Today more than ever, entrepreneurial survival depends on the ability to overcome new and critical situations and to recover from unforeseeable events. Hence, entrepreneurial resilience is becoming increasingly important. The aim of this study is to...

Public health services, as a preventive aspect of health care, are essential for the sustainability of the entire health care system. However, the context of public health services, which focus is primarily on prevention, is not a common setting when measuring the efficiency within nonparametric evidence-based approach. The aim of this study is to measure the efficiency of the financial performance of organizational units of the public health institute in Croatia, the Health Ecology Department in particular, during the period 2016–2018 using data envelopment analysis. Among the many reasons behind choosing this nonparametric method is the fact that it identifies the sources of inefficiency and specifies the directions and magnitudes of improvements required. Two input-oriented models – CCR under constant and BCC under variable returns-to-scale assumption – are employed for evaluating three types of efficiency – technical, pure technical and scale efficiency. Two hypotheses are examined and empirically confirmed: first, that there is significant between-unit variability in financial performance, and second, that investments are the major source of inefficiency among the observed indicators. The results have additionally revealed that the mentioned differences are less pronounced in the case of pure technical efficiency, implying that the overall inefficiency of the Health Ecology Department units can be generally attributed to scale efficiency. Besides, only three out of twelve department units are considered efficient. The implications of the research results are aimed at further research and testing the efficiency of the entire network of public health institutes, as well as to provide policy makers with new insights when considering different modes of organizing and delivering public health services.

Traditionally, ports have been regarded as hubs responsible for the reception of ships and passengers, but nowadays they have a much wider economic function, being clusters of various activities directly or indirectly linked to maritime transportation and seaborne trade, among which container traffic is the most important segment. The Port of Rijeka as the largest Croatian cargo port, positioned in the North Adriatic Sea, has exceptional but not fully exploited opportunities for further economic development of importance not just for the port and the city but for the Republic of Croatia as well. In addition, its geostrategic position makes it an important international port for Central and South Eastern European countries. The aim of this paper is to investigate and identify the current position of the Port of Rijeka (hereinafter Rijeka) in relation to the container business and, using Benchmarking as the research method, to analyse the established five main factors that have to be taken into consideration where its efficiency is compared to the statistically proven “best container port” in the region – the Port of Koper (hereinafter Koper). The results show significant competitive advantages of the Port of Koper almost in any of the analysed factors. Therefore, recommendations are given for further actions and improvement according to the natural advantages that Rijeka has to utilize in order to enhance its competitiveness and overall performance.

Predmet su ovog istraživanja razvoj i primjena prediktivnih modela dobivenih uporabom matematičko statističkih metoda i umjetnom inteligencijom, tj. strojnim učenjem. Cilj je rada razvijanje modela za implementaciju digitalne transformacije u procese operativnog odlučivanja za potrebe kontrolinga i upravljanja. Kontroling je kao disciplina i funkcija u poduzeću zadužen za analizu postojećeg i predviđanje budućeg poslovanja te u tu svrhu i uporabe sofisticiranih metoda i alata. Ovim radom dokazuje se da je poslovno predviđanje temeljeno na povijesnim činjenicama evoluiralo u poslovno predviđanje procijenjeno na temelju strojnog učenja. U radu je prikazan inovativni stohastički model predviđanja na primjeru pružanja usluga u jednoj marini u Hrvatskoj. Rezultati istraživanja su četiri prikazana modela za ugovaranje i pridobivanje klijenata koji se koriste uslugama marine. Dobiveni rezultati potvrđuju visoku razinu pouzdanosti modela predviđanja uporabom digitalnih alata za potrebe kontrolinga u procesu donošenja odluka. Kroz razvijene metode pretvaranja manualnih procesa u izborno automatizirane procese odlučivanja, ovaj rad pridonosi inovativnosti kontrolinga u digitalnom okruženju.