This study aims to provide foundational data for developing sports non-fungible token (NFT) marketing strategies and enhancing fan experiences by analyzing public sentiment and semantic structures of NBA NFTs. Social big data were collected between 2021 and 2025 from six global platforms (Google, YouTube, Twitter, Reddit, Yahoo, Quora) using the TextoM platform. The analyses conducted included text mining, sentiment analysis, semantic network analysis, and CONCOR analysis. Central keywords included NFT, NBA, TopShot, player, team, marketplace, and crypto. Sentiment analysis indicated 67.5% positive and 32.5% negative sentiment. Semantic network analysis revealed a structure centered around NFT, community, news, game, and blockchain. CONCOR analysis identified five clusters: NFT Infrastructure, NBA Branding, Market Economy, Community Engagement, and Temporal Context. Overall, NBA NFTs are perceived as technical assets and as emotionally driven, identity- and community-centered content. The findings of this study offer significant practical implications for practitioners and managers in the sports industry by guiding the development of marketing strategies that integrate emotional engagement and multidimensional consumer value. This study contributes to the emerging literature on sports NFTs by providing exploratory discourse-level insights into how NBA NFTs are discussed across online platforms and by identifying themes that may inform future theory-driven research at the consumer level.
Accurately quantifying enterprise digital capability is fundamental to evaluating industrial modernization, yet conventional empirical inquiries predominantly rely on single-dimensional proxy variables or unweighted keyword counts, failing to capture the multidimensional integration of digital assets. Moving beyond causal regression paradigms and reductionist metrics, this inquiry develops a comprehensive, objective Digital Readiness Index (DRI) for physical manufacturing enterprises using an information-theoretic Entropy Weight Method (EWM). Grounded in multi-source text-mining disclosures and corporate balance sheets across 41,756 firm-year observations of Chinese A-share listed manufacturing enterprises spanning 2000 to 2025, the evaluation framework integrates eight discrete operational indicators across three dimensions: Technical Depth (AI, Big Data, Cloud Computing, Blockchain, and Digital Applications), Intangible Capital Endowments, and Governance Oversight. Objective entropy weighting demonstrates that specialized frontier technologies, particularly Blockchain (w=37.81%), Artificial Intelligence (w=15.95%), Big Data Analytics (w=15.93%), and Cloud Computing (w=15.66%)—constitute the primary sources of informational divergence across manufacturing firms. Longitudinal trajectory evaluation reveals a sustained upward trajectory in mean digital readiness, accelerating markedly after the 2015 macroeconomic policy inflection point. Non-parametric Gaussian Kernel Density Estimation uncovers a distinct dynamic polarization pattern, characterized by a shifting rightward distribution and an elongating upper tail. Cross-sectional decomposition establishes substantial structural disparities: high-tech sectors such as Computers and Electronics exhibit the highest mean digital readiness (DRI=16.28), whereas chemical and pharmaceutical sectors display persistent digital inertia (DRI≈4.06). Furthermore, non-state-owned enterprises (Non-SOEs) systematically outperform state-owned enterprises (SOEs) across all asset scale tiers. These findings provide an objective measurement tool and benchmark for corporate technology auditing and industrial policy calibration.
The study involved a total of 217 respondents. consisting of key stakeholders in higher education, including undergraduate and postgraduate students, academic staff, and administrative personnel. The respondents were selected using a purposive sampling technique to ensure they had relevant experience and understanding of academic data management systems. Among the participants, the majority were students, representing approximately 65%, followed by academic staff at 20%, and administrative personnel at 15%. In terms of gender distribution, 54% were female and 46% were male. Most respondents were aged between 18 and 30 years, reflecting a digitally active population familiar with emerging technologies. Additionally, a significant proportion of respondents reported prior exposure to digital academic systems, while a smaller percentage demonstrated awareness of blockchain technology applications in education. This distribution ensures that the collected data reflects diverse perspectives within the academic ecosystem and supports the reliability of the analysis conducted using Structural Equation Modeling-Partial Least Squares (SEM-PLS).
Alicja Fandrejewska, Monika Eisenbardt, Tomasz Eisenbardt
The chapter provides a comprehensive overview of the functioning of contemporary consumers and services within the digital landscape, emphasizing the transition from traditional marketing and classical market models to customer-oriented approaches in online environments. It examines the impact of rapid technological development, data-driven processes, and the emergence of the information society on the evolution of digital services. Particular attention is given to key technological trends, including e-commerce, e-banking, e-health, e-learning, cloud computing, the Internet of Things, blockchain, and artificial intelligence, as well as to the opportunities and risks associated with these transformations, such as disinformation and increasing uncertainty. The chapter argues that contemporary consumers operate in a complex, dynamic, and highly digitized environment, in which services are increasingly intangible, personalized, and data-driven. It concludes by linking these considerations to the research approach and presenting selected empirical findings related to the issues discussed.
Purpose This study aims to examine how digital cultural values, collaboration, innovation and customer-centricity enable successful technological adoption in the banking sector's digital transformation journey. It explores how emerging technologies such as artificial intelligence (AI), machine learning (ML), blockchain and metaverse-based interfaces are integrated to enhance customer experience and operational efficiency, with emphasis on the role of shared values in shaping strategy, leadership and organizational readiness. Design/methodology/approach A qualitative, case-based exploratory design is adopted. Data were collected through semi-structured interviews with senior managers across strategy, innovation, technology and customer experience functions. These were supplemented with secondary sources, including policy documents, digital strategy reports and industry analyses. Thematic analysis was used to identify cultural patterns and organizational factors influencing digital adoption in a regulated banking context. Findings The findings show that digital cultural values are critical enablers of successful technological adoption. Collaboration enhances cross-functional coordination and accelerates integration of emerging technologies. Innovation fosters experimentation and openness to AI, ML and immersive tools. Customer-centricity ensures that digital investments improve accessibility, transparency and service quality. Collectively, these values strengthen adaptability, operational efficiency and ecosystem integration, highlighting that cultural alignment is as important as technological capability in digital transformation. Originality/value The study positions digital cultural values as central enablers of technology adoption, extending digital transformation literature beyond technological capability perspectives. It contributes to theory by showing how shared values mediate the relationship between emerging technologies and service transformation in regulated banking environments. Practically, it offers guidance for building culturally aligned digital strategies that improve adoption, trust and customer experience.
Omar Al-Jamili, Abdulaziz Fahmi Omar Faqera, Mohd Adan Omar, Shehu M. Sarkintudu · 8 authors
Open Government Data (OGD) has become central to digital transformation and data-driven governance, yet scholarly understanding of how OGD initiatives progress from initial adoption to sustained institutionalization remains fragmented. This study aims to synthesize the existing literature and develop an integrative framework that explains the socio-technical mechanisms underpinning the long-term sustainability and value creation of OGD initiatives. The study integrates bibliometric analysis with a systematic literature review of 481 peer-reviewed articles published between 2010 and 31 December 2024. Quantitative science-mapping techniques are combined with qualitative thematic synthesis to capture the intellectual structure, technological evolution, and theoretical foundations of OGD research. The findings reveal rapid growth and thematic diversification in OGD scholarship, with increasing attention to advanced technologies such as artificial intelligence and blockchain. However, the literature remains theoretically fragmented across behavioral, institutional, and public-value perspectives. Two critical gaps are identified: insufficient theorization of institutional legitimacy as a driver of continuity, and limited exploration of user-centric governance mechanisms shaping sustained data reuse. To address these gaps, the study proposes the Socio-Technical Institutionalization Model (STIM), which conceptualizes OGD sustainability as the dynamic alignment of technological infrastructures, institutional arrangements, and user ecosystems. By combining quantitative science mapping with systematic thematic synthesis and proposing the STIM lifecycle framework, this study offers an integrative synthesis that extends prior OGD reviews. The framework bridges fragmented theoretical perspectives and explains how open data initiatives may evolve from adoption to institutionalized value creation within complex digital governance ecosystems.
Research methodology This case was developed using secondary research methods. Information was collected from publicly available sources including company annual reports, investor presentations, regulatory publications, analyst commentary and reputable media outlets such as Bloomberg and the Financial Times. Industry reports from consulting organizations and international institutions were also used to contextualize developments in the global fintech ecosystem. No primary interviews or confidential company data were used in preparing this case. This case contains no disguised information; all organizations, individuals, financial data and events referenced are real and drawn exclusively from publicly available sources. As this case was developed entirely from publicly available secondary sources and involved no human participants, institutional ethics review board approval was not required. This case offers a distinctive contribution to the published teaching case literature on fintech strategy and platform renewal. While existing cases on fintech strategy tend to focus on a single dimension of disruption, such as Stripe’s developer-led payment infrastructure, Apple’s device ecosystem lock-in or the regulatory challenges facing individual cryptocurrency platforms, this case uniquely combines three simultaneous strategic challenges within a single narrative: the deployment of artificial intelligence (AI)-enabled commerce capabilities, the early-stage integration of stablecoin infrastructure through PYUSD and the organizational complexity created by a decade of acquisition-driven expansion across Venmo, Braintree, Honey and Xoom. No published case in the fintech or platform strategy literature, to the author’s knowledge, addresses this particular combination of AI governance, digital asset experimentation and acquisition integration fragmentation within the context of a large, regulated incumbent facing embedded finance disruption. This case therefore provides a pedagogically distinctive vehicle for exploring strategic renewal in digitally regulated industries. Case overview/synopsis This case places students in the position of Alex Chriss, the newly appointed Chief Executive Officer of PayPal, as he prepares for a critical board strategy review in October 2024. Despite leading one of the world’s largest digital payments platforms, processing over $1.5tn in total payment volume annually and serving more than 430 million active accounts globally, Chriss inherited a company under significant strategic pressure. Revenue growth had slowed, share price performance was deteriorating and analysts were increasingly describing PayPal as a mature incumbent rather than a platform innovator. The organizational inflection point is a dilemma with no comfortable resolution. Chriss must choose a strategic direction to present to the board ahead of PayPal’s quarterly earnings announcement, but every available path carries a different form of risk. Moving aggressively into AI and decentralized finance offers innovation momentum but risks operational disruption and regulatory overexposure across dozens of regulated markets. Consolidating the core platform is operationally safer but risks confirming the narrative that PayPal has lost its competitive edge. Pursuing fintech partnerships accelerates capability building but reduces strategic control. Leading on Environmental, Social, and Governance (ESG) and responsible digital finance builds long-term legitimacy but delivers limited near-term growth. Investors want visible transformation. Merchants need stability. Regulators demand discipline. Employees are already stretched from restructuring. No single option satisfies all of these demands simultaneously, and PayPal’s resource constraints mean Chriss cannot pursue all four directions at once. The case draws on dynamic capabilities theory, the resource-based view, platform ecosystem theory, stakeholder theory and embedded finance literature, and requires no prior knowledge of fintech or payment systems. Complexity academic level This case is appropriate for MBA and Executive MBA courses in strategic management, innovation management and financial technology. It may also be used in final-year undergraduate courses addressing platform strategy, digital transformation or fintech ecosystems. The case is suitable for both in-person and online classroom delivery.
FinTech, Crowdfunding, Digital Finance
Innovations and Analysis in Business and Education
In modern business and trade, digital transformation (DT) has become a key factor in gaining a competitive edge. Rapid developments in blockchain, big data analytics, cloud computing, artificial intelligence (AI), and the Internet of Things (IoT) are changing company models, value generation workflows, and organizational strategies. By combining organizational, strategic, and technological viewpoints, this study offers a multifaceted examination of digital transformation. Secondary data from peer-reviewed literature, international industry publications, and corporate disclosures of top companies, such as Amazon, Alibaba Group, Microsoft, and Tesla, Inc., were analyzed using a descriptive and analytical research design. The study creates a conceptual framework that connects performance results, transformation processes, and digital drivers. The results indicate that ecosystem integration, organizational agility, digital capability development, and strategic alignment are necessary for a successful digital transformation. The paper contributes to digital transformation literature by combining findings from several sectors and putting forth an integrated strategic model that can be empirically validated in further studies; the paper adds to the body of knowledge on digital transformation.