ISPC 2025 participants at Adelphi University in New York

Combined conference event

ISPC 2026

Digital and AI Management

Scientific Research and Practical Implementation

10-11 September 2026Thursday and FridayBrugg-Windisch, SwitzerlandFHNW CampusTwo connected programsThursday on site, Friday hybrid

View program Explore ISPC 2026

ISPC 2025 at Adelphi University, New York

The ISPC partnership

One conference. Five equal partners.

ISPC is a joint undertaking of five international institutions. Together they contribute academic expertise, international reach, conference organization and a shared commitment to researcher development.

Adelphi UniversityUnited States

FHNW School of BusinessSwitzerland

IU International UniversityGermany

East European UniversityGeorgia

E-Commerce Institute CologneGermany

264contributing authors

56institutions

22countries

5continents

Final ISPC 2026 management figures, verified 18 October 2026

The current conference

Two connected programs brought science and practice together.

On Thursday, the E-Commerce Best Practice Day and ISPC 2026 formed one combined event with four parallel tracks. On Friday, the sixth ISPC continued with its final scientific program in ten parallel hybrid tracks.

Thursday: E-Commerce Best Practice Day and ISPC 2026
Friday: Final scientific program of the sixth ISPC
Format: Thursday on site; Friday in person and online
Venue: FHNW Campus Brugg-Windisch
Bahnhofstrasse 6, 5210 Windisch, Switzerland

Friday, 11 September

Ten tracks. Twenty moderators.

98Friday presentations

AI Strategy, Governance & Responsible AI

Moderated byWolfram Pietsch
Patricia Girrbach

AI Applications, Smart Cities & Financial Intelligence

Moderated byChristian Schachtner
Nino Tavberidze

Digital Consumer Experience, Retail & Marketing

Moderated byAlina Holle
Christian Pangaribuan

E-Commerce, Platforms & Digital Business

Moderated byDarius Zumstein
Ulrich Arnold

AI in Higher Education & Academic Quality

Moderated byIa Natsvlishvili
Nurdan Kavaklı Ulutaş

Digital Education, Skills & Internationalization

Moderated byTatia Gherkenashvili
Tinatin Rodonaia

Agentic AI, Decision Intelligence & Digital Procurement

Moderated byAnouk Hodel
Adele Dörner

Digital Innovation, Knowledge Transfer & Recommendation Systems

Moderated byManuel Etzkorn
Yvonne Römer

Digital Work, Leadership, Ethics & Society

Moderated byTeona Shugliashvili
Tea Munjishvili

Digital Health, Trust & Societal AI

Moderated byRichard Geibel
Annalena Sommer

The Friday scientific program was guided by 20 moderators working in pairs across ten parallel tracks. Final program updated 22 August 2026. All times are CEST.

Thursday ISPC contributionsFriday scientific tracks

10:00-10:30

Digitalization in Higher Education - Opportunities and Benefits. Syntea as an AI-supported best-practice example from IU International University

Richard Geibel

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Affiliation(s)

IU International University of Applied Sciences

Abstract

Digitalization is profoundly transforming universities in Germany. Generative artificial intelligence (AI) opens new possibilities for the individualization, scaling, and quality improvement of learning and support processes. At the same time, challenges exist regarding pedagogical quality, governance, data protection, and academic integrity. This paper analyzes the potential benefits of university digitization with a focus on AI and discusses “Syntea” as a best practice - the learning assistant at IU International University, Germany’s largest university with over 130,000 students. It systematically outlines the benefits for students, faculty, and the university as an organization. The paper demonstrates that Syntea can be understood not merely as a chatbot, but as an integrated assistance approach to support learning, interaction, and academic success. Finally, success factors, limitations, and development perspectives for AI-supported higher education digitalization are derived.

Keywords

digitalization of higher education · AI · generative AI · learning assistance · digital teaching · IU International University · Digital Education

Author-provided ORCID iDs

Richard C. Geibel0009-0003-2064-9499

17:10-17:40

From Customer Journey to Customer Moment: The Impact of TikTok Shop on Social Commerce in Germany One Year After Launch

Annalena Sommer; Richard Geibel

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Affiliation(s)

IU International University of Applied Sciences

Abstract

The convergence of social media and e-commerce has accelerated the evolution of social commerce and transformed digital purchasing behavior. While traditional customer journeys involve multiple stages of information search, evaluation, and transaction, recent developments indicate an increasing compression of these processes into highly integrated purchasing situations. Building on the concept of the Customer Moment, this paper examines the impact of TikTok Shop on social commerce in Germany one year after its launch in March 2025. The study adopts a qualitative case study approach based on academic literature, industry reports, platform information, and market observations. TikTok Shop is analyzed from both a consumer and a platform perspective using the concepts of social commerce, discovery commerce, platform ecosystems, and the Customer Moment. The findings indicate that TikTok Shop significantly reduces friction within the purchasing process by integrating product discovery, social validation, transaction processing, payment, and fulfillment within a single platform environment. From a consumer perspective, this integration facilitates Customer Moments, in which product discovery, evaluation, recommendation, and purchase increasingly occur within a single interaction. From a platform perspective, the study suggests that TikTok Shop may represent the first large-scale implementation of Fully Integrated Social Commerce (FISC) in Germany, extending earlier social commerce models that relied on external transaction infrastructures. The analysis also identifies important limitations. Regulatory requirements, privacy concerns, consumer trust issues, and cultural differences continue to influence social commerce adoption and distinguish the German market from more mature social commerce ecosystems such as China. The paper contributes to the literature by extending the Customer Moment framework and introducing FISC as a complementary platform-level perspective. The findings suggest that FISC provides the structural foundation for Customer Moments and may represent the next stage in the evolution of social commerce.

Keywords

Social Commerce · Customer Moment · Fully Integrated Social Commerce · Customer Journey · Discovery Commerce · TikTok Shop · Platform Economy

Author-provided ORCID iDs

Richard C. Geibel0009-0003-2064-9499
Annalena Sommer0009-0002-6564-9516

08:45-09:10Track 1

A Framework for Dynamic AI Risk Management Using Unranked Probabilistic Logic

Merium Hazem Anwar Labib Bishara; Dr. Anriette Michel Fouad Bishara

6.1D13 FIn person

In the quickly developing field of artificial intelligence, the emergence of black box models has sparked serious questions about the transparency, and interpretability of automated decision-making processes. Machine learning models that are currently in use, especially deep learning models, function as "black boxes," generating judgments based on complex patterns found in vast amounts of data without revealing the underlying reasoning behind decisions. Trust and confidence in automated decision-making processes are jeopardized by this lack of openness. End users, and regulators are among the stakeholders who are frequently kept in the dark about the decision-making process, which can cause skepticism, and even resistance to the adoption of AI. Evaluating justice, and dependability of automated systems becomes a challenge in the absence of visibility into decision-making process. Furthermore, biases, mistakes, and unexpected outcomes that might result from the data or the model's architecture might be hidden by the opacity of black box models. Fairness and ethical compliance are at risk because it is difficult to identify and reduce these biases when decision-making processes are opaque. In fields like healthcare, law, and finance, where choices can significantly affect people's lives, this issue is especially important. Explainable AI (XAI) is a key paradigm for guaranteeing interpretability and transparency in prediction models, especially for essential business choices. XAI methods typically emphasize the characteristics that have the biggest influence on AI's decisions. In many circumstances, this is very useful and simple to comprehend, but when judgments include more intricate interactions between factors, it may become a constraint. In this paper, we introduce an Explainable AI framework that optimizes supply chain decision-making through Probabilistic Inductive Logic Programming. One of the primary challenges in artificial intelligence is integrating probabilistic reasoning with machine learning and first order and relational logic representations. This is addressed by probabilistic inductive logic programming.

Keywords

Probabilistic Inductive Logic Programming · Explainable AI · Black box · Automated decision-making processes · Ethical compliance

09:20-09:45Track 8

Impact of Digitalization and Artificial Intelligence on Employee Behaviour in Organizational Settings

Rokhsareh Mobarhan; Roya Anvari; Saba Khergiani

6.2A53Online

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Affiliation(s)

Danesh Alborz University / University of Georgia / Caucasus University

Abstract

Digitalization is one of the most significant transformations impacting organizational behaviour, particularly as artificial intelligence (AI) and advanced technologies become increasingly integrated into workplace operations, reshaping job roles and employee perceptions. While the negative consequences of AI adoption, such as job insecurity, employee stress, burnout, and fear of replacement, have been extensively studied, its positive aspects—such as improved productivity and organizational performance—have received comparatively less attention. Therefore, this study aims to address this gap by examining the impact of digital transformation on employee job-related behavioural outcomes, drawing on Challenge–Hindrance Stress Framework. The objective of this study is to investigate how advanced technologies influence employee perceptions and behaviours, with a particular focus on job design, workplace stress, and work-related attitudes. The study hypothesizes that employee attitudes mediate the relationship between AI adoption and productivity, that meaningful work reduces the negative effects of digitalization on job crafting, and that digitalization can act as both a challenge and a source of workplace stress. This study adopts a conceptual approach based on a narrative literature review. Recent studies on AI adoption, organizational change, and employee behaviour were analyzed using a qualitative review methodology. The findings suggest that employees who perceive digitalization as an opportunity tend to demonstrate more positive workplace attitudes, whereas those who perceive it as a threat report higher levels of stress. Additionally, the positive relationship between job crafting and employee outcomes is strengthened by meaningful work. In conclusion, employees’ perceptions and responses to digital transformation are strongly influenced by leadership quality and supportive organizational practices. Accordingly, organizations can mitigate the negative effects of digitalization while enhancing productivity, creativity, and long-term competitive advantage.

Keywords

Digitalization · Organizational behaviour · Artificial intelligence · Employee perception · Workplace stress

Author-provided ORCID iDs

Rokhsareh Mobarhan0000-0003-3043-0733
Roya Anvari0000-0003-2136-8341

09:20-09:45Track 9

Mindfulness and Decision-Making Under Workplace Uncertainty

Suraiya Nasreen

6.2D55In person

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Affiliation(s)

IU International University of Applied Sciences

Abstract

submission for presentation Paper Title- Mindfulness and Decision-Making Under Workplace Uncertainty: An Exploration of Clarity in Digitally Intensive Work Environments Author’s Name & Surname- Suraiya Nasreen Abstract As a lecturer working in a digitally intensive academic environment, I regularly experience high levels of cognitive demand, multitasking, and continuous decision-making across both professional and personal domains. Academic roles increasingly require rapid transitions between teaching, administrative responsibilities, research activities, student support, and technology-mediated communication, often contributing to information overload and decision fatigue. These experiences motivated an interest in understanding whether mindfulness can support greater cognitive clarity and more effective decision-making under conditions of workplace uncertainty and constant connectivity. Given the growing complexity of contemporary work environments, this study seeks to offer insights not only for academics, but also for professionals operating in knowledge-intensive sectors characterized by escalating cognitive demands and persistent digital interruptions. Contemporary workplaces are increasingly shaped by information overload, multitasking, and heightened uncertainty, all of which may contribute to cognitive fatigue and delayed decision-making (Kahneman, 2011). Within management research, mindfulness has primarily been examined in relation to stress reduction, emotional regulation, and employee wellbeing (Good et al., 2016; Hülsheger et al., 2013). However, comparatively limited attention has been paid to how mindfulness may influence workplace ambiguity, cognitive overload, and decision paralysis in digitally intensive work environments. This study addresses this gap by exploring how employees and managers perceive the role of mindfulness in navigating uncertainty and making timely workplace decisions. Drawing on attentional control perspectives and organizational sensemaking theory (Maitlis & Christianson, 2014; Weick et al., 2005), this study adopts a qualitative research design to investigate the lived experiences of professionals working in knowledge-intensive sectors such as higher education. Semi-structured interviews will be conducted with approximately 20–25 participants who regularly encounter high levels of complexity, ambiguity, and decision pressure. Data will be analyzed using thematic analysis to identify recurring themes related to mindfulness practices, cognitive clarity, emotional regulation, and workplace decision-making processes. The study is expected to contribute to organizational behavior and mindfulness literature in three significant ways. First, it extends workplace mindfulness research beyond wellbeing outcomes toward cognitive and decision-related dimensions (Glomb et al., 2011). Second, it introduces the concept of “mindful cognitive clarity” to explain how mindfulness may support attentional focus and reduce indecisiveness in complex organizational contexts. Third, the study offers practical insights for organizations seeking non-technical approaches to improving employee focus, adaptability, and decision effectiveness in increasingly digital and fast-paced work settings. The findings may provide a foundation for future research on mindfulness, workplace cognition, and managerial decision-making in the context of digital transformation and AI-augmented work.

Keywords

Mindfulness · Cognitive Clarity · Decision-Making · Digital Workplaces · city · country- International University · Duesseldorf · Germany

09:20-09:45Track 10

Digital Trust and Civic Participation in the Era of Artificial Intelligence: The Case of Georgia

Tamar Charkviani

6.2D57In person

Business and Technology University

Abstract

Higher education governance in transitional states, like Georgia, faces a dual pressure: rapid technological development trends on one hand, and international funders and European integration requirements on the other. Georgian domestic reforms across multiple sectors, including higher education, prioritize digital governance. However, the conditions necessary for data-driven decision-making — compatible data infrastructure, interoperable data ecosystems, adequate legal frameworks, and institutional capacity — remain largely absent. This article examines these gaps through the case of Georgia, where higher education governance digitalization began following Bologna accession, was advanced by EU association agreement requirements, and accelerated during the pandemic. Despite this momentum, governance digitalization ambitions consistently exceed the data infrastructure that is essential to sustain them. The article develops the concept of compressed digitalization understood as the process where reforms due to the above-mentioned pressure and exceeded ambitions have inverted the necessary developmental sequence, deploying governance systems before compatible data ecosystems, legal frameworks, and institutional capacity are in place. By developing this concept the article addresses a gap in the digital governance literature, which has explained individual system failures in transitional states but has not theorized the systematic sequencing inversion that occurs when reform pressure outpaces institutional readiness across entire governance architectures. Three empirical dimensions illustrate this argument: the fragmentation of institutional data ecosystems within the Georgian higher education system; the transformation of the Educational Management Information System, originally established to manage school-level databases, into a strategic governance body without commensurate institutional, human, and financial investment; and the pursuit of large-scale digitalization reforms in the absence of reliable and compatible data. Together, these dimensions demonstrate that rather than following a sequential and sustainable developmental path, higher education governance digitalization has been compressed and accelerated, with foundational prerequisites systematically bypassed in favor of advanced governance systems that the existing data infrastructure cannot yet support. Against this diagnosis, the article argues for sequenced digitalization, recognizing that while immediate results may not be visible, building foundational and interoperable data infrastructure is the essential precondition for a sustainable ecosystem that enables reliable data collection, management, and use for evidence-based policy planning. Most critically, meaningful data-based decision-making — even at the sectoral level — requires a systematic and comprehensive view of each case within the broader state governance process, in correlation with other sectoral dimensions. This is only achievable through compatible and integrated data infrastructure that provides decision-makers with a complete and accurate picture.

Keywords

governance digitalisation · data-based decision · compressed digitalisaion · higher education governance

09:55-10:20Track 6

The Impact of Integrating Digital Programs on Students' Digital Competencies in Moroccan Universities

Boutaina Aribia; Hafsa Boukhrisse; Ilham Oumaira; Ahlam Qafas; Zidane Lamdaghri

6.2A06Online

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Affiliation(s)

Ibn Tofail University

Abstract

In an era of accelerating digital transformation, Higher education takes a wide part into this wave pushing universities to equip students with essential digital competencies. This is to foster not only their academic success but also their way towards innovation, polyvalency and employability. This paper is built on a wide base of extensive review of international and national literature, so as to highlight the current state of digital skills in Moroccan higher education with a particular focus on Ibn Tofail University. The aim of this study is to explore how can institutional policies and pedagogical practices contribute to the integration of digital skills within university programs. To this aim, we conducted a mixed study based on semi-structured interviews with 12 administrative and management staff, including department heads and directors. In addition, we conducted a quantitative study based on survey forms administered to a sample of 363 students from Code212 center of Ibn Tofail University in order to assess their perception and level of digital competencies. These interviews mainly aim to illustrate participants’ perceptions, strategies and challenges related to digital transformation and competencies development. The study results show that digital skills integration in higher Education has a very significant and positive impact on students’ digital competencies, especially in access to resources, learning autonomy to the employability aptitude. Yet, these findings show the need for persistent infrastructure, staff training, solid programs and a further institutional coordination. Through this study, the focus is to understand how Moroccan universities can strengthen their digital ecosystems following strategic capacity building and evidence-based decision making.

Keywords

Keywords not yet available.

Author-provided ORCID iDs

Boutaina Aribia0009-0000-8573-9338
Ilham Oumaira0000-0001-7291-6462
Ahlam Qafas0000-0003-3009-379X
Zidane Lamdaghri0000-0002-8920-0898

09:55-10:20Track 7

Predictive Analytics for Capital Investment Planning in Energy and Water Utility Companies

Tsotne Lekishvili

6.2A08In person

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Affiliation(s)

International Black Sea University / Georgian Water and Power

Abstract

Capital investment planning in energy and water utility companies is among the most consequential and complex financial management challenges in the infrastructure sector. Utilities must allocate significant resources across long asset lifecycles, navigate regulatory constraints, manage currency and inflation risk, and maintain service continuity — all while operating under fiscal pressure and increasing stakeholder scrutiny. In emerging economies such as Georgia, these challenges are compounded by ageing infrastructure, limited capital market depth, and an evolving regulatory environment aligned with EU standards. Conventional financial planning approaches — predominantly static spreadsheet models and historical trend extrapolation — are increasingly inadequate for the scale and complexity of decisions utilities face. This paper examines how predictive analytics and AI-driven financial modelling can transform capital investment planning in energy and water utility companies. Specifically, it explores three application areas: demand and revenue forecasting using machine learning to improve the accuracy of long-term cash flow projections; asset lifecycle modelling through predictive maintenance data integration into capital expenditure planning; and scenario-based investment optimisation using Monte Carlo simulation and sensitivity analysis to quantify risk under regulatory, macroeconomic, and operational uncertainty. Drawing on practitioner experience in financial modelling, budgeting, and valuation within Georgia's utility sector, the paper develops a conceptual framework for AI-augmented capital planning, identifying the data inputs, modelling architectures, and organisational preconditions required for effective implementation. The Georgian utility context — characterised by infrastructure renewal pressure, NBG regulatory requirements, and IFRS reporting obligations — provides a representative case for utilities in transitional economies navigating the shift toward data-driven financial governance. The study advances practical recommendations for utility finance teams, senior management, and policymakers, contributing to the broader discourse on digital transformation in infrastructure financial management.

Keywords

Predictive Analytics · Capital Investment Planning · Utility Sector · Financial Modelling · Digital Transformation Tsotne Lekishvili International Black Sea University · Tbilisi

09:55-10:20Track 8

Human-AI Collaboration in Construction Management: Organisational and Cultural Challenges

Giorgi Shaburishvili

6.2A53In person

Caucasus International University; International Black Sea University

Abstract

The FMCG retail sector in Georgia has undergone significant structural changes over the past decade, reflecting a gradual shift in market organization, competitive dynamics, and operational practices. This study examines the evolution of the retail market in Georgia, with a particular focus on the transition from fragmented traditional retail structures toward more organized retail formats, and the emergence of digital transformation as a growing trend within the sector. The research is based on an empirical analysis of secondary sources, including industry reports, statistical publications, academic studies, and systematic observations of t he Georgian retail market. The analysis focuses on key dimensions of retail market evolution, including the coexistence of traditional and organized retail formats, regional disparities in retail development, distribution and logistics structures, retail ecosystem characteristics, and changes in operational efficiency across the FMCG value chain. The findings indicate that the Georgian FMCG retail market is in a transitional stage of development, where traditional retail still plays a substantial role, par ticularly in regional areas, while organized retail chains continue to expand their market presence in urban centers. This coexistence reflects an incomplete but ongoing process of market formalization and consolidation. At the same time, digital transform ation is emerging as an important but still developing trend, primarily observed within organized retail operators, where investments in retail technologies and integrated systems are gradually reshaping operational processes. The study contributes to the understanding of retail market evolution in emerging economies by providing evidence from the Georgian FMCG sector and highlighting the structural transition toward organized retail formats alongside the gradual emergence of digital transformation practices.

Keywords

Retail Market Evolution · FMCG Sector · Georgia · Organized Retail · Traditional Retail · Digital

12:15-12:40Track 5

Examining Generative Artificial Intelligence Literacy Among K12 Turkish EFL Teachers

Ebru Kızıltaş; Gülşah Külekçi; Nurdan Kavaklı Ulutaş

6.2A03Online

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Affiliation(s)

Izmir Demokrasi University; Dokuz Eylül University

Abstract

Generative Artificial Intelligence (GenAI) has reshaped English as a Foreign Language (EFL) instruction, but K12 EFL teachers’ GenAI literacy is underexplored, especially in Türkiye. To determine teachers’ self-reported GenAI literacy levels and determinants, 120 K12 EFL teachers voluntarily completed the GenAI Literacy Scale developed by Wang et al. (2025) and adapted to the Turkish EFL context by Özgün-Çoban et al. (2025). Results indicated that teachers’ overall GenAI literacy was moderate. Among the subdimensions, Ethics yielded the highest mean score, while Design demonstrated the lowest mean score. Teachers who had received AI-related training reported significantly higher literacy scores than those without, and AI use frequency was positively correlated with overall GenAI literacy. A hierarchical regression model further examined these relationships while controlling for demographic variables. Demographic characteristics alone accounted for 20.2% of the variance (R² = .202), adding AI use frequency and AI training significantly improved the model (ΔR² = .064, p = .015), with the final model explaining 26.6% of the variance. School type emerged as the only significant predictor, while AI use frequency and AI training showed only marginal effects. The findings underscore the need for professional development programs that move beyond general AI training toward targeted, hands-on opportunities for designing AI-supported instructional materials, narrowing the gap between teachers’ awareness of AI and their practical capacity to integrate it meaningfully into K12 EFL classrooms.

Keywords

Generative AI Literacy · EFL Teachers · K12 Education · Teacher Professional Development

12:15-12:40Track 6

The Role of Entrepreneurship Education in Shaping Entrepreneurial Success: An Indonesian Perspective

Jessica Alexandria Wahyudi; Sentot Basuki Prayitno; Muhril Ardiansyah; M. Lukman B. Alfakihuddin

6.2A06Online

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Affiliation(s)

Sampoerna University

Abstract

This study investigates the role of entrepreneurship education (EE) in shaping entrepreneurial success (ES) among Indonesian business owners, with a focus on the mediating effects of entrepreneurial bricolage (EB) and opportunity identification (OI). Grounded in the Resource-Based View (RBV) theory, the research aims to empirically examine how education functions as a strategic resource that enhances business performance when activated through adaptive behaviors and cognitive capabilities. Data were collected from 365 entrepreneurs across Indonesia and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 3. The findings indicate that while entrepreneurship education does not have a statistically significant direct effect on entrepreneurial success, it significantly influences both entrepreneurial bricolage and opportunity identification. These two mediators, in turn, exhibit significant positive effects on entrepreneurial success. This suggests that the impact of education on entrepreneurial outcomes is indirect and occurs through behavioral and strategic mechanisms. The structural model demonstrated acceptable model fit (SRMR = 0.097, NFI = 0.829), with R² values indicating moderate explanatory power for all endogenous constructs.

Keywords

entrepreneurship education · entrepreneurial success · entrepreneurial bricolage · opportunity identification · Indonesia · PLS-SEM · resource-based view · business performance · SME development · entrepreneurship research

Author-provided ORCID iDs

Sentot Basuki Prayitno0009-0003-4882-1027
M. Lukman B. Alfakihuddin0000-0003-1260-7136

12:15-12:40Track 7

The Strategic Impact of AI: Mitigating Data Barriers to Foster Business Competitiveness

Rachel John Robinson

6.2A08Online

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Affiliation(s)

IU International University of Applied Sciences

Abstract

Emerging as a critical organizational tool rewriting data analysis and process automation systems to generate competitive advantage is artificial intelligence (AI) inclusion into business strategic decisions and operations. Predictive analytics combined with artificial intelligence technologies such natural language processing and machine learning assist companies better understand vast amounts for better decision-making. Through automation systems, artificial intelligence technology offers the most value of competition in company. AI automation therefore prevents human mistakes and improves operations performance, thereby enabling businesses to relocate their staff to strategic value-added activities. Robotic process automation (RPA) is applied in finance supply chain management and customer service among the popular AI solutions to enhance decision-making. Predictive analytics is a tool that artificial intelligence presents to companies allowing them to effectively use resources with low risk by projecting market trends. By means of accurate forecasts derived from historical and real-time data, artificial intelligence models help companies create strategic plans and reduce their degree of uncertainty. As a highlight, the main difficulties of implementing artificial intelligence systems by companies are the high technology costs, lack of technical knowledge, and ethical questions. Algorithmically bias, data privacy, and incompatibility with present IT systems all impede the implementation of artificial intelligence and provide major obstacles to its acceptance. The strategic importance of artificial intelligence technology in decision support and its effects on corporate competitiveness in medium-sized companies will be investigated in this work. Through a mixed method analytics of industry reports, academic literature, and corporate cases, this study explores how Artificial intelligence (AI) facilitates an organization in its strategic decision-making and promotes business competitiveness. The analysis using Resource-Based View and Dynamic Capabilities theories demonstrates AI produces between 15-25% better decision accuracy and delivers 40% operational efficiency enhancement as cited by PwC and leads to 35% revenue advancement for AI implementers, yet several implementation obstacles exist like high spending ($500k-$10M) and skill deficits (affecting 70% of companies) and GDPR and similar regulatory issues. This project delivers practical AI adoption recommendations to SMEs because it outlines sequential AI implementation approaches along with workforce training programs and protocols for ethical governance thus filling an essential gap in strategic AI research for companies of medium size.

Keywords

Artificial Intelligence (AI) · Robotic process automation (RPA) · strategic decision-making · business competitiveness · operational efficiency · SMEs

12:15-12:40Track 8

The Impact of Sustainability Regulatory Frameworks on the Strategies and Operational Performance of Low-Cost Carriers

Tamta Beridze; Irma Dikhaminjia

6.2A53Online

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Affiliation(s)

Sokhumi State University

Abstract

The aviation industry, like other sectors of the global economy, is undergoing a fundamental transformation, driven not only by technological progress but also by the implementation of stringent sustainability regulations. While the impact of frameworks such as EU ETS, CORSIA, and ReFuelEU on Full-Service Carriers (FSCs) is well-documented, Low-Cost Carriers (LCCs) remain under-researched despite their increasing market dominance. This study addresses this research gap by providing a comprehensive analysis of how these sustainability mandates influence LCC business models, strategies, and operational performance. The research methodology is based on a quantitative analysis of a dataset of 139 international airlines. By examining key performance indicators - specifically the Cost per Available Seat Kilometer (CASK) and CO2 emission intensity - the study evaluates the correlation between sustainability compliance and operational efficiency. Furthermore, the paper includes a comparative analysis of adaptation trends and the quality of ESG reporting between LCCs and FSCs. The results show that LCCs face structural challenges in balancing cost-leadership models with the escalating financial burden of environmental compliance. Data indicate a correlation between CASK levels and the stringency of environmental regulations, which presumably suggests that LCCs need to fundamentally rethink their fleet management, route planning, and pricing strategies. Moreover, a significant disparity in ESG reporting quality was identified, which, presumably, is driven by a lack of investor pressure or the absence of standardized reporting requirements. This study contributes to the academic and practical development of aviation management with empirical evidence on LCC adaptation mechanisms. The findings provide critical implications for policymakers and industry leaders to develop targeted regulatory support that fosters innovation without compromising the economic viability of the sector.

Keywords

Low-Cost Carriers · Aviation Sustainability · Regulatory Frameworks · Operational Efficiency · CASK · ESG Reporting

Author-provided ORCID iDs

Tamta Beridze0009-0008-6291-6635
Irma Dikhaminjia0000-0002-2227-5158

12:15-12:40Track 9

Humanity at the Crossroads: Utopian and Posthuman Scenarios of Future Governance

Elizbar Elizbarashvili; Sofiko Kvantaliani

6.2D55Online

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Affiliation(s)

Tbilisi State University

Abstract

This article examines the transformative impact of contemporary technological development on the future of humanity, focusing particularly on artificial intelligence, biotechnology, nanotechnology, cybernetic engineering, and posthumanist philosophy. It argues that the current stage of civilization represents a historical turning point in which technology transcends its instrumental role and becomes a decisive force shaping human existence, social organization, and political power. The study emphasizes the dual nature of technological progress: on the one hand, advanced technologies create unprecedented opportunities for medical breakthroughs, economic abundance, social equality, and the realization of human freedom; on the other hand, they generate serious risks of total surveillance, dehumanization, social stratification, and the erosion of human autonomy. The article critically engages with the ideas of philosophers and theorists such as Yuval Noah Harari, Nick Land, and Bruno Latour. Through the analysis of concepts including posthumanism, accelerationism, technological singularity, cybernetic transformation, and artificial superintelligence, the paper explores possible scenarios for the future of humanity. Particular attention is devoted to the prospect of a biologically and technologically divided society, where enhanced elites may dominate non-enhanced populations through advanced systems of algorithmic governance and bio-technological control. Furthermore, the article investigates the philosophical implications of dissolving traditional distinctions between human and machine, organic and inorganic life, nature and technology. It highlights the growing possibility that humanity could gradually transition into a posthuman condition in which artificial intelligence and technological systems surpass human cognitive and political capacities. In this context, technological development is interpreted not merely as scientific progress but as a profound ethical and civilizational challenge. The central argument of the article is that technology itself is neither inherently beneficial nor destructive; rather, its consequences depend on the ethical, political, and social frameworks governing its application. Consequently, the future trajectory of civilization will be determined by humanity’s ability to preserve humanistic values, democratic principles, and individual dignity amid accelerating technological change. The study concludes that contemporary civilization stands between two opposing futures: a utopian society grounded in equality, freedom, and collective prosperity, or a dystopian order characterized by technological domination, social inequality, and the disappearance of human autonomy.

Keywords

Artificial Intelligence · Posthumanism · Technological Singularity · Global Governance · Humanism

12:15-12:40Track 10

EU and WHO HR policies in the healthcare system: digital models, strategic aspects and perspectives

Natia Tsiklauri; Marina Endeladze

6.2D57Online

Sampoerna University

Abstract

Social media has become an important key in influencing consumer attitudes and purchasing patterns in the digital age, especially among Generation Z. This study explores the role of Influencer credibility and the homophily effect on purchase intention through the mediating roles of parasocial relationships. This study, which is based on the Stimulus-Organism-Response (S-O-R) framework, defines influencer credibility and influencer homophily as the external stimulus that causes consumers to react psychologically (parasocial relationship) and behaviorally (purchase intention) to responses among consumers. To collect data from 251 respondents who are actively engaged with fashion and beauty influencers on Instagram and TikTok, a structured online survey was implemented with a quantitative methodology. According to Research Model 1, the Parasocial Relationship is substantially influenced by both influencer credibility and influencer homophily, with Homophily having a significantly greater impact. Conversely, Research Model 2 results indicate a substantial “Expertise Paradox"; the direct correlation between Influencer Credibility and Purchase Intention was determined to be statistically non-significant. The results of this study reveal that Indonesian Gen Z does not perceive any commercial value in an influencer’s expertise unless it is personalized through a perceive digital friendships. In addition, the respondents acknowledged that influencers were trustworthy; however, they also indicated a substantial disparity in "thought similarity", suggesting that visual relatability currently surpasses ideology congruences. According to the research, Generation Z is primarily influenced by the “Bestie Effect", which is the intimate, peer-orientated connection of a parasocial relationship. The managerial implications suggest that businesses should transition from “Scripted Expertise” to “Relatable Authority”, utilizing emotional connection to mitigate commercial mistrust. This research advances Parasocial Interaction (PSI) theory by demonstrating that relatability has replaced conventional authority as the primary determinant of economic behavior in modern digital contexts.

Keywords

Influencer Credibility · Influencer Homophily · Parasocial Relationship · Purchase Intention

13:25-13:50Track 4

Exploring P2P Lending Adoption in Indonesia through the Technology Acceptance Model and Trust

Tri Wismiarsi; Angelina Priscilla Tan; Joanna Christie Liem

6.1D53Online

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Affiliation(s)

Sampoerna University

Abstract

The exponential development of financial technology (FinTech) has transformed the financial services industry with the sector’s peer-to-peer (P2P) lending playing a key role in facilitating access to capital. This study is conducted to understand the factors that affect the adoption of P2P lending platforms in the millennials and Generation Z Indonesia, since they have a substantial demographic representation. The Technology Acceptance Model (TAM) with the addition of perceived risk of and trust was used to analyze data from 200 respondents using structural equation modeling. The results showed that all direct relationships of perceived ease of use (PEU), perceived usefulness (PU), and perceived risks (PR) have no significant effects on adoption intention (AI). In contrast PEU has significant influence on PU, similar results were also found in the relationships between PU and trust, and PR and trust. Furthermore, the impact of trust on AI is very significant. The results of this study indicate that trust is a significant mediator between the TAM variables as well as perceived risks in the adoption of P2P. The study highlights issues around trust and technology, in FinTech adoption. These insights help to inform how millennials and Gen-Z adopt P2P, so that the FinTech firms can improve the P2P adoption, while offering a need for future research in different variables and varied demographics. In addition, the results also provide recommendations to the government to improve the governance of the business to be better and responsible.

Keywords

P2P Lending · Technology Acceptance Model · Perceived Risks · Trust · Adoption Intention

Author-provided ORCID iDs

Tri Wismiarsi0000-0002-8797-1306
Angelina Priscilla Tan0009-0004-4044-2222

13:25-13:50Track 5

AI-Driven Educational Management for Migrant Inclusion: A Systematic Review of Language, Contemplative, and Inclusive Practices in Post-Compulsory Education

Nurdan Kavaklı Ulutaş; Sneha Saha; Shalini Balasubramani; Sarmin Haque

6.2A03Online

The evolution of Artificial Intelligence and Machine Learning has fundamentally reshaped organizational management and higher education digital infrastructures. While contemporary educational platforms frequently rely on simplified metrics, evaluating complex pedagogical knowledge acquisition and integrating predictive analytical systems require sophisticated, unified digital frameworks. This study introduces the development, operational architecture, and empirical evaluation of "Kiber3" (Kiber3.tsu.ge), a comprehensive multilingual digital knowledge management ecosystem implemented at Ivane Javakhishvili Tbilisi State University. The platform bridges advanced machine learning sub-systems with hybrid pedagogical methodologies to optimize learning, automated self-assessment, and strategic academic testing. "Kiber3" comprises 13 logically integrated web applications developed in VB.NET. It employs an advanced engineering approach known as "production grammar" to execute real-time semantic analysis on open-ended student text, effectively parsing morphological stems, syntax shifts, synonyms, and abbreviations. Structurally, the platform integrates desktop analytical software—PrognoziReg and DiagLog—which execute matrix-based multiple linear, logarithmic, and logistic regression equations using specialized Excel/VBA non-linear optimization engines (Solver). These frameworks facilitate rigorous predictive modeling across diversified domains, including financial bankruptcy forecasting (FinSim), foreign trade simulation (ForSim), banking credit scoring, and medical diagnostics. Built upon fifteen years of empirical data from predecessor models ("Kiber1" and "Kiber2"), "Kiber3" establishes a highly autonomous homeostatic equilibrium in student testing logistics, neutralizing administrative intervention through precise group-based examination scheduling. The semantic parser successfully validates unstructured open text based on scalable informational weights assigned by domain experts. Furthermore, regression diagnostics seamlessly compute statistical data (F-statistics, Student’s t-criterion, and ROC diagrams) to maintain high mathematical validity. The study demonstrates that digital systems function optimally as symbiotic tools alongside traditional instruction. Developing local architectures like "Kiber3" and cultivating qualified domestic machine learning specialists represent strategic prerequisites for advancing digital education and economic management in Georgia.

Keywords

Knowledge Base Ecosystems · Semantic Processing · Multiple Regression Modelling · Digital Transformation · Academic Assessment Automation

14:00-14:25Track 6

Digital Management of Higher Education in Georgia: Adapting to Global Trends in the National Context

Eter Kharaishvili; Natela Tsiklashvili; Manana Lobzhanidze; Lela Aduashvili

6.2A06Online

View abstract and affiliations

Affiliation(s)

Tbilisi State University / Batumi Shota Rustaveli State University

Abstract

Digital transformation is not only a technological choice, but also a fundamental prerequisite for the global competitiveness of higher education systems. The present article examines digital management in higher education in Georgia, through the lens of adaptation to global trends and the synergy of the national context. The aim of the research is to assess the practices, effectiveness, and challenges of digital management in Georgian universities, to identify existing global trends in this field, and, taking into account national specificities, to develop recommendations for improving digital management in higher education. A quantitative study has been conducted. The number of respondents was determined using Cochran’s methodology. Through a comprehensive approach, three main strata groups were identified: academic staff, administrative staff, and students. Within the framework of the study, special attention is paid to data comparison. Although the questionnaires differed partially across the strata groups, they were categorized and harmonized during the analysis process, which ensured the integration of the data into a unified analytical framework. The survey results reveal the transformative dynamics of the system. The empirical analysis showed that despite the high level of student optimism (75.7% confirm the effectiveness of digital management), the system remains “in a transitional phase of integration”: only 28.1% of academic staff consider digital integration to be fully accomplished, while 54.7% of respondents identify financial and infrastructural deficits as the main limiting barriers. Based on the theoretical frameworks of the resource-based view (RBV) and dynamic capabilities, the paper argues that the success of technological models depends on their localization (contextual intelligence) rather than mechanical copying. The article presents strategic recommendations for the formation of unified digital ecosystems, the enhancement of staff qualifications (the need for which is indicated by 44% of the administration), and the ethical integration of artificial intelligence (AI).

Keywords

higher education · digital management · adaptation to global trends · national context · Georgia

Author-provided ORCID iDs

Eter Kharaishvili0000-0003-4013-7354
Natela Tsiklashvili0000-0003-0638-7376
Manana Lobzhanidze0009-0000-4575-9422
Lela Aduashvili0009-0008-4732-7007

14:00-14:25Track 7

The Right Model Depends on the Data: A Cross-Domain Study of Regression Algorithms

Saipranay Tummala; Nguyen; Xie

6.2A08Online

Adelphi University

Abstract

This paper proposes an exploration into the application of advanced hybrid retrieval models, combining keyword-based (BM25) and semantic (vector) search, within a financial intelligence terminal. Focusing on the structured and unstructured data derived from SEC 10-K and 10-Q filings, we demonstrate how such a system can significantly enhance investment decision-making by enabling portfolio managers to query complex investment themes, identify relevant companies, and uncover underlying evidence with greater precision and recall. The study will particularly highlight the benefits of Reciprocal Rank Fusion (RRF) in reconciling the strengths and weaknesses of different retrieval methodologies, offering a robust solution for navigating the vast and rapidly evolving landscape of digital financial data. We will present a practical implementation using MongoDB Atlas Search and vector embeddings, alongside an in-depth analysis of its performance on various investment themes, showcasing its practical implications for digitalization in business and management. Introduction: The digitalization of financial information has led to an explosion of data, making it challenging for investment professionals to extract actionable insights efficiently. Traditional keyword-based search methods often fall short in capturing the nuanced semantic relationships critical for complex investment thesis development. Conversely, pure semantic search, while powerful for conceptual understanding, can sometimes lack the precision of exact keyword matching. This paper addresses this challenge by presenting a hybrid retrieval framework designed specifically for financial intelligence, leveraging the latest advancements in information retrieval and machine learning. Methodology: Our approach involves building a financial intelligence terminal using a corpus of LLM-extracted company analysis JSONs from SEC filings. We will employ MongoDB as the data store, utilizing its indexing capabilities (compound, multikey) for structured queries. The core of our methodology lies in integrating and comparing: 1. **Keyword Search:** Utilizing MongoDB's `$text` operator and Atlas Search's BM25 algorithm for precise term matching. 2. **Vector Search:** Generating and storing high-dimensional embeddings of executive summaries and analyst notes using state-of-the-art transformer models, and performing similarity searches using MongoDB Atlas Vector Search. 3. **Hybrid Retrieval:** Implementing Reciprocal Rank Fusion (RRF) to combine the ranked lists from both keyword and vector search, aiming to achieve superior overall retrieval effectiveness. A detailed analysis will be conducted to identify scenarios where each method excels and where they diverge, providing insights into their respective strengths and limitations in a real-world financial context. Expected Results: We anticipate demonstrating that hybrid retrieval, particularly through RRF, significantly outperforms individual BM25 and vector search methods in identifying companies relevant to complex investment themes. The study will quantify the improvements in ranking accuracy and relevance for diverse queries, offering a clear blueprint for financial institutions seeking to enhance their analytical capabilities through digital transformation. Furthermore, we expect to highlight how this approach can reduce information overload and accelerate the research process for portfolio managers, enabling more informed and timely investment decisions. File: Application Baker ISPC 2026.docx Topic: Application ISPC 2026 Page: 3/3

Keywords

Keywords not yet available.

14:35-15:00Track 3

Conditions and Values of Hologram Shopping Assistants in Retail: An Integrated Study at LOEB

Celine Alter / Lisa Lüthy

6.1D51Online

The rapid adoption of remote and hybrid work has transformed organizational management, creating new challenges in sustaining employee motivation and fostering a strong sense of belonging within geographically dispersed teams. Despite growing attention to digital work environments, empirical evidence on the combined effects of managerial practices on these employee outcomes remains limited. This study examines how recognition initiatives, performance objectives, performance feedback, and online team development influence work motivation and employees' sense of belonging in remote work settings. A quantitative cross-sectional survey was conducted with 320 remote and hybrid employees, and the data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The measurement model demonstrated satisfactory reliability and validity across all constructs. The structural model revealed that clear performance objectives and online team development significantly enhance both work motivation and sense of belonging, while performance feedback positively influences employees' sense of belonging but does not significantly affect work motivation. Recognition initiatives showed no statistically significant effects on either outcome. These findings highlight that goal clarity, collaborative virtual team-building, and continuous communication are more influential than recognition alone in promoting employee engagement and social connectedness in digital workplaces. The study extends the remote work literature by providing an integrated framework that explains how managerial practices shape motivational and relational outcomes in virtual environments. Practically, the findings offer evidence-based guidance for organizations seeking to strengthen employee engagement, team cohesion, and sustainable organizational performance in increasingly digital workplaces.

Keywords

Digital work environment · Employee motivation · Sense of belonging · Performance management · Virtual teams

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Springer proceedings

Five published volumes. One forthcoming.

The forthcoming proceedings of the sixth ISPC add 72 contributions to the five volumes already published in Springer Proceedings in Business and Economics.

2026Forthcoming proceedings · 72 contributions
2025Digital Management for a Fast-Changing World
2024Digital Management and Artificial Intelligence
2023Digital Management to Shape the Future
2022Chances and Challenges of Digital Management
2021Digital Management in Covid-19 Pandemic and Post-Pandemic Times

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