bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Model for Diversification
Authors: Ansoff H. I. 
Published: November 2008

During the past few years, many interesting papers have been written on the subject of product-market diversification. A majority of the writers have dealt with either case histories of successful diversification or with qualitative check-off lists to be used in analyzing specific diversification opportunities. A much smaller group of papers has been devoted to formulation of a systematic approach which a company can use to compare alternative diversification decisions. This paper falls in the latter category. As a first step, diversification is defined and distinguished from other company growth alternatives. Typical growth perspectives are described which may motivate a company to diversify. Diversification objectives are established and related to the company's long-range objectives. A two-step evaluation scheme is proposed for selection of the preferred diversification strategy. The first is a qualitative step, which narrows a wide field of diversification opportunities to a selected few which are consistent with the company's diversification objectives and long-range policy. In the second step, a quantitative procedure is outlined for evaluating the relative profit potential of the selected alternatives. Finally, limitations of the present method are discussed.
Extensions to Class Dominance Characteristics
Authors: Balachandran Kashi R. , Radhakrishnan Suresh 
Published: November 2008

Balachandran and Schaefer (1979) show that if in the individual optimum class A dominates with a first come first serve queue, there could exist cases where in the public optimum class B dominates. We derive conditions under which providing a nonpreemptive priority allows both classes of users to use the facility and get a higher expected public net benefit than the first come first serve queue. The class that is provided the higher priority may or not be the class that dominates in the public optimum under the first come first serve queue. That means that introducing a priority scheme may switch the class that is given preference in the public optimum. It is shown that there exist situations where the class that is granted the higher priority for service could be levied a lower price than the class that is granted the lower priority. In the case where the users alone have perfect information on the value from using the facility, we derive conditions under which a non preemptive priority is superior to the first come first serve queue. The preemptive priority resume queue is not necessarily always superior to the first come first serve queue. We derive conditions under which the superiority of the preemptive priority resume queue over the first come first serve queue holds.
Feedforward Artificial Neural Networks for Solving Discrete Multiple Criteria Decision Making Problems
Authors: Malakooti Behnam , Zhou Ying Q. 
Published: November 2008

Decision making involves choosing some course of action among various alternatives. In almost all decision making problems, there are several criteria for judging possible alternatives. The main concern of the Decision Maker (DM) is to fulfill his conflicting goals while satisfying the constraints of the system. In this paper, we present an Adaptive Feedforward Artificial Neural Network (AF-ANN) approach to solve discrete Multiple Criteria Decision Making (MCDM) problems. The AF-ANN is used to capture and represent the DM's preferences and then to select the most desirable alternative. The AF-ANN can adjust and improve its representation as more information from the DM becomes available. We begin with the assumption that an AF-ANN topology is given, i.e., specific numbers of nodes and links are predetermined. To adjust the parameters of the AF-ANN, we present an iterative learning algorithm consisting of two steps. (a) generating a direction, and (b) a one-dimensional search along that direction. We then present a methodology to obtain the most appropriate AF-ANN topology and set its parameters. The procedure starts with a small number of nodes and links and then adaptively increases the number of nodes and links until the proper topology is obtained. Furthermore, when the set of training patterns (alternatives with their associated evaluations by the DM) changes, the AF-ANN model can adapt itself by re-training or expanding the existing model. Some illustrative examples are presented. To solve discrete MCDM problems by an AF-ANN, we show how to incorporate basic properties of efficiency, concavity, and convexity into the AF-ANN. We formulate the MCDM problems and use the AF-ANN to rank the set of discrete alternatives where each alternative is associated with a set of conflicting and noncommensurate criteria. We present a method for solving discrete MCDM problems through AF-ANNs which consists of. (a) formulating and assessing the utility function by eliciting information from the DM and then training the AF-ANN, and (b) ranking a rating alternatives by using the trained AF-ANN model. Some computational experiments are presented to show the effectiveness of the method.
Development of Measures to Assess the Extent to Which an Information Technology Application Provides Competitive Advantage
Authors: Sethi Vijay , King William R. 
Published: November 2008

In order to measures the extent to which information technology provides competitive advantages, the construct “Competitive Advantage Provided by an Information Application” (CAPITA) was operationalized. A field survey gathered data from 185 top information systems executives regarding information technology applications which had been developed to gain competitive advantage. A confirmatory analysis revealed that CAPITA may be conceptualized in terms of nine dimensions which satisfy key measurement criteria including unidimensionality and convergent validity, discriminant validity, predictive validity, and reliability. The nine dimensions from the basis of a preliminary multidimensional measure or index of competitive advantage which has practical uses for competitive assessment. These include justifying and evaluating applications and acting as dependent variables in empirical competitive advantage research. Extensions entail formulating alternative measures of CAPITA to clarify the theoretical foundations of the construct, validating the latent-structure model on another data set, use of multiple informants for data collection, and exploring complex factor structures for the construct.
What Common Ground Exists for Descriptive, Prescriptive, and Normative Utility Theories?
Authors: Luce R. Duncan , von Winterfeldt Detlof 
Published: November 2008

Descriptive and normative modeling of decision making under risk and uncertainty have grown apart over the past decade. Psychological models attempt to accommodate the numerous violations of rationality axioms, including independence and transitivity. Meanwhile, normatively oriented decision analysts continue to insist on the applied usefulness of the subjective expected utility (SEU) model. As this gap has widened, two facts have remained largely unobserved. First, most people in real situations attempt to behave in accord with the most basic rationality principles, even though they are likely to fail in more complex situations. Second, the SEU model is likely to provide consistent and rational answers to decision problems within a given problem structure, but may not be invariant across structures. Thus, people may be more rational than the psychological literature gives them credit for, and applications of the SEU model may be susceptible to some violations of invariance principles. This paper attempts to search out the common ground between the normative, descriptive, and prescriptive modeling by exploring three types of axioms concerning structural rationality, preference rationality, and quasi-rationality. Normatively the first two are mandatory and the last, suspect. Descriptively, all have been questioned, but often the inferences involved have confounded preference and structural rationality. We propose a prescriptive view that entails full compliance with preference rationality, modifications of structural rationality, and acceptance of quasi-rationality to the extent of granting a primary role to the status quo and the decomposition of decision problems into gains and losses.
Selection of Product Line Qualities and Prices to Signal Competitive Advantage
Authors: Balachander Subramanian , Srinivasan Kannan 
Published: November 2008

We investigate a firm's choice of prices and qualities of a product line to signal competitive advantage to potential entrants and to discourage entry. The market consists of customer segments with different valuations for product quality. We demonstrate that a higher quality and a higher price of each product in the line convey the firm's advantage to potential competition and prevents entry. We discuss implications for optimal product line selection when customers ‘self-select’ a product from the line. When product quality change is costly, the superior incumbent continues to select a higher quality and price for each product in the line to credibly substantiate its competitive advantage, though the distortions necessary from the optimal values are lower than before. After informative signalling and deterring entry, the firm retains the higher quality product line.
Equipment Selection and Machine Scheduling in General Mail Facilities
Authors: Jarrah Ahmad I. Z. , Bard Jonathan F. , deSilva Anura H. 
Published: November 2008

With the goal of fiscal self-sufficiency, the United States Postal Service (USPS) has embarked upon a 10-year program to modernize, and in some cases radically alter, the way it manages and processes the mail. At the heart of this effort is the goal of automating virtually all of the letter mail by 1995. This means reading, sorting, and then sequencing each mail piece to the order in which it will be delivered by the carrier with only a minimum of manual labor. In support of this goal, a series of long-term planning models has been developed to help select equipment and plan for its use at the more than 250 general mail facilities (GMF) throughout the nation. This paper reports on one of the central studies underlying this effort. Because of the size and complexity of the facility design problem, a hierarchical approach was followed. Three interrelated models were developed starting with a mixed integer linear program to derive equipment needs and initial machine schedules. The latter are post-processed in two stages to produce implementable schedules that reflect current practice. An auxiliary linear program and a heuristic were constructed for this purpose. The models and analysis conducted are demonstrated with data obtained from the Providence GMF. The results indicate savings over the 10-year planning horizon.
Fairness and Social Risk I: Unaggregated Analyses
Authors: Fishburn Peter C. , Sarin Rakesh K. 
Published: November 2008

This paper is the first of a two-paper study of fairness issues for decisions that affect the benefits received and the risks encountered by a population. The study examines fairness for individuals and for homogeneous groups within the population. It considers fairness both for population benefit-risk profiles and for probability distributions over profiles that reflect uncertainty about outcomes of decisions. The present paper focuses on fairness for profiles in which benefits and risks are not aggregated within groups or across the population. It ties fairness to notions of envy among individuals and groups that are based on individuals' preferences. The sequel will discuss aggregation of benefits and risks along with fairness from an aggregated perspective.
Noise and Learning in Semiconductor Manufacturing
Authors: Bohn Roger E. 
Published: November 2008

Rapid technological learning is critical to commercial success in VLSI semiconductor manufacturing. This learning is done through deliberate activities, especially various types of experimentation. Such experiments are vulnerable to confounding by process noise, caused by process variability. Therefore plants with low noise levels can potentially learn more effectively than high noise plants. Detailed die yield data from five semiconductor plants were examined to estimate process noise levels. A bootstrap simulation was used to estimate the error rates of identical controlled experiments conducted in each plant. Absolute noise levels were high for all but the best plants, leading to lost learning. For example, the probability of overlooking a three percent yield improvement was above twenty percent in all but one plant. Brute-force statistical methods are either expensive or ineffective for dealing with these high noise levels. Depending on the criterion used, there was a four- to ten-fold difference among the plants.
Modeling Strategic Investment Decisions Under Sequential Technological Change
Authors: Nair Suresh K. 
Published: November 2008

Strategic decisions to invest in new equipment are critical not only because of the large initial capital costs incurred but even more importantly because they affect future unit production costs, revenues, and the ability of the firm to perform operations that were not possible earlier. Thus these decisions determine the very competitiveness of the firm. Further, decisions regarding the choice of technology are very expensive to correct if incorrect decisions are identified. These decisions have become increasingly urgent and complex because the state of the art in technology is changing rapidly. However, many models available for evaluating these decisions are either too complex and inefficient or too restrictive in the number, types, and the way appearance of future technologies is modeled. This research attempts to relax some of these restrictions using forecast horizon procedures to model capital investment decisions where any number of technologies may appear in the future with purchase costs and revenues that may vary over time. The appearance of these future technologies are considered uncertain with probabilities that may also vary with time. However, we assume that the order in which they appear is sequential, much like the different generations of microchips for microcomputers. We develop a new approach using nonunique terminal rewards to solve a dynamic programming model of the problem by introducing “converse” difference functions and present an algorithm that is both simple and efficient. Despite the increase in state space from the use of “converse” functions, we show that the computational burden of the algorithm does not increase. Numerical examples are given to illustrate our algorithm. Sensitivity of the optimal decision to changes in probabilities, costs, and revenues are also discussed.
Model Selection Criteria: An Investigation of Relative Accuracy, Posterior Probabilities, and Combinations of Criteria
Authors: Rust Roland T. , Simester Duncan , Brodie Roderick J. , Nilikant V. 
Published: November 2008

We investigate the performance of empirical criteria for comparing and selecting quantitative models from among a candidate set. A simulation based on empirically observed parameter values is used to determine which criterion is the most accurate at identifying the correct model specification. The simulation is composed of both nested and nonnested linear regression models. We then derive posterior probability estimates of the superiority of the alternative models from each of the criteria and evaluate the relative accuracy, bias, and information content of these probabilities. To investigate whether additional accuracy can be derived from combining criteria, a method for obtaining a joint prediction from combinations of the criteria is proposed and the incremental improvement in selection accuracy considered. Based on the simulation, we conclude that most leading criteria perform well in selecting the best model, and several criteria also produce accurate probabilities of model superiority. Computationally intensive criteria failed to perform better than criteria which were computationally simpler. Also, the use of several criteria in combination failed to appreciably outperform the use of one model. The Schwarz criterion performed best overall in terms of selection accuracy, accuracy of posterior probabilities, and ease of use. Thus, we suggest that general model comparison, model selection, and model probability estimation be performed using the Schwarz criterion, which can be implemented (given the model log likelihoods) using only a hand calculator.
Improved Implicit Optimal Modeling of the Labor Shift Scheduling Problem
Authors: Thompson Gary M. 
Published: November 2008

This paper presents an integer programming model for developing optimal shift schedules while allowing extensive flexibility in terms of alternative shift starting times, shift lengths, and break placement. The model combines the work of Moondra (Moondra, S. L. 1976. An L. P. model for work force scheduling for banks. J. Bank Res. 7(4) 299–301.) and Bechtold and Jacobs (Bechtold, S. E., L. W. Jacobs. 1990. Implicit optimal modeling of flexible break assignments in labor staffing decisions for service operations. Management Sci. 36(11) 1339–1351.) by implicitly matching meal breaks to implicitly represented shifts. Moreover, the new model extends the work of these authors to enable the scheduling of overtime and the scheduling of rest breaks. We compare the new model to Bechtold and Jacobs' model over a diverse set of 588 test problems. The new model generates optimal solutions more rapidly, solves problems with more shift alternatives, and does not generate schedules violating the operative restrictions on break timing.
Delay Cost and Incentive Schemes for Multiple Users
Authors: Radhakrishnan Suresh , Balachandran Kashi R. 
Published: November 2008

This paper examines the role of cost application in the presence of delay and agency costs. Two risk neutral division managers share a common (production) facility and decide on (a) the demand (usage) rates, and (b) productive action. Each division manager causes costly delays at the common production facility for the other division manager. The expected delay depends on the demand rates chosen by the division managers. An M/G/1 queuing framework is used to characterize delay costs. The unobservability of demand rates leads to stochastic choice hazard, and the unobservability of productive actions leads to moral hazard problems. The headquarters designs incentive schemes such that the use of the common facility is optimal for the firm. We show that a franchise contract is necessary to implement the first-best solution (similar to Harris and Raviv 1979), but is not sufficient. Specifically, when the action aversion of one division manager is small, the use of a franchise contract leads to “greedy” behaviour by the that division manager. The cost application required is greater than the expected marginal cost of delay to preclude the greedy behavior and ensure a stable equilibrium.
Near Optimal Solutions for Product Line Design and Selection: Beam Search Heuristics
Authors: Nair Suresh K. , Thakur Lakshman S. , Wen Kuang-Wei 
Published: November 2008

Many practical product line design problems have large numbers of attributes and levels. In this case, if most attribute level combinations define feasible products, constructing product lines directly from part-worths data is necessary. For three typical formulations of this important problem, Kohli and Sukumar (Kohli, R., R. Sukumar. 1990. Heuristics for product-line design using conjoint analysis. Management Sci. 36 1464–1478.) present state-of-the-art heuristics to find good solutions. In this paper, we develop improved heuristics based on a beam search approach for solving these problems. In our computations for 435 simulated problems, significant improvements occur in five important performance measures used. Our heuristic solutions are closer to the optimal, have smaller standard deviation over replicates, take less computation time, obtain optimal solutions more often and identify a number of “good” product lines explicitly. Computation times for these problems are no more than 22 seconds on a PC, small enough for adequate sensitivity analysis. We also apply the heuristics to a real data set and clarify computational steps by giving a detailed example.
Measuring System Usage: Implications for IS Theory Testing
Authors: Straub Detmar , Limayem Moez , Karahanna-Evaristo Elena 
Published: November 2008

There is widespread agreement among researchers that system usage, defined as the utilization of information technology (IT) by individuals, groups, or organizations, is the primary variable through which IT affects white collar performance. Despite the number of studies targeted at explaining system usage, there are crucial differences in the way the variable has been conceptualized and operationalized. This wide variation of system usage measures hinders the efforts of MIS researchers to compare findings across studies, thus impeding the accumulation of knowledge and theory in this area. The purpose of this paper is to address conceptual as well as methodological issues related to measuring system usage. First, via LISREL measurement modeling techniques, we compare subjective and objective measures of system usage, namely, self-reported versus computer-recorded measures. Next, using a modified form of Davis' Technology Acceptance Model (TAM) as a nomological net, we test the nomological validity of these system usage constructs and measures. Results of the LISREL measurement and nomological net analysis suggest that system usage should be factored into self-reported system usage and computer-recorded system usage. Contrary to expectations, these constructs do not appear to be strongly related to each other. Moreover, while self-reported measures of system usage are related to self-reported measures of TAM independent variables, objective, computer-recorded measures show distinctly weaker links. In the face of such counter-evidence, it is tempting to argue that research that has relied on subjective measures of system usage (for example, research confirming TAM) may be artifactual. There are several alternative explanations, though, that maintain the integrity of TAM and studies that measure system usage subjectively. These alternative explanations suggest directions for further research as well as new approaches to measurement.
When Efficient Is Insufficient: Fairness in Decisions Affecting a Group
Authors: Boiney Lindsley G. 
Published: November 2008

Many key decisions have significant consequences for a group of people, rather than a single individual. In some cases, traditional group decision making techniques such as negotiation, voting, or compromise can be employed by the group members themselves to determine a satisfactory course of action. In other instances, however, the intervention of a central decision maker may be necessary. Intervening on behalf of a group raises additional concerns for the decision maker, one of which is fairness. This paper builds upon envy-based fairness concepts from the fair allocation literature and theories of ex ante and ex post equitable distributions from the social risk literature to develop a model for choice among limited options under uncertainty when preferences are heterogeneous. The model is therefore appropriate when other existing models are not. An example demonstrates an application of the model for choice under uncertainty and contrasts the approach and decision recommendation with that of existing models. By quantifying the degree of envy rather than treating it dichotomously, the approach captures the balance between fairness and efficiency; some envy may be tolerated if it brings greater overall utility and/or if some envy already exists in the status quo option. Lastly, implementation issues and the impact of differing preferences and attitudes toward risk on fairness and efficiency considerations are explored.