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Organisational Learning and Innovation Culture in Fashion Tech Startups: A Grounded Theory Study

Dipa Karmokar ORCID: https://orcid.org/ Department of Fashion Design & Technology Faculty of Design & Technology Shanto-Mariam University of Creative Technology Dhaka, Bangladesh

Prof. Dr Kazi Abdul Mannan Department of Business Administration Faculty of Business Shanto-Mariam University of Creative Technology Dhaka, Bangladesh Email: drkaziabdulmannan@gmail.com ORCID: https://orcid.org/0000-0002-7123-132X   Corresponding author: Dipa Karmokar: dipakarmokar213@gmail.com

Learn. polic. strategies. 2026, 5(2); https://doi.org/10.64907/xkmf.v5i2.lps.6

Submission received: 2 April 2026 / Revised: 20 May 2026 / Accepted: 25 May 2026 / Published: 29 May 2026

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Abstract

The rapid convergence of fashion and digital technology has led to the emergence of fashion tech startups that operate in highly dynamic and innovation-driven environments. This study examines the relationship between organisational learning and innovation culture within such startups using a grounded theory approach based on secondary qualitative data. Drawing on industry reports, academic literature, and case-based evidence, the research identifies key dimensions of organisational learning, including knowledge acquisition, knowledge sharing, experimentation, and leadership-driven learning climates. The findings reveal that these learning processes collectively shape and reinforce an innovation culture by fostering creativity, collaboration, adaptability, and risk-taking. A grounded theoretical model is developed, illustrating a cyclical relationship in which learning processes enable an innovation culture, which in turn enhances learning and innovation outcomes. The study contributes to the literature by extending organisational learning theory to the fashion tech context and conceptualising innovation culture as an emergent property of continuous learning. The findings offer practical implications for startup leaders seeking to build sustainable innovation capabilities in rapidly evolving markets.

Keywords: organisational learning, innovation culture, fashion tech startups, grounded theory, knowledge management, entrepreneurship, digital innovation

1. Introduction

The global fashion industry is experiencing a paradigm shift driven by rapid technological advancement, digital transformation, and changing consumer expectations. The emergence of fashion technology (fashion tech) startups has fundamentally altered traditional business models by integrating design innovation with advanced technologies such as artificial intelligence (AI), big data analytics, blockchain, and virtual/augmented reality (AR/VR). These startups are not merely digitising fashion processes but are redefining value creation, supply chain management, and customer engagement (McKinsey & Company, 2023; Westerman et al., 2014).

Fashion tech startups operate in highly volatile, uncertain, complex, and ambiguous (VUCA) environments, where rapid shifts in trends and consumer preferences require continuous adaptation. Unlike established firms, startups often lack structured processes and formalised systems, making organisational learning a critical capability for survival and growth. Organisational learning enables startups to respond to environmental changes by acquiring, interpreting, and applying knowledge effectively (Argote, 2013). In such contexts, learning is not confined to formal training programs but is embedded in everyday practices such as experimentation, iteration, and customer interaction.

The concept of innovation culture has gained increasing attention as a determinant of organisational performance in dynamic industries. Innovation culture refers to a set of shared values, beliefs, and norms that support creativity, experimentation, and risk-taking (Martins & Terblanche, 2003). In fashion tech startups, innovation culture is particularly significant because it influences how teams approach problem-solving, adopt new technologies, and generate novel ideas. A strong innovation culture encourages employees to challenge conventional practices, collaborate across disciplines, and embrace uncertainty as an opportunity for growth.

Despite the recognised importance of both organisational learning and innovation culture, the relationship between these constructs remains underexplored, especially in the context of fashion tech startups. Existing research has largely focused on manufacturing or high-tech industries, leaving a gap in understanding how learning processes operate in creative, design-driven sectors (Crossan et al., 1999; Grant, 1996). Fashion tech startups present a unique context where creativity and technology intersect, requiring a nuanced understanding of how learning and culture interact to drive innovation.

Organisational learning in startups is often characterised by informality, speed, and adaptability. The lean startup methodology, for example, emphasises iterative learning through build-measure-learn cycles, enabling firms to test hypotheses and refine products based on real-time feedback (Ries, 2011). This approach aligns with experiential learning theory, which posits that knowledge is created through the transformation of experience (Kolb, 1984). In fashion tech startups, experiential learning occurs through rapid prototyping, digital experimentation, and engagement with consumers via online platforms.

At the same time, an innovation culture plays a mediating role in shaping how learning processes translate into innovative outcomes. A supportive culture fosters psychological safety, allowing employees to take risks and share ideas without fear of failure (Amabile et al., 1996). Leadership is a critical factor in establishing such a culture, as leaders influence organisational values, communication patterns, and decision-making processes (Senge, 1990). In startups, where hierarchies are often flat, leadership tends to be more participative, enabling faster knowledge exchange and collaborative learning.

The integration of organisational learning and innovation culture is particularly relevant in the fashion tech sector due to its interdisciplinary nature. These startups combine elements of design, engineering, data science, and marketing, requiring cross-functional collaboration and knowledge integration. The knowledge-based view (KBV) of the firm suggests that knowledge is the most strategically important resource, and firms that effectively manage knowledge can achieve sustainable competitive advantage (Grant, 1996). In fashion tech startups, knowledge about consumer behaviour, technological capabilities, and design trends must be continuously updated and integrated to remain competitive.

Furthermore, the rise of sustainability concerns and ethical fashion has added another layer of complexity to the industry. Startups are increasingly expected to innovate not only in terms of products but also in processes and business models that reduce environmental impact (McKinsey & Company, 2023). This requires learning from diverse sources, including scientific research, industry best practices, and stakeholder feedback.

Given these dynamics, there is a pressing need to develop a theoretical framework that explains how organisational learning contributes to the development of innovation culture in fashion tech startups. This study addresses this gap by employing a grounded theory approach based on secondary qualitative data. By analysing industry reports, case studies, and academic literature, the research aims to identify key learning mechanisms and cultural attributes that drive innovation.

Research Objectives

• To explore the processes of organisational learning in fashion tech startups.
• To identify the key dimensions of innovation culture within these organisations.
• To develop a grounded theoretical model explaining the relationship between organisational learning and innovation culture.

This study contributes to the literature in three ways. First, it extends organisational learning theory to the context of fashion tech startups, a relatively underexplored domain. Second, it provides insights into the role of innovation culture in creative and technology-driven industries. Third, it offers practical implications for entrepreneurs and managers seeking to foster innovation through learning.

2. Literature Review

Organisational learning is a foundational concept in management and organisational theory, referring to the processes through which organisations acquire, interpret, and retain knowledge (Argote, 2013). It encompasses both individual and collective learning, as well as formal and informal mechanisms. According to Crossan et al. (1999), organisational learning occurs across three levels, individual, group, and organisational, and involves processes of intuiting, interpreting, integrating, and institutionalising knowledge.

One of the most influential models of organisational learning is the SECI model developed by Nonaka and Takeuchi (1995), which describes knowledge creation as a dynamic interaction between tacit and explicit knowledge. The model includes four processes: socialisation, externalisation, combination, and internalisation. In the context of startups, these processes are often accelerated due to the need for rapid innovation and adaptation.

Another important perspective is the concept of absorptive capacity, defined as the ability of a firm to recognise the value of new information, assimilate it, and apply it to commercial ends (Cohen & Levinthal, 1990). High absorptive capacity enables organisations to leverage external knowledge sources, such as customer feedback and technological advancements, which are particularly important in fashion tech startups.

Senge (1990) introduced the idea of the learning organisation, emphasising the importance of systems thinking, shared vision, and team learning. Learning organisations are characterised by continuous improvement and the ability to adapt to changing environments. In startup contexts, learning is often embedded in agile practices and iterative processes, as highlighted by Ries (2011).

2.1 Innovation Culture

Innovation culture refers to the organisational environment that supports creativity and innovation. Martins and Terblanche (2003) define it as a set of shared values and beliefs that encourage innovation by promoting risk-taking, openness, and collaboration. Innovation culture is not merely about generating ideas but also about implementing them effectively.

Amabile et al. (1996) identified key components of a supportive work environment for creativity, including organisational encouragement, supervisory support, work group support, and resource availability. These factors influence employees’ intrinsic motivation, which is critical for creative performance.

Leadership plays a central role in shaping an innovation culture. Transformational leaders, for example, inspire employees to transcend self-interest and pursue collective goals, fostering a culture of innovation (Bass & Riggio, 2006). In startups, leadership is often more flexible and participative, enabling rapid decision-making and knowledge sharing.

Digital transformation has further emphasised the importance of an innovation culture. Westerman et al. (2014) argue that organisations must develop a digital culture that supports experimentation and learning to succeed in the digital age.

2.2 Fashion Tech Startups

Fashion tech startups represent a convergence of creativity and technology, operating in a highly dynamic and competitive environment. These startups leverage technologies such as AI for personalised recommendations, blockchain for supply chain transparency, and AR/VR for virtual try-ons (McKinsey & Company, 2023).

Unlike traditional fashion firms, fashion tech startups rely heavily on data-driven decision-making and digital platforms. This requires continuous learning and adaptation, as technologies and consumer preferences evolve rapidly. Moreover, these startups often operate with limited resources, making efficient knowledge management critical.

Sustainability has become a key driver of innovation in the fashion industry. Startups are increasingly focusing on eco-friendly materials, circular economy models, and ethical production practices. This adds complexity to the learning process, as firms must integrate knowledge from diverse domains, including environmental science and social responsibility.

2.3 Organisational Learning and Innovation

The relationship between organisational learning and innovation has been extensively studied in the literature. Learning enables organisations to generate new knowledge, which can be transformed into innovative products, processes, and services (Crossan et al., 1999). The knowledge-based view (KBV) of the firm emphasises that knowledge is the primary source of competitive advantage (Grant, 1996).

Dynamic capabilities theory further highlights the role of learning in enabling firms to sense opportunities, seize them, and reconfigure resources accordingly (Teece et al., 1997). In fashion tech startups, dynamic capabilities are essential for responding to rapid technological and market changes.

However, learning alone is not sufficient for innovation. The organisational context, including culture and leadership, plays a critical role in determining whether learning translates into innovation outcomes. A supportive innovation culture enhances the effectiveness of learning processes by encouraging experimentation and risk-taking (Martins & Terblanche, 2003).

2.4 Research Gap

While existing literature provides valuable insights into organisational learning and innovation, several gaps remain. First, most studies focus on large organisations or traditional industries, with limited attention to startups. Second, the unique characteristics of fashion tech startups, such as the integration of design and technology, are not adequately addressed. Third, the interplay between organisational learning and innovation culture remains underexplored.

This study addresses these gaps by adopting a grounded theory approach to develop a conceptual framework based on secondary qualitative data. By focusing on fashion tech startups, the research provides new insights into how learning and culture interact in emerging industries.

3. Theoretical Framework

This study is grounded in an integrative theoretical framework that draws upon Experiential Learning Theory, the Knowledge-Based View (KBV) of the firm, and Innovation Culture Theory to explain how organisational learning processes shape innovation culture in fashion tech startups. These theoretical perspectives collectively provide a multi-dimensional understanding of how knowledge is generated, shared, and transformed into innovative outcomes within dynamic and uncertain environments.

3.1 Experiential Learning Theory

Experiential Learning Theory (ELT), developed by Kolb (1984), posits that learning is a cyclical process involving four stages: concrete experience, reflective observation, abstract conceptualisation, and active experimentation. This framework emphasises that knowledge is constructed through the transformation of experience rather than through passive acquisition.

In the context of fashion tech startups, ELT is particularly relevant due to the iterative and experimental nature of innovation processes. Startups frequently engage in rapid prototyping, user testing, and feedback-driven product development, which align closely with Kolb’s learning cycle. For example, a startup developing AI-driven fashion recommendations may continuously refine its algorithms based on user interaction data, thereby engaging in a cycle of experimentation and learning.

Furthermore, experiential learning in startups is often collective rather than individual. Teams engage in collaborative problem-solving, sharing insights derived from experimentation and market feedback. This aligns with the notion that learning is socially embedded and context-dependent (Kolb, 1984). The dynamic nature of fashion tech environments requires organisations to continuously adapt, making experiential learning a critical capability for sustaining innovation.

3.2 Knowledge-Based View (KBV) of the Firm

The Knowledge-Based View (KBV) extends the resource-based view by emphasising knowledge as the most strategically significant resource of the firm (Grant, 1996). According to KBV, organisational success depends on the ability to create, integrate, and apply knowledge effectively.

In fashion tech startups, knowledge is inherently multidimensional, encompassing technological expertise, design creativity, consumer behaviour insights, and market intelligence. The integration of these diverse knowledge domains is essential for innovation. For instance, the development of virtual try-on technologies requires collaboration between software engineers, fashion designers, and user experience specialists.

KBV also highlights the importance of tacit knowledge, which is difficult to codify and transfer but plays a crucial role in innovation (Nonaka & Takeuchi, 1995). In startups, tacit knowledge is often shared through informal interactions, mentorship, and collaborative work environments. The SECI model (Socialisation, Externalisation, Combination, Internalisation) provides a useful framework for understanding how tacit and explicit knowledge interact to create new knowledge (Nonaka & Takeuchi, 1995).

Additionally, absorptive capacity, the ability to recognise, assimilate, and apply external knowledge, is a key concept within the KBV framework (Cohen & Levinthal, 1990). Fashion tech startups rely heavily on external knowledge sources, such as technological advancements and consumer trends, making absorptive capacity a critical determinant of innovation performance.

3.3 Innovation Culture Theory

Innovation Culture Theory focuses on the organisational context that supports or hinders innovation. Martins and Terblanche (2003) define innovation culture as a set of shared values, beliefs, and practices that encourage creativity, risk-taking, and experimentation. This perspective emphasises that innovation is not solely a function of individual creativity but is also shaped by organisational norms and structures.

In fashion tech startups, innovation culture is often characterised by flexibility, openness, and a willingness to embrace uncertainty. These organisations typically adopt flat hierarchies and collaborative work environments, which facilitate knowledge sharing and rapid decision-making. Leadership plays a crucial role in shaping innovation culture by fostering psychological safety and encouraging employees to experiment without fear of failure (Amabile et al., 1996).

Moreover, an innovation culture is closely linked to digital transformation. Westerman et al. (2014) argue that organisations must develop a digital culture that supports experimentation and continuous learning to succeed in the digital age. In fashion tech startups, this involves integrating digital tools and platforms into everyday work processes, enabling real-time data analysis and decision-making.

3.4 Integrative Conceptual Framework

By integrating these theoretical perspectives, this study proposes a conceptual framework in which organisational learning processes act as the foundation for the development of an innovation culture, which in turn drives innovation outcomes.

• Experiential Learning Theory explains how learning occurs through iterative processes of experimentation and reflection.
• Knowledge-Based View explains how knowledge is managed and leveraged as a strategic resource.
• Innovation Culture Theory explains how organisational context influences the translation of learning into innovation.

The proposed framework suggests a dynamic and recursive relationship:

Organisational Learning Processes → Knowledge Integration → Innovation Culture → Innovation Outcomes → Feedback Loop → Enhanced Learning

This model highlights the cyclical nature of learning and innovation, where each process reinforces the other. In fashion tech startups, this interplay is particularly pronounced due to the need for continuous adaptation and creativity.

4. Methodology

This study adopts a qualitative research design using a grounded theory approach, as originally developed by Glaser and Strauss (1967). Grounded theory is particularly suitable for exploring complex social phenomena and developing theory inductively from data. Given the limited existing research on the relationship between organisational learning and innovation culture in fashion tech startups, a grounded theory approach enables the generation of new theoretical insights grounded in empirical evidence.

Unlike traditional hypothesis-testing methods, grounded theory emphasises theory development through systematic data analysis. This approach is appropriate for the present study, which seeks to understand how learning processes and innovation culture interact in a relatively underexplored context.

4.1 Data Source and Sampling

The study relies on secondary qualitative data, which includes:

• Industry reports (e.g., McKinsey & Company, Deloitte Insights)
• Published case studies of fashion tech startups
• Academic journal articles
• Company websites, blogs, and innovation reports
• Media articles and expert interviews

Secondary data are particularly valuable for studying startups, as access to primary data may be limited due to confidentiality and resource constraints. Moreover, secondary sources provide a rich and diverse dataset that captures multiple perspectives on organisational practices.

A purposive sampling strategy was employed to select relevant data sources. The criteria for inclusion were:

• Focus on fashion tech or digitally enabled fashion startups
• Discussion of organisational practices related to learning or innovation
• Credibility and reliability of the source

This approach ensures that the data are both relevant and theoretically informative.

4.2 Data Analysis Procedure

The data analysis followed the systematic procedures of grounded theory, including open coding, axial coding, and selective coding (Strauss & Corbin, 1998).

Open Coding: In the initial stage, data were examined line-by-line to identify key concepts and categories. Codes were assigned to segments of text that represented meaningful units of information. Examples of initial codes include “customer feedback learning,” “cross-functional collaboration,” “rapid prototyping,” and “risk-taking behaviour.”

Axial Coding: In the second stage, relationships between categories were identified. Codes were grouped into higher-order categories, such as “knowledge acquisition,” “knowledge sharing,” “experimentation,” and “leadership support.” This process helped to organise the data and identify patterns.

Selective Coding: In the final stage, a core category was identified, and relationships between categories were integrated into a coherent theoretical framework. The core category in this study is “organisational learning as a driver of innovation culture.”

The iterative nature of grounded theory allowed for continuous refinement of categories and relationships, ensuring that the emerging theory is closely grounded in the data.

4.3 Trustworthiness and Rigour

To ensure the quality and credibility of the research, several strategies were employed:

• Triangulation: Multiple data sources were used to validate findings and reduce bias (Denzin, 1978).
• Constant Comparison: Data were continuously compared across sources to identify similarities and differences (Glaser & Strauss, 1967).
• Theoretical Saturation: Data collection and analysis continued until no new categories or insights emerged.
• Audit Trail: Detailed documentation of coding and analysis processes was maintained to enhance transparency.

These measures enhance the trustworthiness of the study and ensure that the findings are robust and reliable.

4.4 Ethical Considerations

Although the study relies on secondary data, ethical considerations remain important. All sources were properly cited to ensure academic integrity and avoid plagiarism (Mannan & Farhana, 2026). Publicly available data were used, and no confidential or sensitive information was accessed.

4.5 Limitations of the Methodology

Despite its strengths, the methodology has several limitations:

• Reliance on Secondary Data: The study depends on existing sources, which may limit the depth of insights.
• Context-Specific Findings: The findings may not be generalizable beyond fashion tech startups.
• Interpretive Bias: As with all qualitative research, the analysis is subject to the researcher’s interpretation.

However, these limitations are mitigated by the use of multiple data sources and rigorous analytical procedures.

5. Findings and Analysis

The grounded theory analysis of secondary qualitative data revealed a set of interrelated categories that explain how organisational learning processes shape innovation culture in fashion tech startups. Through open, axial, and selective coding, four major dimensions of organisational learning emerged: knowledge acquisition, knowledge sharing, experimentation and iterative learning, and leadership-driven learning climate, which collectively contribute to the formation and reinforcement of innovation culture. These dimensions are not isolated; rather, they interact dynamically to produce sustained innovation outcomes.

5.1 Knowledge Acquisition: Building the Learning Foundation

Knowledge acquisition emerged as the foundational element of organisational learning in fashion tech startups. These firms actively gather knowledge from diverse internal and external sources, including customer feedback, social media analytics, technological developments, and industry trends. This aligns with the concept of absorptive capacity, which emphasises the ability of organisations to recognise, assimilate, and apply new knowledge (Cohen & Levinthal, 1990).

In fashion tech startups, knowledge acquisition is often technology-enabled. For example, startups utilise AI-driven analytics tools to monitor consumer preferences and predict fashion trends. This data-driven approach allows firms to make informed decisions and reduce uncertainty in product development. Unlike traditional fashion firms, which rely heavily on seasonal cycles and intuition, fashion tech startups adopt a continuous learning approach based on real-time data.

Moreover, knowledge acquisition is not limited to market information. Startups also acquire technical knowledge through partnerships, collaborations, and open innovation ecosystems. This reflects the increasing importance of external knowledge networks in innovation processes (Grant, 1996). The integration of diverse knowledge sources enhances the organisation’s ability to generate novel ideas and solutions.

The findings indicate that effective knowledge acquisition contributes to an innovation culture by fostering curiosity, openness, and responsiveness. Organisations that actively seek new knowledge are more likely to embrace change and experiment with new ideas, which are essential characteristics of an innovation-oriented culture.

5.2 Knowledge Sharing: Enabling Collective Learning

Knowledge sharing emerged as a critical mechanism for transforming individual learning into organisational capability. In fashion tech startups, knowledge sharing is facilitated through both formal and informal channels, including digital collaboration tools, team meetings, and social interactions.

The analysis reveals that startups often adopt flat organisational structures, which reduce hierarchical barriers and promote open communication. This aligns with the principles of learning organisations, where knowledge flows freely across levels and functions (Senge, 1990). Cross-functional teams, comprising designers, engineers, marketers, and data scientists, play a central role in knowledge sharing, enabling the integration of diverse perspectives.

The role of tacit knowledge is particularly significant in this context. Tacit knowledge, which is difficult to codify, is shared through observation, imitation, and direct interaction (Nonaka & Takeuchi, 1995). For instance, designers may share creative insights with developers, while data analysts provide insights into consumer behaviour. This collaborative exchange enhances the organisation’s ability to innovate.

Furthermore, digital platforms such as internal communication tools and cloud-based systems facilitate real-time knowledge sharing. These technologies support the creation of a collective memory, enabling organisations to retain and reuse knowledge (Argote, 2013).

The findings suggest that knowledge sharing contributes to an innovation culture by promoting collaboration, trust, and inclusivity. When employees feel that their ideas are valued and shared openly, they are more likely to engage in creative problem-solving and innovation.

5.3 Experimentation and Iterative Learning: Driving Innovation

Experimentation and iterative learning emerged as central components of organisational learning in fashion tech startups. These firms adopt lean startup principles, which emphasise rapid prototyping, continuous testing, and learning from failure (Ries, 2011).

The data indicate that startups view experimentation not as a risk but as an opportunity for learning. This reflects a shift from traditional risk-averse approaches to a more risk-tolerant and learning-oriented mindset. Failures are not penalised but are treated as valuable sources of knowledge, contributing to continuous improvement.

Iterative learning is closely linked to experiential learning theory, which emphasises learning through action and reflection (Kolb, 1984). In fashion tech startups, this process is accelerated through digital tools and platforms that enable real-time feedback and rapid adjustments. For example, virtual fashion platforms can test new designs with users before production, reducing costs and enhancing innovation.

The findings also highlight the importance of feedback loops in the learning process. Customer feedback, performance metrics, and internal evaluations provide continuous input for refining products and processes. These feedback loops create a dynamic learning environment where knowledge is constantly updated and applied.

Experimentation and iterative learning contribute to an innovation culture by fostering creativity, adaptability, and resilience. Organisations that embrace experimentation are more likely to generate innovative ideas and respond effectively to changing market conditions.

5.4 Leadership and Learning Climate: Shaping Organisational Culture

Leadership emerged as a critical factor in shaping the learning climate and, consequently, innovation culture. In fashion tech startups, leaders play a pivotal role in establishing values, norms, and practices that support learning and innovation.

The analysis reveals that effective leaders adopt a transformational leadership style, characterised by vision, inspiration, and support for innovation (Bass & Riggio, 2006). These leaders encourage employees to take risks, share ideas, and engage in continuous learning.

Psychological safety is another important aspect of the learning climate. When employees feel safe to express their ideas and experiment without fear of negative consequences, they are more likely to engage in innovative behaviour (Amabile et al., 1996). Leaders contribute to psychological safety by promoting open communication and recognising contributions.

Additionally, leaders play a key role in aligning organisational learning with strategic objectives. By setting clear goals and providing resources for learning and innovation, leaders ensure that learning processes contribute to organisational performance.

The findings suggest that a leadership-driven learning climate contributes to an innovation culture by fostering motivation, empowerment, and alignment. Leaders who prioritise learning create an environment where innovation can thrive.

5.5 Grounded Theory Model

The integration of these categories resulted in the development of a grounded theoretical model:

Knowledge Acquisition → Knowledge Sharing → Experimentation & Iterative Learning → Leadership-Driven Learning Climate → Innovation Culture → Innovation Outcomes

This model is cyclical and dynamic, with feedback loops reinforcing learning and innovation. The findings highlight that innovation culture is not a static attribute but an emergent property of continuous learning processes.

6. Discussion

The findings of this study provide significant insights into the relationship between organisational learning and innovation culture in fashion tech startups. By integrating empirical evidence with existing theoretical frameworks, this discussion elaborates on the implications of the findings for theory and practice.

6.1 Organisational Learning as a Dynamic Capability

The findings support the view that organisational learning is a dynamic capability that enables firms to adapt to changing environments (Teece et al., 1997). In fashion tech startups, learning is not a static process but a continuous and iterative activity that drives innovation.

The emphasis on knowledge acquisition, sharing, and experimentation reflects the importance of learning agility in dynamic industries. Startups that can quickly acquire and apply knowledge are better positioned to respond to technological and market changes. This aligns with the knowledge-based view, which highlights knowledge as a key source of competitive advantage (Grant, 1996).

Moreover, the findings extend the concept of dynamic capabilities by emphasising the role of learning culture. While dynamic capabilities focus on processes and resources, this study highlights the importance of organisational culture in enabling these capabilities.

6.2 Innovation Culture as an Emergent Phenomenon

One of the key contributions of this study is the conceptualisation of innovation culture as an emergent phenomenon arising from organisational learning processes. Rather than being imposed through top-down policies, an innovation culture develops organically through continuous interaction, collaboration, and experimentation.

This perspective challenges traditional views that treat culture as a static or independent variable. Instead, the findings suggest that culture is co-created through learning practices, reinforcing the idea that organisational processes and culture are deeply interconnected (Martins & Terblanche, 2003).

The role of psychological safety and leadership further underscores the importance of social and relational factors in shaping innovation culture. These findings are consistent with prior research emphasising the role of supportive environments in fostering creativity (Amabile et al., 1996).

6.3 Interplay Between Learning and Innovation

The study highlights the mutually reinforcing relationship between organisational learning and innovation culture. Learning processes contribute to the development of an innovation culture, while an innovation culture enhances the effectiveness of learning.

For example, a culture that encourages experimentation and risk-taking enables organisations to engage in more effective learning. Conversely, strong learning processes provide the knowledge and skills necessary for innovation. This reciprocal relationship creates a self-reinforcing cycle that drives continuous improvement and innovation.

This finding contributes to the literature by providing a more nuanced understanding of the relationship between learning and innovation. While previous studies have established a link between the two (Crossan et al., 1999), this study highlights the mechanisms through which they interact in a specific context.

6.4 Implications for Fashion Tech Startups

The findings have important practical implications for fashion tech startups. First, startups should prioritise knowledge management practices, including systems for acquiring, sharing, and applying knowledge. Investments in digital tools and platforms can enhance these processes.

Second, startups should foster a culture of experimentation and learning. This involves encouraging risk-taking, learning from failure, and continuously iterating on products and processes. The adoption of lean startup principles can support this approach (Ries, 2011).

Third, leadership plays a critical role in shaping innovation culture. Startup leaders should adopt transformational leadership practices, promoting vision, collaboration, and psychological safety.

Finally, startups should recognise the importance of interdisciplinary collaboration. The integration of diverse knowledge domains is essential for innovation in fashion tech, requiring effective communication and teamwork.

6.5 Theoretical Contributions

This study makes several contributions to the literature:

• It extends organisational learning theory to the context of fashion tech startups.
• It provides a grounded theoretical model linking learning processes to innovation culture.
• It highlights the role of culture as an emergent outcome of learning, rather than a static factor.

These contributions enhance our understanding of how innovation occurs in emerging industries and provide a foundation for future research.

6.6 Limitations and Future Research Directions

While the study provides valuable insights, it has several limitations. The reliance on secondary data may limit the depth of analysis, and the findings may not be generalizable to other contexts.

Future research could address these limitations by:

• Conducting primary empirical studies using interviews and surveys
• Comparing different industries
• Exploring the role of external factors, such as policy and market conditions

7. Conclusion

This study set out to explore the relationship between organisational learning and innovation culture in fashion tech startups through a grounded theory approach based on secondary qualitative data. The findings demonstrate that organisational learning is not merely a supporting function but a central driver of innovation culture in dynamic and technology-driven environments. By systematically analysing patterns across multiple data sources, the study identifies four core dimensions of organisational learning, knowledge acquisition, knowledge sharing, experimentation, and leadership-driven learning climate, that collectively contribute to the development of a sustainable innovation culture.

One of the key conclusions is that innovation culture emerges as a dynamic and evolving outcome of continuous learning processes rather than as a static organisational attribute. Fashion tech startups that actively engage in acquiring and integrating knowledge, fostering collaborative learning environments, and embracing iterative experimentation are more likely to cultivate a culture that supports creativity, adaptability, and risk-taking. This reinforces the argument that learning and culture are deeply interconnected and mutually reinforcing.

The study also highlights the critical role of leadership in shaping the learning environment. Leaders who promote psychological safety, encourage open communication, and support experimentation enable employees to engage more effectively in innovative activities. In this sense, leadership acts as a catalyst that aligns organisational learning processes with innovation goals.

From a theoretical perspective, the research contributes to the literature by integrating experiential learning theory, the knowledge-based view, and innovation culture theory into a unified framework. This integrative approach provides a deeper understanding of how learning processes translate into innovation outcomes in emerging industries. From a practical standpoint, the findings suggest that fashion tech startups should prioritise knowledge management systems, foster collaborative and inclusive cultures, and adopt agile and experimental approaches to innovation.

However, the study is not without limitations. The reliance on secondary data may restrict the depth of insights, and the findings may not be universally generalizable. Future research could build on this work by incorporating primary data, conducting comparative studies across industries, and examining the impact of external factors such as policy and market conditions.

In conclusion, this study underscores the importance of organisational learning as a foundational capability for fostering innovation culture and sustaining competitive advantage in the rapidly evolving fashion tech landscape.

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