The banking industry has been seeking novel ways to leverage database marketing efficiency. However, the nature of bank marketing data hindered the researchers in the process of finding a reliable analytical scheme. Various studies have attempted to improve the performance of Artificial Neural Networks in predicting clients’ intentions but did not resolve the issue of imbalanced data. This research aims at improving the performance of predicting the willingness of bank clients to apply for a term deposit in highly imbalanced datasets. It proposes enhanced Artificial Neural Network models (i.e., cost-sensitive) to mitigate the dramatic effects of highly imbalanced data, without distorting the original data samples. The generated models are evaluated, validated, and consequently compared to different machine-learning models. A real-world telemarketing dataset from a Portuguese bank is used in all the experiments. The best prediction model achieved 79% of geometric mean, and misclassification errors were minimized to 0.192, 0.229 of Type I & Type II Errors, respectively. In summary, an interesting Meta-Cost method improved the performance of the prediction model without imposing significant processing overhead or altering original data samples.

Agile methods have attracted significant attention in the industry as an approach for software development and IT project management due to fast-changing business environments, cost, and competitive pressures. Choosing the right approach among various agile development models, however, is a complex, multi-criteria decision that can have significant implications on project success. In this article, we present a teaching case designed to help Information Systems students improve their skills in understanding and evaluating complex business requirements and in selecting the most appropriate software development methodology to match the needs of a specific IT project, and the organization. The teaching case includes a comparative overview of various agile methodologies, as well as the use of multi-criteria decision tools for solving the problem of methodology selection.

No abstract

PurposeSharing knowledge of physicians in hospitals is critical and significant in terms of providing better healthcare services. Despite the significance of knowledge sharing in the healthcare setting, very few studies have empirically investigated knowledge sharing drivers among physicians. Particularly, the process of knowledge sharing through the interplay between individual characteristics, knowledge characteristics, and intention in a healthcare setting has received very little empirical support. In this study, the authors draw upon personality traits and knowledge characteristics theories to develop a theoretical model to empirically examine the effect of individual characteristics and knowledge characteristics on physicians' knowledge sharing behavior.Design/methodology/approachBased on a sample of 215 physicians from 20 hospitals in Jordan, the authors conducted data analysis using the partial least squares statistical technique.FindingsThe study revealed that the personality traits (Extraversion, Neuroticism, Agreeableness and Conscientiousness) significantly influence physician intention to share knowledge. Knowledge characteristic (Situatedness) was also found to affect the intention to share knowledge.Originality/valueVery little is known about the effect of individual characteristics and knowledge characteristics on knowledge sharing behavior among physicians. The study contributes to the related literature by empirically investigating how individual characteristics and knowledge characteristics influence physicians' knowledge sharing behavior. The findings add to the understanding of the role of personality traits and knowledge characteristics in physicians' intention to share knowledge and give important insights for practice and theory.

No abstract