Knowledge that Transforms

The “productivity paradox” of information systems (IS) is that, despite enormous improvements in the underlying technology, the benefits of IS spending have not been found in aggregate output statistics. One explanation is that IS spending may lead to increases in product quality or variety which tend to be overlooked in the aggregate statistics, even if they increase output at the firm-level. Furthermore, the restructuring and cost-cutting that are often necessary to realize the potential benefits of IS have only recently been undertaken in many firms. Our study uses new firm-level data on several components of IS spending for 1987–1991. The dataset includes 367 large firms which generated approximately 1.8 trillion dollars in output in 1991. We supplemented the IS data with data on other inputs, output, and price deflators from other sources. As a result, we could assess several econometric models of the contribution of IS to firm-level productivity. Our results indicate that IS spending has made a substantial and statistically significant contribution to firm output. We find that the gross marginal product (MP) for computer capital averaged 81% for the firms in our sample. We find that the MP for computer capital is at least as large as the marginal product of other types of capital investment and that, dollar for dollar, IS labor spending generates at least as much output as spending on non-IS labor and expenses. Because the models we applied were similar to those that have been previously used to assess the contribution of IS and other factors of production, we attribute the different results to the fact that our data set is more current and larger than others explored. We conclude that the productivity paradox disappeared by 1991, at least in our sample of firms.

This paper investigates the psychometric properties of three measures of subjective uncertainty—a zero-to-hundred subjective probability scale and two seven point rating scales. Individual level analysis applied to data obtained from two separate studies suggests that the scales produce fairly similar results: The inter-response mode correlations were high, and individual plots comparing various methods were quite similar. Covariance structure models based on multitrait-multimethod matrices are utilized to assess the reliability and method variance of the scales. The cumulative evidence suggests that rating scales are consistently just as reliable as the subjective probability scale. The probability scale contained significant method error. In fact, the two rating scales were found to have lower systematic method variance and lower random error variance than the subjective probability scale. The paper concludes with a discussion regarding possible explanations of these results and directions for future research.

In this paper, we propose a new interactive procedure for solving multiple objective programming problems. Based upon feed-forward artificial neural networks (FFANNs), the method is called the Interactive FFANN Procedure. In the procedure, the decision maker articulates preference information over representative samples from the nondominated set either by assigning preference “values” to the sample solutions or by making pairwise comparisons in a fashion similar to that in the Analytic Hierarchy Process. With this information, a FFANN is trained to represent the decision maker's preference structure. Then, using the FFANN, an optimization problem is solved to search for improved solutions. An example is given to illustrate the Interactive FFANN Procedure. Also, the procedure is compared computationally with the Tchebycheff Method (Steuer and Choo [Steuer, R. E., E.-U. Choo. 1983. An interactive weighted Tchebycheff procedure for multiple objective programming. Math. Programming 26(1) 326–344.]). The computational results indicate that the Interactive FFANN Procedure produces good solutions and is robust with regard to the neural network architecture.

Many resource-allocation problems in manufacturing and service operations require selecting integer-valued levels for various activities that consume “nondecreasing amounts” of limited resources. System productivity, to be maximized, is limited by the least productive (bottleneck) activity. We first review a basic bisection method that can solve this discrete, monotonic resource-allocation problem even with a nonlinear objective and constraints. We then generalize the basic algorithm to solve an enhanced version of the problem containing additional coupling constraints on the allocation decisions. This generalization applies to assembly-release planning (ARP) in a multiproduct assemble-to-forecast environment with part commonality. The ARP problem requires deciding the number of kits for each product to release for assembly in every time period, using the available parts, to achieve if possible the target service levels for all products and time periods or minimize the maximum deviation of the actual service levels from the targets. We also consider extensions of the ARP model incorporating precedence constraints and part substitutability, and show how to modify the bisection method to solve these problems.

We address the NP-hard problem of scheduling n independent jobs with release dates, due dates, and family setup times on a single machine to minimize the maximum lateness. This problem arises from the constant tug-of-war going on in manufacturing between efficient production and delivery performance, between maximizing machine utilization by batching similar jobs and maximizing customers' satisfaction by completing jobs before their due dates. We develop a branch-and-bound algorithm, and our computational results show that it solves almost all instances with up to about 40 jobs to optimality. The main algorithmic contribution is our lower bounding strategy to deal with family setup times. The key idea is to see a setup time as a setup job with a specific processing time, release date, due date, and precedence relations. We develop several sufficient conditions to derive setup jobs. We specify their parameters and precedence relations such that the optimal solution value of the modified problem obtained by ignoring the setup times, not the setup jobs, is no larger than the optimal solution value of the original problem. One lower bound for the modified problem proceeds by allowing preemption. Due to the agreeable precedence structure, the preemptive problem is solvable in O(n log n) time.

Too often the relationship between clients and external consultants is perceived as one of protagonist versus antagonist. Stories on dramatic, failed consultancies abound, as do related anecdotal quips. A contributing factor to many “apparently” failed consultancies is a poor appreciation by both the client and consultant of the client's true goals for the project and how to assess progress toward these goals. This paper presents and analyses a measurement model for assessing client success when engaging an external consultant. Three main areas of assessment are identified: (1) the consultant's recommendations, (2) client learning, and (3) consultant performance. Engagement success is empirically measured along these dimensions through a series of case studies and a subsequent survey of clients and consultants involved in 85 computer-based information system selection projects. Validation of the model constructs suggests the existence of six distinct and individually important dimensions of engagement success. Both clients and consultants are encouraged to attend to these dimensions in pre-engagement proposal and selection processes, and post-engagement evaluation of outcomes.