A Behavior-Based Model of the Innovativeness Lifecycle: A Meta-Synthesis Approach

Document Type : Original Article

Authors

Majid Malekmohammadi ¹

Seyed Hamid Khodadad Hosseini ²

Kambiz Heidarzadeh Hanzaee ³

¹ Department of Entrepreneurship Management, Science and Research Branch, Islamic Azad University, Tehran, Iran

² Tarbiat Modares University

³ Entrepreneurship Department, Faculty of Management & Economics, Science and Research Branch, Islamic Azad University

https://doi.org/10.22067/tmj.2024.90344.1622

Abstract

1- INTRODUCTION
Startup ventures face exceptionally high failure rates, with empirical studies indicating that up to 90% fail within their early years. While both internal and external factors contribute to these outcomes, behavior-based factors are often more controllable-and therefore more critical-for strategic intervention. This study aims to address high startup failure rates by developing a comprehensive, behavior-oriented lifecycle model. Drawing on organizational lifecycle theories and behavioral entrepreneurship frameworks, it centers entrepreneurial actions, decisions, and adaptive behaviors as key units of analysis. Behavioral approaches are essential for decoding the complexities and dynamic transformations of startup journeys, offering practical frameworks that align with real-world entrepreneurial evolution. Accordingly, this research asks: How can behavior-based lifecycle models, synthesized through meta-synthesis, provide actionable insights into entrepreneurial challenges and decision-making across different lifecycle stages?
 
2- METHODOLOGY
A meta-synthesis approach was employed following the seven-step method developed by Sandelowski and Barroso. This approach enables a comprehensive integration of findings from a wide range of literature. An initial pool of 312 sources was identified from academic and practitioner databases (1980–2024). Following a critical appraisal using the CASP tool, 33 high-relevance articles were selected for final analysis. Keyword-driven searches included terms such as "startup lifecycle," "entrepreneurial behavior," "failure factors," and "development stages." The data were thematically coded and organized using Attride-Stirling’s thematic network framework, comprising basic, organizing, and global themes. Qualitative analysis was conducted using MAXQDA software, with inter-rater reliability and internal validity confirmed through expert review and Cohen’s Kappa coefficients (κ = 0.671 for validity; κ = 0.635 for reliability). Experts were selected via snowball sampling from experienced entrepreneurs and venture capital professionals, enhancing the practical relevance of the resulting framework.
 
3- RESULTS & DISCUSSION
The resulting behavior-oriented lifecycle model is organized into six global phases, each comprising specific organizing themes:

Ideation and Opportunity Validation: This phase focuses on recognizing and testing opportunities through market needs analysis and early prototyping. Key activities include idea development, validation of value propositions, and assessing customer fit. Common failure factors include inadequate market validation, lack of complementary team skills, and unclear strategic direction.
Preparation and Foundation: This phase includes business planning, team formation, legal setup, early MVP development, and seed funding. Risks at this stage arise from regulatory hurdles, excessive spending on R&D, and premature scaling.
Commercialization and Early Operations: This phase covers startup launch, customer validation, achieving break-even, and stabilizing operations. Failures often result from poor financial management, supply chain disruptions, insufficient customer insights, and dysfunctional team dynamics.
Growth (Primary and Secondary): Activities include market expansion, professionalization, capital fundraising (Series A–D), and process optimization. Challenges involve intense competition, failures in intellectual property protection, and inefficiencies in scaling.
Scaling and Maturity: This phase involves aggressive market penetration, IPO readiness, organizational structuring, and sustaining innovation. The risks include capital shortages, inexperienced leadership, and misaligned partnerships.
Decline, Renewal, or Exit: The final phase reflects organizational aging and bureaucratization, with potential for rejuvenation through strategic pivots or structured exits.

The model aligns with the Resource-Based View (RBV) and the theory of dynamic capabilities. RBV emphasizes the orchestration of tangible and intangible resources-human, technological, and financial-while the dynamic capabilities approach focuses on continuous learning, adaptation, and realignment. Each stage features behavioral propositions grounded in these frameworks, addressing tasks such as market sensing, team synergy, capital mobilization, and scaling agility.
 
 
4- CONCLUSIONS & SUGGESTIONS
The proposed model provides a comprehensive framework for understanding startup evolution through a behavioral lens, addressing both strategic and operational dimensions. It highlights that entrepreneurial failure is rarely caused solely by external factors but is often rooted in misaligned behaviors and decisions. Its stepwise structure facilitates proactive diagnosis and early intervention throughout the lifecycle stages. Key recommendations include:

Incorporating behavioral diagnostics into startup evaluation tools.
Utilizing the model as a training framework within incubators and accelerators.
Tailoring policy support mechanisms (e.g., funding, mentorship) to address lifecycle-specific behavioral needs.
Encouraging startups to adopt reflective practices that align team behaviors with lifecycle transitions.

 
Future research could empirically apply the model across different sectors and regions to assess its generalizability and predictive accuracy. Quantitative extensions using behavioral metrics-such as team adaptability and decision-making agility-may enhance its practical relevance. Additionally, integrating digital behavioral trace data (e.g., platform engagement, customer feedback loops) could offer deeper insights into failure prediction and lifecycle optimization.

Keywords

Startup Lifecycle

Entrepreneurial Process

Business Failure

Behavioral Approach

Meta-Synthesis

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