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A Bayesian approach to demand forecasting to optimize spare parts inventory that requires periodic replenishment is examined relative to a non-Bayesian approach when the demand rate is unknown. That is, optimal inventory levels are decided using these two approaches at consecutive time intervals. Simulations were conducted to compare the total inventory cost using a Bayesian approach and a non-Bayesian approach to a theoretical minimum cost over a variety of demand rate conditions including the challenging slow moving or intermittent type of spare parts. Although Bayesian approaches are often recommended, this study’s results reveal that under conditions of large variability across the demand rates of spare parts, the inventory cost using the Bayes model was not superior to that using the non-Bayesian approach. For spare parts with homogeneous demand rates, the inventory cost using the Bayes model for forecasting was generally lower than that of the non-Bayesian model. Practitioners may still opt to use the non-Bayesian model since a prior distribution for the demand does not need to be identified.

Bayesian approaches have been widely applied in construction management (CM) research due to their capacity to deal with uncertain and complicated problems. However, to date, there has been no systematic review of applications of Bayesian approaches in existing CM studies. This paper systematically reviews applications of Bayesian approaches in CM research and provides insights into potential benefits of this technique for driving innovation and productivity in the construction industry.

A total of 148 articles were retrieved for systematic review through two literature selection rounds.

Bayesian approaches have been widely applied to safety management and risk management. The Bayesian network (BN) was the most frequently employed Bayesian method. Elicitation from expert knowledge and case studies were the primary methods for BN development and validation, respectively. Prediction was the most popular type of reasoning with BNs. Research limitations in existing studies mainly related to not fully realizing the potential of Bayesian approaches in CM functional areas, over-reliance on expert knowledge for BN model development and lacking guides on BN model validation, together with pertinent recommendations for future research.

This systematic review contributes to providing a comprehensive understanding of the application of Bayesian approaches in CM research and highlights implications for future research and practice.

After briefly reviewing the past history of Bayesian econometrics and Alan Greenspan's (2004) recent description of his use of Bayesian methods in managing policy-making risk, some of the issues and needs that he mentions are discussed and linked to past and present Bayesian econometric research. Then a review of some recent Bayesian econometric research and needs is presented. Finally, some thoughts are presented that relate to the future of Bayesian econometrics.

This paper aims to present several Bayesian specification tests for both in- and out-of-sample situations.

The authors focus on the Bayesian equivalents of the frequentist approach for testing heteroskedasticity, autocorrelation and functional form specification. For out-of-sample diagnostics, the authors consider several tests to evaluate the predictive ability of the model.

The authors demonstrate the performance of these tests using an application on the relationship between price and occupancy rate from the hotel industry. For purposes of comparison, the authors also provide evidence from traditional frequentist tests.

There certainly exist other issues and diagnostic tests that are not covered in this paper. The issues that are addressed, however, are critically important and can be applied to most modeling situations.

With the increased use of the Bayesian approach in various modeling contexts, this paper serves as an important guide for diagnostic testing in Bayesian analysis. Diagnostic analysis is essential and should always accompany the estimation of regression models.

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.

Several inferential procedures are advocated in the literature. The most commonly used techniques are the frequentist and the Bayesian inferential procedures. Bayesian methods afford inferences based on small data sets and are especially useful in studies with limited data availability. Bayesian approaches also help incorporate prior knowledge, especially subjective knowledge, into predictions. Considering the increasing difficulty in data acquisition, the application of Bayesian techniques can be hugely beneficial to managers, especially in analysing limited data situations like a study of expert opinion. Another factor constraining the broader application of Bayesian statistics in business was computational power requirements and the availability of appropriate analytical tools. However, with the increase in computational power, connectivity and the development of appropriate software programmes, Bayesian applications have become more attractive. This chapter attempts to unravel the applications of the Bayesian inferential procedure in marketing management.

The purpose of this paper is to provide the maintenance personnel with a methodology for modeling and estimating the reliability of critical machine systems using the historical data of their inter‐failure times.

The failure patterns of five different machine systems were modeled with NHPP‐log linear process and HPP belonging to stochastic point process for predicting their reliability in future time frames. Besides the classical approach, Bayesian approach was also used involving Jeffreys's invariant non‐informative independent priors to derive the posterior densities of the model parameters of NHPP‐LLP and HPP with a view to estimating the reliability of the machine systems in future time intervals.

For at least three machine systems, Bayesian approach gave lower reliability estimates and a larger number of (expected) failures than those obtained by the classical approach. Again, Bayesian estimates of the probability that “ROCOF (rate of occurrence of failures) would exceed its upper threshold limit” in future time frames were uniformly higher for these machine systems than those obtained with the classical approach.

This study indicated that, the Bayesian approach would give more realistic estimates of reliability (in future time frames) of the machine systems, which had dependent inter‐failure times. Such information would be helpful to the maintenance team for deciding on appropriate maintenance strategy.

With the help of Bayesian approach, the posterior densities of the model parameters were found analytically by considering Jeffreys's invariant non‐informative independent prior. The case study would serve to motivate the maintenance teams to model the failure patterns of the repairable systems making use of the historical data on inter‐failure times and estimating their reliability in future time frames.

The step-stress accelerated test is the most appropriate statistical method to obtain information about the reliability of new products faster than would be possible if the product was left to fail in normal use. This paper presents the multiple step-stress accelerated life test using type-II censored data and assuming a cumulative exposure model. The authors propose a Bayesian inference with the lifetimes of test item under gamma distribution. The choice of the loss function is an essential part in the Bayesian estimation problems. Therefore, the Bayesian estimators for the parameters are obtained based on different loss functions and a comparison with the usual maximum likelihood (MLE) approach is carried out. Finally, an example is presented to illustrate the proposed procedure in this paper.

A Bayesian inference is performed and the parameter estimators are obtained under symmetric and asymmetric loss functions. A sensitivity analysis of these Bayes and MLE estimators are presented by Monte Carlo simulation to verify if the Bayesian analysis is performed better.

The authors demonstrated that Bayesian estimators give better results than MLE with respect to MSE and bias. The authors also consider three types of loss functions and they show that the most dominant estimator that had the smallest MSE and bias is the Bayesian under general entropy loss function followed closely by the Linex loss function. In this case, the use of a symmetric loss function as the SELF is inappropriate for the SSALT mainly with small data.

Most of papers proposed in the literature present the estimation of SSALT through the MLE. In this paper, the authors developed a Bayesian analysis for the SSALT and discuss the procedures to obtain the Bayes estimators under symmetric and asymmetric loss functions. The choice of the loss function is an essential part in the Bayesian estimation problems.

Decisions pertaining to working capital management have pivotal role for firms’ short-term financial decisions. The purpose of this paper is to examine impact of working capital on profitability for Indian corporate entities.

Both classical panel analysis and Bayesian techniques have been employed that provides opportunity not only to perform comparative analysis but also allows flexibility in prior distribution assumptions.

It is found that longer cash conversion period has detrimental influence on profitability. Financial soundness indicators are playing significant role in determining firm profitability. Larger firms seem to be more profitable and significant as per Bayesian approach. Bayesian approach has led to considerable gain in estimation fit.

Observing the highly skewed distribution of dependent variable, Multivariate Student t-distribution has been considered along with normal distribution to model stochastic term. Accordingly, Bayesian methodology is applied.

Analysis of working capital for firms has been performed in Indian context. Application of Bayesian methodology is performed on balanced panel spanning from 2003 to 2012. As per author’s knowledge, this is the first study which applies Bayesian approach employing panel data for the analysis of working capital management for Indian firms.

Several activity-based transportation models are now becoming operational and are entering the stage of application for the modelling of travel demand. In our application, we will use decision rules to support the decision-making of the model instead of principles of utility maximization, which means our work can be interpreted as an application of the concept of bounded rationality in the transportation domain. In this chapter we explored a novel idea of combining decision trees and Bayesian networks to improve decision-making in order to maintain the potential advantages of both techniques. The results of this study suggest that integrated Bayesian networks and decision trees can be used for modelling the different choice facets of a travel demand model with better predictive power than CHAID decision trees. Another conclusion is that there are initial indications that the new way of integrating decision trees and Bayesian networks has produced a decision tree that is structurally more stable.