PurposeConceptual model in this paper combines existing scientific knowledge grounded in theories of planned behavior, diffusion of innovation and a unified theory of acceptance and use of technology, while aiming to identify relevant determinants of continuous use of e-learning by employees who used e-learning in the past year at their workplace.Design/methodology/approachThe authors developed and empirically tested the positive impact of professional, personal, IT and environmental factors on the continued use of e-learning among 672 employees across different sectors using the structural equation modeling technique.FindingsResearch results suggest that the most powerful determinant of continuous use of e-learning are personal factors. Environmental influences and technological aspects also exhibit a positive and significant impact on the continuous use of e-learning. Research hypothesis related to the positive influence of professional factors on the continuous use of e-learning has not been empirically confirmed. Also, results demonstrated that continued use of e-learning contributes to better individual business performance.Practical implicationsThe practical contribution is threefold: to companies, education institutions and human resource managers. For companies, identification of key determinants will lead to a better understanding of employees needs regarding continuous job improvements. The findings can be used by educational institutions to design e-learning programs according to results and real value to employees. On the other hand, human resource managers can benefit from this study in terms of getting concrete factors that motivate employees for continuous job improvement.Originality/valueThe research sheds light on the proposed integrated model that tests the post-adoption of the continuous use of e-learning within an organizational context.

In the modern days, the amount of the data and information is increasing along with their accessibility and availability, due to the Internet and social media. To be able to search this vast data set and to discover unknown useful data patterns and predictions, the data mining method is used. Data mining allows for unrelated data to be connected in a meaningful way, to analyze the data, and to represent the results in the form of useful data patterns and predictions that help and predict future behavior. The process of data mining can potentially violate sensitive and personal data. Individual privacy is under attack if some of the information leaks and reveals the identity of a person whose personal data were used in the data mining process. There are many privacy‐preserving data mining (PPDM) techniques and methods that have a task to preserve the privacy and sensitive data while providing accurate data mining results at the same time. PPDM techniques and methods incorporate different approaches that protect data in the process of data mining. The methodology that was used in this article is the systematic literature review and bibliometric analysis. This article identifieds the current trends, techniques, and methods that are being used in the privacy‐preserving data mining field to make a clear and concise classification of the PPDM methods and techniques with possibly identifying new methods and techniques that were not included in the previous classification, and to emphasize the future research directions. This article is categorized under: Commercial, Legal, and Ethical Issues > Security and Privacy

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We live in a rapidly changing global society, where no one can predict the outcome of the economic, social, and political structures of the world. Changes in science, technology, and economics are particularly noticeable and are closely linked to human life. These changes create new opportunities but also challenges in new areas of everyday activity in order to achieve sustainable development. For countries to compete with each other, they must be creative and innovative in all fields to cope with domestic, national, and global issues. Current economic competitiveness is based on the capabilities of a country and their respective companies to be and stay innovative. This is the main reason why many governments place innovativeness at the center of their growth strategies so that they can foster economic progress and global competitiveness in general. The recognition and need for identification of innovation as a driver of change are evident on a company level as well. This study will use secondary data collected this year from the World Economic Forum to identify critical challenges and opportunities for B&H competitiveness. Also, the results of this research identified enabling environment and markets impact on the innovation ecosystem. Practical contribution relates to concrete implications and recommendations that can be used for the improvement of Bosnia and Herzegovina innovativeness.

Business intelligence systems are in widespread use today due to the many business benefits. Users are one of the key stakeholders in the business intelligence process. For optimal system adaptation, the user should be able to interact with the application in order to improve its capacity to contribute to decision-making. For the business intelligence process itself to be effective, it is necessary to define the user needs regardless of the type of work they do. If the user is satisfied and thinks that the system improves his/her performance or the quality of decisions made, they will want to use it even more. System usage has sometimes been viewed as a direct reflection of system performance; however, this is difficult to define in organizations where system usage is mandatory. Business intelligence systems are especially mandatory to use, as they are used in large organizations and require greater investment than other systems. This is why it is important to investigate the nature of system usage and its impact on individual performance. This research model deals with determinants that represent dimensions of the information system's success theory. Those determinants are: user satisfaction, intention to use, system usage, and individual performance. Obtained results show that increased user satisfaction and intention to use, lead to increased system usage and that both the increase in user satisfaction and system usage lead to a rise in individual user performance.