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The purpose of this paper is to consider the concepts of individual and complete statistical power used for multiple testing and shows their relevance for determining the number of statistical tests to perform when assessing non-response bias.

A statistical power analysis of 55 survey-based research papers published in three prestigious logistics journals (International Journal of Physical Distribution and Logistics Management, Journal of Business Logistics, Transportation Journal) over the last decade was conducted.

Results show that some of the low complete power levels encountered could have been avoided if fewer tests had been used in the assessment of non-response bias.

The research offers important recommendations to scholars engaged in survey research as they assess the effects of non-respondents on research findings. By following the recommended strategies for testing non-response bias, researchers can improve the statistical power of their findings.

The purpose of this study is to review the literature on strategic management in Africa with special emphasis on how strategy constructs have been measured and present a roadmap to help improve strategy research in Africa.

A content analysis of empirical research on strategic management published in journals using data from Africa from 2000 to 2013 is conducted to examine construct measurement practices.

The findings indicate that the average sample sizes in strategy research in Africa is not large as strategy research in general, and have low statistical power. While the studies rely heavily on single-indicator measures, there were also several studies using scale or multiple measures that report reliabilities.

Limitations of the research include small number of studies used, inability to examine journal effects’ of the findings due to few numbers of papers from many of the journals, and lack of examination of the influence of the context and topical areas of the articles on the use of the construct measurement techniques.

The study provides information about the use of construct measurement techniques and power analysis in strategy research in Africa. It further encourages the use of larger sample sizes, the examination of power, and more focus on variables which allow the assessment of reliabilities and validity.

Little is known about construct measurement practices of the empirical research in and about Africa in the discipline of strategic management. This chapter builds on extant research on construct measurement issues in strategic management research, but with the unique value-added contribution of focusing on the African environment where the discipline is beginning to take hold.

Constructing and evaluating behavioral science models is a complex process. Decisions must be made about which variables to include, which variables are related to each other, the functional forms of the relationships, and so on. The last 10 years have seen a substantial extension of the range of statistical tools available for use in the construction process. The progress in tool development has been accompanied by the publication of handbooks that introduce the methods in general terms (Arminger et al., 1995; Tinsley & Brown, 2000a). Each chapter in these handbooks cites a wide range of books and articles on specific analysis topics.

Attaining high response rates in survey‐based supply chain management (SCM) research is becoming increasingly difficult, but small samples can limit the reliability and validity of empirical research findings. The purpose of this article is to analyze the status quo and provide a discussion of methodological issues related to the use of small samples in SCM research.

An in‐depth review of 75 small sample survey studies published between 1998 and 2007 in three journals in the field that frequently publish survey‐based research papers (TJ, IJPDLM, and JBL) was conducted, and key characteristics of these studies were compared with the characteristics from 44 small sample survey studies published in leading operations management (JOM) and management (AMJ) journals.

The review of papers published in TJ, IJPDLM, and JBL shows that small samples are frequently used in SCM research. This study provides an overview of current practices, opportunities for improvement, and a number of specific recommendations that may help increase the analytical rigor of (future) survey‐based studies that rely on small samples.

The recommendations provided in this article can greatly benefit researchers in the field of SCM. By following these proposals, the reliability and validity of research findings will be increased, researchers will be better equipped to investigate interesting questions where small samples are the norm rather than the exception (e.g., the study of dyadic supply chain relationships), and important and valid contributions to the theory and practice of SCM will be generated.

This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.

This study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.

There are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.

The study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.

This paper aims to investigate whether the best statistical model also corresponds to the best empirical performance in the volatility modeling of financialized commodity markets.

The authors use various p and q values in Value-at-Risk (VaR) GARCH(p, q) estimation and perform backtesting at different confidence levels, different out-of-sample periods and different data frequencies for eight financialized commodities.

They find that the best fitted GARCH(p,q) model tends to generate the best empirical performance for most financialized commodities. Their findings are consistent at different confidence levels and different out-of-sample periods. However, the strong results occur for both daily and weekly returns series. They obtain weak results for the monthly series.

Their research method is limited to the GARCH(p,q) model and the eight discussed financialized commodities.

They conclude that they should continue to rely on the log-likelihood statistical criteria for choosing a GARCH(p,q) model in financialized commodity markets for daily and weekly forecasting horizons.

The log-likelihood statistical criterion has strong predictive power in GARCH high-frequency data series (daily and weekly). This finding justifies the importance of using statistical criterion in financial market modeling.

First, this paper investigates whether the best statistical model corresponds to the best empirical performance. Second, this paper provides an indirect test for evaluating the accuracy of volatility modeling by using the VaR approach.

This study aims to examine the role of an overall model fit assessment in the context of partial least squares path modeling (PLS-PM). In doing so, it will explain when it is important to assess the overall model fit and provides ways of assessing the fit of composite models. Moreover, it will resolve major concerns about model fit assessment that have been raised in the literature on PLS-PM.

This paper explains when and how to assess the fit of PLS path models. Furthermore, it discusses the concerns raised in the PLS-PM literature about the overall model fit assessment and provides concise guidelines on assessing the overall fit of composite models.

This study explains that the model fit assessment is as important for composite models as it is for common factor models. To assess the overall fit of composite models, researchers can use a statistical test and several fit indices known through structural equation modeling (SEM) with latent variables.

Researchers who use PLS-PM to assess composite models that aim to understand the mechanism of an underlying population and draw statistical inferences should take the concept of the overall model fit seriously.

To facilitate the overall fit assessment of composite models, this study presents a two-step procedure adopted from the literature on SEM with latent variables.

This paper clarifies that the necessity to assess model fit is not a question of which estimator will be used (PLS-PM, maximum likelihood, etc). but of the purpose of statistical modeling. Whereas, the model fit assessment is paramount in explanatory modeling, it is not imperative in predictive modeling.

Evidence for adverse impact in applied, organizational settings can often depend on application of the “four-fifths rule.” We analyze the arithmetic four-fifths rule, its operationalization, and related statistical tests. We note that the rule has intuitive appeal and has arithmetic directness. On the other hand, the four-fifths rule contains many ambiguities because of the manner in which it is defined, as well as its use in practice. One purpose of this article is to discuss the arithmetic and statistical facets of the definition. A related purpose of this article is to demonstrate where the ambiguities (and possibly unintended consequences) with the four-fifths rule might arise when numerical interpretations are invoked. Implications for future research and academic dialogues are then noted.