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This study aims to more deeply examine the various types of testing mediations and use the comparison test by using test-based mediation Sobel models and Bayesian approach. The purpose of this study are to apply the traditional (using indirect effect) and Sobel test, extend Yuan and MacKinnon (2009) work on Bayesian mediation analysis. Both analysis methods of mediation (Traditional, Sobel Test and Bayesian estimation) should apply in the research of management, by using structural equation modeling (SEM) in a structural model, with one mediation, one exogenous (independent) and one endogenous variable. The meta-analysis approximation has been used to investigate the job satisfaction as a mediation in the relationship between employee competence and performance (endogenous).

Data were collected from ten dissertations of students of the Management Doctoral Program at the Brawijaya University from 2009 until 2013; data were analyzed for the mediation variable of job satisfaction (M) in the relationship between employee competence (X) and employee performance (Y) (Muindi and Obonyo, 2015; Olcer, 2015; Sattar et al., 2015; Khan and Ahmed, 2015). A researcher can determine the mediating variable and whether it is complete or partial or if mediation exists in several ways.

The results of the above findings using meta-analysis showed that 60% of previous research states that job satisfaction is a partial mediation on relationship competence of the performance, 10% of previous research states that job satisfaction is a full mediation on relationship competence of the performance and 30% stated that job satisfaction is not pemediasi (pemediasi means Mediation variable) on the relationship between competence and performance. This research found that all three approaches provide similar conclusions for ten previous research.

The findings showed that the Sobel approach and the Bayesian approach provide results that are more sensitive than the traditional approach.

In my opinion, the rule to investigate the mediation variable should be completed with the conditions (1) q (theta) is not statistically significant, (2) α (alpha) and β (beta) are significant, and (3) q’ (theta) is significant, and increase when M is include as an additional predictor. This condition called partial mediation.

The traditional method is simpler and easy. The method is less sensitive and is not sufficient for investigating the mediating variables. In general, the method results in a mediation variable, but it cannot be used to determine either partial or complete mediation variables. So, investigation by Baron and Kenny Methods (in Hair et al., 2010), the rule or testing called Sobel Test and another approach such as Bayesian to determine the mediation variable is necessary.

Various methods for detecting mediating/intervening have been widely used in previous research as a method of measurement using indirect effect (Hair et al., 2010), and calculations have been performed using Sobel test (Baron and Kenny, 1986) and Bayesian approach (Enders, 2013). In this study, I wanted to more deeply examine the various types of testing mediations, and use the comparison test by using the test-based mediation Sobel models and Bayesian approach (Baron and Kenny, 1986; Enders, 2013). The statistical application should not be complicated and difficult, it but must rather be simple and easy, so that it is user-friendly. The traditional method is simpler and easier than the other methods, but how sensitive is it? This research is conducted to investigate this problem. The evaluation of mediating mechanisms has become a critical element of behavioral science research (Enders, 2013), especially in the field of management, not only to assess whether (and how) interventions achieve their effects but also, more, broadly, to understand the cause of behavioral change. Methodologists have developed mediation analysis techniques for a broad range of substantive applications. However, methods for estimating mediation mechanisms with various methods have been understudied. The purpose of this study is to apply the traditional (using indirect effect) and Sobel tests and extend Yuan and MacKinnon’s (2009) work on the Bayesian mediation analysis. Both analyses methods of mediation (traditional and Sobel test and Bayesian estimation) should apply in the research of management, by using structural equation modeling (SEM) in a structural model, with one mediation, one exogenous (independent) and one endogenous variable. The meta-analysis approximation has been used to investigate job satisfaction as the mediation in the relationship between employee competence and performance (endogenous). This study uses software R to complete the mediating effect (Enders, 2013). R is a language and environment for statistical computing and graphics. It is a GNU project which is similar to the S language and environment which was developed at Bell Laboratories (formerly AT&T, now Lucent Technologies) by John Chambers et al. R provides a wide variety of statistical analyses such as SEM and Mediation test. R provides an open source route for participation in that activity. The Bayesian estimation approach provides an R function and a macro that applies the method of mediation analysis.

Researchers in management regularly face modelling issues that involve double-moderated mediation models. Here, the author illustrates how to conceptualise, specify and empirically estimate mediation effects when having to simultaneously account for continuous (Likert type) and nominal (i.e. group) moderator variables. Researchers’ estimates of the mediation effects suffer serious bias because of the effects of unaccounted confounders. This is an issue that plagues management research, and this study aims to show how to address these valid reservations for its focus models. In aiming to inform a wider management audience, the study deliberately uses the rich context of a focus case as this allows the author to clarify the nuances that management researchers face applying double-moderated mediation models. Specifically, the study’s focus case is on professionals’ willingness to implement a new government policy. The study also combines traditional and Bayesian statistical approaches and explains the differences in estimation and interpretation that are associated with the Bayesian approach. Explaining, and exemplifying the use of, the models, the author focuses on how one can substantially increase the robustness of the methods used in management research and can considerably improve the quality of the generated theoretical insights. The study also clarifies important assumptions and solutions.

The study uses a doubled moderated mediation Bayesian approach, and draws the sample data from a population of 5,199 professionals, all members of either the Dutch Association of Psychologists or the Dutch Association for Psychiatry. The data collection process resulted in 1,307 questionnaires being returned, a response rate of 25 per cent. All the items were measured using a Likert scale, ranging from “strongly disagree” to “strongly agree”, unless stated otherwise.

Explaining, and exemplifying the use of, the models the study focuses on how one can substantially increase the robustness of the methods used in management research and can considerably improve the quality of the generated theoretical insights.

This is an original approach exemplified for wider use by management researchers.

This paper aims to manage better the conflicts in labor disputes by improving the understanding of mediation dynamics from a game-theoretical perspective.

Signaling game model is adapted to a hypothetical labor dispute based on the legislative regulations on the mandatory mediation system in Turkey.

The paper determines mediation equilibria in which both players get positive payoffs. Analysis of the mediation equilibria helps to improve the understanding about the litigation and mediation dynamics depending on the variables. The variables are clearly separated from each other due to their reverse effects on strategy choices of the parties. Mediation payoff and litigation cost are characterized by their incentive effects on mediation preferences, whereas mediation fee and litigation payoff are characterized by their disincentive effect. While increasing amounts of incentive variables strengthen the mediation tendency of the employee, increasing amounts of disincentive variables reveal the opposite effect. Furthermore, the analysis also indicates that if the litigation payoff is too small to recover litigation costs, accepting the mediation becomes the optimal strategy. This prediction is contrary to that of traditional game-theoretic litigation/settlement models, in which small-claim disputes typically cannot be settled.

The assumption that the mediation fee is not a part of the litigation cost eliminates the disincentive effect of mediation fee and makes it neutral on the strategy choice of employee.

This paper first analyzes the strategic role of mediation in labor disputes by using a signaling game. Despite its mediation focus, the paper also provides practical insights for litigation.

In forecasting unknown quantities, risk and finance decision makers often rely on one or more biased experts, statistical specialists representing parties with an interest in the decision maker's final forecast. This problem arises in a variety of contexts, and the decision maker may represent a corporate enterprise, rating agency, government regulator, etc. The purpose of the paper is to assist decision makers, experts, and others to have a better understanding of the dynamics of the problem, and to adopt strategies and practices that enhance efficiency.

The problem is formulated as a two‐person, non‐cooperative Bayesian game with the decision maker and one expert as players, and perfect Bayesian equilibrium solutions are identified. Then the analysis is extended to variations of the game in which the expert's loss function is not common knowledge, and in which there are multiple experts.

In the struggle for information between the decision maker and the experts, the experts generally benefit from greater uncertainty about the parameters of the model. Thus, in attempting to elicit as much information as possible from the experts, the decision maker must strive to minimize all sources of uncertainty.

As in most Bayesian games, the analysis requires that a variety of process assumptions and model parameters be common knowledge. These conditions may be difficult to satisfy in real‐world applications.

The principal finding of the study is that there is truly a struggle for information between the decision maker and the experts. This generally encourages the experts to inject as much uncertainty as possible into the process. To counter this effect, the decision maker might: provide incentives for the experts to increase their sampling information; try to mitigate specific uncertainties regarding the model parameters; and try to increase the number of experts.

This is the first paper to apply the framework of signaling games to the problem of eliciting information from biased experts. It is of value to decision makers, experts, and economic researchers.

Given the growing importance of spirituality and religion-related factors in the workplace, this paper aims to analyze the effects of spirituality, intrinsic religiosity and Islamic work ethics (IWE) on employee job performance.

Building on these theoretical concepts and perspectives, the authors hypothesized that employee intrinsic motivation mediated these proposed effects. To verify these propositions, the authors examined data collected from 224 full-time employees working for various organizations in Morocco. The authors conducted structural equations modeling (SEM) analyses using the AMOS 24 platform and Bayesian analyses to investigate direct and indirect effects.

First, the authors found that only IWE has positive and significant direct effect on job performance. Second, the findings indicate that employee intrinsic motivation does not mediate the relationship between the independent variables of spirituality and intrinsic religiosity and the dependent variable of job performance. These results may be because of the fact that Islamic principles and values are implemented and more reinforced in the Moroccan workplace than new and modern concepts such as intrinsic religiosity and spirituality.

The present paper used a cross-sectional methodology, which did not allow testing causation between the constructs under study.

Organizations and managers alike ought to resort to IWE insights with the objective to increase the performance of their Muslim members.

This paper is novel as it represents a study on spirituality in a non-Western context. It also uses a relatively sizeable sample (N = 224) of full-time employees in the workplace.

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.

Proposes Bayesian life test sampling plan for non‐repairable products with exponential lifetime distribution, which are sold under a warranty policy. It is assumed that the parameter of the lifetime distribution is a random variable varying from lot to lot according to a known prior distribution. Describes constructions of a cost model with three cost components: test cost, accept cost, and reject cost. Presents an algorithm for finding optimal sampling plans which minimize the expected average cost per lot. Describes sensitivity analyses for the parameters for the prior distribution.

Background subtraction is a particularly popular foreground detection method, whose background model can be updated by using input images. However, foreground object cannot be detected accurately if the background model is broken. In order to improve the performance of foreground detection in human-robot interaction (HRI), the purpose of this paper is to propose a new background subtraction method based on image parameters, which helps to improve the robustness of the existing background subtraction method.

The proposed method evaluates the image and foreground results according to the image parameters representing the change features of the image. It ignores the image that is similar to the first image and the previous image in image sequence, filters the image that may break the background model and detects the abnormal background model. The method also helps to rebuild the background model when the model is broken.

Experimental results of typical interaction scenes validate that the proposed method helps to reduce the broken probability of background model and improve the robustness of background subtraction.

Different threshold values of image parameters may affect the results in different environments. Future researches should focus on the automatic selection of parameters’ threshold values according to the interaction scene.

A useful method for foreground detection in HRI.

This paper proposes a method which employs image parameters to improve the robustness of the background subtraction for foreground detection in HRI.