SPORTS MARKETING IN CROATIAN FOOTBALL CLUB RIJEKA

Popularizacijom natjecateljskog sporta postignuto je da su neki sportovi postali visoko rangirani na ljestvici interesa u društvu. Među njima posebno mjesto zauzima nogomet. Razlog takve popularnosti je financijske prirode. Poznato je da se ulažu ogromna financijska sredstva u klubove i njihove igrače koji na tržištu postižu visoku cijenu. Nesumnjivo je da marketing ima veliku ulogu u tom području. Iako se u suštini ne razlikuje od marketinga proizvodnih poduzeća, sportski marketing ipak ima neke specifičnosti koje se ogledaju kroz pojedine elemente marketing miksa. Tako promatrajući, sportski proizvod ima veći raspon mogućnosti zadovoljenja potreba samih korisnika. U radu je predstavljen HNK Rijeka, čiji zavidni rezultati u sportskom smislu, bez dodatnih ulaganja u marketing, nisu dovoljan preduvjet i za ostvarivanje pozitivnih poslovnih rezultata. Poslovni rezultati se očekuju efikasnim ulaganjem financijskih sredstava i kvalitetno odabranom marketinškom strategijom. Premda postoje brojni oblici financiranja kluba, jedan od ciljeva HNK-a Rijeka jest i da se samofinanciranje kluba podigne na višu razinu. Iz tog su razloga marketinške aktivnosti kluba usmjerene i na oblik promocije koji rezultira povećanjem financijskih resursa kroz svoje obožavatelje, navijače i publiku u širem smislu.Certain sports have become highly ranked on the scale of interest in today`s society due to popularization of competitive sports. Football occupies a very special place among modern popular competitive sports. One of the many reasons of such popularity is certainly it`s financial nature. It is widely known that huge funds are invested in football clubs and their respective players that can reach notably high prices on the player markets. Marketing plays an important role in the football world, without a doubt. Although Sports marketing is essentialy no different than marketing of any other business on the market, there are some differences that can be observed in individual elements of the marketing mix. With before mentioned in mind, sport products have a very large range of possibilities to meet the needs of consumers themselves. This paper examines CFC Rijeka whose impressive results in terms of sport achievements, and without additional investments in marketing, are not sufficient enough for obtaining positive business results. Satisfying business results can only be expected by investing financial resources and by implementing a specificaly selected marketing strategy. There are numerous forms of financing the FC Rijeka right now, but the longterm goal is to be a self-financing football club on a higher level than now. Because of this objective, marketing activities of the club are focused on a form of promotion that result in increased financial resources induced through fandom, fans and the public in general

Poduzetnički mitovi (ili stvarnosti) i poduzetničke namjere

The development of modern entrepreneurship research began some 50 years ago. In the meantime, various definitions of entrepreneurship led to different interpretations of its scope, and subsequently, the entrepreneurial myths emerged. This topic was studied quite intensively in the 1980s and 1990s, and numerous results succeeded in demystifying most of the entrepreneurial myths. Despite that, the entrepreneurial myths persist and regularly appear on the web portals, media, and bloggers’ articles. On the other side, contemporary entrepreneurship textbooks include entrepreneurial myths as a lesson which could eliminate some obstacles for the nascent entrepreneurs. This study revealed that the entrepreneurial myths are somewhat present among younger examinees, nonentrepreneurs in Croatia, who have a low or medium level of education. Still, not even these demographic groups view those myths as realities. Further analysis pointed out that there is a statistically significant, although negative and weak correlation between the perception of entrepreneurial myths as myths (not as realities) and a higher level of entrepreneurial intentions.Razvoj suvremenih istraživanja u području poduzetništva započeo je prije pedesetak godina. U međuvremenu, ponuđene su različite definicije poduzetništva koje su dovele do raznolikih interpretacija obuhvata poduzetništva te naposlijetku do nastanka poduzetničkih mitova. Ova tema je intenzivno istraživana 1980-ih i 1990-ih te su brojni rezultati uspješno demistificirali većinu poduzetničkih mitova. Usprkos tome, poduzetnički mitovi opstaju i redovito se pojavljuju na web portalima, u medijima i člancima blogera. S druge strane suvremeni udžbenici poduzetništva uključuju poduzetničke mitove kao lekciju koja može eliminirati neke prepreke za nove poduzetnike. Ovim istraživanjem utvrđeno je da su poduzetnički mitovi ponešto prisutni među mlađim, niže obrazovanim, ispitanicima ne-poduzetnicima u Hrvatskoj. Ipak, niti ove skupine ispitanika u prosjeku ne drže poduzetničke mitove, realnošću. Daljnja analiza je istaknula da postoji statistički značajna, ali slaba i negativna povezanost između percepcije poduzetničkih mitova kao mitova (ne kao realnosti) i više razine poduzentičkih namjera

MAPPING THE CUSTOMER EXPERIENCE: UNVEILING THE CUSTOMER JOURNEY IN THE NAIL INDUSTRY

After a period of a low-touch customer service approach due to the recent pandemic, delivering value to customers again becomes a focus for entrepreneurs in originally designed business models in the service industry. Pandemic experiences have prompted customers to accept the usual service process changes readily. A valuable tool for analyzing customer behavior through the most critical touchpoints of the value delivery process is customer journey mapping. In this research, the CJM tool was applied using the example of a micro-entrepreneur in the nail industry. Customer experiences (N = 476) indicate that users attach the most significant importance to appointment availability, loyalty/reward programs, staff expertise, and price. By creating a customer journey map for customers of the analyzed business entity and other surveyed service users of different providers, differences and potential improvement touchpoints in the entrepreneur\u27s service process have been identified

Considerations of target surface area and the risk of radiosurgical toxicity.

OBJECTIVE:The goal of this study was to explore conceptual benefits of characterizing delineated target volumes based on surface area and to utilize the concept for assessing risk of therapeutic toxicity in radiosurgery. METHODS AND MATERIALS:Four computer-generated targets, a sphere, a cylinder, an ellipsoid and a box, were designed for two distinct scenarios. In the first scenario, all targets had identical volumes, and in the second one, all targets had identical surface areas. High quality stereotactic radiosurgery plans with at least 95% target coverage and selectivity were created for each target in both scenarios. Normal brain volumes V12Gy, V14Gy and V16Gy corresponding to received dose of 12 Gy, 14 Gy and 16 Gy, respectively, were computed and analyzed. Additionally, V12Gy and V14Gy volumes and values for seven prospective toxicity variables were recorded for 100 meningioma patients after Gamma Knife radiosurgery. Multivariable stepwise linear regression and best subset linear regression analyses were performed in two statistical software packages, SAS/STAT and R, respectively. RESULTS:In a phantom study, for the constant volume targets, the volumes of 12 Gy, 14 Gy and 16 Gy isodose clouds were the lowest for the spherical target as an expected corollary of the isoperimetric inequality. For the constant surface area targets, a conventional wisdom is confirmed, as the target volume increases the corresponding volumes V12Gy, V14Gy and V16Gy also increase. In the 100-meningioma patient cohort, the best univariate model featured tumor surface area as the most significantly associated variable with both V12Gy and V14Gy volumes, corresponding to the adjusted R2 values of 0.82 and 0.77, respectively. Two statistical methods converged to matching multivariable models. CONCLUSIONS:In a univariate model, target surface area is a better predictor of spilled dose to normal tissue than target largest dimension or target volume itself. In complex multivariate models, target surface area is an independent variable for modeling radiosurgical normal tissue toxicity risk

A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery.

Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases

Segmentation results.

<p>(a) the large tumor (2.532cc), auto-contours from intensity-based method (blue) and En-DeepMedic method (green) overlaid on physician drawn contour (red); (b) zoomed-in view of ROI in orange rectangle in (a); (c) the small tumor (0.537cc), auto-contours from En-DeepMedic method (green) overlaid on physician drawn contour (red); (d) zoomed-in view of ROI in orange rectangle in (c).</p

Quantitative geometrical accuracy of each lesion of sample cases.

<p>Quantitative geometrical accuracy of each lesion of sample cases.</p

ROC curves of using En-DeepMedic for patient data segmentation.

<p>ROC curves of using En-DeepMedic for patient data segmentation.</p

Workflow of the proposed deep learning-based brain metastases auto-segmentation.

<p>Workflow of the proposed deep learning-based brain metastases auto-segmentation.</p