bluuebunny/crossref_metadata_2025_split huggingface dataset:
Presenting Geographic Information: Effects of Data Aggregation, Dispersion, and Users' Spatial Orientation
Authors: Swink Morgan , Speier Cheri 
Published: June 2007

ABSTRACTGeographic information systems (GIS) have taken on an increasingly important role supporting decision making in many organizations. GIS have been used to support a breadth of tasks including oil and mineral exploration, facility location, logistics support, and facilities management decisions. The effectiveness of GIS as a decision support tool comes primarily from the visual display of data in the form of maps. When presenting information as a geographic map, the level of data aggregation potentially affects aspects of task complexity such as information load and the potential for pattern recognition by the user. Other task attributes expected to be related to data aggregation effects include problem size, the degree of data dispersion, and users' spatial orientation skills.We conducted an experiment to study these effects and their interactions. Subjects used a GIS including map‐based information characterized by different levels of problem size, data dispersion, and data aggregation. Spatial orientation skill was examined as a covariate in the experimental treatments. The results indicate that all of these geographic information characteristics have significant impacts on decision performance. Moreover, many interactions are present among the factors. We evaluate these interactions in order to derive implications for practice and for future research.
The Influence of Task Interruption on Individual Decision Making: An Information Overload Perspective
Authors: Speier Cheri , Valacich Joseph S. , Vessey Iris 
Published: June 2007

ABSTRACTInterruptions are a common aspect of the work environment of most organizations. Yet little is known about how interruptions and their characteristics, such as frequency of occurrence, influence decision‐making performance of individuals. Consequently, this paper reports the results of two experiments investigating the influence of interruptions on individual decision making. Interruptions were found to improve decision‐making performance on simple tasks and to lower performance on complex tasks. For complex tasks, the frequency of interruptions and the dissimilarity of content between the primary and interruption tasks was found to exacerbate this effect. The implications of these results for future research and practice are discussed.
Optimizing Natural Gas Flows with Linear Programming and Scenarios
Authors: Butler John C. , Dyer James S. 
Published: June 2007

ABSTRACTRecently, we developed a linear programming model to aid an electric utility company in evaluating several long‐term operating decisions. Variations of this model provide insights into daily operations and valuation of supply contracts. This paper outlines the important characteristics of this class of network models including the usage of a gas storage facility, the use of market forecasts of gas prices, and the accommodation of gas contract structures. In addition, we develop a scenario optimization procedure in which demand forecasts provide scenarios. We also describe ways in which these models were automated to drive a decision support system.
Marketing Category Forecasting: An Alternative of BVAR‐Artificial Neural Networks*
Authors: Jiang James J. , Zhong Maosen , Klein Gary 
Published: June 2007

ABSTRACTAnalyzing scanner data in brand management activities presents unique difficulties due to the vast quantity of the data. Time series methods that are able to handle the volume effectively often are inappropriate due to the violation of many statistical assumptions in the data characteristics. We examine scanner data sets for three brand categories and examine properties associated with many time series forecasting methods. Many violations are found with respect to linearity, normality, autocorrelation, and heteroscedasticity. With this in mind we compare the forecasting ability of neural networks that require no assumptions to two of the more robust time series techniques. Neural networks provide similar forecasts to Bayesian vector autoregression (BVAR), and both outperform generalized autoregressive conditional herteroscedasticty (GARCH) models.
Cost Efficiency Benchmarking for Operational Units with Multiple Cost Drivers
Authors: Troutt Marvin D. , Gribbin Donald W. , Shanker Murali , Zhang Aimao 
Published: June 2007

ABSTRACTWe consider the activity‐based costing situation, in which for each of several comparable operational units, multiple cost drivers generate a single cost pool. Our study focuses on published data from a set of property tax collection offices, called rates departments, for the London metropolitan area. We define what may be called benchmark or most efficient costs per unit of driver. A principle of maximum performance efficiency is proposed, and an approach to estimating the benchmark unit costs is derived from this principle. A validation approach for this estimation method is developed in terms of what we call normal‐like‐or‐better performance effectiveness. Application to longitudinal data on a single unit is briefly discussed. We also consider some implications for the more routine case when costs are disaggregated to subpools associated with individual cost drivers.
Organizational Learning in Global Purchasing: A Model and Test of Internal Users and Corporate Buyers*
Authors: Hult G. Tomas M. , Hurley Robert F. , Giunipero Larry C. , Nichols Ernest L. 
Published: June 2007

ABSTRACTThis research examines a model centered on organizational learning in purchasing. Two different studies are conducted to test the hypotheses among purchasing users (Study 1) and buyers (Study 2). The user sample consists of users representing 355 strategic business units of a Fortune 500 multinational corporation. The buyer sample consists of corporate buyers of 200 multinational corporations drawn from the membership directory of the National Association of Purchasing Management (NAPM). In each study, the focus is on the learning relationships between corporate buyers and internal users in the purchasing organization. Based on the two studies, the results suggest that organizational learning in the purchasing process is influenced by the organizational culture factors of localness, transformational leadership, and openness. Organizational learning has a positive effect on information processing in the purchasing system, which, in turn, has a positive influence on the cycle time of the purchasing process.
Ranking State Financial Management: A Multilevel Fuzzy Rule‐based System
Authors: Ammar Salwa , Wright Ronald , Selden Sally 
Published: June 2007

ABSTRACTIn 1996 the Alan K. Campbell Institute in the Maxwell School of Citizenship and Public Affairs at Syracuse University was awarded a grant to rate management performance of state and local governments and selected federal agencies. The project includes several parallel initiatives to evaluate government performance. This article contains a description of a multilevel fuzzy rule‐based system developed to evaluate state government performance. The objective is to measure effectiveness in state financial management and produce a relative ranking of performance. The system incorporates evaluation criteria and expert judgment. It utilizes survey and other publicly available information relevant to state financial management. Fuzzy set theory is used to represent imprecision in evaluated information and judgments. The results of this evaluation are compared to a parallel journalistic effort to rank state performance. The article highlights the differences between the two approaches and outlines the advantages of the fuzzy rule‐based system.
Testing the Interaction Effects of Task Complexity in Computer Training Using the Social Cognitive Model
Authors: Bolt Melesa Altizer , Killough Larry N. , Koh Hian Chye 
Published: June 2007

AbstractUsing a Modified Social Cognitive Theory framework, this study examines the behavior modeling and lecture‐based training approaches to computer training. It extends the existing Social Cognitive Model for computer training by adding the task complexity construct to training method, prior performance, computer self‐efficacy, outcome expectations, and performance. A sample of 249 students from a large state university served as participants in a laboratory experiment that was conducted to determine the task complexity*training method and task complexity* self‐efficacy interaction effects on performance. Structural equation modeling with interaction effects was used to analyze the data. The results show that behavior modeling outperforms lecture‐based training in a measure of final performance when task complexity is high. Further, it is found that computer self‐efficacy has a greater positive effect on performance when task complexity is high than when task complexity is low. Prior performance is also found to be an important variable in the model.
An Exploratory Analysis of the Value of the Skills of IT Personnel: Their Relationship to IS Infrastructure and Competitive Advantage
Authors: Byrd Terry Anthony , Turner Douglas E. 
Published: June 2007

AbstractDetermining and assessing the requisite skills of information technology (IT) personnel have become critical as the value of IT has risen in modern organizations. In addition to technical skills traditionally expected of IT personnel, softer skills like managerial, business, and interpersonal skills have been increasingly cited in previous studies as mandatory for these employees. This paper uses a typology of IT personnel skills—technology management skills, business functional skills, interpersonal skills, and technical skills—and investigates their relationships to two information systems (IS) success variables, IS infrastructure flexibility and the competitive advantage provided by IS. The study investigates these relationships using the perceptions of chief information officers (CIOs) from mostly Fortune 2000 companies. The contributions of this study are: IT personnel skills do affect IS success, technical skills are viewed as the most important skill set in affecting IS infrastructure flexibility and competitive advantage, and modularity is viewed as more valuable to competitive advantage than integration. Several explanations are offered for the lack of positive relationships between the softer IT personnel skills and the dimensions of IS success used in this study.
Lot‐sizing Decisions Under Limited‐time Price Reduction*
Authors: Ramasesh Ranga V. , Rachamadugu Ram 
Published: June 2007

AbstractWe consider the optimal lot‐sizing policy for an inventoried item when the vendor offers a limited‐time price reduction. We use the discounted cash flow (DCF) approach in our analysis, thereby eliminating the sources of approximation found in most of the earlier studies that use an average annual cost approach. We first characterize the optimal lot‐sizing policies and their properties, then develop an algorithm for determining the optimal lot sizes. We analytically demonstrate that the lot sizes derived using an average annual cost approach for the different variants of the problem are, in general, larger than the DCF optimum. While DCF analysis is more rigorous and yields precise lot sizes, we recognize that the associated mathematical models and the solution procedure are rather complex. Since simple and easy‐to‐understand policies have a strong practical appeal to decision makers, we propose a DCF version of a simple and easy‐to‐implement heuristic called the “Early Purchase” (EP) strategy and discuss its performance. We supplement our analytical developments with a detailed computational analysis and discuss the implications of our findings for decision making.
Effects of Information Sequence and Irrelevant Distractor Information When Using a Computer‐based Decision Aid*
Authors: Reneau J. Hal , Blanthorne Cindy 
Published: June 2007

AbstractComputer‐based decision aids are intended to support and improve human judgments. Frequently, the largest portion of the design effort is devoted to the technical aspects of the system; behavioral aspects are often overlooked. As a result, the decision aid may be ineffective. An experiment was conducted to examine the effects of two information structure variables that theoretically affect judgments: information sequence and irrelevant distractor information. Auditor subjects made continuing existence judgments for client‐banks after interacting with one of four alternative decision aids. The decision aids are modifications of a system developed by an international CPA firm. Judgments were predicted to be more accurate when: (1) diagnostic information is presented late rather than early in the information sequence and (2) when no irrelevant distractor information is presented. Further, judgment confidence was predicted to be unrelated to either information sequence or irrelevant distractor information. The experimental data support all three predictions.
Optimizing Service Attributes: The Seller's Utility Problem*
Authors: Easton Fred F. , Pullman Madeleine E. 
Published: June 2007

AbstractService designers predict market share and sales for their new designs by estimating consumer utilities. The service's technical features (for example, overnight parcel delivery), its price, and the nature of consumer interactions with the service delivery system influence those utilities. Price and the service's technical features are usually quite objective and readily ascertained by the consumer. However, consumer perceptions about their interactions with the service delivery system are usually far more subjective. Furthermore, service designers can only hope to influence those perceptions indirectly through their decisions about nonlinear processes such as employee recruiting, training, and scheduling policies. Like the service's technical features, these process choices affect quality perceptions, market share, revenues, costs, and profits.We propose a heuristic for the NP‐hard service design problem that integrates realistic service delivery cost models with conjoint analysis. The resulting seller's utility function links expected profits to the intensity of a service's influential attributes and also reveals an ideal setting or level for each service attribute. In tests with simulated service design problems, our proposed configurations compare quite favorably with the designs suggested by other normative service design heuristics.
Utility‐consistent Brand Demand Systems with Endogenous Category Consumption: Principles and Marketing Applications*
Authors: Baltas George 
Published: June 2007

AbstractThis paper introduces the design and implementation of utility‐consistent, brand, and category demand systems. It extends formal demand analysis to the area of brand and category demand, which directly concerns marketing researchers and managers. The proposed brand demand system is a set of interrelated demand functions that are derived explicitly from a utility function describing consumer preferences. The model generalizes by the integration of category expenditures, which are determined endogenously. The theoretical plausibility of the proposed demand model is demonstrated first and, subsequently, brand and category level systems are derived. Econometric methods for estimating the systems are also developed and illustrated in empirical data. The results yield empirically determined, quantitative insights into the structure of consumer demand for brands and product categories. The proposed approach has the attractive feature of structuring the interdependencies of consumer decisions and ensuring an explicit role for theory in applied research.
A Cascaded Inference Model for Evaluation of the Internal Audit Report*
Authors: Krishnamoorthy Ganesh 
Published: June 2007

AbstractThis paper provides a normative framework for how external auditors should evaluate internal audit (IA) work, with a view to assessing the risk of material misstatement. The central issue facing the external auditor when evaluating IA work is the reliability of IA work. Reliability assessments are structured using the cascaded inference framework from behavioral decision theory, in which attributes of source reliability are explicitly modeled and combined using Bayes' rule in order to determine the inferential value of IA work. Results suggest that the inferential value of an IA report is highly sensitive to internal auditor reporting bias, but relatively insensitive to reporting veracity. Veracity refers to internal auditors' propensity to report truthfully, whereas bias refers to the propensity to misreport findings. Results also indicate that this sensitivity to reporting bias is conditional on the level of internal auditor competence, thus suggesting significant interaction effects between the objectivity and competence factors. Collectively, these findings suggest that the impact of source reliability attributes may be more complex than portrayed in the auditing standards and that recognizing these subtleties may lead to greater efficiency and effectiveness.
A Decision Support System for Production/ Distribution Planning in Continuous Manufacturing
Authors: Mallya Sudhir , Banerjee Snehamay , Bistline William G. 
Published: June 2007

AbstractThis paper presents a decision support system (DSS) for managing production/distribution planning in a continuous manufacturing environment. The vendor has multiple plants and distribution centers (DCs). The trading partners have widely varying independent demand patterns. The DSS is designed for use in a multiproduct environment with overlapping raw materials and processing requirements. The production and distribution lead times at plants may span multiple planning periods. The impact of any manual override of a suggested solution can also be evaluated. The DSS is based on a linear programming model with a rolling horizon and was originally designed for a large process industry. Results of a pilot implementation using actual data are also presented, which show potential for significant savings for the company.
Data Mining for Network Intrusion Detection: A Comparison of Alternative Methods*
Authors: Zhu Dan , Premkumar G. , Zhang Xiaoning , Chu Chao‐Hsien 
Published: June 2007

AbstractIntrusion detection systems help network administrators prepare for and deal with network security attacks. These systems collect information from a variety of systems and network sources, and analyze them for signs of intrusion and misuse. A variety of techniques have been employed for analysis ranging from traditional statistical methods to new data mining approaches. In this study the performance of three data mining methods in detecting network intrusion is examined. An experimental design (3times2x2) is created to evaluate the impact of three data mining methods, two data representation formats, and two data proportion schemes on the classification accuracy of intrusion detection systems. The results indicate that data mining methods and data proportion have a significant impact on classification accuracy. Within data mining methods, rough sets provide better accuracy, followed by neural networks and inductive learning. Balanced data proportion performs better than unbalanced data proportion. There are no major differences in performance between binary and integer data representation.
Service Package Switching in Hotel Revenue Management Systems
Authors: Baker Tim , Murthy Nagesh N. , Jayaraman Vaidyanathan 
Published: June 2007

ABSTRACTRevenue Management Systems (RMS) are commonly used in the hotel industry to maximize revenues in the short term. The forecasting‐allocation module is a key tactical component of a hotel RMS. Forecasting involves estimating demand for service packages across all stayover nights in a planning horizon. A service package is a unique combination of physical room, amenities, room price, and advance purchase restrictions. Allocation involves parsing the room inventory among these service packages to maximize revenues. Previous research and existing revenue management systems assume the demand for a service package to be independent of which service packages are available for sale. We develop a new forecasting‐allocation approach that explicitly accounts for this dependence. We compare the performance of the new approach against a baseline approach using a realistic hotel RMS simulation. The baseline approach reflects previous research and existing industry practice. The new approach produces an average revenue increase of at least 16% across scenarios that reflect existing industry conditions.
Estimating Moments of Subjectively Assessed Distributions*
Authors: Reilly Terence 
Published: June 2007

ABSTRACTMoment‐matching discrete distributions were developed by Miller and Rice (1983) as a method to translate continuous probability distributions into discrete distributions for use in decision and risk analysis. Using gaussian quadrature, they showed that an n‐point discrete distribution can be constructed that exactly matches the first 2n ‐ 1 moments of the underlying distribution. These moment‐matching discrete distributions offer several theoretical advantages over the typical discrete approximations as shown in Smith (1993), but they also pose practical problems. In particular, how does the analyst estimate the moments given only the subjective assessments of the continuous probability distribution? Smith suggests that the moments can be estimated by fitting a distribution to the assessments. This research note shows that the quality of the moment estimates cannot be judged solely by how close the fitted distribution is to the true distribution. Examples are used to show that the relative errors in higher order moment estimates can be greater than 100%, even though the cumulative distribution function is estimated within a Kolmogorov‐Smirnov distance less than 1%.
An Evaluation of Flexible Workday Policies in Job Shops*
Authors: Yang Kum‐Khiong , Webster Scott , Ruben Robert A. 
Published: June 2007

ABSTRACTJob shops have long faced pressures for improvement in a challenging and volatile environment. Today's trends of global competition and shortening of product life cycles suggest that both the challenges and the intensity of market volatility will only increase. Consequently, the study of tactics for maximizing the flexibility and responsiveness of a job shop is important. Indeed, there is a significant body of literature that has produced guidelines on when and how to deploy tactics such as alternate routings for jobs and transfers of cross‐trained workers between machines.In this paper we consider a different tactic by adjusting the length of workdays. Hours in excess of a 40‐hour week are exchanged for compensatory time off at time and a half, and the total amount of accrued compensatory time is limited to no more than 160 hours in accordance with pending legislation. We propose several simple flexible workday policies that are based on an input/output control approach and investigate their performance in a simulated job shop. We find significant gains in performance over a fixed schedule of eight hours per day. Our results also provide insights into the selection of policy parameters.