Innovation and Entrepreneurship: Theory and Practice

Permanent URI for this collectionhttps://hdl.handle.net/10125/112524

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Small College Technology Transfer - Options and Efficacy of Oversight Frameworks
(2026-01-06) Likarish, Daniel; Moore, Erik; Fulton, Steven
This case study defines processes that were developed to initiate a technology transfer program at a university that was expanding beyond teaching and research to include commercialization opportunities for the faculty. The case analyzes how the university’s effort to support a particular research group triggered the development of technology transfer processes to be used across the institution. In addition, an analysis of institutions with technology transfer offices provides context for the case. Several models are applied to the case including Carayannis’ Quintuple Helix model and Chen’s stages of development. The conclusion provides insights on how the drivers to improve faculty culture and societal good opened up a new pathway sustainable with a leaner and more long-term funding model.
Reclaiming a Pedagogy for Entrepreneurial Creativity: A Perspective Based on Intellectual Agility and Design-driven Entrepreneurship
(2026-01-06) Iandoli, Luca; Patel, Nadya Shaznay; Zollo, Giuseppe
Market validation-driven pedagogies, such as Lean Startup and Design Thinking, have become widely popular over the last two decades. By emphasizing hypothesis testing, these approaches risk marginalizing the cultivation of inquiry, imagination, questioning, and reframing entrepreneurial ideas before they are tested. This position paper presents a pedagogical framework to support intellectual agility in iterating between validation and speculation, thereby improving the generation of market hypotheses. We carry out a preliminary review of empirical and conceptual studies in Design-driven Entrepreneurship Education (EE) pedagogy, guided by the criteria of emphasizing early ideation, reflective inquiry, and futures-oriented learning. Building on this literature, we identify five types of transformations (material, conceptual, linguistic, behavioral, and temporal) to support intellectual agility and integrate creative inquiry with market hypotheses validation. Using our framework, we identify and classify methods that can foster creative inquiry in EE. These include reflective prototyping, metaphor-based reframing, futures thinking, constraint-driven storytelling, identity exploration, and anticipatory design. We advocate for constraint as a catalyst for entrepreneurial creativity, challenging EE to move beyond idea testing toward transformative ideation capable of supporting learners’ intellectual agility and their ability to navigate complexity and leveraging ambiguity as a resource.
Cross-Border Exits in Venture Capital: Evidence from the Global AI Ecosystem
(2026-01-06) Zava, Marta
This paper investigates whether startups backed by foreign venture capital (VC) investors are more likely to achieve cross-border exits - namely, IPOs or acquisitions in the foreign investor’s home country. We argue that the effect is stronger in geographically unbounded sectors, such as Artificial Intelligence (AI), where market access and scalability transcend national borders. Using a dataset of 21,312 global VC-backed startups (deals from 2020 to 2024), we estimate a logistic regression model that incorporates investor origin, sector characteristics, and their interaction. Our findings suggest that foreign backing significantly enhances the likelihood of cross-border exits in AI, but not in more bounded industries such as healthcare, energy, or media. The results have implications for cross-border capital flows, startup strategy, and international VC syndication.
Is Integration the New Incubation? A Systematic Literature Review on the Shift from Supply to Demand Models of Corporate-Startup Engagement
(2026-01-06) Kotzian, Tobias; Przybilla, Leonard; Kernstock, Philipp; Krcmar, Helmut
As corporations increasingly adopt open innovation strategies, engaging with startups has become vital. Corporate accelerators (CA) and venture clienting (VCL) offer distinct approaches to startup collaboration: supply-driven incubation and demand-driven integration. While CAs have been widely implemented, their impact on long-term innovation integration remains debated. VCL emphasizes direct business application, but is only emerging in research. Drawing on a systematic literature review of 46 publications, we analyze each model’s key phases and how challenges manifest in design and outcome alignment. Our findings highlight that accelerators support exploratory innovation and ecosystem engagement but lack mechanisms for adoption. VCL promotes problem-driven, measurable innovation with higher demands on startup maturity. Despite its increasing adoption in practice, the lack of academic research on VCL is surprising. This study contributes a conceptual foundation for future empirical studies and calls for deeper investigation into VCL’s mechanisms and startups’ perspectives.
Polarized Distinctiveness: How Platform Designs and Superstar Connections Shape Crowdfunding Success
(2026-01-06) Pan, Jingwen; Jiang, Haochen; Lu, Angela; Tan, Chee-Wee
Crowdfunding platforms often see a few top campaigns succeed while most struggle. Optimal distinctiveness theory (ODT) suggests campaigns need to balance fitting in and standing out, but platform designs can interfere with this balance. This study examines how Indiegogo's “keep-what-you-raise” model changes optimal distinctiveness levels. We find a U-shaped relationship between distinctiveness and crowdfunding success. In contrast to the “all-or-nothing” model that favors moderate distinctiveness, the “keep-what-you-raise” model demands that campaigns either closely conform or be exceptionally distinct to succeed. Collaborations with superstars steepen this U-shape, amplifying penalties for moderate distinctiveness while boosting gains for extremes through knowledge transfer and legitimacy spillovers. We extend ODT theoretically to demonstrate how platform dynamics disrupt conventional balancing acts by incentivizing strategic extremism. Methodologically, we advance the distinctiveness measurement through a contextualized heterogeneous graph neural network. Practically, our findings guide campaigners to adopt platform-specific distinctiveness strategies and advise platforms to design mechanisms that support niches.
The Path to Comprehensiveness: An LLM-Enhanced Systematic Literature Review on the Innovation Mindset
(2026-01-06) Kulturel-Konak, Sadan; Konak, Abdullah; Passerini, Katia; Bandera, Cesar; Bartolacci, Michael
The study of the innovation mindset is not a new endeavor within and outside business and management. However, most of the studies and meta-analyses that have been undertaken on the topic rely on manual coding or simple keyword filters, thereby possibly missing some key artifacts due to the sheer scope of the daunting task. In this work, we try to overcome the comprehensiveness problem by introducing a multi‑LLM ensemble pipeline that integrates DeepSeekR1, Llama3, and QWEN models to retrieve, classify, and thematically cluster scholarly articles. Applying the pipeline to 106 peer‑reviewed publications, we identify four recurrent themes: (A) Creativity‑Risk Synergy, (B) Innovation Capacity, (C) Entrepreneurial Orientation, and (D) Adaptability and Problem Solving. These combinations improve over author‑supplied keywords, demonstrating the methodological value of using LLM models. The identified themes clarify the key needs for further research into the innovation mindset and offer an agenda for future explorations in information systems sciences.
Quantifying Founder-Market Fit: A Machine Learning Approach to Startup Success Prediction
(2026-01-06) Gonchar, Ekaterina; Diaz, Sebastian; Schmidt, Benjamin; Yadav, Priyanshu; Han, Qiwei
The high failure rate of early-stage startups poses persistent challenges for venture capitalists and innovation policymakers alike. Although Founder-Market Fit (FMF), defined as the alignment between a founder's background and the domain of their startup, has rarely been systematically quantified, it is widely acknowledged in practice as a key determinant of success. In this paper, we present a novel, data-driven framework to operationalize and predict FMF using machine learning and natural language processing. We construct high-dimensional representations of founder profiles by aggregating structured data from Crunchbase, LinkedIn, and X, and apply transformer-based embeddings to quantify semantic alignment with industry verticals. FMF scores, together with features related to prestige, experience, seniority, and inferred personality traits, are incorporated into supervised models to predict startup success. Our findings show that FMF significantly improves predictive performance over baseline models and remains robust across weighting schemes and learning algorithms. By providing a scalable, interpretable, and auditable approach to founder evaluation, this study advances algorithmic entrepreneurship and offers practical insights for investors, accelerators, and policymakers seeking to improve early-stage startup assessments.
Introduction to the Minitrack on Innovation and Entrepreneurship: Theory and Practice
(2026-01-06) Bandera, Cesar; Bartolacci, Michael; Passerini, Katia; Kulturel-Konak, Sadan

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