Analysis.

Expected Monetary Value (EMV)―The expected monetary value (EMV) for a given course of action is the weighted sum of all possible payoffs for each alternative. The expected (or mean) value is the long-run average value that would result if the decision were repeated a large number of time. Mathematically EMV is stated as follows:

EMV (Course of action, S_{j}) =

where m = number of possible states of nature;

P_{i} = probability of occurrence of states of nature, N_{i};

P_{ij} = payoff associated with state of nature N_{i} and course of action, S_{j}.

Expected Opportunity Loss (EOL)-An alternative approach to maximizing expected monetary value (EMV) is to maximize the expected opportunity loss (EOL), also called expected value of regret. The EOL is defined as the difference between the highest profit (OR payoff) of a state of nature and the actual profit obtained by the particular course of action taken. In order words, EOL is the amount of payoff that is lost by not selecting that course of action which has the greatest payoff for the state of nature that actually occurs. The course of action due to which EOL is minimum is recommended. Since EOL is an alternative decision criterion for decision making under risk, therefore, the results will always be the same as these obtained by EMV criterion discussed earlier. Thus, only one of the two methods should be applied to reach a decision. Mathematically, it is stated as follows.

EOL (state of nature, N_{i}) =

where I_{ij} = opportunity loss due to state of nature, N_{i} and course of action, S_{j};

P_{i} = probability of occurrence of state of nature, N_{i}.

Expected Value of Perfect Information (EVPI)―In the decision-making under risk, each state of nature is associated with the probability of its occurrence. However, if the decision maker can acquire perfect (complete and accurate) information about the occurrence of various states of nature, then he would be able to select a course of action that yields the desired payoff for whatever state of nature that actually occurs. It has been seen that the EMV or EOl criterion helps the decision maker select a particular course of action that optimizes the expected payoff, without the need of any additional information. Expected value of perfect information (EVPI) represents the maximum amount of money the decision maker has to pay in order to get this additional information about the occurrence of various states of nature, before a decision has to be made. Mathematically it is stated as:

EVPI = (Expected profit with perfect information)-(Expected profit without perfect information).

EVPI =

where, P_{ij} = best payoff when action, S_{j} is taken in the presence of state of nature, N_{i};

P_{i} = probability of state of nature, N_{i};

EMV^{*} = maximum expected monetary value.

Posterior Probabilities and Bayesian Analysis―The search and evaluation of decision alternatives often reveal new information. If such information is regarding the effects of alternatives, the consequences are restated. When the uncontrollable factors are involved, either the states of nature themselves are reconsidered or their likelihoods are revised. The value of new information is evaluated in terms of its impact on the expected payoff. The expected value and the cost of the new information are compared to determine whether it is worth acquiring. An initial probability statement to evaluate expected payoff is called a prior probability distribution, the one which has, been revised in the light of new information called a posterior probability distribution. It will be evident that what is a posterior to one sequence of state of nature becomes the prior to others that are yet to happen. The method of computing posterior probabilities from prior probabilities is by using a called Bayes’ theorem. A further analysis of problems using these probabilities, with respect to new expected payoffs with additional information, is called prior-posterior analysis. The Bayes’ theorem, in general terms, can be stated as follows: Let _{i}, i.e.

Since each joint probability can be expressed as the product of a known marginal (prior) and conditional probability,

Finally, the big advantage of Bayes’ decision rule is that it incorporates all the available information including all the payoffs and the best available estimates of the probabilities of the respective states of nature [

All organizations face the challenge of decision-making about outcomes that maximizes their expected utilities respectively. But these decisions hardly are taken with perfect knowledge and information by the decision- maker, because the business environment is fluid and portends tsunami changes. These changes create decision-making environments of uncertainty, risk and ambiguity whilst, the organizations objective sign posts for the optima value creation (utility maximization) have not shifted. In order to achieve the decision objective function optimally, organizations deploy decision theory and analysis approach as a premise for taking informed decisions within the confine of utility function maximization. The absence of effective decision theory and analysis makes many faults and failures in decision-making and consequent failure in organizational performance leading to subsequent survival challenges. The emergence of decision theory as a systematic study and a multidimensional field of knowledge in the late 20th century make it a definite precursor for decision-making effectiveness in organizations.

Decision theory and analysis show that based on the objective function maximization quest and the fast changing environment of the decision-maker, programmed decisions are not obvious business solutions but non- programmed decision types. And because nonprogrammed decisions are often characterized by uncertainty, risk and ambiguity, the decision maker takes on approaches of administrative (to satisfice) and sometimes political (to build alliances for coalition and scenario analysis) approaches. The classical and normative approaches are not applicable due to problems of boundary rationality in the decision-making process. Mostly, intuition based on executive’s experiential surveys plays a better role in the objective utility optimization. This humanistic decision approach to decision-making holds the sway for an optimum value creation in presence of Simon’s theorem on decision-making.

Decision theory and analysis also argue that since there is no one way and hit method managers codify to take all decisions, it is very important to clone a decision-making model contingent to the decision issues at hand. The models then abstract the real world situations of the decision variables. Top on the list of these models are the deterministic and probabilistic (or stochastic) models. The dynamic models put the decision-maker in the position of perfect knowledge and information, but not prevalent in real world. Whilst, the stochastic models use probability as a substitute for knowledge and information (i.e. risks and uncertainties) to mould optima value creation decisions, representing the real world.

This study analyses decision models under pure uncertainty and risk and find that for organizations to realize the optima value creation in pure uncertainty environments, decision theory and analysis must be their precursor for decision-making process and must deploy either of the following models for effective decision-making:

a) Optimism (Maximax or Minimim) criterion;

b) Pessimism (Maximin or Minimax) criterion;

c) Equal probabilities (Laplace) criterion;

d) Coefficient of optimism (Hurwiez) criterion;

e) Regret (Salvage) criterion.

Concomitantly, in risky environments, this study succinctly recommends either of the following models for effective decision-making process if organizations crave optima value creation:

1) Expected Monetary Value (EMV);

2) Expected Opportunity Loss (EOL);

3) Expected Value of Perfect Information (EVPI);

4) Posterior Probabilities and Bayesian Analysis.

Finally, this paper wonders why with the availability of these real world models for uncertainty and risky environments of decision-making, organizations still strives hard with issues of decisions-making. It is also generally held that entrepreneurs are risk aversive in business venturing and avoids uncertainties. However, risks and uncertainties are the real world business environment that is the domain of entrepreneurship venturing. Is entrepreneurship excluded in decision theory and analysis? In other words, are entrepreneurs not needed to deploy decision theory and analysis in their business venturing? Therefore, this paper recommends specific decision theory and analysis research in entrepreneurship venturing to answer the question directed at entrepreneurship decision-making. Also, a cross-sectional research to be conducted on executives and managers regarding their attitudinal capacity to engage in decision theory and analysis, if not, they should engage decision-making services from consultants and specialists.

Cephas A.Gbande,Paul T.Akuhwa, (2015) Decision Theory and Analysis: An Optima Value Creation Precursor for Organizations. Open Journal of Applied Sciences,05,355-367. doi: 10.4236/ojapps.2015.57036