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Adolescent students with disabilities often struggle with completing writing tasks efficiently. Until recently, most research regarding writing efficiency or fluency has examined production skills such as handwriting with young writers or examined how to use measures of fluency to assess student performance. In this chapter, 10 studies that directly address the impact of instruction on adolescents' writing fluency will be reviewed. Findings indicated that when teacher modeling and structured practice was provided for writing within a time limit, students' writing improved in the number of ideas or text parts written and in holistic quality. When measured, improvement generalized to a standardized writing fluency test. Implications for future research are noted.

Machine learning (ML) has recently gained momentum as a method for measurement in strategy research. Yet, little guidance exists regarding how to appropriately apply the method for this purpose in our discipline. We address this by offering a guide to the application of ML in strategy research, with a particular emphasis on data handling practices that should improve our ability to accurately measure our constructs of interest using ML techniques. We offer a brief overview of ML methodologies that can be used for measurement before describing key challenges that exist when applying those methods for this purpose in strategy research (i.e., sample sizes, data noise, and construct complexity). We then outline a theory-driven approach to help scholars overcome these challenges and improve data handling and the subsequent application of ML techniques in strategy research. We demonstrate the efficacy of our approach by applying it to create a linguistic measure of CEOs' motivational needs in a sample of S&P 500 firms. We conclude by describing steps scholars can take after creating ML-based measures to continue to improve the application of ML in strategy research.

The traditional predictor of technical inefficiency proposed by Jondrow, Lovell, Materov, and Schmidt (1982) is a conditional expectation. This chapter explores whether, and by how much, the predictor can be improved by using auxiliary information in the conditioning set. It considers two types of stochastic frontier models. The first type is a panel data model where composed errors from past and future time periods contain information about contemporaneous technical inefficiency. The second type is when the stochastic frontier model is augmented by input ratio equations in which allocative inefficiency is correlated with technical inefficiency. Compared to the standard kernel-smoothing estimator, a newer estimator based on a local linear random forest helps mitigate the curse of dimensionality when the conditioning set is large. Besides numerous simulations, there is an illustrative empirical example.

We address the problem of selecting a topological design for a network having a single traffic source and uncertain demand at the remaining nodes. Solving the associated fixed charge network flow (FCF) problem requires finding a network design that limits both the fixed costs of establishing links and the variable costs of sending flow to the destinations. In this paper, we discuss how to obtain a sequence of optimal solutions that arise as the demand intensity varies from low levels to high. One of the network design alternatives associated with these solutions will be chosen based upon the dominant selection criteria of the decision maker. We consider both probabilistic and non-probabilistic criteria and compare the network designs associated with each. We show that the entire sequence of optimal solutions can be identified with little more effort than solving a single FCF problem instance. We also provide solution approaches that are relatively efficient and suggest good design alternatives based upon approximations to the optimal sequence.

Several activity-based transportation models are now becoming operational and are entering the stage of application for the modelling of travel demand. In our application, we will use decision rules to support the decision-making of the model instead of principles of utility maximization, which means our work can be interpreted as an application of the concept of bounded rationality in the transportation domain. In this chapter we explored a novel idea of combining decision trees and Bayesian networks to improve decision-making in order to maintain the potential advantages of both techniques. The results of this study suggest that integrated Bayesian networks and decision trees can be used for modelling the different choice facets of a travel demand model with better predictive power than CHAID decision trees. Another conclusion is that there are initial indications that the new way of integrating decision trees and Bayesian networks has produced a decision tree that is structurally more stable.

Vector error-correction models (VECMs) have become increasingly important in their application to financial markets. Standard full-order VECM models assume non-zero entries in all their coefficient matrices. However, applications of VECM models to financial market data have revealed that zero entries are often a necessary part of efficient modelling. In such cases, the use of full-order VECM models may lead to incorrect inferences. Specifically, if indirect causality or Granger non-causality exists among the variables, the use of over-parameterised full-order VECM models may weaken the power of statistical inference. In this paper, it is argued that the zero–non-zero (ZNZ) patterned VECM is a more straightforward and effective means of testing for both indirect causality and Granger non-causality. For a ZNZ patterned VECM framework for time series of integrated order two, we provide a new algorithm to select cointegrating and loading vectors that can contain zero entries. Two case studies are used to demonstrate the usefulness of the algorithm in tests of purchasing power parity and a three-variable system involving the stock market.

Accounting’s definition of accountability should include attributes of socioenvironmental degradation manufactured by unsustainable technologies. Beck argues that emergent accounts should reflect the following primary characteristics of technological degradation: complexity, uncertainty, and diffused responsibility. Financial stewardship accounts and probabilistic assessments of risk, which are traditionally employed to allay the public’s fear of uncontrollable technological hazards, cannot reflect these characteristics because they are constructed to perpetuate the status quo by fabricating certainty and security. The process through which safety thresholds are constructed and contested represents the ultimate form of socialized accountability because these thresholds shape how much risk people consent to be exposed to. Beck’s socialized total accountability is suggested as a way forward: It has two dimensions, extended spatiotemporal responsibility and the psychology of decision-making. These dimensions are teased out from the following constructs of Beck’s Risk Society thesis: manufactured risks and hazards, organized irresponsibility, politics of risk, radical individualization and social learning. These dimensions are then used to critically evaluate the capacity of full cost accounting (FCA), and two emergent socialized risk accounts, to integrate the multiple attributes of sustainability. This critique should inform the journey of constructing more representative accounts of technological degradation.