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The prediction of graduation rates of college students has become increasingly important to colleges and universities across the USA and the world. Graduation rates, also referred to as completion rates, directly impact university rankings and represent a measurement of institutional performance and student success. In recent years, there has been a concerted effort by federal and state governments to increase the transparency and accountability of institutions, making “graduation rates” an important and challenging university goal. In line with this, the main purpose of this paper is to propose a hybrid data analytic approach which can be flexibly implemented not only in the USA but also at various colleges across the world which would help predict the graduation status of undergraduate students due to its generic nature. It is also aimed at providing a means of determining and ranking the critical factors of graduation status.

This study focuses on developing a novel hybrid data analytic approach to predict the degree completion of undergraduate students at a four-year public university in the USA. Via the deployment of the proposed methodology, the data were analyzed using three popular data mining classifications methods (i.e. decision trees, artificial neural networks, and support vector machines) to develop predictive degree completion models. Finally, a sensitivity analysis is performed to identify the relative importance of each predictor factor driving the graduation.

The sensitivity analysis of the most critical factors in predicting graduation rates is determined to be fall-term grade-point average, housing status (on campus or commuter), and which high school the student attended. The least influential factors of graduation status are ethnicity, whether or not a student had work study, and whether or not a student applied for financial aid. All three data analytic models yielded high accuracies ranging from 71.56 to 77.61 percent, which validates the proposed model.

This study presents uniqueness in that it presents an unbiased means of determining the driving factors of college graduation status with a flexible and powerful hybrid methodology to be implemented at other similar decision-making settings.

The purpose of this paper is to provide a model that tests to what extent researchers’ interactions in the early stage of their collaborative network activities affect the number of collaborative outputs (COs) produced (e.g. joint publications, joint grant proposals and joint patents).

Using self-reports from 100 tenured/tenure-track faculty at a US-based university, partial least squares (PLS) path models are run to test the extent to which researchers’ individual innovativeness (Iinnov) affects the number of COs they produced taking into account the tie strength (TS) of a researcher to other conversational partners. Iinnov is determined by the specific indicators obtained from researchers’ interactions in the early stage of their collaborative network activities.

The results indicate that researchers’ Iinnov positively affects the volume of their COs. Furthermore, TS negatively affects the relationship between researchers’ Iinnov and the volume of their COs, which is consistent with the famous “Strength of Weak Ties” theory.

By investigating the degree of impact of researchers’ Iinnov on their CO, college administration could be informed regarding the extent that the social cohesion formed by interpersonal ties affects or drives the collaboration activity that results in COs. When this paper is extended to the entire university, university administration would know the capability of the different colleges, or even the university as a whole, in transforming the ideas embedded in researchers’ networks into a productive work in a collaborative manner.

It is one of the foremost attempts to investigate the relationship between researchers’ Iinnov during ideation phase and their CO. Moreover, this paper contributes to the literature regarding the transformation of tacit knowledge into explicit knowledge at a university context.

Previous researches have proven that customer satisfaction and loyalty are affected by complicated relationships and are challenging to European customer satisfaction index (ECSI) model. Existing approaches mostly limit their hypotheses to linear relationships, which hinder much information that would lead to better modeling and understanding the relationship between customer satisfaction and loyalty. The purpose of this paper is to reveal potential nonlinear and interaction effects that might be embedded in antecedents of ECSI by exemplifying it in Turkish telecommunications sector.

This papar has justified the validity and reliability of the ECSI model implementation in Turk Telekom Company. The path models are tested via conventional structural equation modeling (SEM) and using a novel method, i.e. universal structure modeling with Bayesian neural networks.

The findings of this study reveal that quality has the most important impact on customer satisfaction. The next important construct was found to be the company image. The relationship between customer expectation and customer satisfaction was revealed to be insignificant. This study reveals the fact that while using the ECSI model more attention must be paid to the consideration of potential nonlinear relationships that might be available among model constructs.

This research presents uniqueness in that it reveals significant nonlinear relationships between the model constructs of the ECSI model. Previous studies have identified purely linear relationships, which may not hold true in reality. However, in this study it is revealed that improving one determinant of customer satisfaction may not be as worthy as it is assumed to be in theory, which refers to a nonlinear relationship.