Knowledge that Transforms

The present research develops and tests a theoretical extension of the Technology Acceptance Model (TAM) that explains perceived usefulness and usage intentions in terms of social influence and cognitive instrumental processes. The extended model, referred to as TAM2, was tested using longitudinal data collected regarding four different systems at four organizations (N = 156), two involving voluntary usage and two involving mandatory usage. Model constructs were measured at three points in time at each organization: preimplementation, one month postimplementation, and three months postimplementation. The extended model was strongly supported for all four organizations at all three points of measurement, accounting for 40%–60% of the variance in usefulness perceptions and 34%–52% of the variance in usage intentions. Both social influence processes (subjective norm, voluntariness, and image) and cognitive instrumental processes (job relevance, output quality, result demonstrability, and perceived ease of use) significantly influenced user acceptance. These findings advance theory and contribute to the foundation for future research aimed at improving our understanding of user adoption behavior.

Many companies have embarked on initiatives that enable more demand information sharing between retailers and their upstream suppliers. While the literature on such initiatives in the business press is proliferating, it is not clear how one can quantify the benefits of these initiatives and how one can identify the drivers of the magnitudes of these benefits. Using analytical models, this paper aims at addressing these questions for a simple two-level supply chain with nonstationary end demands. Our analysis suggests that the value of demand information sharing can be quite high, especially when demands are significantly correlated over time.

Stochastic trees are extensions of decision trees that facilitate the modeling of temporal uncertainties. Their primary application has been to medical treatment decisions. It is often convenient to present stochastic trees in factored form, allowing loosely coupled pieces of the model to be formulated and presented separately. In this paper, we show how the notion of factoring can be extended as well to preference components of the stochastic model. We examine updateable-state utility, a flexible class of expected utility models that permit stochastic trees to be rolled back much in the manner of decision trees. We show that preference summaries for updateable-state utility can be factored out of the stochastic tree. In addition, we examine utility decompositions which can arise when factors in a stochastic tree are treated as attributes in a multiattribute utility function.

A model is introduced to guide a profit maximizing firm in its quest to enhance performance through process change. The key benefit sought from process change is a long term increase in effective capacity. However, realizing success from process change is not trivial. First, while process change may increase effective capacity in the long run, the disruptions during implementation typically reduce short term capacity. Second, competitive forces such as decreasing revenue streams and shrinking product life cycles complicate the implementation of process change. Third, while knowledge may enhance the ultimate benefits derived from process change, the correct timing and means of knowledge creation are difficult to discern. Lastly, a variety of trade-offs must be evaluated when selecting the particular process change to pursue. For example, choices range from hardware and software replacements to modification of manufacturing procedures. The model introduced here explicitly considers both the short term loss due to disruption and the long term gain in effective capacity associated with the process change. In addition, investments in the accumulation of knowledge are investigated for their potential to enhance process change effectiveness. Knowledge is generated from investment in preparation and training (learning-before-doing) and as a by-product of process change (learning-by-doing). Analysis of the model provides managerial recommendations for several key decisions relating to process change implementation including: (i) the selection of an appropriate process change alternative, (ii) the rate and timing for investment in process change, and (iii) the rate and timing for investment in preparation and training. New results are reported reflecting the important relationship between process change and knowledge. For example, we show that under certain conditions, a firm should optimally delay investment in process change until sufficient accumulation of knowledge is achieved. More generally, we identify conditions whereby investment in process change occurs at an increasing rate over time. This result is particularly important since it demonstrates a limitation of the existing literature where process change always occurs at a decreasing rate.

A system with heavy tailed service requirements under heavy load having a single server has an equilibrium waiting time distribution which is approximated by the Mittag-Leffler distribution. This fact is understood by a direct analysis of the weak convergence of a sequence of negative drift random walks with heavy right tail and the associated all time maxima of these random walks. This approach complements the recent transform view of Boxma and Cohen (1997).

This paper provides a new portfolio selection rule. The objective is to minimize the maximum individual risk and we use an l ∞ function as the risk measure. We provide an explicit analytical solution for the model and are thus able to plot the entire efficient frontier. Our selection rule is very conservative. One of the features of the solution is that it does not explicitly involve the covariance of the asset returns.

Consider a two-echelon repairable inventory system consisting of a central depot and multiple stocking centers. The centers provide parts replacement service to customers and replenish their inventory from the depot, following a one-for-one policy. The depot fills center replenishment orders on a first-come-first-serve basis. Defectives received at the centers are passed to the depot for repair and depot inventory replenishment. For this system, existing models (e.g., the METRIC model) usually assume that the depot replenishment lead times (DRLTs) are i.i.d., which however, does not fit well into the service parts logistics system that motivated this research. Because the DRLTs consist of the sum of repair times, defective return times, and transportation times, they are different across stocking centers, which are located globally. We study the impact of such center-dependent DRLTs on system performance. We derive probability distributions of the random delays at the depot experienced by center replenishment orders. We prove that a center with shorter DRLTs experiences shorter delays, and therefore, delivers better customer service. We show that for such systems, using the i.i.d. DRLT assumption introduces errors in estimating system performance. These errors become significant when both the demand rates and the depot planned inventory level are low.

In this article, we explore the connection between information system design and incentives for project search. The choice of an information system affects the level of managerial slack that is generated during project implementation. Whether slack is beneficial or costly to an organization has been the subject of debate. In our model of the hold-up problem in capital budgeting, there are both costs and benefits to having managerial slack. The cost of slack is the consumption of perquisites by the manager. The benefit of slack is that it can serve as a motivational tool. The possibility of increasing his slack may encourage a self-interested manager to conduct a more diligent search for a profitable project. To trade off the costs and benefits of slack in our model, an optimal information system sometimes incorporates coarse information, late information, and a mix of monitored and self-reported information. These features are familiar to accountants. Accounting incorporates both verified (monitored) and unverified (self-reported) information and provides information that is aggregated (coarse) and historical (late).

This paper extends the behavioral results reported in Fischer et al. (2000) by developing a model addressing preference uncertainty in multiattribute evaluation. The model is motivated by two hypotheses regarding properties of multiattribute profiles that lead to greater preference uncertainty. Our attribute conflict hypothesis predicts that greater within-alternative conflict (discrepancy among the attributes of an alternative) leads to more preference uncertainty. Our attribute extremity hypothesis predicts that greater attribute extremity (very high or low attribute values) leads to less preference uncertainty. To provide a deeper explanation of attribute conflict and extremity effects, we develop RandMAU, a family of additive (RandAUF) and multiplicative (RandMUF) random weights multiattribute utility models. In RandMAU models, preference uncertainty is represented as random variation in both the weighting parameters governing trade-offs among attributes and the curvature parameters governing single-attribute evaluations. Simulation results show that RandMUF successfully predicts both the attribute conflict and attribute extremity effects exhibited by the experimental participants in Fischer et al. (2000). It also predicts an outcome value effect on error whose form depends on the shape of single-attribute functions and on the type of multiattribute combination rule.

Understanding the wide range of outcomes achieved by firms trying to implement Total Quality Management (TQM) and similar process improvement initiatives presents a challenge to management theory: A few firms reap sustained benefits from their programs, but most efforts fail and are abandoned. In this paper I study one dimension of this phenomenon: the role of impending layoffs in determining employee commitment to process improvement. Currently, the literature provides two opposing theories concerning the effect of job security on the ability of firms to implement change initiatives. The “Drive Out Fear” school argues that firms must commit to job security, while the “Drive In Fear” school emphasizes the positive role that insecurity plays in motivating change. In this study a contract theoretic model is developed to analyze the role of agency in process improvement. The main insight of the study is that there are two types of job security, internal and external, that have opposing impacts on the firm's ability to implement improvement initiatives. The distinction is useful in explaining the results of different case studies and can reconcile the two change theories.