Exploring Time Perspective in Greek Young Adults: Validation of the Zimbardo Time Perspective Inventory and Relationships with Mental Health Indicators

In this article we examine the factorial structure of the Greek version of the Zimbardo Time Perspective Inventory (ZTPI; Zimbardo and Boyd in J Personal Soc Psychol 77:1271–1288, 1999), in a sample of 337 university students, using principal axis factoring (PAF) with oblique rotation, and its dimensionality using parallel analysis. Moreover, we evaluate the internal consistency reliability, the convergent validity (through associations with mental health indicators such as dispositional optimism, self-esteem, trait anxiety, depression, and proactive coping), as well as discriminant, and differential validity of this instrument. The results indicated that the ZTPI had a 5-factor structure (past negative, past positive, present fatalistic, present hedonistic, future). Correlational analyses indicated that an aversive view of the past, and a fatalistic attitude toward life were positively and significantly associated with trait anxiety and depression, while they were negatively correlated with self-esteem, proactive coping, and dispositional optimism. Future time perspective was positively associated with proactive coping, whereas a positive attitude toward the past was negatively associated with depression and trait anxiety. Psychometric properties of the five ZTPI scales were satisfactory (Cronbach’s alphas ranging from .710 to .845), thus facilitating the robust investigation of time perspective in Greek speaking populations. However confirmatory factor analyses revealed that the “positive attitude toward the past” dimension might not be a good indicator of time perspective. We discuss the theoretical implications of these findings for future studies of time perspective.     

Human resource development (HRD) resilience: a new ‘success element’ of organizational resilience?

ABSTRACT

Endlessly changing business and economic landscapes urge organizations to become resilient to ensure business survival and growth. Yet, in many cases, business world is becoming turbulent faster than organizations are becoming resilient. Relevant research indicates the ways through which organizations could respond to unforeseen events, mainly through suggesting that individual and group resilience could lead to an organizational one. However, research is nascent on how particularly human resource development (HRD) resilience could be built, and thus to contribute to organizational resilience as well. Within today’s business uncertainty and complexity, HRD resilience comes in line with the developmental strategies of organizations. Therefore, the purpose of this perspective article is to set the foundations of the term (HRD resilience) in order to initiate a dialogue around its ability to make a substantial contribution to organizational practice, and thus to be seen as a new ‘success element’ of organizational resilience.     

Assessing bank soundness with classification techniques

The recent crisis highlighted, once again, the importance of early warning models to assess the soundness of individual banks. In the present study, we use six quantitative techniques originating from various disciplines to classify banks in three groups. The first group includes very strong and strong banks; the second one includes adequate banks, while the third group includes banks with weaknesses or serious problems. We compare models developed with financial variables only, with models that incorporate additional information in relation to the regulatory environment, institutional development, and macroeconomic conditions. The accuracy of classification of the models that include only financial variables is rather poor. We observe a substantial improvement in accuracy when we consider the country-level variables, with five out of the six models achieving out-of-sample classification accuracy above 70% on average. The models developed with multi-criteria decision aid and artificial neural networks achieve the highest accuracies. We also explore the development of stacked models that combine the predictions of the individual models at a higher level. While the stacked models outperform the corresponding individual models in most cases, we found no evidence that the best stacked model can outperform the best individual model.     

Taking Big Data apart: local readings of composite media collections

If we are to think critically about Big Data initiatives, we must learn to take them apart. This paper explains how to interrogate Big Data, not as large homogenous resources, but as heterogeneous collections with varied and discordant local ties. My argument focuses on the Big Data of media collections: composite digital repositories of texts, images, and video created in different contexts, but brought together online. The primary example used in this paper is the Digital Public Library of America (DPLA), a collection composed of digitized library, museum and archive records from institutions across the United States. I demonstrate how local readings of DPLA data can uncover schemata, errors, infrastructures, classifications, absences, and rituals that have important origins. Moreover, I explain how identifying these local features can support new forms of scholarship, pedagogy, and advocacy in the face of Big Data. For this last point, I use two additional cases: NewsScape, a real-time archive of broadcast news, and Zillow, a marketplace for real estate listings. The range of examples demonstrates how the stakes change from one Big Data initiative to the next. The paper concludes with a set of speculative guidelines for attending to the local conditions in Big Data: get dirty, take a comparative approach, show context, use data to connect people, and create opportunities for the collection of counter-data. When working with Big Data, I argue that thinking locally is thinking critically.     

An application of multicriteria decision aid models in the prediction of open market share repurchases

This study presents the first attempt to develop classification models for the prediction of share repurchase announcements using multicriteria decision aid (MCDA) techniques. We use three samples consisting of 434 UK firms, 330 French firms, and 296 German firms, to develop country-specific models. The MCDA techniques that are applied for the development of the models are the UTilités Additives DIScriminantes (UTADIS) and the ELimination and Choice Expressing REality (ELECTRE) TRI. We adopt a 10-fold cross validation approach, a re-sampling technique that allows us to split the datasets in training and validation sub-samples. Thus, at the first stage of the analysis the aim is the development of a model capable of reproducing the classification of the firms considered in the training samples. Once this stage is completed, the model can be used for the classification of new firms not included in the training samples (i.e. validation stage). The results show that both MCDA models achieve quite satisfactory classification accuracies in the validation sample and they outperform both logistic regression and chance predictions. The developed models could provide the basis for a decision tool for various stakeholders such as managers, shareholders, and investment analysts.