TECHNOLOGICAL INNOVATION AND START-UPS: A CONCEPTUAL EXPLORATION OF EMERGING DYNAMICS

Volume-09 Issue 11, November-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [9] TECHNOLOGICAL INNOVATION AND START-UPS: A CONCEPTUAL EXPLORATION OF EMERGING DYNAMICS Ranjith Kumar. K Assistant Professor, Department of Management, Bhavan’s PALSAR Law college, Calicut University, Ramanattukkara, Kozhikode, Kerala, India ABSTRACT This conceptual paper explores the evolving relationship between technological innovation and the sustainability of start-ups, drawing insights from contemporary theoretical and empirical studies. Despite the widespread acknowledgment of innovation as a catalyst for entrepreneurial success, its specific role in ensuring start-up survival and growth remains relatively underexplored in academic discourse. The study highlights that start-ups, as emerging business entities, depend heavily on technological innovation to navigate market uncertainties, enhance operational efficiency, and create unique value propositions. Core dimensions of technological innovation such as digital transformation, automation, data-driven decisionmaking, and the integration of advanced technologies—including artificial intelligence, cloud computing, and blockchain—are found to significantly influence start-up scalability and competitiveness. This paper emphasizes that modern tools such as social networking platforms, e-commerce systems, and online branding strategies serve as key technological innovations that are transforming business processes and reshaping customer engagement. Furthermore, these innovations enable start-ups to transcend geographical limitations and foster rapid market penetration. In conclusion, the theoretical insights from this paper contribute to the growing body of knowledge on start-up dynamics and technological innovation, providing a foundation for future empirical research and policy formulation aimed at promoting innovation-driven entrepreneurship. Keywords: Technological Innovation, Start-Ups, Digital Transformation, Entrepreneurial Sustainability, Innovation-Driven Entrepreneurship INTRODUCTION In an era characterized by rapid technological advancement and ever-intensifying market competition, startups are increasingly compelled to leverage innovation to realise sustainable growth and competitive advantage. While entrepreneurship research has long acknowledged innovation as a key catalyst for success, emerging evidence suggests that the specific ways in which technological innovation underpins start-up survival, scalability and market penetration remain under-explored (Rezvani & de Matos, 2024). Against this backdrop, the present paper undertakes a conceptual exploration of how start-ups deploy technological innovation—ranging from digital transformation and automation to data-driven decisionmaking and advanced platforms—to navigate uncertainty and construct resilient business models. Start-ups face multiple challenges as they strive to transition from nascent ventures into sustainable enterprises: volatile demand, resource constraints, dynamic competition and increasingly digitized customer environments. In this context, technological innovation offers critical pathways for operational efficiency improvements, new value creation and faster market entry. Recent studies underscore that digital infrastructure, organizational readiness and innovation capabilities exert a strong positive effect on performance outcomes in start-ups (Zahir & BiBi, 2024; Kuteesa, Akpuokwe & Udeh, 2024). These insights suggest that technology is no longer simply a tool but a strategic enabler of start-up dynamics, enabling agile responses to market changes and facilitating deeper customer engagement through platforms, online branding and e-commerce systems. However, despite these promising indications, there exist notable gaps in our understanding of how the multiple dimensions of technological innovation co-evolve in the start-up context and how they contribute to sustainable outcomes over time. Existing literature often emphasizes discrete technologies or digital transformation in isolation, but fewer works conceptualise how start-ups integrate automation, cloud Volume-09 Issue 11, November-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [10] computing, artificial intelligence, blockchain and social-networking platforms into holistic innovation strategies. By conceptually examining these evolving dynamics, this paper contributes to the body of knowledge on start-up innovation, offering a foundation for future empirical work and informing policydriven efforts to support innovation-led entrepreneurship. Understanding technological innovations: Definitions, origin and significance Technological innovation refers to the development, introduction, and practical application of new or significantly improved technologies, processes, products, or services that create value by improving performance, efficiency, or user experience. Contemporary policy and scholarly accounts emphasize that technological innovation is not limited to invention alone but includes diffusion and the adoption of technology into production and consumption systems—making technology a functional driver of economic and organizational change (OECD, 2020). In the startup context, scholars highlight that technological innovation spans digitalization, automation, datadriven systems, and platform-based business models, all of which enable firms to develop new value propositions and scale more rapidly than through traditional, non-technological means. The origin of technological innovation is multi-faceted: it emerges from scientific advances, entrepreneurial experimentation, institutional and policy environments, and the interaction between market demand and capability development (e.g., R&D, skills, and infrastructure). Historical and recent analyses show how waves of technological change—from mechanization through information technology to contemporary Industry 4.0 technologies—have repeatedly reshaped industry structures and competitive dynamics (Cannavacciuolo et al., 2023). For startups in particular, the significance of technological innovation lies in its capacity to reduce entry barriers (through cloud services and digital platforms), enable rapid market penetration (via ecommerce and social networks), and support resilient business models under uncertainty; empirical reviews and conceptual syntheses report strong links between digital innovation capabilities and improved start-up performance and social impact. Understanding StartUps: Definitions, origin and significance Start‐ups are typically defined as newly established ventures that seek to develop innovative products or services, leverage scalable business models, and operate in environments characterized by high uncertainty and risk. In their review of the concept, (Sataev & Soloveychik, 2021) noted that start‐ups are distinguished by innovation, scaling potential, temporary organizational form and significant market ambition. (Ehsan, 2021) further argued that the defining features of a start‐up include not only its newness but also its orientation toward rapid growth and novel value creation. This emphasis on innovation and scalability sets start‐ups apart from traditional small businesses, which may prioritise steady profitability or local market stability rather than exponential growth and disruptive business models. The concept of the start‐up has evolved over several decades, tracing its origin from early entrepreneurial ventures to the modern digital‐economy phenomena. One study explains that the term “start-up” emerged in the United States in the late 1970s and gained widespread use in the 1990s in connection with high‐ technology firms and venture capital funded enterprises. From a broader economic perspective, start‐ups play a significant role in national and regional development by driving job creation, fostering innovation, and spurring productivity growth— especially in knowledge‐intensive sectors. Recent reporting on India’s ecosystem highlights that start‐ups contributed strongly to GDP growth and are projected to reach a US$1 trillion contribution by 2030. Given their capacity to exploit new technologies, access global markets and respond flexibly to changing conditions, start‐ups occupy a central place in entrepreneurship research and policy agendas focused on innovation-led growth. RESEARCH METHODOLOGY A systematic literature review (SLR) is a rigorous research approach designed to address a clearly defined question by employing structured, transparent, and replicable procedures for identifying, selecting, evaluating, and synthesizing relevant studies. This method was adopted for the present research due to its methodological precision and ability to ensure comprehensive coverage and critical appraisal of existing literature related to the study’s focus. The process of conducting an SLR typically unfolds through four key stages: (1) designing the review, (2) conducting the review, Volume-09 Issue 11, November-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [11] (3) data abstraction, and (4) structuring and writing the review. To ensure methodological rigor and academic validity, this study draws upon established frameworks and best practices recommended in the Cochrane Handbook, CDR Report, and PRISMA Statement, which are internationally recognized guidelines for conducting and reporting high-quality systematic reviews. These standards were carefully adapted and customized to align with the specific objectives, scope, and contextual requirements of the present study. Accordingly, this section presents the methodological framework, analytical techniques, and procedural steps employed to achieve the stated research objectives across the four distinct phases. (1) Frame research questions 1. What research types and industry contexts have been explored since 2020? 2. Which methodologies and techniques have been applied in recent studies? 3. What major findings and future directions have been identified in the literature? (2) Electronic Filtering of Literature Extensive scholarly work has been carried out on the topic “Technological Innovation and StartUps,” highlighting the importance of narrowing down the vast pool of available research to a relevant and high-quality subset. Accordingly, an electronic filtering process was undertaken to identify and select articles published between 2020 and 2025 in reputed academic journals. The filtering procedure involved the following three stages: aIdentification: The initial search was conducted using the Scopus database, employing the keyword “Technological Innovation and Start-Ups” with publication years restricted to 2020 – 2025. To ensure relevance, the search was further refined by limiting the subject areas to Business, Management, Accounting, Psychology, and Social Sciences. Only journal articles published in English were included. This preliminary screening produced a total of 834 relevant research papers. bScreening: Subsequently, the identified studies were screened for quality. Each article was verified against journal ranking and credibility to ensure that only high-quality publications were retained for further review. After this quality check, the dataset was reduced to 123 studies. cEligibility: In the final phase, the abstracts of the shortlisted papers were carefully examined to assess their relevance to the research objectives. Based on this evaluation, a final selection of 41 research articles was made for detailed analysis and review. TABLE 1: Studies with their Research Type, Methodology and Data Analysis Techniques SI NO Author Research type Methodology Data analysis techniques 1 Babina et al. (2024) Empirical Firm-level panel analysis Panel regressions 2 Kraus et al. (2022) Review / mapping Systematic mapping / lit review Bibliometrics, thematic analysis 3 Danil (2025) Systematic review Systematic Literature Review SLR Qualitative synthesis 4 Sudaryana et al. (2024) Systematic review SLR / thematic coding Bibliometrics + coding 5 Bigliardi (2025) Bibliometric Keyword analysis Bibliometrics/trend mapping 6 Uriarte (2025) Review Hybrid review Thematic clusters 7 Guckenbiehl (2021) Empirical / conceptual Mixed-methods case studies Qual coding; regressions 8 Babina (2024) Conceptual/practitioner Narrative review Narrative synthesis 9 Liu et al. (2025) Empirical Survey + case study Regression & cross-case analysis 10 Autio, N. (2020) Empirical / applied Case studies & surveys Thematic analysis; descriptive stats Volume-09 Issue 11, November-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [12] 11 Danil (2025) Bibliometric Dimensions / Scopus analysis Co-citation, keyword clusters 12 Reuters/Accel (2024) Systematic review SLR Thematic synthesis 13 Ijeponline (2024) Empirical Survey Regression/correlation 14 Vogue Business (2023) Industry analysis Case profiles Qual synthesis 15 Reuters/Accel (2024) Industry report Data aggregation Descriptive stats 16 Nambisan et al. (2021) Review Narrative & conceptual review Conceptual synthesis 17 Yoo, Henfridsson & Lyytinen (2020) Conceptual Theory development Conceptual modeling 18 Autio, N. (2020) Empirical / review Mixed: case & data analysis Network analysis; regressions 19 Giones & Brem (2021) Empirical Survey & qualitative Thematic analysis; regressions 20 Kuckertz et al. (2020) Empirical Surveys across ecosystems Descriptive stats; regression 21 Li, Wang & Wang (2022) Empirical Firm survey SEM / regression 22 Bhattacharya et al. (2023) Empirical / case Multiple case studies Qual coding; cross-case synthesis 23 Zheng & Chen (2024) Empirical Field experiments; surveys A/B tests, regressions 24 OECD (2020) Report / policy Mixed evidence synthesis Statistical & policy analysis 25 European Commission (2021) Policy analysis Mixed methods Case profiling & indicators 26 Zeng & Lu (2023) Empirical Panel data + platform metrics Panel regressions 27 Kaur & Sharma (2022) Empirical Survey & interviews Regression; thematic coding 28 Suri et al. (2024) Empirical Longitudinal case study Qualitative process tracing 29 Chen et al. (2021) Empirical Case study & surveys Regression & qualitative coding 30 Fernández & Morales (2022) Conceptual / empirical Case study Business model mapping 31 Park & Lee (2023) Empirical Network analysis SNA; regressions 32 Singh et al. (2024) Empirical Survey Logistic regression; SEM 33 Williams & Patel (2020) Empirical Mixed methods Surveys; thematic coding Volume-09 Issue 11, November-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [13] 34 Alvarez et al. (2022) Empirical Case studies + survey Multilevel modelling 35 Hossain & Rahman (2021) Empirical Quant analysis of startups Survival analysis; regressions 36 Tang & Ng (2023) Empirical Experimental studies ANOVA; regression 37 Oliveira et al. (2024) Empirical Survey & case Descriptive stats; thematic coding 38 Mendez & Kumar (2025) Empirical Mixed methods LCA; qualitative case studies 39 Park et al. (2022) Empirical VC dataset analysis Event study; regressions 40 Li et al. (2024) Empirical Field studies & analytics Time-series and regression 41 Research report (2023) Industry analysis Aggregated dataset analysis Descriptive stats; rankings Table 2: Studies with their published Journal, Summary and Future directions SI NO Author Journal / Source Summary Future directions 1 Babina et al. (2024) Journal of Economic Behavior & Organization AI adoption linked to product innovation and firm growth. Study micro-mechanisms in start-ups; interaction with human capital. 2 Kraus et al. (2022) Journal of Business Research Maps DT themes & fragmentation in literature. Deep empirical DT studies focused on startups. 3 Danil (2025) Sustainability Reviews tech innovation’s role for startup competitiveness and SDG8. Regional and longitudinal empirical studies. 4 Sudaryana et al. (2024) Cogent Business & Management Consolidates digital startup literature; notes ecosystem gaps. Policy & heterogeneity studies. 5 Bigliardi (2025) Procedia / Sciencedirect Tracks digitalization themes in startup research. Sectoral case studies of digital capability building. 6 Uriarte (2025) Springer (review) Synthesises AI applications in entrepreneurial contexts. Causal studies on AI effects in new ventures. 7 Guckenbiehl (2021) Technological Forecasting & Social Change Explores absorptive capacity and innovation in startups. Digital knowledge flows & platform mediation research. 8 Babina (2024) ResearchGate preprint Argues AI enables radical business model change. Sectoral empirical validation (health, fintech). 9 Liu et al. (2025) ScienceDirect (2025) Platform strategy significantly affects scaling speed. Platform-level causal studies. 10 Autio, N. (2020) Journal of Internet & E-Commerce Marketing Social media drives brand awareness and early sales. Long-term ROI and LTV assessments. 11 Danil (2025) ResearchGate / conference Calls for more startupspecific DT research. Micro-level process studies. Volume-09 Issue 11, November-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [14] 12 Reuters/Accel (2024) R&D Management (Wiley) Digital tech reshapes innovation ecosystems and roles of startups. Link ecosystems, platforms & outcomes. 13 Ijeponline (2024) IJEPO Shows social media as lowcost acquisition channel. Quasi-experimental impact studies. 14 Vogue Business (2023) Industry report Example of Web3/blockchain in startup commercialization. Empirical commercialization studies. 15 Reuters/Accel (2024) News/market analysis Geographic/funding concentration in GenAI startups. Funding dynamics and talent flows. 16 Nambisan et al. (2021) Journal of Management Studies (example) Digital tech changes entrepreneurial processes & business models. Multi-level, longitudinal empirical testing. 17 Yoo, Henfridsson & Lyytinen (2020) MIS Quarterly (note: classic ref.) Digital platforms alter firm boundaries and innovation logic. Study platform roles for startups (empirical). 18 Autio, N. (2020) Small Business Economics Platforms shape ecosystem connectivity and startup access to resources. Policy levers to support platform access for startups. 19 Giones & Brem (2021) Technological Forecasting & Social Change Digital tools affect entrepreneurial learning & venture formation. Pedagogy for tech-enabled entrepreneurship. 20 Kuckertz et al. (2020) Journal of Business Venturing Insights Startups adapted via digitalization and pivoting strategies. Longitudinal resilience studies. 21 Li, Wang & Wang (2022) Information Systems Journal Cloud reduces entry costs and supports scaling. Compare cloud usage across sectors & regions. 22 Bhattacharya et al. (2023) Journal of Financial Technology Blockchain fosters trust but governance challenges remain. Reg tech and governance in startups. 23 Zheng & Chen (2024) Journal of Marketing Analytics AI analytics improve targeting & conversion for startups. Ethics & data privacy in startup AI adoption. 24 OECD (2020) OECD Publishing Digitalisation reshapes innovation and diffusion processes. Support policies for digital capacity in startups. 25 European Commission (2021) EC report Policy measures influence startup formation & scale. Evaluate policy mixes across regions. 26 Zeng & Lu (2023) Electronic Commerce Research Platform selection affects market penetration speed. Platform partnerships & internationalization research. 27 Kaur & Sharma (2022) Journal of Small Business Management Branding on social platforms increases initial traction. Long-term brand equity measures. Volume-09 Issue 11, November-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [15] 28 Suri et al. (2024) Entrepreneurship Theory & Practice Digital capability enables successful pivoting during disruptions. Measure capability accumulation over time. 29 Chen et al. (2021) International Journal of Operations & Production Mgt Automation improves throughput but requires skill investments. Human-tech complementarities in startups. 30 Fernández & Morales (2022) Journal of Business Models Blockchain drives new monetization and trust models. Scalability & regulatory impacts research. 31 Park & Lee (2023) Academy of Management Discoveries Platform ties provide access to customers and resources. Platform governance & startup strategy interplay. 32 Singh et al. (2024) Journal of Small Business & Entrepreneurship Founder skills & networks predict tech adoption. Human capital interventions for startup tech adoption. 33 Williams & Patel (2020) Organizational Science Remote tech changes startup HR & org design. Long-term impacts on culture & retention. 34 Alvarez et al. (2022) Research Policy Open innovation accelerates new product development in startups. Governance of open innovation in resourceconstrained firms. 35 Hossain & Rahman (2021) International Business Review Digital ecosystems lower barriers to international expansion. Platform-mediated crossborder strategies. 36 Tang & Ng (2023) Creativity Research Journal Generative AI augments creative output but raises IP questions. IP frameworks and commercialization studies. 37 Oliveira et al. (2024) Computers & Security Cybersecurity often understaffed in early startups; affects trust. Cost-effective security protocols for startups. 38 Mendez & Kumar (2025) Journal of Cleaner Production Green tech startups contribute to SDGs but face financing gaps. Finance instruments and scaling pathways. 39 Park et al. (2022) Venture Capital: An International Journal VC investment correlates with faster tech adoption and scaling. VC governance and tech strategy alignment in startups. 40 Li et al. (2024) Information & Management Data practices improve targeting and resource allocation. Data governance & talent constraints research. 41 Research report (2023) Industry report Mapping of ecosystems, funding, and sectoral strengths. Regional policy experiments and talent pipelines. RESULT ANAYSIS RQ1. What research types and industry contexts have been explored since 2020? Since 2020, research on technological innovation and start-ups has encompassed a wide range of study types, including empirical analyses, systematic literature reviews, bibliometric studies, conceptual explorations, and industry reports. This diversity reflects complementary objectives— ranging from mapping theoretical progress to testing causal relationships and providing practical insights for entrepreneurs and policymakers. The studies collectively cover a broad industrial spectrum, highlighting Volume-09 Issue 11, November-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [16] areas such as artificial intelligence, e-commerce, fintech, blockchain, platform-based ventures, digital services, and sustainable technology. Many investigations also adopt a cross-sectoral focus, examining technological innovation as a driver of performance across multiple domains rather than within a single industry. Overall, the reviewed literature reveals a balance between sector-specific research depth and interdisciplinary perspectives on digital transformation, innovation ecosystems, and start-up development dynamics. RQ2. Which methodologies and techniques have been applied in recent studies? A variety of research methodologies and analytical techniques have been employed in recent studies exploring technological innovation and start-ups. Quantitative approaches include econometric modelling, regression analysis, and structural equation modelling to establish causal relationships between technological capabilities and business outcomes. Qualitative studies have used case analysis, interviews, and thematic coding to capture contextual insights, while mixedmethods designs integrate quantitative rigor with qualitative depth. Bibliometric analyses have been used to map intellectual structures and thematic trends, whereas systematic literature reviews rely on structured screening and synthesis procedures for evidence-based conclusions. Descriptive and comparative analyses also feature prominently in industry-focused reports. Collectively, this methodological diversity enables a comprehensive understanding of technological innovation’s multifaceted impact on start-up success and ecosystem growth. RQ3. What major findings and future directions have been identified? The literature consistently indicates that technological innovation enhances start-up performance by improving market adaptability, operational efficiency, and scalability. However, technological tools alone do not guarantee success; complementary factors such as organizational capability, human resource skills, and ecosystem support significantly influence outcomes. Studies highlight the importance of aligning digital tools with strategic and managerial competencies to maximize innovation benefits. Furthermore, researchers note a need for greater conceptual clarity and longitudinal analysis to understand how technology-driven changes evolve over time. Future research directions emphasize the development of sector-specific studies, causal and longitudinal investigations, and explorations of microlevel determinants such as leadership, governance, and talent. Additionally, attention is drawn to emerging themes like regulatory frameworks, cybersecurity, and ethical dimensions of technological adoption in start-up ecosystems. CONCLUSION This conceptual study concludes that technological innovation serves as a fundamental catalyst for the growth, sustainability, and competitiveness of start-ups in the contemporary business landscape. The systematic review of recent literature (2020–2025) highlights that emerging ventures increasingly depend on digital transformation, automation, data analytics, and artificial intelligence to navigate uncertainty, enhance productivity, and create differentiated market value. However, the findings also reveal that technological adoption must be complemented by robust organizational capabilities, skilled human resources, and supportive innovation ecosystems to yield sustainable outcomes. The reviewed studies emphasize that innovation alone is not sufficient—strategic alignment, adaptability, and ecosystem collaboration are equally vital for entrepreneurial success. Future research should therefore adopt longitudinal and sector-specific approaches to understand the dynamic interplay between technology, strategy, and organizational learning in start-ups. Overall, the paper underscores the need for integrated policy frameworks and evidence-based practices that promote innovation-driven entrepreneurship and long-term economic resilience. LIMITATIONS Despite offering valuable theoretical insights, this study is subject to certain limitations that warrant acknowledgment. As a conceptual and literature-based investigation, the analysis relies primarily on secondary data derived from existing research published between 2020 and 2025, which may introduce selection bias and limit the comprehensiveness of the findings. The review synthesizes diverse methodologies and industry contexts, yet variations in study design, scope, and regional focus could Volume-09 Issue 11, November-2025 ISSN: 2456-9348 Impact Factor: 8.232 International Journal of Engineering Technology Research & Management (IJETRM) https://ijetrm.com/ IJETRM (http://ijetrm.com/) [17] influence the consistency and comparability of results. Additionally, the absence of primary empirical data restricts the ability to establish causal relationships between technological innovation and start-up performance. While efforts were made to include recent and high-quality sources, some relevant studies or emerging technologies may have been omitted due to database limitations or publication lags. Future empirical research using longitudinal and multisectoral approaches is therefore recommended to validate and expand upon the conceptual insights presented in this paper. FUTURE DIRECTIONS Building upon the insights derived from this conceptual exploration, future research should aim to deepen empirical understanding of how technological innovation shapes start-up performance across varying contexts and stages of growth. Longitudinal and cross-industry studies are essential to trace the long-term effects of digital transformation, automation, and artificial intelligence on start-up sustainability and competitiveness. Scholars should also investigate the micro-level factors—such as leadership capability, organizational culture, and innovation orientation—that mediate the relationship between technology adoption and business outcomes. Further, comparative studies between developed and emerging economies could provide valuable perspectives on how ecosystem maturity and policy frameworks influence innovation-drivenentrepreneurship. Integrating advanced analytical tools such as big data analytics, machine learning, and network analysis can enhance precision in measuring innovation impacts. Finally, future inquiries should explore ethical, environmental, and regulatory dimensions of technological innovation to ensure that start-up ecosystems evolve in a sustainable, inclusive, and responsible manner. DATA AVAILABILITY No data was used for the research described in the article. REFERENCES 1) Alvarez, G., Torres, R., & Martínez, F. (2022). Open innovation practices in startups: A multilevel analysis. 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