Interorganizational Governance Value Creation: Coordinating for Information Visibility and Flexibility in Supply Chains*

More thoroughly understanding how interorganizational governance value can be created by information technology and other governance mechanisms is critical for supply chain management. Based primarily on transaction‐cost economics and supplemented by the resource‐based view, this study investigates how interorganizational governance (i.e., relational governance and virtual integration) can create value (i.e., information visibility and supply chain flexibility) in the supply chain context. The findings show that both relational governance and virtual integration benefit information visibility. Those results also support both direct and indirect (via information visibility) effects of relational governance on supply chain flexibility. Although failing to affect supply chain flexibility directly, virtual integration can still improve supply chain flexibility with its ability to enhance information visibility. Thus, interorganizational governance mechanisms emphasizing both control and collaboration can influence the gain from collaboration‐specific capabilities, leading to the competitive advantage of a supply chain. The results of the study suggest that firms can gain greater supply chain flexibility within existing interfirm relationships by enhancing information visibility through virtual integration and relational governance.     

Estimation of dynamic panel data models with both individual and time-specific effects

This paper proposes a generalized least squares and a generalized method of moment estimators for dynamic panel data models with both individual-specific and time-specific effects. We also demonstrate that the common estimators ignoring the presence of time-specific effects are inconsistent when N→∞$N\to \infty$ but T is finite if the time-specific effects are indeed present. Monte Carlo studies are also conducted to investigate the finite sample properties of various estimators. It is found that the generalized least squares estimator has the smallest bias and root mean square error, and also has nominal size close to the empirical size. It is also found that even when there is no presence of time-specific effects, there is hardly any efficiency loss of the generalized least squares estimator assuming its presence compared to the generalized least squares estimator allowing only the presence of individual-specific effects.     

Assessing the contribution of R&;D to total factor productivity—a Bayesian approach to account for heterogeneity and heteroskedasticity

This paper proposes a hierarchical Bayes estimator for a panel data random coefficient model with heteroskedasticity to assess the contribution of R&D capital to total factor productivity. Based on Hall (1993) data for 323 US firms over 1976–1990, we find that there appear to have substantial unobserved heterogeneity and heteroskedasticity across firms and industries that support the use of our Bayes inference procedure. We find much higher returns to R&D capital and a more pronounced downswing for the 1981–1985 period, followed by a more pronounced upswing than those yielded by the conventional feasible generalized least squares estimators or other estimates. The estimated elasticities of R&D capital are 0.062 for 1976–1980, 0.036 for 1981–1985 and 0.081 for 1986–1990, while the estimated elasticities of ordinary capital are much more stable over these periods.     

SUBSAMPLING THE JOHANSEN TEST WITH STABLE INNOVATIONS

By taking into account the thick-tail property of the errors, cointegration analysis in vector error-correction models with infinite-variance stable errors is a natural generalization of cointegration analysis in error-correction models with normally distributed errors. We study the Johansen test for cointegrated systems under symmetric stable innovations with discrete spectral measures. The results show that the distributions of the Johansen test statistics under these innovations involve nuisance parameters. To overcome the problem of nuisance parameters, we implement a nonparametric subsampling procedure. We document some subsampling simulation results and demonstrate in an empirical example how the test can be used in practice.     

A Borda count for partially ordered ballots

The application of the theory of partially ordered sets to voting systems is an important development in the mathematical theory of elections. Many of the results in this area are on the comparative properties between traditional elections with linearly ordered ballots and those with partially ordered ballots. In this paper we present a scoring procedure, called the partial Borda count, that extends the classic Borda count to allow for arbitrary partially ordered preference rankings. We characterize the partial Borda count in the context of weighting procedures and in the context of social choice functions.     

Setting aside mood-biased thoughts and judgements: theory-based bias correction / Apartar las opiniones y los pensamientos sesgados por el estado de ánimo: la corrección del sesgo basada en teoría

Abstract

When people perceive their thoughts and judgements as unduly affected by some biasing factor (in themselves or in the judgement setting), they often attempt to avoid or remove those biases. Theories describe different psychological mechanisms guiding these efforts. We review the primary theories of bias correction and focus on the use of naive theories of bias in attempts to form accurate assessments of judgement targets. One distinguishing feature of the theory-based correction approach is its ability to deal with corrections for contrastive biases (i.e., biases that lead judgements in a direction opposite to the meaning of the biasing factor). Another is that any perceived biasing factor can result in corrections, even when those factors are not easily mapped into the kinds of mental representation mechanisms that form the heart of alternative approaches. We use corrections for mood-based biases to illustrate the utility of the theory-based correction approach, even though many mood-based biases can be conceptualized in ways that fit alternative approaches to bias correction. Implications for correction of many potential types of biases are discussed.     

Taiwan quality indicator project and hospital productivity growth

The Taiwan Quality Indicator Project (TQIP) is a quality management program that measures and monitors the healthcare quality of hospitals in Taiwan. This paper examines the impact of TQIP participation on hospital productivity growth with the application of the Malmquist productivity change index based on data envelopment analysis (DEA). Analyzing operations data from 31 TQIP regional hospitals over the period 1998–2004, we find that TQIP hospitals improved their productivity in the post-TQIP period. This improvement is attributable to quality change and relative efficiency progress. The simultaneous enhancement in both quality and relative efficiency coincides with the philosophy of total quality management (TQM) spirit, and confirms the efficiency improvement and quality assurance functions of TQIP.     

An empirical Bayes approach to evaluation of results for a specific region in multiregional clinical trials

To accelerate the drug development process and shorten approval time, the design of multiregional clinical trials (MRCTs) incorporates subjects from many countries/regions around the world under the same protocol. After showing the overall efficacy of a drug in all global regions, one can also simultaneously evaluate the possibility of applying the overall trial results to all regions and subsequently support drug registration in each of them. In this paper, we focus on a specific region and establish a statistical criterion to assess the consistency between the specific region and overall results in an MRCT. More specifically, we treat each region in an MRCT as an independent clinical trial, and each perhaps has different treatment effect. We then construct the empirical prior information for the treatment effect for the specific region on the basis of all of the observed data from other regions. We will conclude similarity between the specific region and all regions if the posterior probability of deriving a positive treatment effect in the specific region is large, say 80%. Numerical examples illustrate applications of the proposed approach in different scenarios. Copyright © 2013 John Wiley & Sons, Ltd.     

Bayesian marginal inference via candidate's formula

Computing marginal probabilities is an important and fundamental issue in Bayesian inference. We present a simple method which arises from a likelihood identity for computation. The likelihood identity, called Candidate's formula, sets the marginal probability as a ratio of the prior likelihood to the posterior density. Based on Markov chain Monte Carlo output simulated from the posterior distribution, a nonparametric kernel estimate is used to estimate the posterior density contained in that ratio. This derived nonparametric Candidate's estimate requires only one evaluation of the posterior density estimate at a point. The optimal point for such evaluation can be chosen to minimize the expected mean square relative error. The results show that the best point is not necessarily the posterior mode, but rather a point compromising between high density and low Hessian. For high dimensional problems, we introduce a variance reduction approach to ease the tension caused by data sparseness. A simulation study is presented.     

Minimum Aberration Regular Two-Level Designs in the Presence of Dispersion Factors

In practice, the variance of the response variable may change as some specific factors change from one setting to another in a factorial experiment. These factors affecting the variation of the response are called dispersion factors, which can violate the usual assumption of variance homogeneity. In this study, we modify the conventional minimum aberration criterion to take the impact of dispersion factors into account. The situations of one or two dispersion factors are investigated. As a result, we present regular 2^{n ? p} designs with run sizes equal to 16 and 32 using the modified minimum aberration criterion.