Values and happiness among Asian adolescents: a cross-national study

Despite policy efforts to increase adolescent happiness, their impact has been unsatisfactory. Their limited impact may be rooted from a discrepancy between values that adolescents pursued and those that the policies were based on. To provide policy implications, our study aims to identify prevailing values for South Korean, Japanese, and Chinese adolescents and to examine the relationship between the values and self-rated happiness (SRH). A cross-sectional study was conducted using survey data collected on approximately 2000 middle and high school students (7th to 12th school grade) from each country in 2008. Firstly, an explanatory factor analysis was conducted to identify salient adolescent values from each country. Subsequently, a multiple logistic regression analysis was conducted for each population group to examine the relationship between the identified values and adolescent SRH after controlling for sociodemographic characteristics. We found that benevolence and altruism were positively associated with adolescent SRH in all three population groups. Patriarchy was associated with SRH positively in Chinese yet inversely in Japanese. Success pursuit was inversely associated with SRH in Korean. Policy efforts based on values of communities or social harmony may benefit adolescents’ SRH in these three countries.     

All Politics is Not Local: National Forces in State Abortion Initiatives

Objective. Although research suggests that national forces can play a role in local and state elections, most of this work has only recently begun to examine the potential role of national forces in state or local ballot initiative or referenda elections. Methods. Our research addresses this gap in the literature by exploring the influence of national forces, such as the timing of elections, Supreme Court rulings, the activities of interest groups, and public opinion, on state direct legislation elections. We incorporate national forces into the morality politics framework and derive specific hypotheses. We then test these hypotheses by conducting a multivariate analysis of county–level voting patterns across 16 abortion–related direct legislation elections. Results. Our results confirm most of the hypotheses derived from the morality politics framework, including those concerning the role of national forces. Conclusions. Voting patterns on abortion tend to be influenced by the presence of presidential elections, Supreme Court rulings, interest–group activity, public opinion, partisanship, college education, and conservative religious forces. We discuss the implications of our findings for research on elections, abortion policy, and morality politics.     

Frequency domain bootstrap for ratio statistics under long-range dependence

A frequency domain bootstrap (FDB) is a common technique to apply Efron’s independent and identically distributed resampling technique (Efron, 1979) to periodogram ordinates – especially normalized periodogram ordinates – by using spectral density estimates. The FDB method is applicable to several classes of statistics, such as estimators of the normalized spectral mean, the autocorrelation (but not autocovariance), the normalized spectral density function, and Whittle parameters. While this FDB method has been extensively studied with respect to short-range dependent time processes, there is a dearth of research on its use with long-range dependent time processes. Therefore, we propose an FDB methodology for ratio statistics under long-range dependence, using semi- and nonparametric spectral density estimates as a normalizing factor. It is shown that the FDB approximation allows for valid distribution estimation for a broad class of stationary, long-range (or short-range) dependent linear processes, without any stringent assumptions on the distribution of the underlying process. The results of a large simulation study show that the FDB approximation using a semi- or nonparametric spectral density estimator is often robust for various values of a long-memory parameter reflecting magnitude of dependence. We apply the proposed procedure to two data examples.     

The Effect of Single Rater Bias in Multi‐Stakeholder Research: A Methodological Evaluation of Buyer‐Supplier Relationships

As the global competitive landscape intensifies, firms have looked to their supply chain organizations to improve cost, visibility, and cycle time performance across functions, products, and markets. As a result, the scope of supply chain related operations have increasingly cut across organizational boundaries. To understand and capture such cross‐organizational activities, researchers have broadened the focus of their studies and included multiple stakeholders in their analysis (e.g., integration, sustainability, and buyer‐supplier relationships). However, multi‐stakeholder research has also increased the complexity and effort required to conduct studies across organizational boundaries. Unfortunately, many studies that use multi‐stakeholder constructs fail to fully address their multi‐sided nature during both construct conceptualization and data collection. Several studies suggest that neglecting the multi‐sided nature of certain constructs can affect the research validity and reliability and may invalidate research inferences and results, although such concerns have not been empirically demonstrated. The current study addresses this gap by performing a series of tests using data from 105 matched pairs of buyers and their suppliers to illustrate key methodological considerations for conducting multi‐stakeholder research. This study also offers practical guidance regarding assumptions routinely made in single rater research and proposes when single rater data may be appropriate for multi‐stakeholder research.     

Bayesian spectral analysis models for quantile regression with Dirichlet process mixtures

This paper presents a Bayesian analysis of partially linear additive models for quantile regression. We develop a semiparametric Bayesian approach to quantile regression models using a spectral representation of the nonparametric regression functions and the Dirichlet process (DP) mixture for error distribution. We also consider Bayesian variable selection procedures for both parametric and nonparametric components in a partially linear additive model structure based on the Bayesian shrinkage priors via a stochastic search algorithm. Based on the proposed Bayesian semiparametric additive quantile regression model referred to as BSAQ, the Bayesian inference is considered for estimation and model selection. For the posterior computation, we design a simple and efficient Gibbs sampler based on a location-scale mixture of exponential and normal distributions for an asymmetric Laplace distribution, which facilitates the commonly used collapsed Gibbs sampling algorithms for the DP mixture models. Additionally, we discuss the asymptotic property of the sempiparametric quantile regression model in terms of consistency of posterior distribution. Simulation studies and real data application examples illustrate the proposed method and compare it with Bayesian quantile regression methods in the literature.     

Taxonomy of ERP integrations and performance outcomes: an exploratory study of manufacturing firms

Building and deploying network capabilities of firms are crucial for sustaining competitive advantage. Enterprise resource planning (ERP) systems are useful to enhance such network capabilities thorough effective information flows. Their intended goals are measured in terms of production costs, operational flexibility and supply chain performance outcomes. However, the impact of ERP system implementation on firm performance has been reported as somewhat inconclusive. This study contends that a missing link in the story is the scope and extent of ERP system implementation after investigating how the extent of ERP integration is associated with the performance outcomes of manufacturing firms. The study also posits that restructuring in the organisation and supply chains are positively associated with manufacturing performance. Since ERP often entails restructuring in an organisation and supply chains, it is anticipated that restructuring plays an important role in inducing positive impact of ERP implementation to the firm. Using a global sample of 641 manufacturers, this research identifies four distinct ERP systems integration patterns, epitomised by different extents and directions of integration, and finds a significant association among the broadest degree of ERP systems integration, restructuring and plant performance improvement. The empirical results also show that restructuring takes place most actively in a firm that implement ERP with widest scope and scale.     

The virtual waiting time of the M/G/1 queue with customers of n types of impatience

We consider the $M/G/1$ queue in which the customers are classified into $n+1$ classes by their impatience times. First, we analyze the model with two types of customers; one is the customer with constant impatience time $k$ and the other is the patient customer whose impatience time is $\infty$. The expected busy period of the server and the limiting distribution of the virtual waiting time process are obtained. Then, the model is generalized to the one in which the impatience time of each customer is anyone in $\{k_1,k_2,\dots ,k_n,\infty \}$.     

Bayesian analysis of semiparametric Bernstein polynomial regression models for data with sample selection

In regression analysis, a sample selection scheme often applies to the response variable, which results in missing not at random observations on the variable. In this case, a regression analysis using only the selected cases would lead to biased results. This paper proposes a Bayesian methodology to correct this bias based on a semiparametric Bernstein polynomial regression model that incorporates the sample selection scheme into a stochastic monotone trend constraint, variable selection, and robustness against departures from the normality assumption. We present the basic theoretical properties of the proposed model that include its stochastic representation, sample selection bias quantification, and hierarchical model specification to deal with the stochastic monotone trend constraint in the nonparametric component, simple bias corrected estimation, and variable selection for the linear components. We then develop computationally feasible Markov chain Monte Carlo methods for semiparametric Bernstein polynomial functions with stochastically constrained parameter estimation and variable selection procedures. We demonstrate the finite-sample performance of the proposed model compared to existing methods using simulation studies and illustrate its use based on two real data applications.