Computer systems cannot improve organizational performance if they aren't used. Unfortunately, resistance to end-user systems by managers and professionals is a widespread problem. To better predict, explain, and increase user acceptance, we need to better understand why people accept or reject computers. This research addresses the ability to predict peoples' computer acceptance from a measure of their intentions, and the ability to explain their intentions in terms of their attitudes, subjective norms, perceived usefulness, perceived ease of use, and related variables. In a longitudinal study of 107 users, intentions to use a specific system, measured after a one-hour introduction to the system, were correlated 0.35 with system use 14 weeks later. The intention-usage correlation was 0.63 at the end of this time period. Perceived usefulness strongly influenced peoples' intentions, explaining more than half of the variance in intentions at the end of 14 weeks. Perceived ease of use had a small but significant effect on intentions as well, although this effect subsided over time. Attitudes only partially mediated the effects of these beliefs on intentions. Subjective norms had no effect on intentions. These results suggest the possibility of simple but powerful models of the determinants of user acceptance, with practical value for evaluating systems and guiding managerial interventions aimed at reducing the problem of underutilized computer technology.

User Acceptance of Computer Technology: A Comparison of Two Theoretical Models

This paper briefly reviews the analytic hierarchy process (AHP) and its applications in diverse decision problems. It addresses some of the major extensions and criticisms of the method, as well.

The Analytic Hierarchy Process—A Survey of the Method and its Applications

Multi-attribute decision making: A simulation comparison of select methods

Inter-company comparison using modified TOPSIS with objective weights

An overview of the analytic hierarchy process and its applications

Several studies this past decade have examined differences between holistic and decomposed approaches to determining weights in additive utility models. Some have argued that it matters little which procedure is used, whereas others strongly favored particular methods. In this paper we address this controversy experimentally by comparing five conceptually different approaches in terms of their weights and predictive ability. The five methods are 1 multiple linear and non-linear regression analyses of ten and fifteen holistic assessments, 2 direct decomposed tradeoffs as proposed by Keeney and Raiffa Keeney, R. L., H. Raiffa. 1977. Decisions with Multiple Objectives. Wiley, New York., 3 a recent eigen-vector technique of Saaty Saaty, T. L. 1977. A scaling method for priorities in hierarchical structures. J. Math. Psych.15 3 234-281. involving redundant pairwise comparisons of attributes, 4 a straightforward allocation of hundred importance points, and 5 unit weighting i.e., equal weighting after standardizing the attributes.

The decision task involved college admissions. Subjects were asked to evaluate hypothetical college applicants on the basis of verbal SAT, quantitative SAT, high-school grade point average, and a measure of extra-curricular activity. Linear as well as nonlinear attribute utility functions were used in constructing the additive models. The nonlinear functions were specified graphically by the subjects through selection from five different shapes i.e., one per attribute.

To test the predictive ability of the various models, each subject made twenty separate pairwise comparisons of alternatives including direction and strength of preference. The prediction criteria were percentage correct predictions as well as correlations using these twenty pairs. Seventy subjects were tested, using an order-controlled within-subject design, in comparing the different methods of weight determination. Monetary incentives were used to enhance motivation.

In terms of findings, the methods generally differed systematically concerning the weights given to the various attributes, as well as the variances of the resulting predictions. On average, however, the methods predicted about equally well, except for unit weighting which was clearly inferior. The findings differ in this regard from the general literature. Furthermore, nonlinear models were found to be inferior to linear ones. Finally, subjects judged the methods to differ significantly in difficulty and trustworthiness, which were found to correlate inversely. The overall results raise various applied and theoretical issues, which are discussed.

https://www.jstor.org/stable/info/2631303

An Experimental Comparison of Different Approaches to Determining Weights in Additive Utility Models

A probabilistic dominance measure for binary choices: analytic aspects of a multi-attribute random weights model

This paper defines a quantity, called fidelity, which measures the degree of correspondence between a multiattribute utility function and a decision maker’s actual preferences. It ranges from zero to one, and indicates the probability that a given utility function correctly predicts the ordinal preference (of the decision maker) for a randomly selected pair of alternatives. This concept of fidelity is extended to one which determines how well a particular method for constructing utility functions performs. Experimental procedures for estimating these fidelities are discussed.

On the Fidelity of Multiattribute Preference Representations: Some Analytical Considerations

An algorithm for determining stratigraphic dip

Detailed log-derived stratigraphic interpretation requires the use of high vertical resolution logging devices such as a dipmeter. The Dresser Atlas Strata Dip® program, originally intro­ duced in the early 1980's, is a computer program designed to give a detailed point-wise estimate of the orientation and depth of very fme formation laminae. Numerous improvements in the computational algorithms coupled with features such as the removal of the effects of structural dip and intelligent handling of floating pad data have resulted in a second generation product which is described in this paper. The accuracy of the program's results was tested using synthetic data generated by a dip simu­ lation program. The repeatability of the program (in fact of the entire data acquisition and processing system) is demonstrated by separate analyses of three independent runs over the same well interval. Two field examples demonstrate the value of this new program as an aid to stratigraphic analysis. Since its introduction in the early 1980s, numerous improvements in the program have resulted in a second generation product. A description of the program logic is given so that others may gain a greater understanding of the correlation technique employed. While this technique is an intuitively valid approach to detailed dip .analysis, experiments were conducted to verify its application in practice. The first of these experiments demonstrates the accuracy of the program in estimating the location and orientation of bedding planes traversed by the borehole. The second experiment demonstrates the repeatability of the entire Diplog® system which comprises both data acquisition and Strata Dip processing. The usefulness of the program as an aid to stratigraphic analysis is shown via two field examples. The first of these examples is an eolian dune analysis in which the program clearly indicates the individual dune sets and the paleo-wind directions. The second example is an analysis of a sandstone formation comprising sediments deposited in a fluvial environment. This formation is interpreted as a point bar sequence consisting of channel lag, trough cross set, and ripple cross laminae deposits.

/pdf/advances-in-diplog-data-processing-for-stratigraphic-532eeqfyyt.pdf

C. Carter Waid

Papers

An Experimental Comparison of Different Approaches to Determining Weights in Additive Utility Models

A probabilistic dominance measure for binary choices: analytic aspects of a multi-attribute random weights model

On the Fidelity of Multiattribute Preference Representations: Some Analytical Considerations

An algorithm for determining stratigraphic dip

Advances in Diplog® Data Processing for Stratigraphic Analysis