Abstracts Track 2025

Area 1 - Economics
Area 2 - Emerging Areas in FEMIB
Area 3 - IT Business

Area 1 - Economics

Nr: 67
Title:

Anthropogenic Effects of Climate Change: Further Evidence from a Fractionally Integrated Ice-Age Model
Authors:

Szabolcs Blazsek, Alvaro Escribano and Adrian Licht
Abstract: We introduce the fractionally integrated quasi-vector autoregressive (FI-QVAR) model. We apply FI-QVAR to climate data and introduce the fractionally integrated score-driven ice-age model. We use global sea ice volume, atmospheric carbon dioxide (CO2) concentration, and Antarctic land surface temperature data from 798,000 to 1,000 years ago. We control for the eccentricity of the Earth's orbit, the obliquity of Earth, and the precession of the equinoxes (i.e. Milankovitch cycles). We estimate FI-QVAR using the maximum likelihood (ML) method for fractional integration parameters in (-1/2,1/2). The statistical and forecasting performances of FI-QVAR are superior to QVAR and VAR. The impulse response functions (IRF) for FI-QVAR capture better dynamic effects of the shocks than QVAR and VAR. We confirm, with more confidence than previous works for these data, that for the last 12,000-15,000 years when humanity influenced the Earth's climate (i.e. Anthropocene), the global sea ice volume forecasts are above the observed sea ice volume, the atmospheric CO2 concentration forecasts are below the observed atmospheric CO2 concentration, and the Antarctic land surface temperature forecasts are below the observed Antarctic land surface temperature, after controlling for natural forces of climate change due to orbital variables.
Nr: 71
Title:

Exogenous, Observable, and Endogenous Switching Models of Industrial Production in the United Kingdom
Authors:

Astrid Loretta Ayala
Abstract: We forecast the real industrial production (IP) growth in the United Kingdom (UK) and real stock market returns in the UK and United States (US). We use the exogenous-switching vector autoregressive (VAR) model (EXS-VAR), the novel observable-switching VAR model (OS-VAR), and the novel endogenous-switching VAR model (ENS-VAR). In EXS-VAR, the transition probabilities are constant, i.e. it is a Markov-switching (MS) VAR model. OS-VAR is a multivariate regime-switching score-driven model where a score-driven filter drives the predictive probabilities. In ENS-VAR, the transition probabilities are dynamic and driven by observable regime predictor variables. The in-sample analysis is from April 1963 to December 2023. The out-of-sample forecasting is from January 2014 to December 2023. We compare the statistical and forecasting performances of the regime-switching VAR models and the classical VAR model. The results show that the ENS-VAR is superior to the competing VAR specifications and provides the most accurate predictions of real UK IP growth.

Area 2 - Emerging Areas in FEMIB

Nr: 44
Title:

Advertising in the Era of Artificial Intelligence: The Impact of AI-Generated Ads on Consumer Purchase Behavior
Authors:

Galina Kondrateva, Tatiana Khvatova and Zeling Zhong
Abstract: This paper explores the influence of automated advertising through the lens of artificial intelligence (AI) technology, particularly generative AI (Gen-AI). Gen-AI offers marketers novel opportunities for personalization, refined targeting, and optimized interactions. This study, which investigates AI-generated advertising from a customer perspective, tests factors that can predict trust and purchase intention with a quantitative approach based on the results of the survey with 255 participants in France. The research addresses gaps in understanding consumer behavior changes due to the Gen-AI capabilities to automate advertising production. Findings demonstrate the positive influence of brand message authenticity and reputation on trust, underscoring the importance of clear communication. Moreover, the study pioneers the exploration of AI creativity's perception and its implications in advertising.

Area 3 - IT Business

Nr: 79
Title:

Provocative Imagery's Impact on Charitable Giving: A Comparative Analysis of Donation Platforms in the US and Korea
Authors:

Sang-Yong Tom Lee
Abstract: With the growing prominence of online crowdfunding, charitable donation platforms increasingly utilize provocative imagery—often termed "poverty porn"—to evoke emotional responses and drive contributions. This study examines the impact of emotionally charged images on donation-based crowdfunding success, comparing donor behaviors in the United States and South Korea. Leveraging deep learning methodologies, the research employs an image emotion detection model to analyze key emotional triggers, including depression, fear, shame, powerlessness, and humiliation. The study finds that while "powerlessness" positively influences donations on U.S. platforms like GoFundMe, "humiliation" exerts a negative effect, suggesting a nuanced relationship between emotional appeal and donor engagement. Conversely, Korean donors on HappyBean exhibit a strong aversion to all poverty-related emotional imagery, signaling cultural differences in responses to visual stimuli. These findings highlight the dual-edged nature of provocative imagery in charitable appeals, with implications for cross-cultural marketing strategies and the ethical considerations of poverty-based fundraising. The study contributes to the literature by integrating impulsive nudges within behavioral economics with AI-driven emotion analysis, offering insights for optimizing crowdfunding campaigns. Future research will explore additional psychological and cultural factors influencing donation behaviors, further refining the role of image-based nudges in global fundraising efforts.