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1 – 10 of over 7000Franz T. Lohrke, Charles M. Carson and Archie Lockamy
The purpose of this paper is to review Bayesian analysis in recent entrepreneurship research to assess how scholars have employed these methods to study the entrepreneurship…
Abstract
Purpose
The purpose of this paper is to review Bayesian analysis in recent entrepreneurship research to assess how scholars have employed these methods to study the entrepreneurship process. Researchers in other business fields (e.g. management science, marketing, and finance) have increasingly employed Bayesian methods to study issues like decision making. To date, however, Bayesian methods have seen only limited use in entrepreneurship research.
Design/methodology/approach
After providing a general overview of Bayesian methods, this study examines how extant entrepreneurship research published in leading journals has employed Bayesian analysis and highlights topics these studies have investigated most frequently. It next reviews topics that scholars from other business disciplines have investigated using these methods, focusing on issues related to decision making, in particular.
Findings
Only seven articles published in leading management and entrepreneurship journals between 2000 and 2016 employed or discussed Bayesian methods in depth when studying the entrepreneurship process. In addition, some of these studies were conceptual.
Research limitations/implications
This review suggests that Bayesian methods may provide another important tool for researchers to employ when studying decision making in high uncertainty situations or the impact of entrepreneurial experience on decision making over time.
Originality/value
This review demonstrates that Bayesian analysis may be particularly appropriate for entrepreneurship research. By employing these methods, scholars may gain additional insights into entrepreneurial phenomenon by allowing researchers to examine entrepreneurial decision making. Through this review and these recommendations, this study hopes to encourage greater Bayesian analysis usage in future entrepreneurship research.
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The purpose of this paper is to present a new efficient method for the tolerance–reliability analysis and quality control of complex nonlinear assemblies where explicit assembly…
Abstract
Purpose
The purpose of this paper is to present a new efficient method for the tolerance–reliability analysis and quality control of complex nonlinear assemblies where explicit assembly functions are difficult or impossible to extract based on Bayesian modeling.
Design/methodology/approach
In the proposed method, first, tolerances are modelled as the random uncertain variables. Then, based on the assembly data, the explicit assembly function can be expressed by the Bayesian model in terms of manufacturing and assembly tolerances. According to the obtained assembly tolerance, reliability of the mechanical assembly to meet the assembly requirement can be estimated by a proper first-order reliability method.
Findings
The Bayesian modeling leads to an appropriate assembly function for the tolerance and reliability analysis of mechanical assemblies for assessment of the assembly quality, by evaluation of the assembly requirement(s) at the key characteristics in the assembly process. The efficiency of the proposed method by considering a case study has been illustrated and validated by comparison to Monte Carlo simulations.
Practical implications
The method is practically easy to be automated for use within CAD/CAM software for the assembly quality control in industrial applications.
Originality/value
Bayesian modeling for tolerance–reliability analysis of mechanical assemblies, which has not been previously considered in the literature, is a potentially interesting concept that can be extended to other corresponding fields of the tolerance design and the quality control.
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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…
Abstract
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.
Carol K.H. Hon, Chenjunyan Sun, Bo Xia, Nerina L. Jimmieson, Kïrsten A. Way and Paul Pao-Yen Wu
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…
Abstract
Purpose
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.
Design/methodology/approach
A total of 148 articles were retrieved for systematic review through two literature selection rounds.
Findings
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.
Originality/value
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.
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Richa Srivastava and M A Sanjeev
Several inferential procedures are advocated in the literature. The most commonly used techniques are the frequentist and the Bayesian inferential procedures. Bayesian methods…
Abstract
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.
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Laura E. Jackson, M. Ayhan Kose, Christopher Otrok and Michael T. Owyang
We compare methods to measure comovement in business cycle data using multi-level dynamic factor models. To do so, we employ a Monte Carlo procedure to evaluate model performance…
Abstract
We compare methods to measure comovement in business cycle data using multi-level dynamic factor models. To do so, we employ a Monte Carlo procedure to evaluate model performance for different specifications of factor models across three different estimation procedures. We consider three general factor model specifications used in applied work. The first is a single-factor model, the second a two-level factor model, and the third a three-level factor model. Our estimation procedures are the Bayesian approach of Otrok and Whiteman (1998), the Bayesian state-space approach of Kim and Nelson (1998) and a frequentist principal components approach. The latter serves as a benchmark to measure any potential gains from the more computationally intensive Bayesian procedures. We then apply the three methods to a novel new dataset on house prices in advanced and emerging markets from Cesa-Bianchi, Cespedes, and Rebucci (2015) and interpret the empirical results in light of the Monte Carlo results.
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Mohammad Mahdi Ershadi and Abbas Seifi
This study aims to differential diagnosis of some diseases using classification methods to support effective medical treatment. For this purpose, different classification methods…
Abstract
Purpose
This study aims to differential diagnosis of some diseases using classification methods to support effective medical treatment. For this purpose, different classification methods based on data, experts’ knowledge and both are considered in some cases. Besides, feature reduction and some clustering methods are used to improve their performance.
Design/methodology/approach
First, the performances of classification methods are evaluated for differential diagnosis of different diseases. Then, experts' knowledge is utilized to modify the Bayesian networks' structures. Analyses of the results show that using experts' knowledge is more effective than other algorithms for increasing the accuracy of Bayesian network classification. A total of ten different diseases are used for testing, taken from the Machine Learning Repository datasets of the University of California at Irvine (UCI).
Findings
The proposed method improves both the computation time and accuracy of the classification methods used in this paper. Bayesian networks based on experts' knowledge achieve a maximum average accuracy of 87 percent, with a minimum standard deviation average of 0.04 over the sample datasets among all classification methods.
Practical implications
The proposed methodology can be applied to perform disease differential diagnosis analysis.
Originality/value
This study presents the usefulness of experts' knowledge in the diagnosis while proposing an adopted improvement method for classifications. Besides, the Bayesian network based on experts' knowledge is useful for different diseases neglected by previous papers.
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Chaoyu Zheng, Benhong Peng and Guo Wei
The operational management of cold chain logistics has an important impact on the quality of cold chain products, but the service delivery process is subject to a series of…
Abstract
Purpose
The operational management of cold chain logistics has an important impact on the quality of cold chain products, but the service delivery process is subject to a series of potential problems such as product loss and cold storage temperature in the actual operation.
Design/methodology/approach
In this paper, the whole cold chain logistics system and risk events are analyzed. A Bayesian network is used for modeling and simulation to identify the main influencing factors and to conduct a sensitivity analysis of the main factors.
Findings
It is found that the operation of cold chain logistics systems can be divided into four links according to the degree of influence as follows: transportation and distribution, processing and packaging, information processing and warehousing. Transportation and distribution is the most influential factor of system failure, and extreme weather is the most risky event. At the same time, the four risk events that have the greatest impact on the operation of the cold chain system are in descending order: transportation equipment failure, extreme weather, unqualified pre-cooling and violation operation.
Originality/value
Therefore, enterprises should develop appropriate interventions for securing the transportation services, design strategies to deal with extreme weather conditions prior to and in the early stage of product delivery, and prepare additional effective measures for managing emergency events.
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Farzana Akbari, Mahdi Salehi and Mohammad Ali Bagherpour Vlashani
The purpose of this paper is to investigate the effect of managerial ability on tax avoidance in Tehran Stock Exchange (TSE) and OTC by classifying the income smoother and…
Abstract
Purpose
The purpose of this paper is to investigate the effect of managerial ability on tax avoidance in Tehran Stock Exchange (TSE) and OTC by classifying the income smoother and non-income smoother companies based on the theoretical approaches.
Design/methodology/approach
To measure the managerial ability the data envelopment analysis is applied based on the accounting data, company and industry characteristics. In this research, hypotheses are tested for the first time by three statistical methods, namely classical regression models, mixed effect multilevel models, and Bayesian multilevel models, which have never been addressed in Persian accounting research studies. The hypotheses are estimated during a 12-year period from 2004 to 2015 in TSE and OTC. In this research, according to Lucas’s critique, structural change test is used in order to control macroeconomic and political variables affecting the results of the study.
Findings
The results of hypothesis testing by employing three statistical methods suggest that only one hypothesis of this investigation is significant, which shows the significant association of type of market’s impact (exchange of OTC) on the relationship between managerial ability and tax avoidance.
Originality/value
Each company’s performance is affected by various factors. The study intends to mention that the performance of listed companies in the stock market depends heavily on its financial reports. And investors with perceived perception by the reports, can indirectly squeeze their stock indexes with their sudden sale of stocks, and question the company’s performance with losses to the company.
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