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In the context of Dynamic Factor Models, we compare point and interval estimates of the underlying unobserved factors extracted using small- and big-data procedures. Our paper differs from previous works in the related literature in several ways. First, we focus on factor extraction rather than on prediction of a given variable in the system. Second, the comparisons are carried out by implementing the procedures considered to the same data. Third, we are interested not only on point estimates but also on confidence intervals for the factors. Based on a simulated system and the macroeconomic data set popularized by Stock and Watson (2012), we show that, for a given procedure, factor estimates based on different cross-sectional dimensions are highly correlated. On the other hand, given the cross-sectional dimension, the maximum likelihood Kalman filter and smoother factor estimates are highly correlated with those obtained using hybrid procedures. The PC estimates are somehow less correlated. Finally, the PC intervals based on asymptotic approximations are unrealistically tiny.

The purpose of the research is to analyse the ability of nonfinancial factors to predict value creation in Finnish technology firms. Nonfinancial factors are defined in terms of a large set of variables on organizational characteristics, strategy, competitive stance, consistency of performance measurement, management control systems (MCSs), and quality of MCSs. Financial ratios are used as a benchmark. The hypotheses are that, firstly, nonfinancial factors include important information for prediction and, secondly, that they provide incremental information over financial ratios. The nonfinancial variables are drawn from a postal survey carried out in 1999. Financial variables for 1998–2001 are obtained for 40 private firms of the 110 firms responding to the survey. Shareholder value is estimated on the basis of the four‐year financial data for 2001. This value divided by the shareholder book value (estimated‐to‐book value ratio, EBV) as well as its drivers are predicted by past non‐financial and financial data. Partial Least Squares (PLS) method is used to analyse the importance of information in prediction. The results give support to the hypotheses. Moreover, the results show that nonfinancial factors yield important incremental information over financial ratios when predicting value drivers, that is, growth, profitability, and risk. Especially, financial ratios are weak in predicting growth.

This chapter focusses on the identification of sustainability factors. Out of the 77 variables used in the questionnaire for collection of the information, on sustainability of financial innovation in e-payment system (EPS), the important factors are identified and derived using exploratory factor analysis (EFA). This chapter further presents validation of the identified factors through confirmatory factor analysis (CFA). Based on the identified factors, a model for EPS is proposed. The chapter also presents a scale developed based on identified factors.

The purpose of this paper is to identify the factor structure of the Ways of Coping Questionnaire (WCQ) among abused Iranian women.

This paper is a methodological study. The participants included 554 abused married women who were selected by convenience sampling from the women presenting to health centers and hospitals and their companions as well as women visiting mosques and parks in Shahroud, Iran. The exploratory factor analysis was used to investigate the factor structure of the WCQ. The reliability of the questionnaire was estimated using Cronbach’s alpha coefficient.

Following the exploratory factor analysis, 36 items and 5 factors were extracted, which could explain 73.24% of the variance in the concept of coping with intimate partner violence. The extracted factors included distancing, planning, wishful thinking, seeking social support and problem-solving. Cronbach’s alpha coefficient of the questionnaire as a whole was 0.91.

This paper has practice and research implications for promoting coping among abused women. The factor structure of the WCQ obtained in this paper can validly measure coping with intimate partner violence among abused Iranian women. Learning about the coping strategies used by abused women can help design context-based interventions to assist these women in better coping with their husband’s violence and improving their well-being. This version of WCQ also enables assessing the effectiveness of the designed interventions.

The purpose of this paper is to examine characteristics of project management assets and project management performance outcomes as a step towards exploring the link between assets being valuable, rare, inimitable, and having organizational support and the achievement of competitive advantage.

This paper analyzes data from responses to an online survey by 198 North American Project Management Institute^® members. Exploratory factor analysis is used to identify characteristics of project management assets and project management performance outcomes.

In total, six factors that comprised the characteristics of project management assets, three factors that comprised organizational support for project management assets, and two factors that comprised the project management performance outcomes were extracted.

Limitations of this study include sample size, response rate, and self‐report bias, calling for a larger sample in ongoing research. This study is a step towards making the link between project management assets and performance outcomes.

This study draws managerial attention to project management assets as sources of competitive advantage, applying the resource based view of the firm that assets are sources of competitive advantage if they add economic value, are rare, are difficult to imitate, and have organizational support.

Few papers have applied the resource based view of the firm to examine project management capabilities as a source of competitive advantage. This paper contributes to the literature on the resource based view of the firm and contributes to an improved understanding of project management as a source of competitive advantage.

The importance of retail store attributes for apparel shopping was examined for elderly (65 and over) men and women (65 and older). A self‐administered mail‐in survey questionnaire was used to collect data for the study. The data were analysed using descriptive statistics, factor analysis, and one‐way analysis of variance (ANOVA). Cronbach's alpha was executed to determine the reliability of the extracted factors. Four extracted factors explained 67.76 per cent of the total variance with regard to the preferences for the retail store attributes by the participating older men and women. Sex was identified as the most discriminating variable for both the extracted factors and fashion opinion leadership variables. Implications of the results for future research in both the US and international settings are discussed.

This research draws on the resource-based view of the firm from strategic management and applies it to a study of competitive advantage in the project management context. The relationship between the characteristics of project management resources, focusing on organizational support for the project management process, and outcomes of the project management process are examined.

This study uses data gathered from 437 North American project management professionals with an existing survey tool that was used in a prior smaller sample study. The study uses Barney’s VRIO framework that assesses resources as valuable (V), rare (R), inimitable (I) and organizationally supported to leverage their value (O). The conceptual model hypothesizes relationships between the project management asset characteristics (valuable, rare, and inimitable), organizational support for the project management process, and project management performance outcomes (both project and firm level). Hypotheses are tested using factors extracted from a confirmatory factor analysis (CFA). The factors extracted include two factors representing valuable project management asset characteristics, one factor representing rare project management asset characteristics, one factor representing inimitable project management asset characteristics, two factors representing organizational support for the project management process, one factor representing project-level performance and one factor representing firm-level performance.

Project management assets that are considered valuable and organizational support for the project management process are found to contribute positively to project management process outcomes. No advantage was perceived from rare and inimitable project management assets. Project-level performance was found to significantly mediate the relationship between organizational support and firm-level performance.

This study draws managerial attention to organizational support for the project management process as a source of competitive advantage through its positive affect on both project-as well as firm-level performance.

The study uses a survey tool from previous research with a new, larger dataset to contribute to the understanding of the importance of organizational support for the project management process in a quest for both project success as well as a firm's competitive advantage.

Upon the realization of the need for guideline in cross-organizational data integration, in an exploratory manner, this study developed a public data governance framework, specifically, the governance for integrated public data (GIPD) framework and identified the influential factors of its successful implementation. This framework was then subjected to an analysis of a real data integration case in the South Korean public sector to test its efficacy.

To develop the GIPD framework, the authors conducted an extensive meta study, focus group interviews and the analytic hierarchy process involving field experts. Further, the authors performed topic modeling on documents from Korean research and development data integration projects, and compared the extracted factors to those of the GIPD to illustrate the latter's usefulness in a real case.

Legislation, policy goals and strategies, operation organization, decision-making council, financial support size and objective, system development and operation, data integration, data generation, system/data standardization and master data management were derived as the 10 important factors in implementing the GIPD framework. The illustrative case of Korea revealed that decision-making council, financial support size and objective, legislation, data generation and data integration were insufficient.

Although this study reveals important findings, it has a few limitations. First, the potential factors for data governance might vary depending on the attribute of the “interviewee” (such as their career or experience period) and the goal and area of GIPD framework building. Second, the inherent limitation of topic modeling in determining topics from groups of extracted keywords means that topics may be interpreted in various ways, depending on the perspective of the expert.

This study is highly significant in that it provides a starting point for discussions on the issue of data integration among public institutions. Therefore, although this study examined public data governance based on R&D data, it will contribute to providing a sufficient guideline for any type of inter-institutional data governance framework, what to discuss and how to discuss between institutions.

The findings are expected to provide a roadmap to formulate practical guidelines on inter-institutional data cooperation and a diagnostic matrix to improve the existing data governance system, especially in the public sector, from the existing practice of empirical analysis using a mixed methodology approach.

This study aimed to use enrollment information including demographic, family background and financial status, which can be gathered before the first semester starts, to construct early prediction models (EPMs) and extract crucial factors associated with first-year student dropout probability.

The real-world samples comprised the enrolled records of 2,412 first-year students of a private university (UNI) in Taiwan. This work utilized decision trees (DT), multilayer perceptron (MLP) and logistic regression (LR) algorithms for constructing EPMs; under-sampling, random oversampling and synthetic minority over sampling technique (SMOTE) methods for solving data imbalance problems; accuracy, precision, recall, F1-score, receiver operator characteristic (ROC) curve and area under ROC curve (AUC) for evaluating constructed EPMs.

DT outperformed MLP and LR with accuracy (97.59%), precision (98%), recall (97%), F1_score (97%), and ROC-AUC (98%). The top-ranking factors comprised “student loan,” “dad occupations,” “mom educational level,” “department,” “mom occupations,” “admission type,” “school fee waiver” and “main sources of living.”

This work only used enrollment information to identify dropout students and crucial factors associated with dropout probability as soon as students enter universities. The extracted rules could be utilized to enhance student retention.

Although first-year student dropouts have gained non-stop attention from researchers in educational practices and theories worldwide, diverse previous studies utilized while-and/or post-semester factors, and/or questionnaires for predicting. These methods failed to offer universities early warning systems (EWS) and/or assist them in providing in-time assistance to dropouts, who face economic difficulties. This work provided universities with an EWS and extracted rules for early dropout prevention and intervention.

The purpose of the paper is to investigate the global and regional influences on the domestic term structure of nine Asian economies.

The dynamic Nelson Siegel model was used to extract the latent factors of a country’s yield curve movements in a state-space framework using the Kalman filter. The global and regional factors of the yield curve were extracted using the dynamic factor model. Further, the Bayesian inference of Gibbs sampling approach was used to identify the influence of global and regional factors on the domestic yield curve.

The results suggest that financial integration does not reduce the control of monetary authorities on the front end of the yield curve, and long-term interest rate is the potential transmission channel through which the contagion of the financial crisis spreads.

The results of this study would help the monetary authorities to understand the efficacy of the monetary policy transmission mechanism. It also offers the global investors diversification opportunities for investing in the Asian bond markets.

It is one of the earliest attempts to capture the global and regional yield curve movements and their impact on the emerging Asian economies yield curve. It contributes to literature by identifying the linkages in the long-term factor that is the potential channel through which crisis spreads.