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The purpose of this paper is to provide an update on various methodological issues and statistical techniques pertinent to the conflict management literature. First, issues related to use of laboratory studies, college students, and the study situation are reviewed. Second, two recent innovative statistical techniques, meta‐analysis and confirmatory modeling are described and potential applications in the conflict management field are given.

The study analyses psychometric features of the Grasha–Riechmann student learning style scale. It measures the instructional preferences of students attending different educational stages.

The scale was translated from English to Spanish. Content and face validity evidences were analysed. After that, construct validity evidences – exploratory factor analysis, confirmatory factor analysis, factorial invariance analysis – and internal consistency were examined. Data were collected from samples composed of high school and college students from Argentina.

The adapted version, a four-factor 12-item scale, suitable to be used in local students, measures four learning styles – competitive, independent, dependent and collaborative. The model showed a better fit when compared to rival models – three-factor and six-factor. Besides, the four-factor model verified its factorial invariance in high school and college students' groups composing the sample. The internal consistency indices were adequate for every dimension (ordinal α > 0.70).

Despite satisfactory results of the internal validity evidences and the internal consistency analysis, further studies should analyse external validity evidences – criterion and predictive evidences.

The adapted version of the scale is suitable to be used by teachers in order to examine learning preferences in their students. Such information will allow the adaptation of teaching methods regarding the actual students' needs.

The Grasha–Riechmann student learning style scale’s Argentinian adaptation is presented. It is a valid and reliable measure of learning styles suitable to be used in high school and college.

The purpose of this paper is to examine different possible issues that can contribute to cost escalations in pharmaceutical capital projects and identify remedial measures to control them.

The study is supported by a detailed survey performed around some big pharmaceutical companies of Indian sub-continent. Various issues that lead to cost escalations of projects have been identified. Both exploratory and confirmatory factor analyzes have been applied to the feedback data.

The study derived and validated four prominent factors leading to cost escalations. The paper discusses these factors along with the remedies identified to control project cost. Convergent and discriminant validity have been well established for the model.

The data needful for the study were collected from only four big pharmaceutical companies established in Southern India. Through frequent and elaborate interactions with senior project managers of those companies, it became possible to get a consolidated list of reasons that contribute to cost escalations in pharmaceutical projects. The list and the feedback data may be more exhaustive if some more companies would have been surveyed. Future research is committed to cover some more notable pharmaceutical companies both nationally and internationally and enhance the sample size. This would add more strength to the analysis and derive more consistent and validated results.

This study provides necessary support to the project people to analyze different issues that stand as hurdles for project success and enable them to look for remedies to resolve them. The results would help the project managers to enhance their awareness in controlling the project costs. The study stands as a stepping stone and a roadmap to embed further research in this direction.

Since several significant issues impact the progress of the projects, this work focussed on analyzing cost-related issues in projects; since there is meager research work done in the area of pharmaceutical project management, as an attempt to fill the research gap, this work carried out detailed analysis of different issues leading to cost escalations in pharmaceutical capital projects; factor analysis, including exploratory and confirmatory, has been applied to the data collected and several important factors derived and the measurement model well validated; the research work was done in close interaction with the project people working in four big Indian pharmaceutical companies and useful information was collected; in addition to generation of important factors from the factor analysis, the study was further extended to collect various remedies to mitigate the issues that lead to cost escalations; the research work has enough strength to act as a role model to motivate researchers and project people to further their research on other issues and also refine the present work.

The purpose of this paper is to propose a model (structural equation modeling (SEM)) from the 16 identified just in time (JIT) elements useful for implementation of JIT in maintenance.

Data were collected using questionnaires posted to 421 manufacturing industries and automotive service industries in India from which 133 usable responses were obtained. First, exploratory factor analysis (EFA) is carried out to identify the factor structure after that confirmatory factor analysis (CFA) is carried out to verify the factor structure of a set of identified JIT elements. CFA is conceded by an SEM statistical technique. In this paper, EFA is applied to extract the factors in JIT implementation by the statistical package for social sciences (SPSS 24) software and confirming these factors by CFA through analysis of moment structures (AMOS 18) software.

Out of 18 identified JIT elements through literature and expert opinion only 16 JIT elements are selected for the study, two JIT elements removed due to the low value of correlation item-total correlation (CITC). Three factors extracted through EFA, which affects the benefits of JIT implementation in maintenance in quality improvement, production improvement and process control. SEM using AMOS 18.0 was used to perform the first-order three-factor structure (quality improvement, production improvement and process control) of the JIT implementation in maintenance.

The results will be useful for maintenance managers and maintenance professionals to understand the process of implementation of JIT in maintenance and to gain benefits after the implementation of JIT in maintenance in their respective organization.

The purpose of the present study is to identify the determinants that potentially influence quality education in private universities in Bangladesh.

To attain this objective, 234 data were collected through face-to-face interviews on campus during February-March 2013 from Bachelor of Business Administration students. The bootstrapping procedure through AMOS was applied to analyze the data apart from exploratory and confirmatory factor analysis.

The bootstrap path coefficients suggest that seven factors are statistically significant among the eight postulated independent variables. The outcomes of the confirmatory factor analysis, such as factor loadings, eigenvalues and percentage of variance explained, as well as reliability coefficients, are observed to conform to the results of path analysis, such as item loadings and path coefficients, which consistently increased the robustness of the study.

The overall results of the study would be beneficial to the planners to formulate the proper policy to ensure the quality excellence in the private higher educational institutes.

The research is based on empirical evidence which deserves originality in terms of data and methods of analysis.

This paper aims to demonstrate that aspects of a rational or functional character and aspects of an emotional and social nature condition the perceived value of the exchange relationships among firms in an industrial cluster.

The research uses structural equation models (SEM) to test the models and applies the maximum likelihood estimation method in all the causal models obtained in the course of the data analysis.

The results obtained support the hypotheses put forward, and in particular the importance of emotional and social values in industrial relations.

Recent advances in consumer and services marketing consider that perceived value and satisfaction are central to explaining customer loyalty. However, very few studies in B2B explain the multidimensionality of perceived value. For this purpose, the authors test their working hypotheses in the Spanish ceramic tiles cluster. Specifically, they analyze the relationships between manufacturers and a supplier of frits, enamels and ceramic colors and related services.

An empirical study demonstrates that experience and interpersonal relationships are more important than price and switching costs in an industrial cluster context. The empirical study and the results provide evidence for managers: the critical influence of the emotional and social values perceived by the customer on his level of satisfaction and on the achievement of final loyalty.

The paper highlights the importance of the most intangible dimensions of value for the relations between companies in an industrial cluster.

Employee dissatisfaction with merit pay is a long‐standing problem. This study introduces four explanatory constructs, based on decisional and interactional fairness notions, that describe how supervisors implement merit pay and predict merit pay satisfaction. Multigroup confirmatory factor analyses, applied to a sample of American employees (N = 415) and a sample of Venezuelan employees (N = 239), show that the five constructs introduced here are distinct from each other and that their measures generalize across countries (cultures and languages).

Factor analysis is the most used tool in organizational research and its widespread use in scale validations contribute to decision-making in management. However, standard factor analysis is not always applied correctly mainly due to the misuse of ordinal data as interval data and the inadequacy of the former for classical factor analysis. The purpose of this paper is to present and apply the Bayesian factor analysis for mixed data (BFAMD) in the context of empirical using the Bayesian paradigm for the construction of scales.

Ignoring the categorical nature of some variables often used in management studies, as the popular Likert scale, may result in a model with false accuracy and possibly biased estimates. To address this issue, Quinn (2004) proposed a Bayesian factor analysis model for mixed data, which is capable of modeling ordinal (qualitative measure) and continuous data (quantitative measure) jointly and allows the inclusion of qualitative information through prior distributions for the parameters’ model. This model, adopted here, presents considering advantages and allows the estimation of the posterior distribution for the latent variables estimated, making the process of inference easier.

The results show that BFAMD is an effective approach for scale validation in management studies making both exploratory and confirmatory analyses possible for the estimated factors and also allowing the analysts to insert a priori information regardless of the sample size, either by using the credible intervals for Factor Loadings or by conducting specific hypotheses tests. The flexibility of the Bayesian approach presented is counterbalanced by the fact that the main estimates used in factor analysis as uniqueness and communalities commonly lose their usual interpretation due to the choice of using prior distributions.

Considering that the development of scales through factor analysis aims to contribute to appropriate decision-making in management and the increasing misuse of ordinal scales as interval in organizational studies, this proposal seems to be effective for mixed data analyses. The findings found here are not intended to be conclusive or limiting but offer a useful starting point from which further theoretical and empirical research of Bayesian factor analysis can be built.

Construction material management is an important process in supporting construction operations that affect project performance. Previous studies attempt to identify factors influencing material management in different stages such as procurement, transportation and utilization. However, they lack a model to explain the relationship between influential factors and the effectiveness of material management. Therefore, this study aims to validate the variables and key factors influencing the effectiveness of material management processes.

A total of 42 variables were reviewed from literature in different stages of material management process. Survey questionnaires were used to collect data about participants' perceptions on these variables. The respondents were 200 project engineers and project managers from construction sites and offices in Vietnam. Then factor analysis techniques were undertaken to validate the structure of factor groups. Two methods of exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were, respectively, performed to evaluate and verify the model's fit.

Factors influencing the effectiveness of material management were grouped into nine main factors, which are procurement issues, site conditions, planning and handling on site, industrial environments, contractual issues, quality control, suppliers and manufacturers' issues, transportation in and out site and security on site.

The paper has several implications for theory and methodology related to material management. It features influential factors in association with the material management effectiveness. Therefore, senior managers can more fully understand the errors in their works and propose timely solutions to limit the unwanted risks.

This research contributes on theoretical development on factors influencing effectiveness of material management processes. The key findings on influential factors can be applied to measure the effectiveness of material management processes.

The purpose of this research is to identify and validate a measurement model for assessing the quality management practices among small and medium-sized enterprises, specifically for the food processing industry in developing countries such as Malaysia.

A survey was initially conducted among SMEs using a questionnaire mailed to the managing director of companies selected from the SMECorp directory. A total of 207 respondents from SMEs were used for further analysis. Two steps of analysis were undertaken to validate the measurement model of critical success factors: principal component analysis and confirmatory analysis.

Eight critical success factors of quality management practices are proposed for assessing quality management practices among SMEs in the food processing industry in Malaysia. A measurement model was then developed. PCA with Varimax rotation revealed 13 components, eight of which were retained for further analysis. First- and second-order CFAs identified the CSF measurement model along with the goodness-of-fit index. Thus, the findings also reveal the status of quality management practices among food processing SMEs in Malaysia.

This paper is to evaluate only the CSFs; therefore, further work is needed to evaluate the relationship between CSFs and organisational performance of SMEs in the food processing industry in Malaysia.

There are various papers regarding the assessment of quality management, especially on TQM practices in various industries. However, few assessments of the critical success factors of quality management practices of SMEs in the food processing industry, especially in developing countries like Malaysia, have been found to date. The findings of this paper will help the industry to identify its current quality management practice to focus on improving its performance.