Ivan Jajić

This study focuses on the digital development of European Union Member States. The analysis was conducted using 2022 Digital Economy and Society Index (DESI) data and covered three key areas: the basic digitalisation of business, the adoption of advanced online activities, and the availability of 5G network infrastructure. The results show that most Member States have achieved a high level of basic digitalisation. At the same time, the use of advanced solutions, such as cloud computing and e-commerce, remains relatively limited. Particularly noteworthy is that the indicators are less strongly associated than might be expected. For example, countries with well-developed 5G infrastructure do not necessarily make greater use of advanced technologies. Cluster analysis divided the countries into three groups distinguished by different combinations of infrastructure development and the adoption of digital tools. The findings suggest that digital transformation is not linear, but a complex mix of technological, organisational, and cultural factors. The study highlights the importance of coordinated policies that support not only investment in infrastructure but also the development of digital skills and innovative business models.

This paper proposes a data-driven framework for segmenting European fixed broadband markets using purchasing power parity (PPP)-adjusted prices. The analysis focuses on the least expensive fixed broadband offers in 27 European countries for 2019 and 2023, based on Eurostat data. The aim is to identify stable and changing affordability segments, rather than to analyse any single country in detail. Methodologically, the paper combines descriptive statistics, k-means clustering with internal validation indices, an analysis of cluster transitions and repeatedmeasures inference, complemented by a country fixed-effects model. The results indicate a substantial decline in PPP-adjusted prices and a persistent, yet partially mobile, segmentation of the market into cheaper and more expensive groups of countries. In both clusters, the 2023 centroids are lower than in 2019, while the internal validation indices suggest clearer separation between clusters in the later year. A transition matrix shows that around two-thirds of countries remain in the same cluster, whereas the remaining third move between segments. The paper demonstrates how relatively simple clustering and panel techniques can be effectively integrated into a business intelligence framework for monitoring price-based market segments over time.

Background: Online purchasing of consumer electronics has expanded rapidly, and differences between Generations X and Y, as well as social contagion phenomena such as bandwagoning and herding, may shape how consumers form online purchase intentions. Purpose: To assess how microeconomic bandwagon/herding effects interact with social influence and effort expectancy, moderated by generational groups (Gen X vs. GEN Y), to shape online purchase intention for consumer electronics in an extended UTAUT framework. Study design/methodology/approach: The authors modified the Unified Theory of Acceptance and Use of Technology (UTAUT) and have implemented Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) to validate the research model as well as to research the generational groups' influence. Furthermore, the authors imposed the microeconomic theoretical bandwagon effect and herding behavior to clarify if there are statistically significant results in the e-commerce landscape for consumer electronics products. Findings/conclusions: The paper's major findings showed a direct and indirect (via effort expectancy) social influence on Generation X's consumer intention to purchase consumer electronics products online. However, such influence was not identified for Generation Y, suggesting that e-commerce forms a stronger bandwagon effect on the generation that was not born with the e-commerce technology, unlike Generation Y. Limitations/future research: The limitations of the paper are seen in the unequal generational groups' data size. The potential for further research is evident in capturing a longer time frame, conducting cross-cultural comparisons, and including new generational groups.

Autori rada analiziraju Eurostatove podatke o putničkom zračnom prometu u 29 europskih zemalja u godinama 2018. (prije COVID-a), 2020. (COVID-19) i 2023. (oporavak). Primjenom metode klasteriranja k-srednje vrijednosti na standardiziranim varijablama...

Rad analizira najniže cijene fiksnog širokopojasnog interneta u Europi koristeći Eurostatove podatke za dvije referentne godine (2019. i 2023.), standardizirane paritetom kupovne moći. Primijenjena je k-means klasterska analiza (odvojeno po godinama) uz standardne pokazatelje valjanosti klastera, a promjene kroz vrijeme procijenjene su testovima ponovljenih mjerenja i modelom s fiksnim učincima po državama. Rezultati pokazuju izražen pad cijena u prosjeku na razini uzorka te stabilnu segmentaciju tržišta u dva jasno razdvojena klastera (jeftiniji i skuplji). Nadalje, centri oba klastera 2023. niži su nego 2019. godine, što upućuje na jasan smjer prema nižim cijenama. Republika Hrvatska bilježi smanjenje cijene, ali ostaje u skupljem klasteru i na približno istom relativnom položaju u usporedbi s ostalim državama. Doprinos rada je dvostruk: (i) pruža konzistentno, metodološki transparentno klastersko mapiranje priuštivosti fiksnog interneta za usporedbu zemalja i praćenje promjena, te (ii) daje relevantan uvid prema poboljšanju apsolutne priuštivosti, dok se relativna pozicija država, uključujući Republiku Hrvatsku, sporo mijenja.

Background: Digital technology and broad Internet access have significantly changed consumer behaviour, primarily through the rise of online shopping and improved information-sharing channels. Objectives: The COVID-19 epidemic fuelled the adoption of digital technologies, leading to a significant increase in online shopping. The authors of this paper captured and showed this increasing trend. Methods/Approach: The authors use bibliometric and text-mining methods to demonstrate the importance of COVID-19 in the e-commerce development literature. Results: A search of the Scopus database identified 845 relevant papers from 2020 to 2023, which were then refined to 417 within the fields of Business Management, Accounting, and Social Sciences. Most of these publications were scholarly articles (71.9%), reflecting a wide range of interdisciplinary contributions across Computer Science, Economics, and Engineering. The text mining analysis identified common keywords, including COVID-19, online shopping, purchase intention, e-commerce, and consumption behaviour. These keywords were grouped into six main clusters: consumer behaviour, online shopping intentions, impulsive buying, consumer satisfaction, sustainability considerations, and smartphone shopping behaviours during the pandemic. By implementing such an approach, it became evident which research topics and groups are the primary focus, which authors are the most productive, and which countries collaborate most on this topic. Conclusions: The analysis reveals that the COVID-19 pandemic significantly shaped research directions in e-commerce, with a strong emphasis on consumer behaviour, trust, satisfaction, and impulsive purchasing. The identification of six distinct thematic clusters provides a structured overview of the most prominent topics explored during this period. Additionally, the study highlights which institutions and countries led the research efforts, showing robust collaboration networks in Asia and Europe, where international co-authorship has notably intensified.

Purpose: This paper examines consumer intention to use 5G technology in the personal and professional aspects. It focuses on how perceived value, ease of use, performance expectancy, perceived security, and employment status influence this intention in the Republic of Croatia.Methodology: Data was collected through an online survey targeting consumers with prior experience using 5G technology in September and October 2023 in the Republic of Croatia. The multivariate linear regression statistical method was implemented using JASP statistical software.Results: The authors found that ease of use, perceived security, and performance expectancy significantly and positively impact consumer intention to use 5G technology. Contrary to initial expectations, perceived value showed a non-significant relation towards the intention to use 5G technology, which could be dueto the research sample reasons. The employment status variable showed a negative and significant relationship with 5G adoption intentions. However, it indirectly moderated the ease-of-use variable, which significantly impacted the intention to use 5G technology. Consequently, employment status emerged as a significant predictor when included in the analysis.Conclusion: The key contribution of this paper lies in the analysis of the employment status variable and its explanatory power: while consumers may not view 5G technology as essential (direct relationship), they are more likely to intend to use it if it makes their everyday lives easier (indirect relationship). Additionally, the paper showed the extension of the TAM and UTAUT theoretical models, thus covering the researchgap. Furthermore, the paper recommends future research to explore the economic and cultural impacts of the intention to use 5G technology, including factors such as income, cultural variation, and infrastructure.

Due to its severity, the outbreak of COVID-19 led to unprecedented levels of social isolation that affected educational institutions, among others. Digital technologies such as cloud computing and video broadcasting helped the adoption of e-learning during the crisis. However, the speed and efficiency of e-learning adoption during the COVID-19 period varied across countries. This paper compares the adoption of e-learning in European countries before and during the COVID-19 pandemic and the relationship between the pandemic, e-learning, and economic development. First, the adoption of e-learning in European countries before and during the pandemic is compared. Second, using fuzzy C-means clustering, homogeneous groups of European countries are formed based on e-learning indicators for the periods before and during the pandemic. Third, GDP per capita is used as an indicator of economic development and severity indices are used as an indicator of the severity of the response to the pandemic to compare the different clusters. The research results show that economically and digitally advanced countries led the adoption of e-learning in both the period before and the period during the pandemic. However, they also responded less strictly to the pandemic. Less-advanced countries responded more strictly to the pandemic, likely due to a lack of healthcare resources, and also fell behind in the adoption of e-learning.

Organic food consumption has become a significant trend in consumer behaviour, determined by various motives, among which the price does not play a major role, thus reflecting the Lancaster approach to the microeconomic consumer theory. Additionally, artificial neural networks (ANNs) have proven to have significant potential in providing accurate and efficient models for predicting consumer behaviour. Considering these two trends, this study aims to deploy the Lancaster approach in the emerging area of artificial intelligence. The paper aims to develop the ANN-based predictive model to investigate the relationship between organic food consumption, demographic characteristics, and health awareness attitudes. Survey research has been conducted on a sample of Croatian inhabitants, and ANN models have been used to assess the importance of various determinants for organic food consumption. A Three-layer Multilayer Perceptron Neural Networks (MLPNN) structure has been constructed and validated to select the optimal number of neurons and transfer functions. One layer is used as the first input, while the other two are hidden layers (the first covers the radially symmetrical input, sigmoid function; the second covers the output, softmax function). Three versions of the testing, training, and holdout data structures were used to develop ANNs. The highest accuracy was achieved with a 7-2-1 partition. The best ANN model was determined as the model that was showing the smallest percent of incorrect predictions in the holdout stage, the second-lowest cross-entropy error, the correct classification rate, and the area under the ROC curve. The research results show that the availability of healthy food shops and consumer awareness of these shops strongly impacts organic food consumption. Using the ANN methodology, this analysis confirmed the validity of the Lancaster approach, stating that the characteristics or attributes of goods are defined by the consumer and not by the product itself.

Data mining has become the omnipresent requirement in the business environment of abundant data. At the beginning of data mining development, the one-solution approach was mostly used. In the last 20 years, mostly due to the development of various educational programs and the availability of open-source software, data mining has evolved into a mature-leveled software solution that does not necessarily require high-level expertise since numerous prototype solutions have emerged. Small and medium enterprises (SMEs) are still lagging behind large companies in data mining implementation, which raises the question of the determinants of data mining adoption. This paper aims to test the preliminary model of data mining adoption using the Technology-Organization-Environment framework. The survey on the sample of Italian SMEs was conducted to test the model, and the collected company data was analyzed using the PLS-SEM approach.