Mobile banking is nowadays a standard service provided by banks worldwide because it adds convenience for people. There is no more rushing to a bank or waiting in lines for a simple transaction that can be conducted from anywhere and at any time in the blink of an eye. To be consumed by a respective amount of bank clients regularly, mobile banking applications are required to be continuously improved and updated, be in line with recent security standards, and meet quality requirements. This paper tackles the perceived quality of mobile banking applications that are most commonly used in Croatia and has three objectives in that respect. The first one is to identify the extent to which pragmatic and hedonic dimensions of quality contribute to customers’ satisfaction and their behavioral intentions related to the continuous use of mobile banking applications. The second one is to determine if there are significant differences in the perceived quality between users of diverse mobile banking applications as well as between users who belong to different age groups. The last one is to uncover the advantages and disadvantages of evaluated mobile banking applications. For this purpose, an empirical study was carried out, during which data were collected with an online questionnaire. The sample was composed of 130 participants who are representative and regular users of mobile banking applications. The psychometric features of the proposed research model, which represents an interplay of perceived quality attributes, were tested using the partial least squares structural equation modeling (PLS-SEM) method. Differences in the perceived quality among different mobile banking applications and customers of various age groups were explored with Kruskal–Wallis tests. Pros and cons of mobile banking applications were identified with the help of descriptive statistics. Study findings indicate that, in the context of mobile banking applications used in Croatia, feedback quality and responsiveness contribute to the ease of use, usefulness is affected by both ease of use and efficiency, responsiveness has a significant impact on efficiency while ease of use, usefulness, and security of personal data are predictors of customers’ satisfaction which in turn influences their behavioral intentions. While no significant difference exists in the perceived quality of four examined mobile banking applications, we found a significant difference in the perceived quality among three age groups of users of mobile banking applications. The most commonly reported advantages of mobile banking applications were related to facets of their efficiency and usefulness, whereas their main drawback appeared to be the lack of features dealing with the personalization of offered services. The reported and discussed results of an empirical study can be used as a set of guidelines for future advances in the evaluation and design of mobile banking applications.

Video games have become a ubiquitous form of entertainment and have been enjoyed by people of all ages around the world. The gaming industry has evolved rapidly, with new games being released every year that push the boundaries of technology and creativity. To ensure that video games are not just technically advanced, but also enjoyable and engaging, measuring the gaming experience is essential because it helps game designers understand how players interact with the game and identify areas for its improvement. The objective of this paper is to examine an interplay of gaming experience dimensions in the context of platform video games and to determine the extent to which they contribute to players’ behavioral intentions. To fulfil this objective, an empirical study was undertaken, involving participants with diverse gaming backgrounds. They were requested to engage in the gameplay of the Stranded Away platformer game and subsequently respond to a post-use questionnaire. The psychometric features of the introduced conceptual model were evaluated with the partial least squares structural equation modeling (PLS-SEM) method. The reported findings demonstrate the importance of evaluating different facets of the gaming experience in video games and showcase the potential of the proposed model and measuring instrument as tools for game designers to enhance the overall quality of their products.

Electronic health records (EHRs) are typically stored in relational databases, making them difficult to query for nontechnical users, especially under privacy constraints. We evaluate two practical clinical NLP workflows, natural language to SQL (NL2SQL) for EHR querying and retrieval-augmented generation for clinical question answering (RAG-QA), with a focus on privacy-preserving deployment. We benchmark nine large language models, spanning open-weight options (DeepSeek V3/V3.1, Llama-3.3-70B, Qwen2.5-32B, Mixtral-8 × 22B, BioMistral-7B, and GPT-OSS-20B) and proprietary APIs (GPT-4o and GPT-5). The models were chosen to represent a diverse cross-section spanning sparse MoE, dense general-purpose, domain-adapted, and proprietary LLMs. On MIMICSQL (27,000 generations; nine models × three runs), the best NL2SQL execution accuracy (EX) is 66.1% (GPT-4o), followed by 64.6% (GPT-5). Among open-weight models, DeepSeek V3.1 reaches 59.8% EX, while DeepSeek V3 reaches 58.8%, with Llama-3.3-70B at 54.5% and BioMistral-7B achieving only 11.8%, underscoring a persistent gap relative to general-domain benchmarks. We introduce SQL-EC, a deterministic SQL error-classification framework with adjudication, revealing string mismatches as the dominant failure (86.3%), followed by query-join misinterpretations (49.7%), while incorrect aggregation-function usage accounts for only 6.7%. This highlights lexical/ontology grounding as the key bottleneck for NL2SQL in the biomedical domain. For RAG-QA, evaluated on 100 synthetic patient records across 20 questions (54,000 reference–generation pairs; three runs), BLEU and ROUGE-L fluctuate more strongly across models, whereas BERTScore remains high on most, with DeepSeek V3.1 and GPT-4o among the top performers; pairwise t-tests confirm that significant differences were observed among the LLMs. Cost–performance analysis based on measured token usage shows per-query costs ranging from USD 0.000285 (GPT-OSS-20B) to USD 0.005918 (GPT-4o); DeepSeek V3.1 offers the best open-weight cost–accuracy trade-off, and GPT-5 provides a balanced API alternative. Overall, the privacy-conscious RAG-QA attains strong semantic fidelity, whereas the clinical NL2SQL remains brittle under lexical variation. SQL-EC pinpoints actionable failure modes, motivating ontology-aware normalization and schema-linked prompting for robust clinical querying.