Three types of trust in economic exchanges are identified: weak form trust, semi‐strong form trust, and strong form trust. It is shown that weak form trust can only be a source of competitive advantage when competitors invest in unnecessary and expensive governance mechanisms. Semi‐strong form trust can be a source of competitive advantage when competitors have differential exchange governance skills and abilities, and when these skills and abilities are costly to imitate. The conditions under which strong form trust can be a source of competitive advantage are also identified. Implications of this analysis for theoretical and empirical work in strategic management are discussed.

As mobile phones advance in functionality and capability, they are being used for more than just communication. Increasingly, these devices are being employed as instruments for introspection into habits and situations of individuals and communities. Many of the applications enabled by this new use of mobile phones rely on contextual information. The focus of this work is on one dimension of context, the transportation mode of an individual when outside. We create a convenient (no specific position and orientation setting) classification system that uses a mobile phone with a built-in GPS receiver and an accelerometer. The transportation modes identified include whether an individual is stationary, walking, running, biking, or in motorized transport. The overall classification system consists of a decision tree followed by a first-order discrete Hidden Markov Model and achieves an accuracy level of 93.6% when tested on a dataset obtained from sixteen individuals.

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This tutorial presents a detailed study of sensor faults that occur in deployed sensor networks and a systematic approach to model these faults. We begin by reviewing the fault detection literature for sensor networks. We draw from current literature, our own experience, and data collected from scientific deployments to develop a set of commonly used features useful in detecting and diagnosing sensor faults. We use this feature set to systematically define commonly observed faults, and provide examples of each of these faults from sensor data collected at recent deployments.

Full-information item bifactor analysis is an important statistical method in psychological and educational measurement. Current methods are limited to single group analysis and inflexible in the types of item response models supported. We propose a flexible multiple-group item bifactor analysis framework that supports a variety of multidimensional item response theory models for an arbitrary mixing of dichotomous, ordinal, and nominal items. The extended item bifactor model also enables the estimation of latent variable means and variances when data from more than one group are present. Generalized user-defined parameter restrictions are permitted within or across groups. We derive an efficient full-information maximum marginal likelihood estimator. Our estimation method achieves substantial computational savings by extending Gibbons and Hedeker's (1992) bifactor dimension reduction method so that the optimization of the marginal log-likelihood only requires two-dimensional integration regardless of the dimensionality of the latent variables. We use simulation studies to demonstrate the flexibility and accuracy of the proposed methods. We apply the model to study cross-country differences, including differential item functioning, using data from a large international education survey on mathematics literacy. Keywordshierarchical factor model; item response theory; multidimensional IRT; item factor analysis; differential item functioning Full-information item bifactor analysis (Gibbons & Hedeker, 1992;Gibbons et al., 2007) has been increasingly recognized as an important statistical method in psychological and educational measurement. Item bifactor analysis, as a special case of confirmatory multidimensional item response theory (IRT) modeling, provides information about the dimensionality of the measurement instrument, strategies for scaling individual differences, and new approaches to computerized adaptive testing. For instance, Reise, Morizot, and Hays (2007) applied item bifactor analysis to patient reported health outcomes data and concluded that the item bifactor model provides a valuable tool for exploring dimensionality. In psychopathology research, Simms, Grös, Watson, and O'Hara (2008) found that a bifactor structure is needed for describing mood and anxiety symptoms. In the area of psychiatric services research, Gibbons et al. (2008) applied the bifactor model to the construction of item banks and computerized adaptive tests and demonstrated dramatic reductions in patient and clinician burden. In educational measurement, DeMars (2006) applied the item bifactor model to data from testlet-based assessments and found the bifactor model a practical alternative to more specialized testlet response models (e.g. Wainer, Bradlow, & Wang, 2007).Address correspondence to: Li Cai, UCLA, Los Angeles, CA, USA 90095-1521. lcai@ucla.edu. Phone: 310.206.0583. Fax: 310.206.5830. NIH Public Access Author ManuscriptPsychol Methods. Author manuscript; available in PMC 2012 September 1. NIH-PA Author Manuscript...

This paper argues that the gap between the theoretical utility and the practical utility of the resource-based view (RBV) may be narrowed by operationalizing the theory more consistently with Penrose's original framework. The operationalization proposed here is a twofold approach. First, the RBV may be enhanced by the explicit recognition of Penrose's two classes of resources, namely, administrative resources and productive resources. This distinction suggests a focus on the administrative decisions of managers that lead to economic performance. Second, we argue that the RBV is a theory about extraordinary performers or outliers-not averages. Therefore, the statistical methods used in applying the theory must account for individual firm differences, and not be based on means, which statistically neutralize firm differences. We propose a novel Bayesian hierarchical methodology to examine the relationship between administrative decisions and economic performance over time. We develop and explain a measure of competitive advantage that goes beyond comparisons of economic performance. This Bayesian methodology allows us to make meaningful probability statements about specific, individual firms and the effects of the administrative decisions examined in this study.