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This paper aims to shed light on gender differences in firm performance in a period that entails an unprecedented crisis with specific effects on gender roles, i.e., COVID-19. The analysis focuses on Croatian high-tech manufacturing and knowledge-intensive service sector SMEs. Previous literature indicates that the obstacles the SMEs face may be even more significant for women-owned firms. Specifically, women entrepreneurs find it more challenging to secure social and financial capital. Women often face restrictions on their working hours due to societal pressure and family obligations, and they are rarely well-connected because they are often not members of influential business networks. Literature also suggests that the usual pressures on female working hours have disproportionally increased during the COVID-19 imposed lockdowns, so the general expectation is that women entrepreneurs were not able to cope equally with the changed market circumstances. In this study, we consider a causationeffectuation management framework to investigate how women- and men-owned SMEs used these management styles to address the business challenges in the COVID-19 crisis. Our contribution aims explicitly to answer the invitation made in recent literature to explore how gender influences the effects of the four dimensions of effectuation on firm performance.

Studies show that women are very productive employees, but that as business owners, compared to their male counterparts, they run companies that earn less, grow at a lesser rate, and employ a smaller number of employees. One of the explanations for this discrepancy argues that indicators such as sales, turnover or profit do not measure performance adequately because they are dependent on the size of the firm. Since women often make a conscious decision to keep their companies small, these performance measures may not adequately represent women-owned businesses. We study a panel of micro firms across all industries, from three EU countries of comparable size (Croatia, Slovenia, and Slovakia) in the period 2010 – 2019. Results indicate that female-owned firms have higher values of both turnover per asset and value added per asset. Additionally, results suggest that during recession years, female-led firms show a degree of resilience to these adverse effects, and they manage to increase their turnover per asset by 3 to 4 percent on average, compared to male-led firms. We conclude that although women-owned micro firms tend to have less resources compared to men’s, women can create larger output per asset, suggesting capability to combine those resources in a very effective way.

Several recent papers draw attention to a lack of rigorous research on public policies supporting women entrepreneurs’ competitiveness. This paper evaluates the effect of small business development gender- specific matching grants using a quasi- experimental approach. The grants have a positive effect on firm survival, as well as positive effects on obtaining bank loans, turnover, value added, employment, and total factor productivity. Heterogeneous treatment effects show that the grants increase the chance of young women entrepreneurs’ firm survival and are even more effective for firms owned by mature women. Cost-benefit analysis estimates an increase in value added, which outweighs scheme-induced costs by 80% in the short-run and 170% in the long run.

Odnosi između znanosti i gospodarstva smatraju se vrlo važnim dijelom inovacijskoga sustava i jednim od krucijalnih čimbenika nacionalnoga inovacijskog kapaciteta. Dostupne studije pokazuju da zamjetan dio proizvoda i procesa koji su u prodaji ili u upotrebi ne bi bili razvijeni bez znanstvenog istraživanja. Gospodarstvo i znanost mogu imati obostrane koristi od suradnje. U ovom članku žarište je istraživanja na odnosu između znanosti i gospodarstva, promatranog sa stajališta znanstvenika, a koji se temelji na prikupljenim izvornim podacima u Hrvatskoj. U članku se nastoji razviti razumijevanje o determinantama koje utječu na suradnju s gospodarstvom i na zadovoljstvo znanstvenika tim odnosom. Rezultati istraživanja pokazuju snažan interes znanstvenika za suradnju, a motivi koji mogu objasniti njihov interes jesu intelektualni izazovi i dodatni izvor prihoda. Znanstvenici smatraju da se stanovita ograničenja suradnje nalaze više u njihovoj internoj organizaciji nego u vanjskim odnosima s gospodarstvom. Oni su uvjereni da gospodarstvo nije tako zainteresirano za suradnju kao što su to oni sami. Manje su kritični oni znanstvenici koji su već uspostavili veze s gospodarstvom. Industry-science relationship is considered to be a very important part of the innovation system and it represents one of the crucial factors in national innovation capacity. Extant studies show that a significant proportion of the products and processes that are currently sold and used could not have been developed without academic research. Both industry and academia can benefit from this relationship. Research focus in this paper is on interactions between science and the industry from the researchers' point of view based on field study in Croatia. This paper seeks to develop understanding of determinants of collaboration intensity with industry and scientists' satisfaction in this relationship. It explores scientists' motivations to collaborate and perceptions of barriers for collaboration as well as how they perceive the other side in this relationship. Research findings show strong scientist interest in the collaboration with industry. Significant motives that can explain their interest are intellectual challenge and additional income. Scientists find more powerful obstacles in their internal organizations than in external relationship with industry. They are persuaded that industry is not as interested in collaboration as they are. Less critical are those groups of scientists who have already built closer working ties with industry. Die Beziehungen zwischen Wissenschaft und Wirtschaft gelten als äußerst wichtiger Bestandteil des Innovationssystems und als ein Schlüsselfaktor der nationalen Innovationskapazitäten. Verfügbare Studien zeigen, dass ein wesentlicher Teil der im Verkauf oder im Gebrauch befindlichen Artikel oder Dienstleistungen ohne das Zutun der wissenschaftlichen Forschung nicht entwickelt worden wäre. Sowohl Wirtschaft als auch Wissenschaft können von ihrer Zusammenarbeit profitieren. Dieser Artikel richtet seine Aufmerksamkeit auf das Verhältnis von Wissenschaft und Wirtschaft und betrachtet diese Wechselbeziehung aus akademischer Sicht. Mit ins Spiel kommen hierbei Daten, die die Autorinnen in Kroatien gesammelt haben. Die Verfasserinnen bemühen sich um ein Verständnis der Determinanten, die Einfluss haben auf die Zusammenarbeit zwischen Industrie und Wissenschaft sowie auf die diesbezügliche Zufriedenheit der Akademiker. Die Untersuchungsergebnisse zeigen ein reges Interesse der Wissenschaftler an dieser Zusammenarbeit; als Ausschlag gebende Motive dafür gelten die intellektuelle Herausforderung und Nebenverdienstmöglichkeiten. Nach Meinung der Wissenschaftler ist im internen Aufbau wissenschaftlicher Einrichtungen eher nach diese Zussammenarbeit behindernden Barrieren zu suchen als in den äußeren Beziehungen zur Wirtschaft. Sie sind jedoch davon überzeugt, dass die Wirtschaft an der Zusammenarbeit nicht im selben Maße interessiert ist wie die wissenschaftliche Seite. Weniger kritisch zeigen sich Akademiker, die bereits Beziehungen zu Wirtschaftskreisen ausgebaut haben.

Mnogi suvremeni ekonomisti se slažu da ono što izdvaja uspješne tvrtke od njihovih manje uspješnih rivala je sposobnost neprestanog inoviranja, te da gospodaisko stanje cijele nacije ovisi o inovativnosti njenih tvrtki. Stvaranje novih pioizvoda, usluga ili procesa je kompleksan proces u kojem ima mnogo neizvjesnosti i rizika. U ovom članku je ukratko predočen pioces razvoja novog proizvoda-usluge, i opisane su metode koje pomažu tvrtkama da maksimiziraju šanse za uspjeh., Nowadays most economists agree that capability to successfully and consistently innovate is the crucial element that differentiates successful firms from their less successful rivals. As a consequence, a country's economic situation depends on its innovative capability. New product, process or service development is a complex process, wrought with uncertainty and risk. This prompted development of methods whose purpose is to alleviate uncertainly and increase the chance for success. In this article author gives a short overview of the new product development process and the methods involved.

U ovom diplomskom radu smo pokazali da su pravila ishoda odličan alat za procjenu predikcije analitičara te uz svojstvo prikladnosti, koje osigurava iskrenost prognostičara, nagrađuje prognostičara s obzirom na predikciju i realizaciju događaja. Karakterizacijski teorem 1.2.3 pokazuje povezanost prikladnosti i postojanja konveksne očekivane funkcije ishoda \(G\), koju još nazivamo informacijska mjera ili generalizirana entropijska funkcija. Informacijska mjera je maksimalna korisnost koja se može postići pri predikciji nekog događaja. Razlika između maksimalnog dobitka i dobitka kojeg smo dobili pri nekoj predikciji nazivamo funkcija divergencije. Osim nagrade analitičara, zanima nas i kvaliteta predikcije te potreba za usporedbom različitih predikcijskih procedura zbog čega uvodimo vještine ishoda. Pravila ishoda mogu se primjenjivati na raznovrsne podatke. Glavni predstavnici kategorijskih su Savage i Schervish. Savage je konstruirao reprezentaciju za kategorijske varijable, dok Schervish reprezentacija definira pravila ishoda s 2 moguća ishoda. Do sada spominjana pravila ishoda su bila simetrična, ali ako imamo potrebu za asimetričnim, samo teorem 2.1.2 primjenimo na konveksnu funkciju \(G\) koja nije invarijantna na permutaciju koordinata. Beta familija i Winklerov ishod za binarne varijable prikazuju rješenje asimetričnog problema. Beta familija nagrađuje analitičara samo u slučaju ako je vjerojatnost događaja veća nego unaprijed zadana granica \(c\), uz uvjet realizacije tog događaja, dok Winklerov ishod rješava situaciju ako želimo više nagraditi analitičara u slučaju realizacije, inače manje. Promatrajući neprekidne varijable, pravila ishoda za predikciju gustoće zamjenjujemo s predikcijom za kumulativnu distribuciju zbog poticanja na preveliku umjetnu gustoću. Takvo pravilo ishoda nazivamo neprekidno rangirani ishod vjerojatnosti ako se radi o jednodimezionalnom prostoru, a ako ga generaliziramo na \(m\)-dimenzionalni, uz ispunjeni uvjet za euklidskom normom, ishod energije. Negativna jezgra poboljšava generalizaciju jer nema uvjeta na normu, dok postoji i konstrukcija s kompleksnom jezgrom. Postoje situacije u kojima je teško odrediti cijelu prediktivnu distribuciju, zato analitičari odlučuju predvidjeti kvantile i intervale, stoga definiramo i pravila ishoda za dotične te dajemo primjer u case study-u u odjeljku 5.3 i 5.4. Nadalje, predikciju distribucije možemo prikazati uz pomoć predikcije kvantila. Bayesovi faktori uspoređuju vjerojatnosne modele, a modeli generiraju vjerojatnosna predikcijska pravila. Bayesov faktor je ekvivalentan logaritamskom ishodu u slučaju bez parametara te Bayesovi faktori se mogu primijeniti i za usporedbu vjerojatnosnih predikcijskih pravila, ne samo modela. Ako pretpostavimo da postoje nepoznati parametri predikcijskih pravila koje dobivamo iz podataka te ako su podaci dani u određenom redoslijedu, uz logaritamski ishod, možemo primjeniti Bayesove faktore. Problem nastaje ako ne postoji redoslijed danih podataka, tada je rješenje primjena slučajne k-struke unakrsne provjere. Jedno zanimljivo pravilo ishoda je Bayesov serum istine koji predstavlja način dolaska do iskrenih odgovora na osobna, ali i objektivna pitanja, nagrađujući ljude koji su iskreni te kažnjavajući one koje lažu. Zahtjev za istinom i iskrenošću ispitanika je ispunjen jer se time maksimizira nagrada ispitanika, što je upravo svojstvo prikladnosti. Za kraj diplomskog rada, dali smo primjer istraživanja S. Radas i D. Preleca koje primjenjuje Bayesov serum istine da bi dobili odgovore o potražnji i kvaliteti za određenim proizvodom, kao i o „dobrim" i „lošim" ispitanicima s obzirom na njihovu informiranost i zainteresiranost za istraživanje. In this paper, we have shown that the scoring rules are an excellent tool for evaluating the analyst’s prediction. Proper scoring rules ensures the forecasters honesty and rewards him, regard to the prediction and realization of event. The characterization theorem 1.2.3 shows the connection of the propriety and existence of the convex expected function score \(G\), which is also called the information measure or the generalized entropy function. The information measure is the maximum utility that can be achieved in the prediction of an event. The difference between the maximum score and expected score we get at some prediction, is called the divergence function. In addition to the scoring rules, we are interested in the quality of the prediction and the need to compare different prediction procedures. Because of that, we introduce skill scores. Scoring rules can be applied to a variety of data. Major representatives of the categorical are Savage and Schervish. Savage has constructed a representation for categorical variables, while the Schervish defines scoring rules for binary. So far the mentioned scoring rules have been symmetric, but if we have the need for asymmetric, we apply the theorem 2.1.2 to the convex function \(G\) which is not invariant on the permutation of the coordinates. The Beta family and Winkler’s score for binary variables show the solution for an asymmetric problem. The Beta family rewards the analyst only if probabilistic forecast is greater than the lower limit \(c\), for event or value that materializes, while Winkler’s score resolves the situation if we want to give more importance to one outcome with respect to the other. Further, if we have continuous variables, the rules for density forecasts are replaced with rules for a predictive cumulative distribution functions, due to the stimulation of artificially high density. This kind of scoring rule is called continuous ranked probability score if variables are in one-dimensional space, and if we generalize it to \(m\)-dimensional, with the fulfilled need for the Euclidean norm, energy score. Negative kernel improves generalization because there are no conditions on the norm, and also, there exist scoring rules constructed with complex-valued kernels. There are situations where it is difficult to determine the entire predictive cumulative distribution function, so analysts decide to predict quantiles, and with help of quantiles we are able to make scoring rules for interval forecasts. We give an example in the case study in the 5.3 and 5.4 section. Further, scoring rules for predictive distributions can be made from scoring rules for quantiles. Bayesian factors compare probability models, and models generate probable prediction rules. Bayesian factor is equivalent to a logarithmic score in the case of no parameters and that Bayesian factors can also be applied to compare probable prediction rules, not just the model. If we assume that there are unknown prediction parameters that we get from the data, and if the data is given in a particular order, with the logarithmic score, we can apply Bayesian factors. The problem arises if there is no sequence of data given, then the solution is the use of a random-fold cross-validation. One interesting scoring rule is the Bayesian truth serum (invented by Prelec, 2004), which is the way of getting honest answers for personal, and objective questions, rewarding honesty and punishing those who lie. The demand for the truth and honesty of the respondents is fulfilled because it maximizes the prize for subjects, which is precisely a description of propriety. We have given an example of S. Radas and D. Prelec’s research using Bayesian truth serum to get answers about a need for a given product, and it’s quality. Also, can be used to identify ”good” respondents from the collected data, thus improving data reliability, regard to their knowledge about product and interest in research.

U ovome radu koncentrirali smo se na procjenu prosječnog efekta tretmana koristeći različite pretpostavke i ovisno o njima regresijske metode, metode bazirane na vjerojatnosti sklonosti ili metode instrumentalnih varijabli. Varijable \(y_1\) i \(y_0\) označavale su ishod sa, odnosno bez tretmana, dok je binarna varijabla \(w\) označavala tretman, a \(\textbf{x}\) vektor opaženih kovarijabli. Ono što smo htjeli procijeniti bila je razlika između ishoda sa i bez tretmana, \(y_1-y_0\). Budući da se pojedinac ne može istovremeno nalaziti u oba stanja, tu razliku nije moguće direktno izračunati. Stoga smo promatrali sljedeća dva očekivanja: prosječan efekt tretmana (average treatment effect - ATE) koji je definiran kao: \(AT E \equiv E( y_1-y_0 )\) i prosječan efekt tretmana tretiranih jedinki (average treatment effect on the treated \(AT E_1\)) definiran sa: \(AT E_1 \equiv E( y_1-y_0 | w = 1)\). Također, promatrali smo i prosječan efekt tretmana uvjetno na \(\textbf{x}\) i prosječan efekt tretmana tretiranih uvjetno na \(\textbf{x}\) : \(AT E_1 (\textbf{x}) = E( y_1-y_0 | \textbf{x} , w = 1)\) i \(E( y_1-y_0 | \textbf{x}) = AT E(\textbf{x})\). Ključna pretpostavka bila je nezavisnost o tretmanu (eng. ignorability of a treatment) dana uvjetima: ATE.1: Uvjetno na \(\textbf{x}\), \(w\) i (\( y_0, y_1\) ) su nezavisni. ATE.1’: (a) \(E(y_0 | \textbf{x}, w) = E(y_0 | \textbf{x}\)); (b) \(E(y_1 | \textbf{x}, w) = E(y_1 | \textbf{x})\). Propensity score ili vjerojatnost sklonosti \(p(\textbf{x})\) označava vjerojatnost da jedinka primi tretman ako nam je poznat vektor kovarijabli \(\textbf{x}\). Važna pretpostavka pri korištenju metoda baziranih na vjerojatnosti sklonosti bila je jaka nezavisnost o tretmanu (eng. strong ignorability of a treatment) (uz danu kovarijablu \(\textbf{x}\)) koja se sastoji od pretpostavke ATE.1 i uvjeta \(0 < p(\textbf{x}) < 1, \forall \textbf{x}\). Koristeći te metode objasnili smo ideju uparivanja na temelju vjerojatnosti sklonosti, koju su prvi put predložili Rosenbaum i Rubin 1983. godine. Metoda instrumentalnih varijabli (IV) koristi se za procjenu \(AT E\) i \(AT E_1\) kada nismo sigurni vrijedi li nezavisnost o tretmanu, ali i za procjenu lokalnog prosječnog efekta tretmana (LATE). Promatrali smo najjednostavniji slučaj - kada je instrumentalna varijabla z binarna - te smo LATE definirali na subpopulaciji sastavljenoj od jedinki kod kojih varijabla z određuje hoće li te jedinke sudjelovati u tretmanu. U drugom dijelu rada pokazali smo korištenje uparivanja na temelju vjerojatnosti sklonosti u praksi. Procjenjivali smo utjecaj IWT subvencija na razvoj R&D-a u kompanijama u Flandriji i kako se taj utjecaj mijenja ovisno o određenim parametrima. In this paper focus was on estimating average treatment effects. We used different assumptions and different methods- regression methods, methods based on the propensity score and instrumental variables methods. Variable \(y_1\) denoted the outcome with treatment and \(y_0\) without the treatment, while \(\textbf{x}\) denoted a vector of observed covariates and w denoted receiving the treatment. To estimate the difference \(y_1-y_0\), we observed average treatment effect (ATE) defined as \(AT E \equiv E( y_1-y_0 )\) and average treatment effect on the treated (\(AT E_1\) ) defined as \(AT E_1 \equiv E( y_1-y_0 | w = 1)\). Key assumption was assumption called ignorability of treatment (given observed covariates \(\textbf{x}\)): ATE.1: Conditional on \(\textbf{x}\), \(w\) and ( \( y_0, y_1\)) are independent. ATE.1’: (a) \(E(y_0 | \textbf{x}, w) = E(y_0 | \textbf{x}\)); (b) \(E(y_1 | \textbf{x}, w) = E(y_1 | \textbf{x})\). Propensity score \(p(\textbf{x})\) is the probability of receiving the treatment given the covariates. For using methods based on the propensity score we needed strong ignorability of a treatment assumption. We described matching algorithm based on the propensity score. For estimating the local average treatment effect (LATE), we used the instrumental variables method in the simplest scenario- when an instrumental variable \(z\) is binary. Finally, we showed how to use the propensity score matching in practice. We estimated the impact of IWT subsidies on firms’ R&D intensity and employment in the Flemish region.