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In order to determine the status quo of PLS path modeling in international marketing research, we conducted an exhaustive literature review. An evaluation of double-blind reviewed journals through important academic publishing databases (e.g., ABI/Inform, Elsevier ScienceDirect, Emerald Insight, Google Scholar, PsycINFO, Swetswise) revealed that more than 30 academic articles in the domain of international marketing (in a broad sense) used PLS path modeling as means of statistical analysis. We assessed what the main motivation for the use of PLS was in respect of each article. Moreover, we checked for applications of PLS in combination with one or more additional methods, and whether the main reason for conducting any additional method(s) was mentioned.

We evaluate the hypothesis of temporal stability in risk preferences using two recent data sets from longitudinal lab experiments. Both experiments included a combination of decision tasks that allows one to identify a full set of structural parameters characterizing risk preferences under Cumulative Prospect Theory (CPT), including loss aversion. We consider temporal stability in those structural parameters at both population and individual levels. The population-level stability pertains to whether the distribution of risk preferences across individuals in the subject population remains stable over time. The individual-level stability pertains to within-individual correlation in risk preferences over time. We embed the CPT structure in a random coefficient model that allows us to evaluate temporal stability at both levels in a coherent manner, without having to switch between different sets of models to draw inferences at a specific level.

Using data from the 2013 European Company Survey, this chapter operationalizes the representation gap as the desire for greater employee involvement in decision-making expressed by the representative of the leading employee representative body at the workplace. According to this measure, there is evidence of a substantial shortfall in employee involvement in the European Union, not dissimilar to that reported for the United States. The chapter proceeds to investigate how the size of this representation gap varies by type of representative structure, information provided by management, the resource base available to the representatives, and the status of trust between the parties. Perceived deficits are found to be smaller where workplace representation is via works councils rather than union bodies. Furthermore, the desire for greater involvement is reduced where information provided the employee representative on a range of establishment issues is judged satisfactory. A higher frequency of meetings with management also appears to mitigate the expressed desire for greater involvement. Each of these results is robust to estimation over different country clusters. However, unlike the other arguments, the conclusion that shortfalls in employee involvement representation are smaller under works councils than union bodies is nullified where trust in management is lacking.

Standard estimation of ARMA models in which the AR and MA roots nearly cancel, so that individual coefficients are only weakly identified, often produces inferential ranges for individual coefficients that give a spurious appearance of accuracy. We remedy this problem with a model that uses a simple mixture prior. The posterior mixing probability is derived using Bayesian methods, but we show that the method works well in both Bayesian and frequentist setups. In particular, we show that our mixture procedure weights standard results heavily when given data from a well-identified ARMA model (which does not exhibit near root cancellation) and weights heavily an uninformative inferential region when given data from a weakly-identified ARMA model (with near root cancellation). When our procedure is applied to a well-identified process the investigator gets the “usual results,” so there is no important statistical cost to using our procedure. On the other hand, when our procedure is applied to a weakly identified process, the investigator learns that the data tell us little about the parameters – and is thus protected against making spurious inferences. We recommend that mixture models be computed routinely when inference about ARMA coefficients is of interest.

Heavy-tailed distributions present a tough setting for inference. They are also common in industrial applications, particularly with internet transaction datasets, and machine learners often analyze such data without considering the biases and risks associated with the misuse of standard tools. This chapter outlines a procedure for inference about the mean of a (possibly conditional) heavy-tailed distribution that combines nonparametric analysis for the bulk of the support with Bayesian parametric modeling – motivated from extreme value theory – for the heavy tail. The procedure is fast and massively scalable. The work should find application in settings wherever correct inference is important and reward tails are heavy; we illustrate the framework in causal inference for A/B experiments involving hundreds of millions of users of eBay.com.

This chapter presents a novel human arm gesture tracking and recognition technique based on fuzzy logic and nonlinear Kalman filtering with applications in crane guidance. A Kinect visual sensor and a Myo armband sensor are jointly utilised to perform data fusion to provide more accurate and reliable information on Euler angles, angular velocity, linear acceleration and electromyography data in real time. Dynamic equations for arm gesture movement are formulated with Newton–Euler equations based on Denavit–Hartenberg parameters. Nonlinear Kalman filtering techniques, including the extended Kalman filter and the unscented Kalman filter, are applied in order to perform reliable sensor fusion, and their tracking accuracies are compared. A Sugeno-type fuzzy inference system is proposed for arm gesture recognition. Hardware experiments have shown the efficacy of the proposed method for crane guidance applications.

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.

There are the interesting words of Myrdal in respect of the universality and commonness of what a scientific problem means:From then on more definitely I came to see that in reality there are no economic, sociological, psychological problems, but just problems and they are all mixed and composite. In research the only permissible demarcation is between relevant and irrelevant conditions. The problems are regularly also political and have moreover to be seen in historical perspective. (Myrdal, 1979, p. 106)