An empirical Bayes rule for testing simple hypothesis against simple alternative

This paper gives a short introduction into the empirical B ayes approach. To illustrate this approach the classical problem of testing a simple hypothesis against a simple alternative is discussed. Some explicit results are given for testing H_o: N(- 1, 1) against H,: N( 1, 1). This paper is a further elaboration of section 3.1 of the author's dissertation W an H ouwelingen (1973)).     

Efficient approach to health care industry material resource management: an empirical research

The feasibility of JIT implementation in the health care industry was examined through in-depth case studies and survey research. Specifically, this research examined two different groups in the health care industry: health care institutions that have introduced JIT philosophy into their operations and those that have not. Based on this classification, the research reveals the different impacts that the introduction of JIT philosophy has had on the health care industry's inventory management, service quality, and competitiveness. According to the research result, the introduction of the JIT philosophy in the health care industry's material management system improved that system and reduced implementation problems even though the differences between the JH and the NJH were not high. However, this research found that great potentials exist for the successful implementation of JIT philosophy for material management even though most of the JIT in the health care industry is still in its early stages. Moreover, with the maturing of the stockless material management system, differences between the JH and the NJH groups will be even more significant.     

Design considerations for neyman-pearson and wald hypothesis testing

Summary The Neyman-Pearson Lemma describes a test for two simple hypotheses that, for a given sample size, is most powerful for its level. It is usually implemented by choosing the smallest sample size that achieves a prespecified power for a fixed level. The Lemma does not describe how to select either the level or the power of the test. In the usual Wald decision-theoretic structure there exists a sampling cost function, an initial prior over the hypothesis space and various payoffs to right/wrong hypothesis selections. The optimal Wald test is a Bayes decision rule that maximizes the expected payoff net of sampling costs. This paper shows that the Wald-optimal test and the Neyman-Pearson test can be the same and how the Neyman-Pearson test, with fixed level and power, can be viewed as a Wald test subject to restrictions on the payoff vector, cost function and prior distribution.     

Forecasting methods for marketing: Review of empirical research

This paper reviews the empirical research on forecasting in marketing. In addition, it presents results from some small scale surveys. We offer a framework for discussing forecasts in the area of marketing, and then review the literature in light of that framework. Particular emphasis is given to a pragmatic interpretation of the literature and findings. Suggestions are made on what research is needed.     

Environmental regulation and productivity: testing the porter hypothesis

Abstract  This paper provides an empirical analysis of the relationship between the stringency of environmental regulation and total factor productivity (TFP) growth in the Quebec manufacturing sector. This allows us to investigate more fully the Porter hypothesis in three directions. First, the dynamic aspect of the hypothesis is captured through the use of lagged regulatory variables. Second, we argue that the hypothesis is more relevant for more polluting sectors. Third, we argue that the hypothesis is more relevant for sectors which are more exposed to international competition. Our empirical results suggest that: (1) the contemporaneous impact of environmental regulation on productivity is negative; (2) the opposite result is observed with lagged regulatory variables, which is consistent with Michel Porter’s conjecture; and (3) this effect is stronger in a subgroup of industries which are more exposed to international competition.

Robust adaptive rate-optimal testing for the white noise hypothesis

A new test is proposed for the weak white noise null hypothesis. The test is based on a new automatic selection of the order for a Box–Pierce (1970) test statistic or the test statistic of Hong (1996). The heteroskedasticity and autocorrelation-consistent (HAC) critical values from Lee (2007) are used, allowing for estimation of the error term. The data-driven order selection is tailored to detect a new class of alternatives with autocorrelation coefficients which can be o(n^−1/2)$o\left(n^{-1/2}\right)$ provided there are sufficiently many of such coefficients. A simulation experiment illustrates the good statistical properties of the test both under the weak white noise null and the alternative.     

Assessing total factor productivity across Africa: an empirical investigation

Journal of Productivity Analysis - Even though Africa is home to many emerging markets, the attention on African growth might be neglected by economists and scholars due to data unavailability and...     

An improved empirical Bayes test for positive exponential families

We exhibit an empirical Bayes test δ^* _n for a decision problem using a linear error loss in a class of positive exponential families. This empirical Bayes test δ^* _n possesses the asymptotic optimality, and its associated regret converges to zero with rate n ⁻¹(ln n )⁶ This rate of convergence improves the previous results in the literature in the sense that a faster rate of convergence is achieved under much weaker conditions. Examples are presented to illustrate the performance of the empirical Bayes test δ^* _n     

Emergent clustering methods for empirical OM research

To date, the vast majority of cluster analysis applications in OM research have relied on traditional hierarchical (e.g., Ward's algorithm) and nonhierarchical (e.g., K-means algorithms) methods. Although these venerable methods should continue to be employed effectively in the OM literature, we also believe there is a significant opportunity to expand the scope of clustering methods to emergent techniques. We provide an overview of some alternative clustering procedures (including advantages and disadvantages), identify software programs for implementing them, and discuss the circumstances where they might be employed gainfully in OM research. The implementation of emergent clustering methods in the OM literature should enable researchers to offer implications for practice that might not have been uncovered with traditional methods.     

Optimal hypothesis testing: from semi to fully Bayes factors

We propose and examine statistical test-strategies that are somewhat between the maximum likelihood ratio and Bayes factor methods that are well addressed in the literature. The paper shows an optimality of the proposed tests of hypothesis. We demonstrate that our approach can be easily applied to practical studies, because execution of the tests does not require deriving of asymptotical analytical solutions regarding the type I error. However, when the proposed method is utilized, the classical significance level of tests can be controlled.     

Addressing the endogeneity dilemma in operations management research: Theoretical,empirical, and pragmatic considerations

In this paper, we examine the problem of endogeneity in the context of operations management research. Whereas the extant literature has focused primarily on the statistical aspect of the problem, a comprehensive treatment requires an examination of theoretical and pragmatic considerations as complements. The prevailing problem with the focus on statistical techniques is that the standards tend to be derived from idealizations: the correlation between a regressor and a disturbance term must be exactly zero, or the analysis will be invalid. In actual empirical research settings, such a knife-edge assumption can never be satisfied, indeed it cannot even be directly tested. Idealizations are useful in helping us understand what it would take to eliminate endogeneity, but when applied directly and unconditionally, they lead to unreasonable standards that may unnecessarily stifle substantive inquiry. We believe that it is far more productive and meaningful to ask: “What can we realistically expect empirical scientists to be able to achieve?” To this end, we cover and revisit some of the general technical material on endogeneity, paying special attention to the idiosyncrasies of operations management research and what could constitute reasonable criteria for addressing endogeneity in empirical operations management studies.     

Gender and management on Spanish SMEs: an empirical analysis

Few studies have investigated the presence of women on the boards of directors of companies. Those that have been done have focused on large firms. In this study we analyzed the gender diversity of a sample of Spanish small and medium enterprises. These firms are of great importance in terms of their number, the employment they provide, and their sales. Furthermore, there is an open debate in Spain about gender equality after the passing of several laws against gender discrimination. We found that women's presence on boards generates a negative impact on firm performance and this result may be due to less risky strategies implemented by women directors. This finding is interesting because it sheds light on how women can affect the functioning of a board. We also found that family firms and firms with a financial institution as the main shareholder tend to have more women on the board. Finally, we show that firms with less debt, more assets, and larger boards have more women as directors.     

组织学习能力对知识创新的影响机理与实证研究

本文从组织学习理论出发,构建学习能力对知识创新的影响模式。实证研究结果表明学习能力对知识创新有正向的影响。即组织若具备较好的学习能力,则知识创新的绩效越好。学习能力的各观测变量中,应用能力最具解释力,其次为获取能力,再次为整合能力与转换能力。     

Appropriate roles for statistical decision theory and hypothesis testing in model selection: An exposition

Statistical studies in the social sciences often analyze a cross section of communities that are assumed to be homogeneous for certain characteristics. However, the researcher might be interested in investigating the general behavior of all communities as well as particular differences between groups of communities that are spatially separated. This paper suggests, and empirically examines, a procedure which utilizes both statistical decision theory and hypothesis testing and enables us to determine the best model for estimating behavioral relationships for the full sample and for subgroups. The paper examines property crime occurrences in 94 New Jersey suburban communities of Philadelphia utilizing the full sample as well as subgroups within it. The purpose of grouping is to further understand the possible interjurisdictional mobility of urban criminals. The study suggests possible factors that induce urban criminals to commit crimes in the suburbs.     

Would you mind completing this survey: Assessing the state of survey research in supply chain management

In supply chain management, survey research is one of the primary methodologies used by researchers to generate empirical data. Critical to its effectiveness is an acceptable response rate. Recently, concerns have been raised regarding perceived falling response rates. Using data taken from 464 survey-based studies, as drawn from five representative journals that publish survey-based supply chain studies, this study assesses whether these concerns are valid. Furthermore, it assesses whether the various tactics recommended for improving response rates have been successful. The findings indicate that (1) responses rates have been falling (with a significant decrease beginning in 2001); (2) it is difficult to evaluate the effectiveness of the various techniques because of unevenness in reporting of techniques used; and, (3) response rates are significantly influenced by factors such as the number of questions, source of survey population, method of survey delivery, specific respondents targeted, and use of prequalification/pre-notification.     

A Measurement Framework for Assessing the Contribution of Social Media to Public Engagement: An empirical analysis on Facebook

The public sector is increasingly turning to social media as a means to communicate and interact with citizens, but little is known about the contribution that these social technologies make to public engagement. This paper used a scoping literature review of studies examining social media in order to develop a framework that measures two Facebook features (popularity and commitment), which was then used to evaluate two different levels of public engagement (public communication and public participation). The framework was validated by applying it to the Facebook pages of several Italian city administrations, and a social media engagement matrix was proposed to interpret the findings.     

Helping at NASA: Guidelines for using process consultation to develop impactful research

Management research has long been criticized for its perceived lack of relevance or impact beyond academia. How can we, as management scholars, create research that is more relevant and impactful? I argue that Edgar Schein's process consultation approach can be part of the answer. Process consultation's ultimate aim is to help client organizations. Key aspects of what is now recognized as engaged scholarship were fundamental to process consultation even before engaged scholarship was part of the management vocabulary. Based on my engagement process with the National Aeronautics and Space Administration since 2013, I argue that trust can fruitfully be seen as an outcome and an enabler of productive long-term helping relationships and of engaged scholarship; that what I call pragmatic bricolage is important in terms of offering help based on the client's needs as they develop at different junctures; and that unexpected dilemmas in such relationships are inevitable, but ways forward can be found by applying key principles of social systems. I conclude by outlining guidelines for impactful research and for disseminating research to wider audiences.     

Alternation of parties in power and economic volatility: testing the rational partisan hypothesis and policy learning hypothesis

Right and left parties have distinct macroeconomic preferences that could create different levels of volatility during their executive tenures. But rational partisan theory argues that, because actors in the economy anticipate ruling party behavior, partisan differences only matter when election outcomes are uncertain. We argue that policy risk from ruling parties extends beyond elections, leading to important variation in growth volatility that occurs during a ruling party’s tenure. Building on theories of policy risk and learning, we argue that after elections, economic actors still face uncertainty about the policies of new ruling parties. With time in power, new ruling parties build policy track records, reducing policy risk and, thus, volatility. We estimate a learning curve model of ruling party duration’s effect on the variation in quarterly GDP growth rates. Using data from 44 democracies between 1981 and 2009, we find that learning processes extend beyond the conclusion of uncertain elections.