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Az indiai történelemkép változásának tendenciái
Get PDFIndia jelentősége számottevő mértékben növekszik napjainkban. A hatalmas ország népessége az elmúlt években már megelőzte Kína lakosságát, s ezzel a világ legnépesebb országává vált. Emellett gazdasági teljesítménye is figyelemre méltó. Ma a világ negyedik legnagyobb nemzeti össztermékével rendelkező nemzetgazdasága. Csak az USA, Kína és Németország múlja felül. Jelzésértékű, hogy a közelmúltban megelőzte Japánt. Ráadásul az elmúlt harminc évben az éves GDP növekedése sok esetben 5 és 8% között volt, ami egyfajta tartós gazdasági dinamizmus kibontakozására utal. A független Indiára sokáig jellemző alacsony növekedési ráta – hindu rate of growth – nemzeti büszkeségre nézve megalázó korszakának az 1990-es évektől vége szakadt. India ma nemcsak gyors ütemben gyarapszik, hanem a gazdasági elemzők szerint az elkövetkező évtizedekben tartani is tudja növekedését néhány alapvető tényezőnek – mint a „fiatalos” korpiramisú népesség munkaerőpiacra gyakorolt pozitív hatásának – köszönhetően
The optimal timing of clean technology adoption : a stochastic cost–benefit analysis
Get PDFThis paper develops a quantitative framework to determine the optimal timing for transitioning to clean technologies, which is crucial for sustainable development and climate action. We propose a stochastic model using optimal stopping theory, analyzing the dynamic cost advantages of clean versus conventional technologies. The model derives explicit timing solutions adaptable to market trends and user-specific factors. To illustrate the model’s practical application, we apply it to an empirical case study focused on the adoption of electric vehicles (EVs). Our results indicate that users with higher usage intensity or greater anticipated improvements in cost advantages related to future running costs tend to adopt EVs earlier. In contrast, factors such as increased volatility in cost advantages - often affected by fluctuating energy prices - or unpredictable negative jumps in initial EV costs can delay adoption decisions. This finding highlights the role of stable energy markets, potentially supported by policies like renewable energy investments, grid stabilization, and price guarantees, in promoting EV adoption. Additionally, our results underscore the importance of technological advancements in accelerating cost reductions. Policies that establish financial incentives to reduce initial EV costs can significantly lower adoption barriers, encouraging broader and earlier EV uptake, particularly among high-mileage users
Adaptive law-based feature representation for time series classification
Get PDFTime series classification (TSC) underpins applications across finance, healthcare, and environmental monitoring, yet real-world series often contain noise, local misalignment, and multiscale patterns. We introduce adaptive law-based transformation (ALT), a multiscale generalization of the earlier linear law-based transformation (LLT). ALT scans each series with variable-length, shifted windows, constructs symmetric delay embeddings, and extracts eigenvectors associated with the eigenvalue of minimal magnitude (“shapelet laws”) that capture locally stable patterns. These laws are assembled into class-specific dictionaries, and new series are projected onto them to yield compact, transparent features that enhance linear separability while remaining compatible with standard classifiers. On the BasicMotions dataset with synthetic Gaussian noise, ALT sustained test accuracy roughly 15–20 percentage points (pp) above raw inputs and 5–10 pp above LLT at moderate noise levels. Across ten datasets from the UCR Time Series Classification Archive—spanning motion, biomedical, spectroscopy, and industrial domains - ALT improved median test accuracy by + 7.6 pp with k-nearest neighbors (KNN) and + 4.8 pp with support vector machines (SVMs), with particularly large gains on long, noisy industrial series (FordA/B: + 23.1–25.3 pp). In addition, ALT often reduced SVM training time (median reductions of 340.6 s on FordB and 717.5 s on FordA) while maintaining or improving accuracy. ALT thus offers a lightweight and transparent alternative to heavyweight TSC pipelines: it requires only a small hyperparameter set, produces stable and discriminative features, and delivers competitive or superior accuracy under challenging conditions
Social embeddedness and consumer preferences for farmers’ markets : evidence from three European countries
Get PDFThe study aims to explore the role of social embeddedness in consumer shopping behaviour across different retail environments, with a particular focus on farmers’ markets. Drawing on a sample of 1,800 European consumers from Hungary, Italy, and the United Kingdom, the study examines apple purchase preferences regarding different product attributes (e.g., price, origin, quality certification) using a discrete choice experiment. A hybrid logit model is estimated to capture the impact of social embeddedness on purchase decisions. The results show that, across the three countries, farmers’ markets are the preferred outlet, more so than supermarkets or greengrocers, especially by those consumers who are most embedded in community relations. For Italian consumers, community and cultural aspects are key drivers of purchasing behaviour, while health and quality attributes are the most significant for their British counterparts. Hungarian respondents’ decisions are mostly influenced by price factors, although community-driven considerations also matter. The research confirms that farmers’ markets are not just places to buy food, but also community spaces where trust, personal connections, and local identity play a significant role. The findings have important theoretical, managerial, and policy implications, particularly for promoting more sustainable, community-based food systems, including short food supply chains
Artificial Intelligence in global marketing campaigns – Between human creativity and algorithmic precision
Get PDFThis article analyzes the impact of artificial intelligence (AI) on global marketing campaigns through a qualitative comparative analysis of nine case studies from various industries and markets. Using a four-level typology of human–AI collaboration, the study demonstrates that AI enhances personalization, automation, and operational efficiency but cannot replace human creativity, intuition, and cultural sensitivity. Combining a structured literature review with case-based evidence, the paper reveals the growing importance of hybrid models in which algorithmic technologies support creative processes. The findings show that the most successful campaigns emerge from the synergy between AI’s analytical capabilities and human emotional competence. The article contributes to international marketing theory by integrating technological, creative, and cultural perspectives and offers practical recommendations for managers on the ethical and sustainable use of AI in complex, multicultural market contexts
A zaj, mint lehetséges energiaforrás
Get PDFAz irodalmi áttekintés alapján a tanulmány fókuszában azon fordulat áll, amely szerint a zajhatás akár környezeti erőforrásként is felhasználható. A zajenergiára már tekinthetünk úgy is mint tiszta, zöld, megújuló villamosenergia-forrásra, amely leginkább a mikroelektronika és az anyagtudomány fejlődésével vált elérhetővé. A jelen kutatás újdonsága, hogy a hagyományos közgazdasági értelemben vett „származékos keresletként” tekint a zajhasznosításra. És, habár a zajenergia-termelési potenciált eredendően korlátozza a zajcsökkentés sikeressége, a hangkörnyezet fenntarthatósági céljának mégis annak kell lennie, hogy egyensúlyba hozza az elfogadható zajszintet és a kívánt zajenergia-termelést
Cross-cultural challenges in generative AI : Addressing homophobia in diverse sociocultural contexts
Get PDFPrevious discussions have highlighted the need for generative AI tools to become more culturally sensitive, yet often neglect the complexities of handling content about marginalized groups, who are perceived differently across cultures and religions. Our study examined the responses of two generative AI systems to homophobic statements and explored how their outputs varied when different societal and religious context information about the user was provided. Findings showed that ChatGPT 3.5's replies frequently reflected cultural relativism, as evidenced by an emphasis in the outputs on the idea that different cultures hold distinct perspectives and that these diverse viewpoints should be respected. In contrast, Bard's responses often stressed human rights and provided more support for gay people and lesbian, gay, bisexual, trans, and queer (LGBTQ)+ issues. Both systems demonstrated significant variation in their responses depending on the contextual information provided in the prompts, suggesting that AI systems may adjust the degree and form of support they express for LGBTQ+ people and issues according to the information they receive about a user's background. While our analysis focused specifically on chatbot responses to homophobic statements, the study underscores a broader dilemma concerning the tension between cultural relativism and universal human rights in generative AI—an issue that extends beyond homophobia to include animosity toward other marginalized groups that are perceived differently across societies and religions. The study contributes to understanding the social and ethical implications of AI responses and argues that any work to make generative AI outputs more culturally diverse requires grounding in fundamental human rights
Engagement of true intelligence in financial forecasting: interactions of blockchained sectors and artificial intelligence
Get PDFIn this study, we examine the connectedness between the NASDAQ artificial intelligence index and sectoral cryptocurrency indices. Empirical analyses were conducted via the quantile‒quantile methodology and cross-multiquantilogram tests across 15 cryptocurrency sectors from June 1, 2021, to May 28, 2024. The results show that dynamic total spillovers primarily occur in extremely low and high quantiles, corresponding to the left and right tails of the return distributions. Net directional spillovers indicate the dominance of the AI sector over the cryptocurrency market, which intensifies during significant crashes or booms. The most substantial effect of AI is observed in the DeFi, NFT, and Smart Contracts sectors, highlighting the prominence of financial operation-based blockchain applications in their interaction with artificial intelligence. The cross-multiquantilogram results also suggest that developments in artificial intelligence dominate the cryptocurrency market and have high predictability in its price movements. On the basis of our findings, we recommend using the AI market as an early indicator for the cryptocurrency market and advise against combining these two asset groups in the same portfolio to maintain diversification benefits
Understanding the temporal dynamics of agri-environmental climate scheme adoption
Get PDFThe research explores the intricate dynamics of farmers’ decision-making in the context of the European Union’s agri-environmental-climate schemes (AECS), with a focus on the temporal factors that impact participation and the long-term viability of environmentally friendly practices. The study utilises data from the Hungarian Farm Accountancy Data Network (FADN) from 2014 to 2021. It employs two-step approaches, including duration analysis using the Kaplan-Meier survival function and discrete-time duration logit, probit and complementary log-log models to examine the length of adoption and the determinants of the temporal dynamics of farmers’ decision-making in AECS. The results indicate the trade-off-based preference for market income over the AECS subsidy of Hungarian FADN farms, indicating a relatively weak relationship between adoption and maintenance. The research emphasises the importance of taking into account the farm business model (pertaining to farm characteristics) versus policy measures when analysing farmers’ choices regarding the (non)renewal of AECS contracts. The study provides valuable insights into farmers’ decision-making patterns in relation to changes in their participation in AECS over time
The impact of acculturative stress and sociocultural adaptation on international student satisfaction and loyalty
Get PDFThis paper examines the interrelationship between experienced acculturative stress, sociocultural adaptation, satisfaction, and loyalty within the context of higher education and international students. It was hypothesized that acculturative stress negatively impacts sociocultural adaptation, satisfaction, and loyalty, whereas sociocultural adaptation positively influences satisfaction, and that satisfaction positively impacts loyalty. A sample of 426 international students from 56 countries was analyzed via PLS-SEM. This research is unique because it quantitatively demonstrates that acculturative stress has a significant negative impact on sociocultural adaptation, essentially hindering culturally appropriate behavior, and negatively impacts international student satisfaction, whereas sociocultural adaptation positively affects international student satisfaction. The construct of acculturative stress is slightly better explained by core culture shock items compared to interpersonal stress items. The primary factors contributing to core culture
shock were students’ confusion about their identity in a receiving culture, encountering shocking and disgusting elements in a new environment, and feeling helpless when trying to cope with a new culture. The primary factor influencing the interpersonal stress among international students was their anxiety or discomfort when interacting with local individuals. The acquisition of culturally appropriate behavior in an academic setting contributed the most to the construct of sociocultural adaptation. Based on our insights, higher education institutions could optimize resource allocation by preparing students to handle acculturative stress and facilitating sociocultural adaptation. This is expected to enhance the satisfaction of international students and reduce recruitment costs through the positive word of mouth of loyal students