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1 – 10 of over 2000Juan Prieto-Rodríguez, Juan Gabriel Rodríguez and Rafael Salas
Studies on wage discrimination assume that independent observers are able to distinguish a priori which workers are suffering from discrimination. However, this may not be a good…
Abstract
Studies on wage discrimination assume that independent observers are able to distinguish a priori which workers are suffering from discrimination. However, this may not be a good assumption when anti-discrimination laws mean that severe penalties can be imposed on discriminatory employers or when unobserved heterogeneity is significant. We develop a wage discrimination model in which workers are not classified a priori. It can be thought of as a generalization of the standard empirical framework, whereas the Oaxaca–Blinder model can be thought of as an extreme case. We propose a finite mixture model to explicitly model unobserved heterogeneity in individual characteristics and estimate the probabilities of being a discriminated or a non-discriminated worker. We illustrate this proposal by estimating wage discrimination in Germany and the UK.
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Damaris Serigatto Vicentin, Brena Bezerra Silva, Isabela Piccirillo, Fernanda Campos Bueno and Pedro Carlos Oprime
The purpose of this paper is to develop a monitoring multiple-stream processes control chart with a finite mixture of probability distributions in the manufacture industry.
Abstract
Purpose
The purpose of this paper is to develop a monitoring multiple-stream processes control chart with a finite mixture of probability distributions in the manufacture industry.
Design/methodology/approach
Data were collected during production of a wheat-based dough in a food industry and the control charts were developed with these steps: to collect the master sample from different production batches; to verify, by graphical methods, the quantity and the characterization of the number of mixing probability distributions in the production batch; to adjust the theoretical model of probability distribution of each subpopulation in the production batch; to make a statistical model considering the mixture distribution of probability and assuming that the statistical parameters are unknown; to determine control limits; and to compare the mixture chart with traditional control chart.
Findings
A graph was developed for monitoring a multi-stream process composed by some parameters considered in its calculation with similar efficiency to the traditional control chart.
Originality/value
The control chart can be an efficient tool for customers that receive product batches continuously from a supplier and need to monitor statistically the critical quality parameters.
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The purpose of this paper is to critically review the latest approaches for capturing and explaining heterogeneity in partial least squares (PLS) path modelling and to classify…
Abstract
Purpose
The purpose of this paper is to critically review the latest approaches for capturing and explaining heterogeneity in partial least squares (PLS) path modelling and to classify these into a methodological taxonomy. Furthermore, several areas for future research effort are introduced in order to stimulate ongoing development in this important research field.
Design/methodology/approach
Different approaches to treat heterogeneity in PLS path models are introduced, critically evaluated and classified into a methodological taxonomy. Future research directions are derived from a comparison of benefits and limitations of the procedures.
Findings
The review reveals that finite mixture‐PLS can be regarded as the most comprehensive and commonly used procedure for capturing heterogeneity within a PLS path modelling framework. However, further research is necessary to explore the capabilities and limitations of the approach.
Research limitations/implications
Directions for additional research, common to most latent class detection procedures include the verification and comparison of available approaches, the handling of large data sets, the allowance of varying structures of path models, the profiling of segments and the problem of model selection.
Originality/value
Whereas modelling heterogeneity in covariance structure analysis has been studied for several years, research interest has only recently been devoted to the question of clustering in PLS path modelling. This is the first contribution which critically consolidates available approaches, discloses problematic aspects and addresses significant areas for future research.
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Wayne S. DeSarbo, Qiong Wang and Simon J. Blanchard
The paper aims to examine the nature of competition within an industry by proposing and examining three separate sources of competitive heterogeneity: the strategies that industry…
Abstract
Purpose
The paper aims to examine the nature of competition within an industry by proposing and examining three separate sources of competitive heterogeneity: the strategies that industry members use, the performance that they obtain, and how effectively the strategies are utilized to obtain such performance results.
Design/methodology/approach
To do so, a restricted latent structure finite mixture model is devised that can quantify the contribution of these three potential sources of heterogeneity in the formulation of latent competitive groups within an industry. The paper illustrate this modeling framework with respect to COMPUSTAT strategy and performance data collected for public banks in the USA.
Findings
The paper shows how traditional conceptualizations via strategic or performance groups are inadequate to fully represent intra‐industry heterogeneity.
Originality/value
This research paper proposes a new class of restricted finite mixture‐based models, which fit a variety of alternative forms/models of heterogeneity. Information heuristics are developed to indicate “best model.”
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Isobel Claire Gormley and Thomas Brendan Murphy
Ranked preference data arise when a set of judges rank, in order of their preference, a set of objects. Such data arise in preferential voting systems and market research surveys…
Abstract
Ranked preference data arise when a set of judges rank, in order of their preference, a set of objects. Such data arise in preferential voting systems and market research surveys. Covariate data associated with the judges are also often recorded. Such covariate data should be used in conjunction with preference data when drawing inferences about judges.
To cluster a population of judges, the population is modeled as a collection of homogeneous groups. The Plackett-Luce model for ranked data is employed to model a judge's ranked preferences within a group. A mixture of Plackett- Luce models is employed to model the population of judges, where each component in the mixture represents a group of judges.
Mixture of experts models provide a framework in which covariates are included in mixture models. Covariates are included through the mixing proportions and the component density parameters. A mixture of experts model for ranked preference data is developed by combining a mixture of experts model and a mixture of Plackett-Luce models. Particular attention is given to the manner in which covariates enter the model. The mixing proportions and group specific parameters are potentially dependent on covariates. Model selection procedures are employed to choose optimal models.
Model parameters are estimated via the ‘EMM algorithm’, a hybrid of the expectation–maximization and the minorization–maximization algorithms. Examples are provided through a menu survey and through Irish election data. Results indicate mixture modeling using covariates is insightful when examining a population of judges who express preferences.
Thomas Sproul and Clayton P. Michaud
Prospect theory is now widely accepted as the dominant model of choice under risk, but has not been fully incorporated into applied research because of uncertainty about how to…
Abstract
Purpose
Prospect theory is now widely accepted as the dominant model of choice under risk, but has not been fully incorporated into applied research because of uncertainty about how to include population-level parameter estimates. The purpose of this paper is to characterize heterogeneity across people to lay a foundation for future applied research.
Design/methodology/approach
The paper uses elicitation data from field experiments in Vietnam to fit a finite Gaussian mixture model using the expectation maximization algorithm. Applied results are simulated for investment allocations under myopic loss aversion.
Findings
The authors find that about 20 percent of the sample is classified as extremely loss averse, while the rest of the population is only mildly loss averse. This implies a bimodal distribution of loss aversion in the population.
Research limitations/implications
The data set is only moderately sized: 181 subjects. Future research will be needed to extend these results out of sample, and to other regions.
Originality/value
This paper provides empirical evidence that heterogeneity matters in prospect theory modeling. It highlights how policy makers might be misled by assuming that average prospect theory parameters are typical within the population.
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Edward E. Rigdon, Christian M. Ringle and Marko Sarstedt
Alongside structural equation modeling (SEM), the complementary technique of partial least squares (PLS) path modeling helps researchers understand relations among sets of…
Abstract
Alongside structural equation modeling (SEM), the complementary technique of partial least squares (PLS) path modeling helps researchers understand relations among sets of observed variables. Like SEM, PLS began with an assumption of homogeneity – one population and one model – but has developed techniques for modeling data from heterogeneous populations, consistent with a marketing emphasis on segmentation. Heterogeneity can be expressed through interactions and nonlinear terms. Additionally, researchers can use multiple group analysis and latent class methods. This chapter reviews these techniques for modeling heterogeneous data in PLS, and illustrates key developments in finite mixture modeling in PLS using the SmartPLS 2.0 package.
Prosenjit Ghosh and Sabyasachi Mukherjee
The study aims to cluster the travellers based on their social media interactions as well as to find the different segments with similar and dissimilar categories according to…
Abstract
Purpose
The study aims to cluster the travellers based on their social media interactions as well as to find the different segments with similar and dissimilar categories according to traveller's choice. The study also aims to understand the behaviour of clusters of the travellers towards destination selection and accordingly make the tour packages in order to improve tourists' satisfaction and gain viable benefits.
Design/methodology/approach
Agglomerative hierarchical clustering with Ward's minimum variance linkage algorithm and model-based clustering with parameterized finite Gaussian mixture models has been implemented to achieve the respective goals. The dimension reduction (DR) technique was introduced for better visualizing clustering structure obtained from a finite mixture of Gaussian densities.
Findings
A total of 980 travellers have been clustered into 8 different interest groups according to their tourism destinations selection across East Asia based on individual social media feedback. For selecting the optimal number of clusters as well as the behaviour of the interested travellers groups, both these proposed methods have shown remarkable similarities. DR technique ensures the reduction in dimensionality with seven directions, of which the first two directions explained 95% of total variability.
Practical implications
Tourism organizations focus on marketing efforts to promote the most attractive benefits to the clusters of travellers. By segmenting travellers of East Asia into homogeneous groups, it is feasible to choose a similar area to test different marketing techniques. Finally, it can be identified to which segments, new respondents or potential clients belong; consequently, the tourism organizations can design the tour packages.
Originality/value
The study has uniqueness in two aspects. Firstly, the study empirically revealed tourists' experience and behavioural intention to select tourism destinations and secondly, it finds quantifiable insights into the tourism phenomenon in East Asia, which helps tourism organizations to understand the buying behaviours of tourists' segments. Finally, the application of clustering algorithms to achieve the purpose of this study and the findings are very new in the literature on tourism, to understand the tourist behaviour towards destination selection based on social media reviews.
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Abdullah Alharbi, Wajdi Alhakami, Sami Bourouis, Fatma Najar and Nizar Bouguila
We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is…
Abstract
We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is extremely challenging and important for many applications. The proposed approach involves developing new probabilistic support vector machines (SVMs) kernels from a flexible generative statistical model named “bounded generalized Gaussian mixture model”. The developed learning framework has the advantage to combine properly the benefits of both discriminative and generative models and to include prior knowledge about the nature of data. It can effectively recognize if an image is a tampered one and also to identify both forged and authentic images. The obtained results confirmed that the developed framework has good performance under numerous inpainted images.
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Torben Pedersen, Christine Soo and Timothy M. Devinney
This research examines the differential impact of the importance of internally and externally sourced information and knowledge and their relationship to absorptive capacity and…
Abstract
This research examines the differential impact of the importance of internally and externally sourced information and knowledge and their relationship to absorptive capacity and firm performance. In addition, this analysis deals directly with the unobservable heterogeneity amongst firms that is generally viewed as the raison d'être for a unique resource-based perspective of organizational performance. Latent class, finite mixture regression models are used that show that a single model relating knowledge sourcing, absorptive capacity and firm performance is inadequate in explaining even a minor portion of the variation which is seen between firms.