Abstract Background: Social networks allow real-time interaction that enhances a bank’s ability to respond to customers in a timely, intuitive and personalized manner. By using social networks, banks can improve the understanding of their clients and bank’s products they need. Also, banks can enhance relations with clients and strengthen their brand through raising client loyalty. Objectives: The paper explores and analyses the current presence of banks in Bosnia and Herzegovina on social networks. Methods/Approach: The paper studies the presence of 24 banks in Bosnia and Herzegovina on social networks and analyses the basic characteristics of profiles/pages of the banks on the most popular social networks. Results: A half of the banks have their profiles/pages on different social networks (mostly on Facebook and YouTube). They use the profiles/pages mainly for content marketing, i.e. for presenting their business operations. Unfortunately, banks do not encourage interaction with clients, except through likes. Conclusions: The analysis does not show that banks have a systematized and planned appearance on social networks. There is a plenty of room for improvement, and it is necessary primarily to address the interaction between clients and banks through social networks.

Student engagement should be one of the most powerful drivers for improvement of quality teaching in higher education. As students are direct beneficiaries of quality teaching, they are able to provide crucial feedback not only on what works well but also on what they would like to be done differently and how. The paper presents results of research related to students' perception of course Accounting Information Systems (AIS) and way of its implementation at the Faculty of Economic - University of Mostar. At this course lectures include many opportunities for active student engagement through cooperative learning activities (debates, team work, and presentation of project results). The authors developed two questionnaires in order to investigate the students' understanding of AIS course at the beginning and at the end of lectures. At the first class students completed a questionnaire about their expectations from AIS lectures (content, their engagement, learning activities, learning outcomes, assessment) and at the last class they completed another questionnaire about their real experiences related to AIS lectures. Research has been conducted for last two years and it enabled authors to use its results to tailor lectures in accordance with student's expectation and accordingly to improve teaching process. DOI: 10.5901/jesr.2015.v5n1s1p147

The aim of this paper is a presentation of data mining model that could be used for the measurement of current and forecasting of the future customer profitability. The purpose of this model is to forecast activities of individual customers in the future, and to value that company could expect in doing business with them. Modern customer profitability analysis shows that product cost is just one part of the relation enterprise-customer. A general framework for defining customer profitability, besides pure financial items, has to include a lot of non-linear and non-financial elements. Data mining methods do not use conventional learning methods that suffer from imperfections such as inability to explicitly transfer the knowledge from experts to machines or nonexistence of experts' will for knowledge transfer. Data mining can identify and adopt patterns and rules that exist in historical data stored in databases and/or data warehouses. It can work equally well with nonlinear and nonfinancial elements of environment which have influence on profitability results. Neural networks approved their capability for approximate description of any continuous function. Together with robust methods of genetic algorithms used in the learning process of networks, they make a good choice in the process of selecting methods for forecasting customer profitability. The proposed model for the forecasting of the customer profitability uses two data mining methods: neural networks and genetic algorithm. The paper presents results of empirical research related to forecasting of customer determination to specific segment made in a company which produces and distributes products like dry fruits, nuts, seeds and cereals for the market of South-East Europe.