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The purpose of this paper is to determine predictors of social network services use among college students at two Turkish universities, with particular emphasis on Facebook, Twitter, Foursquare, and Instagram.

A written questionnaire was administered to 822 college students from various faculties. The data obtained from the corresponding questionnaire were analyzed using ordered logit analysis and four different models were independently estimated with respect to social network services.

Ordered logit estimation results revealed that frequency of log in to social network sites, time spent on social network sites, and following event alerts were three predictors that had significant impact on the frequency of all four social network sites’ use. Particularly; event participation, information sharing, following on top ranked topics, and purchasing a product through social media were the other additional predictors of college students’ willingness to use the corresponding social network sites.

Although a vast literature exists on the use of social network services among college students, relatively little work was published which compares several social network services and predictors of their use. This paper may contribute to the existing literature by presenting the predictors of college students’ use of four different social network services in a specific area of Turkey and providing a comprehensive comparison between them.

This chapter aims to verify predictors of marital quality in Croatia. As a theoretical starting point, the Huston Socio-ecological model was used. Huston’s so-called “wide-angle and close range” variables were included in to study as predictors of marital quality. A two-level hypothetical model was created consisting of Six groups of predictor variables: Level 1 predictors included Partners’ demographic variables, Partners’ personality, Partners’ value system, Marital processes or dynamics, and Partners’ wellbeing. Level 2 predictors included four Marriage characteristics. Altogether at both levels, 42 variables represented predictors. Marital quality in the marriage was a dependent variable. Eight hundred and eighty-four marital couples from 14 counties in various parts of Croatia and from Zagreb, the country’s capital, were included in the study. Factor analysis, Maximum likelihood with Promax rotation was used to extract factors. Eight factors were extracted: Marital harmony, Distress, Partners’ personality, Negative spillover from work, Traditionalism, Engagement in child care, Participation in decision-making, and Economic hardship. Multilevel analysis using the Mix model in Statistical Package for Social scientist, version 20 was used in data analysis. Predictive on Marital quality in a marriage turned out variables: Marital harmony, Distress, Partners’ personality, Traditionalism, Engagement in child care, and Participation in decision-making as level 1 and Marriage duration (Marriage stages) as a level 2 variable. Huston’s Ecological model proved to be adequate and useful in explaining marital quality.

The purpose of this paper is to find out the best method for forecasting European Union Allowance (EUA) returns and determine its price determinants. The previous studies in this area have focused on a particular subset of EUA data and do not take care of the multicollinearities. The authors take EUA data from all three phases and the continuous series, adopt the principal component analysis (PCA) to eliminate multicollinearities and fit seven different homoscedastic models for a comprehensive analysis.

PCA is adopted to extract independent factors. Seven different linear regression and auto regressive integrated moving average (ARIMA) models are employed for forecasting EUA returns and isolating their price determinants. The seven models are then compared and the one with minimum (root mean square error is adjudged as the best model.

The best model for forecasting the EUA returns of all three phases is dynamic linear regression with lagged predictors and that for forecasting EUA continuous series is ARIMA errors. The latent factors such as switch to gas (STG) and clean spread (capturing the effects of the clean dark spread, clean spark spread, switching price and natural gas price), National Allocation Plan announcements events, energy variables, German Stock Exchange index and extreme temperature events have been isolated as the price determinants of EUA returns.

The current study contributes to effective carbon management by providing a quantitative framework for analyzing cap-and-trade schemes.

This study differs from earlier studies mainly in three aspects. First, instead of focusing on a particular subset of EUA data, it comprehensively analyses the data of all the three phases of EUA along with the EUA continuous series. Second, it expressly adopts PCA to eliminate multicollinearities, thereby reducing the error variance. Finally, it evaluates both linear and non-linear homoscedastic models incorporating lags of predictor variables to isolate the price determinants of EUA.

To examine the efficacy of admission requirements as predictors of academic success in core business coursework, and as a rationing mechanism for limited course capacity, for both transfer and non‐transfer students following integration of the core business curriculum.

Regression analysis is used to test the efficacy of admission standards in explaining transfer and non‐transfer student performance in the core business curriculum, before and after substantial curricular revision. Fisher's r‐to‐z transformation is used to test differences between student groups and core curriculum formats. Stepwise regression was used to identify an accurate predictor of transfer student performance for the integrated business core.

Efficacy of the admission standard decreased for transfer students following introduction of the new curriculum. While adequate for all students taking the traditional business core, it is a much less effective predictor of success for transfer students under the new curriculum. A modified admission standard for transfer students restored efficacy to previous levels.

The paper considers only one school's experience with revision of its core curriculum.

Re‐examination of admission standards following curricular revision is necessary to ensure effective screening of transfer students. The root problem, however, may not be addressed in its entirety by a unique transfer student admission standard. Non‐transfer students benefit from acculturation as freshman and sophomores, as well as prerequisite courses specifically modified to prepare them for the integrated curriculum.

This paper documents a potential problem for business schools that have, or are considering, significant curricular revisions.

This chapter describes a research study in which data about the uses of Facebook by higher education students were gathered simultaneously with measurements of a set of psychological dimensions (personal and collective self-esteem, self-concept, general self-efficacy, satisfaction with social support and with academic life, and several aspects of academic experiences: interpersonal, career, institutional, personal and course satisfaction). The final result of the study is a path model inspired on the structural model proposed by Mazman and Usluel (2010) in which the psychological variables that have a significant influence on the academic use of Facebook were incorporated. A positive total effect of identity collective self-esteem in the educational use of Facebook was found and a negative total effect was found for public collective self-esteem in the educational usage of Facebook (EUF). Institutional adaptation proved to have a significant positive total effect on students’ willingness to use Facebook for educational purposes. Satisfaction with life was not a direct predictor of the educational use of Facebook. However, it was a direct predictor of the use of Facebook for work-related purposes, which was the strongest predictor of educational use of Facebook. Therefore, although the effect of satisfaction with life in the educational use of Facebook was only indirect, it was nevertheless positive and statistically significant.

To better understand the use intensity of e-government service offerings among citizens, a considerable number of studies have examined correlations between various attitudinal constructs related to such offerings and citizens’ service adoption (intentions). This investigational paper aims to take a different angle by exploring associations between a set of 11 objectively identifiable household and individual behavioral and socio-demographic characteristics on the one side and three levels of e-government services use on the other.

The empirical analysis is based on survey responses of a random sample of 17,012 individuals residing in Germany.

Ordinal logistic regression analysis suggests that citizens with low use levels of public e-service offerings are most likely younger male persons with low levels of computer literacy, internet affinity and education, who have a migration background and live in small mid-level-income households located in rural communities.

The findings imply that public institutions may find it difficult to rapidly raise e-government acceptance by distributing only “technocratic” information explaining various service options. Public authorities should consider supplementing “pure” information programs by measures which ensure that the software of e-government service platforms is designed in a way guaranteeing a very high level of “usability”. Furthermore, they should analyze whether the benefits of providing e-government services in specific foreign languages outweigh the costs of such a service extension. If this is the case, an easy-to-use software menu item should be introduced which enables citizens to switch to another common foreign language.

The contribution of this paper results from the analysis of a set of objective predictors of e-government service use in a large random sample of citizens residing in Germany, whereas most prior studies are based on surveys of small convenience samples in other countries.

Prior research on the police decision to use deadly force has tended to neglect multivariate relationships, particularly at the situational level. This paper makes use of data describing deadly force incidents in Philadelphia during two time periods (1970‐1978 and 1987‐1992) and employs multivariate analyses to identify situational predictors of police shootings involving gun‐assaultive suspects. Findings from the multivariate analyses are then used in a pilot effort to develop predictive risk classifications of deadly force incidents. Identification of predictors of deadly force is helpful not only in assessing the relative contributions of situational variables but also in shaping our understanding of the behavior of line officers who are forced, by the nature of their work, to make split‐second decisions involving life and liberty with minimal guidance and support from the police department.

This chapter considers the estimation of a parametric single-index predictive regression model with integrated predictors. This model can handle a wide variety of non-linear relationships between the regressand and the single-index component containing either the cointegrated predictors or the non-cointegrated predictors. The authors introduce a new estimation procedure for the model and investigate its finite sample properties via Monte Carlo simulations. This model is then used to examine stock return predictability via various combinations of integrated lagged economic and financial variables.