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.

In this paper, the adoption of Augmented Reality, as one of the emerging and intriguing digital technologies, has been investigated. This research uses the extended Unified Theory on Acceptance and Use of Technology framework to analyze these factors. The student population respondents' data about Augmented Reality adoption was collected. The student population has been chosen due to the highest probability of accepting new technologies. The research results show a positive and significant performance expectancy and enjoyment, while effort expectancy showed a negative and significant impact on the behavioural intention dependent variable. These research results can be used for the potential development of Augmented Reality apps in the retail industry and the academic implications of the connections between variables in the UTAUT framework.

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.

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.

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.