FEMIB 2025 Abstracts

Area 1 - Accounting and Finance
Full Papers Short Papers
Area 2 - Economics
Short Papers
Area 3 - Emerging Areas in FEMIB
Full Papers Short Papers
Area 4 - IT Business
Full Papers Short Papers
Area 5 - Management
Full Papers Short Papers

Area 1 - Accounting and Finance

Full Papers
Paper Nr: 31
Title:

SDG Disclosure and Financial Performance: Evidence from Europe
Authors:

Salah Kayed and Rasmi Meqbel
Abstract: This study investigates the impact of Sustainable Development Goals (SDGs) disclosure on the financial performance of non-financial companies listed in European countries from 2019 to 2021. As companies increasingly face pressure to address social and environmental challenges, the extent of their engagement with SDGs has become a focal point. The research utilizes an SDG disclosure index based on the 17 SDGs as the primary independent variable. Financial performance is assessed using two key metrics: Return on Assets (ROA, and Tobin’s Q), analyzed through panel data regression models. The results reveal a significant and positive relationship between SDG disclosure and financial performance, consistent with Stakeholder Theory. This suggests that SDG initiatives enhance corporate reputation, reduce regulatory risks, and strengthen stakeholder relations, thereby contributing to superior financial outcomes. The findings provide valuable insights into the strategic importance of SDGs for firms and highlight the benefits of aligning business practices with sustainable development objectives.
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Paper Nr: 33
Title:

A Clustering Approach for S&P 500 Index Based on Environmental, Social and Governance Ratings of Multiple Agencies
Authors:

Celma de Oliveira Ribeiro and Gabriela Curti Geraldo
Abstract: This article addresses the lack of standardization in the assessment of companies' environmental, social and governance (ESG) practices. To avoid implicit bias in selecting a specific rating, this study suggests using multiple assessment sources simultaneously to categorize companies as good or bad from an ESG perspective. Even with the differences in scope, measurement, and weighting between the agencies' methodologies, when applying the clustering algorithm to the ratings of companies within the S&P 500 index, it was possible to observe that the groups formed exhibited significantly different average scores for ESG practices. In this way, this article offers an alternative to mitigate the impact of rating plurality on the results of empirical studies and on the analysis process conducted by investors.
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Paper Nr: 46
Title:

Portfolio Optimization Based on Prospect Theory
Authors:

Celma de Oliveira Ribeiro and Alan Teixeira dos Santos
Abstract: This paper investigates the application of prospect theory in the context of portfolio optimization and presents a model based on the mean absolute deviation and on Prospect Theory. By analyzing historical returns from assets of three critical sectors traded on B3 (Brazilian Stock Exchange) and over an eight-year period, a prospect optimization approach was implemented and its results were compared to those obtained from the Conditional Value at Risk (CVaR) approach. An additional application was held regarding one of the most relevant sector of assets in terms of contribution to the S&P500’s composition with the purpose to test the new model under different market conditions. Such results revealed the effectiveness of prospect theory in optimizing portfolios since those results were considered similar to the CVaR’s, but at higher returns. Both models were compared through different portfolio performance metrics and, notably, the prospect model exhibited competitive results in most cases. However, the study also identified opportunities for further refinements. Overall conclusions herein suggests the promise of prospect theory in addressing the needs of decision makers in portfolio management, delivering a singular approach that balances the possibility of gains and losses under different scenarios.
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Paper Nr: 64
Title:

Synthetic Data Generation and Federated Learning as Innovative Solutions for Data Privacy in Finance
Authors:

Elif Özcan, Ruşen Akkuş Halepmollası and Yusuf Yaslan
Abstract: Financial services generate vast, complex and diverse datasets, yet data privacy issues pose significant challenges for secure usage and collaborative analysis. Synthetic data generation can offer an innovative solution while preserving privacy without exposing sensitive information. Also, federated learning enables collaborative model training across clients while maintaining data privacy. In this study, we used Default Credit Card dataset and employed diffusion based synthetic data generation to evaluate its impact on centralized and federated learning approaches. To this end, we offer comprehensive benchmarking of synthetic, real, and hybrid datasets by employing four machine learning classifiers both centrally and federated. Our findings demonstrate that synthetic data effectively improves results, especially when combined with real data. We also conduct client specific experiments in federated learning when addressing highly imbalanced or incomplete class distributions. Moreover, we evaluate FedF1 aggregation method, which aims to improve global model performance by optimizing F1-score. To the best of our knowledge, this is the first study to integrate synthetic data generation and federated learning on a financial dataset to provide valuable insights for secure and collaborative learning.
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Paper Nr: 65
Title:

Exploring the Role of Brownian Motion in Financial Modeling: A Stochastic Approach to the Black-Scholes Model for European Call Options
Authors:

Mehul Zawar
Abstract: Stochastic processes, particularly Brownian motion, have become foundational tools in financial modeling, enabling the development of more accurate and insightful representations of market behavior. This paper delves into the mathematical framework behind stochastic differential equations (SDEs) and their critical role in the Black-Scholes model, specifically focusing on its application to European call options. We explore the influence of key parameters, such as stock drift, volatility, and risk-free interest rate, on option pricing by incorporating Brownian motion (Wiener processes) into the model. Through this exploration, we provide a detailed analysis of how these stochastic components shape the dynamics of stock prices and the option's value over time. The stability of the Black-Scholes model is evaluated under various boundary conditions, revealing its robustness in financial modeling. However, limitations of the Black-Scholes approach, including assumptions regarding constant volatility and market efficiency, are discussed, and potential improvements are suggested. This paper underscores the significance of stochastic integration methods, including the Ito and Stratonovich calculus, in refining the modeling of financial systems, thereby offering a comprehensive understanding of the Black-Scholes framework's applicability and areas for enhancement.
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Paper Nr: 76
Title:

Market Reactions in China to the US-Houthi Conflict: An Event Study Approach
Authors:

Rizky Yudaruddin, Dadang Lesmana, Felisitas Defung and Ardi Paminto
Abstract: This study aims to examine market reactions in the Chinese market to the US-Houthi conflict, employing the event study methodology with cumulative abnormal returns (CAR) as a proxy for market reactions. The analysis focuses on a sample of 2,114 Chinese companies. The findings reveal that the Chinese market exhibited significant reactions during the post-event period, with nearly all sectors affected rather than a single sector. This suggests that the conflict disrupted the Suez Canal trade route, a critical pathway for China's trade with Europe, leading to increased investor pessimism. These results provide implications for policy makers and managers in overcoming supply chain disruptions due to the war.
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Paper Nr: 78
Title:

Advanced Supervised Machine Learning Algorithms in Credit Card Fraud Detection
Authors:

Simin Yu, Victor Chang, Gia Linh Huỳnh, Vitor Jesus and Jiabin Luo
Abstract: The rapid growth of online transactions has increased convenience but also risks like money laundering, threatening financial systems. Financial institutions use machine learning to detect suspicious activities, but imbalanced datasets challenge algorithm performance. This study uses resampling techniques (SMOTE, ADASYN, Random Undersampling, NearMiss) and ensemble algorithms (XGBoost, CatBoost, Random Forest) on a simulated money laundering dataset provided by IBM (2023) to address this. Our findings reveal that each resampling technique offers unique advantages and trade-offs. CatBoost consistently outperforms XGBoost and Random Forest across sampling techniques, achieving the best balance between precision and recall while maintaining strong ROC curve scores. This strong performance could reduce the number of transactions banks must examine, as investigations would only focus on the predicted laundering cases.
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Short Papers
Paper Nr: 32
Title:

Introducing the Cluster-Momentum Portfolio in Alternative Risk Premia Investing
Authors:

Berouz Fatemi, Alireza Kobravi, Duncan Larraz, Francesc Naya and Nils S. Tuchschmid
Abstract: Managing alternative risk premia (ARP) portfolios is a challenging task, due to the complexities of these types of investments. In this article, we present a purely quantitative approach that relies on performance persistence among ARP strategies while ensuring diversification by classifying the ARP indices using unsupervised hierarchical clustering. This cluster-momentum portfolio shows a superior performance when compared to a set of internally built benchmarks and also of existing ARP asset manager funds. It seems that persistence in performance can be capitalized in ARP, while the clustering technique achieves its objective of risk-reduction due to portfolio diversification. Moreover, the cluster-momentum portfolio appears to be resilient to parameter modifications.
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Paper Nr: 52
Title:

The Role of Sustainable Loan Products in Managing Sustainability Risks in German Regional Banks
Authors:

Dominic Strube
Abstract: This study examines the role of sustainable loan products, such as Green Loans and Sustainability-Linked Loans, in managing sustainability risks for regional banks in Germany. Based on a survey of 88 Less Significant Institutions and 18 Significant Institutions, the findings show that regional banks predominantly rely on subsidized loans due to their simplicity and low administrative costs. However, these loans lack flexibility to address specific sustainability risks. In contrast, Green Loans and Sustainability-Linked Loans offer greater adaptability but require significant resources for implementation, monitoring, and verification. Challenges such as limited demand, technical constraints, and insufficient sustainability data, particularly from small businesses, further limit their adoption by regional banks. To overcome these barriers, support from IT service providers, banking associations, and targeted market education is essential. Despite the challenges, sustainable loan products present an opportunity for regional banks to enhance resilience, strengthen local economies, and contribute to a sustainable financial system.
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Paper Nr: 53
Title:

Do ESG Ratings Drive Financial Performance? A Systematic Analysis of Trends and Challenges
Authors:

Amelie Heinelt, Dominic Strube and Christian Daase
Abstract: This study examines the relationship between ESG (Environmental, Social, and Governance) ratings and financial performance through a systematic analysis of studies published between 2019 and 2024. The findings reveal that a significant correlation between ESG ratings and financial performance was only demonstrated in a portion of the studies. Regression-based models were the most frequently used methods, followed by panel data and time series analyses. However, no clear statistical relationship between the choice of methodology and the results could be established. Variations in findings are attributed to differences in ESG rating methodologies, data sources, and external factors such as macroeconomic conditions and market volatility. While ESG investments may involve short-term costs, they can contribute to long-term stability. The study highlights the need for standardized ESG ratings and consistent analytical approaches to enable more reliable conclusions.
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Paper Nr: 55
Title:

COVID-19 and Macro-Financial Forces: Who Drives the Conventional and Islamic Stock Markets?
Authors:

Melissa Putritama, Natanael Christian Adinata, Nathalie Noviani and Shinta Amalina Hazrati Havidz
Abstract: Although WHO has declared the pandemic end, the underexplored area of study around COVID-19, macro-financial, conventional, and Islamic stock markets should be conducted. Therefore, this research remains relevant since a market downturn can happen anytime in the future, and the world will face dynamic changes in investors' behavior. We aim to investigate the drivers of the Conventional and Islamic stock markets, which mainly consider the global pandemic COVID-19 and Macro-financial forces. The main methodology applied panel autoregressive distributed lag (ARDL). This research discovers the following findings: (1) conventional stocks highly rely on the confidence index of the COVID-19 vaccine, whereas Islamic stocks remain more resilient; (2) A safe-haven role of Islamic stocks during global market turbulence and outperform their counterparts; (3) government policies boost the confidence of both stock markets; and (4) conventional stocks are much more dominant than Islamic stocks. Islamic stocks provide safe-haven attributes during market turmoil, whereas conventional stocks take time to recover. We offer suggestions to investor decision-making, regulators, and government policies.
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Paper Nr: 62
Title:

Unicorn Illusions: A Novel Approach to Startup Valuation Using ESG
Authors:

Veda Ganesan
Abstract: Overvalued startups with unsustainable business models remain a critical issue, driven by market irrationality and overlooked risks. This study introduces an ESG-integrated Discounted Cash Flow (DCF) model to address these valuation inaccuracies. By incorporating Environmental, Social, and Governance (ESG) metrics into the Weighted Average Cost of Capital (WACC), the model effectively accounts for ESG-related risks and opportunities. The analysis reveals that startups with higher ESG ratings experience reduced costs of equity and debt, resulting in a lower WACC and more accurate valuations. This approach highlights the benefits of integrating sustainable practices into business models, promoting long-term stability and investor confidence. A comprehensive review of existing valuation methods identified key gaps, particularly in accounting for qualitative ESG factors. Regression analysis of case studies demonstrated how ESG-adjusted discount rates improve valuation precision without double-counting risks. Findings suggest an inverse relationship between ESG ratings and capital costs, emphasizing the financial advantages of robust ESG frameworks. This research underscores the need for investors and venture capitalists to incorporate ESG considerations systematically, reducing the risk of market bubbles and fostering sustainable business practices. Future studies should explore nonlinear modeling and behavioral finance to further enhance ESG-integrated valuation frameworks.
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Paper Nr: 70
Title:

Earnings Management Practices During the Covid-19 Pandemic: A Comparative Study of Jordanian Family and Non-Family Controlled Firms
Authors:

Rasmi Meqbel and Hanady Qamoom
Abstract: This study aims to examine the association between family ownership and earnings management before and during the Covid-19 pandemic. Drawing upon the socioemotional wealth and agency theories, the research suggests that family-controlled firms might resort to earnings management as a strategic measure during the Covid-19 crisis to safeguard their endowment and legacy. To test the hypotheses, a panel data analysis is conducted using STATA software on a comprehensive sample of companies listed on the Amman Stock Exchange spanning from 2015 to 2022. Earnings management is measured using the Performance-matched Accrual Earnings Management model as proposed by Kothari et al. (2005). The findings show a significant insight into the behavior of family-controlled firms. Specifically, in the years leading up to the Covid-19 outbreak, these firms displayed a reduced inclination to engage in earnings manipulation. Conversely, within the Covid-19 pandemic, the outcomes indicate an increased likelihood of family firms adopting earnings management strategies. Practical implications include guiding family business decisions in challenging times, while theoretical insights contribute to our understanding of how family ownership influences financial strategies during crises. Societally, the study highlights the significance of transparent financial reporting for stakeholder trust and informs policy considerations to support family firms' resilience during economic disruptions.
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Paper Nr: 72
Title:

The Impact of Digital Transformation on Financial Performance and Green Development: Evidence from Chinese Manufacturing Companies
Authors:

Mohammad Alzyod, Ling Yi and Mahmoud Al-Sayed
Abstract: Digital transformation, driven by advancements in Artificial Intelligence (AI), Big Data, and the Internet of Things (IoT), has become essential for modern manufacturing companies in reshaping their manufacturing processes and business strategies. While prior research has largely focused on the financial benefits of digital transformation, its environmental implications remain underexplored. This study examines the dual impact of digital transformation on financial performance and green development, using panel data from Chinese A-share listed manufacturing firms between 2010 and 2021. Applying a multiple regression model, the analysis integrates Schumpeterian innovation theory and the Resource-Based View (RBV) to provide a comprehensive understanding of how digitalisation influences both economic and environmental outcomes. The findings reveal that digital transformation significantly enhances financial performance while also promoting sustainable business practices. By bridging the gap in existing literature, this study offers new insights into the broader value of digital transformation, providing practical implications for corporate decision-makers and policymakers seeking to align financial growth with sustainability objectives.
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Paper Nr: 16
Title:

Impact of Project Delays on Financial Losses on the Green Economy
Authors:

Dewi Nusraningrum and Agus Jerry Suarjana Putra
Abstract: The facility construction and land leasing projects often face various challenges that can cause delays, ultimately significantly impacting costs and financial losses. The study analyses the impact of project delays resulting in green economic losses that exceed the total initial investment cost. With a total investment of IDR 6,188,481,522, this project suffered significant losses, reaching IDR 8,095,928,364 due to various factors, including operational disruptions due to non-fulfilment of licensing documents, additional licensing items, and extension of work time. The method used is qualitative risk analysis. These findings emphasize the importance of effective project management, especially permit fulfilment and schedule planning, to minimize financial risks and ensure smooth project operations. This research provides important insights for project managers facing similar future challenges.
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Paper Nr: 77
Title:

Bank Risks, and Bank Stability: The Moderating Role of State Ownership in the MENA Region
Authors:

Ahmed Rashed and Dexiang Wu
Abstract: This paper empirically examines the impact of state ownership on the relationship between bank risks and financial stability for a sample of 110 banks within the period 2007-2021 with 1650 bank observations listed in the Middle East and North Africa regions. The findings show that there is no simultaneous link between credit risk and liquidity risk. Liquidity and credit risks can be managed jointly to affect banking stability. State banks are more stable, less likely to engage in risky behavior, and more concerned with social welfare. State banks eliminate the impact of banks' risks on banking stability. Results enhance good governance, economic development, and employment opportunities, maintain financial safety, and ultimately enhance growth. Our results are consistent with the present regulatory framework, particularly Basel III, which confirms the importance of joint management of liquidity and credit risk.
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Area 2 - Economics

Short Papers
Paper Nr: 63
Title:

Economic Determinants and Oil Shocks: Unravelling the Impact of Kuala Lumpur Composite Index (KLCI) Performance
Authors:

Dhia Damia Husni and Abd Hadi Mustaffa
Abstract: The Kuala Lumpur Composite Index (KLCI) increase steadily from 1970 to 2023 despite major swings in important economic indicators such as inflation, exchange rates, GDP, and oil shocks. This divergence between economic issues and stock market performance emphasises the importance of delving deeper into the underlying causes. This study examines the impact of economic forces and oil shocks on KLCI performance using World Bank data from 1970 to 2023. The Auto Regression Distribution Lag (ARDL) model was used to determine how these variables influence stock market performance in the short and long run. Two findings are highlighted based on ARDL analysis. First, the short-run findings indicate that Oil Price, GDP, and Exchange Rate positively impact KLCI. However, inflation delivers a significant and negative impact on KLCI. Second, the long-run findings indicate that oil prices and GDP deliver significant and positive impacts towards KLCI. However, inflation and exchange rates have significant and negative impacts on KLCI. Those findings lead towards further discussion and policy recommendations in the later section, aligning with SDG 7 (Affordable and clean energy) and 8 (Decent Work and economic growth).
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Area 3 - Emerging Areas in FEMIB

Full Papers
Paper Nr: 38
Title:

Corporate Venturing in Sustainability Transition: Conceptual Framework
Authors:

Diana Smite
Abstract: Corporate venturing serves as a bridge between the innovative potential of startups and the scale and resources of established corporations. Corporate venturing has become an increasingly important mechanism for facilitating sustainability transitions, but its unique attributes in the sustainability context are yet to be adequately addressed. In response, this study seeks to fill this gap by proposing a conceptual framework that emerges from an integrative literature review and qualitative content analysis of 42 scholarly articles. The five primary themes that emerged as essential are innovation, ecosystems, partnerships/networks, transition/transformation, shared value creation, and new business models. The proposed framework contributes to the theoretical conversations around sustainable corporate venturing and offers practical insights for practitioners seeking to integrate corporate strategies with sustainability objectives. This study lays a foundation for future empirical and theoretical research by synthesizing fragmented perspectives and offering structured guidance.
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Short Papers
Paper Nr: 47
Title:

Using Adaptive Neuro-Fuzzy Inference System and Deep Learning to Predict and Estimate the Current Stock Prices
Authors:

Ying Bai and Dali Wang
Abstract: To correctly and accurately predict and estimate the stock prices to get the maximum profit is a challenging task, and it is critical important to all financial institutions under the current fluctuation situation. In this study, we try to use different AI methods and algorithms, such as Adaptive Neuro Fuzzy Inference System (ANFIS) and Deep Learning (DL), to easily and correctly predict and estimate the current and future possible stock prices. Combining with some appropriate pre-data-processing techniques, the current stock prices could be accurately and quickly estimated via those models. In this research, both algorithms are designed and built to help decision makers working in the financial institutions to easily and conveniently predict the current stock prices. The minimum training and checking RMSE values for ANFIS model can be 0.0009828 and 0.001713. The minimum MSE value for DL model is 0.0000047 with a regression value of 0.9958.
Paper Nr: 48
Title:

Trust and Risk Management Interplay: A Review in the Digital Context
Authors:

Julija Saveljeva
Abstract: This paper provides a comprehensive overview of previous studies on the relationship between trust and risk management in the digital environment, highlighting multiple ways trust elements can enhance risk management practices. PRISMA 2020 methodology was used to perform this analysis, and 281 papers retrieved from Scopus and Web of Science databases were examined. 45 papers selected based on specific inclusion and exclusion criteria formed the foundation of this study. The main research findings are: 1. A strong, mutual relationship exists between trust and perceived risk. Increased trust reduces perceived risk and leads to more user adoption and engagement with digital services. In turn, higher perceived risk lowers trust and discourages the adoption. 2. Trust integration into assessments for decision-making improves risk management by enhancing accuracy, fairness, and uncertainty handling in online environments. 3. Since the trust is dynamic by its nature, its regular reassessments are important. 4. Furthermore, even when cooperating with trusted services and platforms, it is necessary to continuously monitor providers to avoid over-reliance risks.
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Paper Nr: 74
Title:

A Review on Large Language Models and Generative AI in Banking
Authors:

Daniel Staegemann, Christian Haertel, Christian Daase, Matthias Pohl, Mohammad Abdallah and Klaus Turowski
Abstract: Since ChatGPT was presented to the public in 2022, generative artificial intelligence and especially large language models (LLM) have attracted a lot of interest in academia and industry alike. One of the arguably most interesting domains in that regard is banking. This is because it could, theoretically, heavily benefit from their application but also brings very strict regulations and demands. To provide an overview of the current state of research in this field of tension, a literature review across four major scientific databases was conducted and the identified papers were analysed to, inter alia, determine, which types of studies are common, for which tasks the use of LLMs is explored, and which challenges and concerns became apparent. Further, the findings are discussed and some general observations are made.
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Area 4 - IT Business

Full Papers
Paper Nr: 69
Title:

Fuzzy Based Model for Mitigating Employee Attrition
Authors:

Nida Hasib, Syed Wajahat Abbas Rizvi and Vinodani Katiyar
Abstract: Employee attrition is a major concern for IT firms in today's corporate environment. Aside from the loss of human resources, employee turnover also diminishes the organization's ability to use the expertise and revenue-generating potential of those individuals. This study proposes a fuzzy logic-based phase-wise Model for Mitigating Employee Attrition (MMEA) that evaluates employee attrition at each stage of the software development life cycle using the most pertinent risk measures. The research has made use of the fuzzy inference process power in creating a model based on the anticipated and reduced staff attrition. Using data from sixteen actual software projects, the suggested model's predictive accuracy is confirmed. The MMEA model developed as per the guidelines of the proposed framework may help software professionals to take appropriate corrective measures to predict and reduce employee attrition during software development life cycle for efficient and accurate software development process in IT sector. By giving management of the organization the ability to proactively address attrition-related issues and make long-term strategic decisions that benefit the company, the model effectively maximizes staff retention, according to the research. Our results produced proof that the alternate strategy was valid. As a result, managers and companies may find a more practical tool in the used method for evaluating employee decline.
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Short Papers
Paper Nr: 30
Title:

An Exploratory Investigation of the Artificial Intelligence Adoption on Teachers Job Designs
Authors:

Tarek El Mourad, Lykourgos Hadjiphannis, Kyriakos Christofi, Pieris Chourides and Alexios Kythreotis
Abstract: In a rapidly transforming and increasingly digitalized society, interest in artificial intelligence (AI) is growing. Artificial intelligence (AI) has received increasing attention from various areas of culture, industry, and business. AI-based systems will significantly change the nature of the workforce. This study shows that educators should be prepared to adopt artificial intelligence. Integrating artificial intelligence into the education system will require teachers and educators to acquire new skills and update some of them. AI integration transforms the role of the teacher inside the classroom from teacher to facilitator. Integrating AI into education will require educators to be trained in new skills to have a better effect on the outcome of education and educators' careers and life in general. The study addresses the research question: How does AI adoption in schools impact teacher job designs and the skills required? Principal findings emphasize that AI creates opportunities to reframe teachers' skills, yet also poses challenges requiring targeted professional development. This study also shows how some of the activities will be redundant by the AI integration and there will be no need to be done anymore so there will be no asking in those domains which will make these skills obsolete. Future work will explore practical recommendations for educators to manage these shifts and their implications for educational careers.
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Area 5 - Management

Full Papers
Paper Nr: 56
Title:

Industrial Parks in Italy: A Systematic Overview and Preliminary Analysis of the Fosso Imperatore Case Study
Authors:

Carlotta D’Alessandro, Giuseppe Ioppolo, Grazia Calabrò and Giuseppe Caristi
Abstract: This paper presents a systematic overview of Italian Industrial Parks (IPs) and introduces a preliminary analysis of an understudied case in Southern Italy. PRISMA guidelines were employed, to identify eight relevant studies primarily focused solely on North-Centre Italy. The analysis uncovered a certain degree of diversity in park dimensions and industrial sectors, with specialized single-sector and multi-sector parks. This diversity enables integrated supply networks and optimized resource flow in multi-sector parks, while single-sector parks achieve operational efficiency through sector-specific shared services and infrastructure. This study has then examined the unstudied case of Fosso Imperatore Industrial Park (Campania). Based on publicly available information, the analysis evaluated key elements that characterize successful Eco-Industrial Parks (EIPs). The preliminary findings have suggested the potential presence of several EIP elements, particularly regarding networking capabilities and shared services. Further research is needed to validate these characteristics through primary data collection, focusing also on investigating additional cases of EIPs in Southern Italy. Additionally, supply chain management practices and operational strategies within EIPs should be further explored. This research provides an initial exploration of an understudied area in Italy, revealing its potential for industrial symbiosis (IS) and setting the stage for future comprehensive studies.
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Paper Nr: 57
Title:

The Effect of Consumer Acquisition Process on Consumer Satisfaction in Purchasing Fresh Food Online in the Context of Uncertainty
Authors:

Xinyi Chu, Ruilong Li and Zengwen Yan
Abstract: This study aims to identify the key factors influencing Chinese consumers' satisfaction when purchasing fresh food online in the context of uncertainty. It explores how these factors impact consumer satisfaction, electronic word-of-mouth (e-WOM), and behavioral intentions, providing insights into the unique challenges of the online fresh food market. A conceptual framework was developed based on prior literature, identifying seven key determinants of consumer satisfaction: information quality, website design, merchandise attributes, security, payment, delivery, and customer service. The study employs a quantitative research approach, using path analysis through linear regression to test hypotheses. Data were collected from 266 respondents with prior experience in online fresh food shopping, and reliability and validity were confirmed through Cronbach’s alpha and confirmatory factor analysis. The results confirm that all seven determinants positively influence consumer satisfaction in the online fresh food market. Additionally, consumer satisfaction is found to have a significant positive impact on both behavioral intentions and e-WOM. These findings highlight the importance of addressing perishability, quality sensitivity, and uncertainty in shaping consumer satisfaction. This research contributes to the theoretical understanding of consumer satisfaction in e-commerce by extending existing models to the context of uncertainty. It provides a comprehensive hierarchical model that evaluates the consumer acquisition process from pre-purchase to post-purchase stages. The findings offer actionable insights for online retailers to enhance their strategies and meet consumer needs in a highly competitive and volatile market.
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Short Papers
Paper Nr: 22
Title:

Developing a Research Framework Model for Assessing the Impact of Social Media Marketing Activities on Brand Loyalty
Authors:

Meng Xin, Kyriakos Christofi, Lycourgos Hadjiphanis, Pieris Chourides and Nikolaos Boukas
Abstract: The rapid growth of internet of things has driven a shift in consumer behaviour, prompting businesses to adopt social media as a vital communication channel. In the hospitality sector, social media marketing activities （SMMA）is the cornerstone in business management, marketing research and brand promotion, however, there is a scarcity of research on the comparative effectiveness of social media strategies within this industry. This study seeks to fill this gap by establishing a research framework that examine the connections among SMMA, the consumer-brand relationship (CBR), and brand loyalty (BL) in a systemic way. Specifically, the research model, draws on the Stimulus-Organism-Response (SOR) framework, proposed that factors like entertainment, interaction, customization, trendiness, and word of mouth (WOM) can bolster the CBR and subsequently boost brand loyalty. Moreover, the study considers the mediating and moderating roles of CBR, gender and age in the relationship to social media stimuli and user behaviour. The study’s outcomes can be utilized as a solid foundation to host future empirical investigations aiding in the optimization of marketing strategies and the preservation of a competitive advantage in the digital landscape.
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Paper Nr: 24
Title:

Bits and Biases: Exploring Perceptions in Human-like AI Interactions Using the Stereotype Content Model
Authors:

Fernando Jorge F. Macieira, Diego Costa Pinto, Tiago Oliveira and Mitsuru Yanaze
Abstract: In an AI-infused world, user trust in responses generated by autonomous systems is of critical importance. Building upon the work of Ahn, Kim, and Sung (2022), this study examines the impact of stereotypes attributed to chatbots on user trust using the Stereotype Content Model (SCM), which relies on dimensions like warmth and competence for universal cross-culture social judgment. This research investigates how age-related stereotypes influence user perceptions of anthropomorphic AI, specifically chatbots, and their perceived warmth and competence. We conducted two experiments: Study 1 used AI-generated illustrations to present "young" and "old" chatbot personas, while Study 2 used realistic photos. Participants watched pre-recorded interactions with the chatbot "Dave" and evaluated its warmth and competence on a 9-point Likert scale. Data were collected through Prolific, ensuring a diverse sample. Study 1 found no significant differences in perceptions of warmth and competence between the young and old chatbot personas. However, Study 2 revealed that the younger persona was perceived as warmer than the older one, indicating that the realism of the chatbot's appearance affects stereotype activation. These results underscore the importance of aligning chatbot personas with user expectations to enhance trust and satisfaction.
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Paper Nr: 59
Title:

Analysing Italian Historical Small Towns: A Cultural and Geographic Mosaic of Identity
Authors:

Cristina Ciliberto, Giuseppe Ioppolo, Giuseppe Caristi and Grazia Calabrò
Abstract: The recent increase in the Tourism sector has underlined its economic centrality, contributing to 9,1% of global GDP in 2023. The European Union holds a significant position, counting more than 50% of international arrivals. This, in turn, can be translated into considerable economic effects that positively affect the member states. Among such states, Italy has been ranked in the top five international destinations, registering over 57 million tourist inflows. Such an increase has been driven by affluence in major cities and the modern trend of rediscovering historical small towns (HST). This research aims to analyse the components of this trend, underscoring the geographical position and features of the HSTs throughout the Italian territory. Moreover, a descriptive analysis with quantitative data and a SWOT analysis will be conducted to assess their distribution through the Italian territory and their strengths, weaknesses, opportunities, and threats. Preliminary findings reveal that regions such as Central Italy host the highest concentration of villages, while climate change and depopulation threaten their viability. By analysing these HSTs, the study aims to inform strategic planning for sustainable tourism development, enhancing local identities and preserving cultural heritage while positioning these areas as viable alternatives in the global tourism landscape.
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Paper Nr: 21
Title:

Fuzzy MCDM Framework for Risk Management in Construction Supply Chain
Authors:

Abdullah Ali Salamai
Abstract: Risk management in the construction supply chain (CSC) is vital in construction project risks. CSC has various risks affecting product quality and project timeline, such as operational, social, financial, technical, design, and safety risks. These risks should be mitigated in project construction. So, this paper proposed a set of technologies to overcome risks in CSC, like artificial intelligence (AI), blockchain, data analytics, and IoT, to select the best one. So, the multi-criteria decision-making (MCDM) methodology is used to deal with various risks. The Multi-Attribute Utility Theory (MAUT) method is used to rank technologies. The weights of risks are obtained by the average method by using the decision matrix. The MCDM methodology is integrated with a fuzzy set to overcome uncertainty data. Experts used triangular fuzzy numbers to express their opinions instead of exact numbers. These allow the model to overcome inconsistent and vague data. The MCDM methodology was applied to 18 risks and 5 technologies. The results show that social risks have the highest weight. AI is the best technology for overcoming risks in CSC. AI can integrate with CSC from raw data to final products to deliver to the usert.
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Paper Nr: 60
Title:

Implementing Healthcare Innovation via ISO Standards: An Exploratory Literature Overview
Authors:

Carlotta D’Alessandro, Antonio Licastro, Alberto Bongiorno, Katarzyna Szopik-Depczyńska and Giuseppe Ioppolo
Abstract: The healthcare sector faces increasing pressure to improve quality while reducing its environmental impact. This study presents an exploratory semi-systematic literature review investigating the implementation of the most important ISO standards (9001, 14001, 45001, 26000, and 50001) in healthcare organizations, focusing on implementation of drivers, barriers, and the role of digital technologies. Through analysis of peer-reviewed articles from Web of Science published between 2010 and 2024, the study aimed to examine driving forces and barriers affecting ISO implementation in healthcare settings, while also investigating the potential role of digital technologies in addressing implementation obstacles. While ISO 9001 dominates implementations, driven by desires for process optimization and improved patient care, significant barriers persist, including lack of commitment, financial constraints, and administrative burdens. Despite limited explicit discussion of technological solutions in the literature, digital technologies could facilitate ISO implementation, particularly through integration with healthcare-specific ISO standards. However, technology adoption might exacerbate existing challenges related to training and organizational commitment. Understanding the implementation dynamics provides healthcare organizations with insights for decision-making regarding ISO adoption. Furthermore, the findings can support policymakers in developing targeted initiatives for smoother ISO standard implementation across the healthcare sector, laying the groundwork for future research in this important area.
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