Open Innovation Strategies for Specialty Coffee Chains: An Innovation Management Model (IMM)

Abstract

The specialty coffee sector faces increasing competitive and sustainability challenges and lacks structured innovation frameworks. This study proposes an Innovation Management Model (IMM) based on open innovation for specialty coffee value chains, applied to the agro-industrial chain in Quindío, Colombia. This research adopts a mixed-methods approach and model development to identify, prioritize, and validate key variables for an IMM based on open innovation. Among the data collection techniques and instruments used, the study incorporated, in its first phase, a literature review based on the PRISMA protocol and, in its second phase, validation through workshops with experts to identify key variables and select the proposed or winning IMM. As a result of this research process, three (3) Innovation Management Models were developed. It is concluded that the specialty coffee sector requires multiple interventions to consolidate the proposed IMM-3 as the winning model, emphasizing the imperative need for alignment with the concept of Open Innovation. The importance of this study lies in the practical recommendations for implementing MGI-3 within the studied chain, with potential applicability to other agro-industrial chains in different regions of Colombia.

1. Introduction

The coffee sector represents a strategic agro-industrial activity for many economies worldwide, particularly in developing countries where it plays a critical role in rural livelihoods, employment generation, and export revenues. Colombia is one of the most prominent actors in the global coffee market. During the 2022–2023 period, the country produced 10.6 million sacks of coffee, with domestic consumption of 2.2 million sacks and exports reaching 10.3 million sacks, of which North America constitutes the primary international market with imports of 5.37 million sacks [1]. Beyond its economic importance, the Colombian coffee sector is internationally recognized for its quality and differentiation, particularly due to the production of 100% Arabica coffee, which has historically positioned the country as a producer of some of the world’s finest mild coffees [2].

In recent decades, the specialty coffee segment has gained increasing relevance in international markets due to the growing demand for differentiated, high-quality, and sustainably produced agricultural products. Specialty coffees are characterized by distinctive attributes related to cultivation conditions, harvesting practices, post-harvest processing, and sensory quality. These coffees are typically grown at high altitudes under favorable climatic conditions, which contribute to their exceptional organoleptic properties. Their production involves careful selection of beans, manual harvesting to ensure optimal ripeness, and precise roasting processes designed to highlight unique flavor profiles ranging from fruity and floral notes to chocolate or nut characteristics. Additionally, many specialty coffees are associated with sustainability certifications and origin denominations that ensure product quality, environmental responsibility, and fair compensation for producers [3].

Despite the increasing sophistication of the specialty coffee market, producers and supply chain actors face significant challenges related to competitiveness, market differentiation, sustainability requirements, and technological change. Addressing these challenges requires not only improvements in production processes but also the development of systematic approaches to innovation. Innovation management has therefore become a key strategic capability for organizations operating in dynamic and highly competitive agri-food sectors. In this context, innovation management models and the concept of Open Innovation (hereafter referred to as OI) have attracted growing attention from scholars and practitioners as mechanisms to facilitate knowledge exchange, collaboration, and the development of new products, services, and processes across organizational boundaries.

OI emphasizes the integration of internal and external knowledge sources to accelerate innovation processes and enhance organizational performance. Within agri-food systems, and particularly in sectors dominated by small and medium-sized producers, innovation increasingly depends on collaborative networks involving producers, research institutions, technology providers, government agencies, and market intermediaries. However, although innovation management and OI have been widely discussed in industrial and technological contexts, their systematic application in agricultural value chains—especially in the specialty coffee sector—remains limited. Existing research has largely focused on production characteristics, quality differentiation, and market dynamics, while comparatively little attention has been given to structured innovation management models that support collaborative innovation processes in coffee-producing regions.

This situation reveals a significant research gap. While the specialty coffee sector is characterized by strong knowledge intensity, quality differentiation, and sustainability demands, there is still a lack of studies that identify and prioritize the key variables required to design innovation management models adapted to this sector. In particular, little empirical work has examined how principles of innovation management and OI can be operationalized within coffee supply chains to strengthen competitiveness and sustainability at the regional level.

As a contribution to overcoming this gap, the present study aims to identify and prioritize the key variables required for the development of an innovation management model for the specialty coffee sector in the Department of Quindío, Colombia, with potential applicability to other coffee-producing regions in the country. Specifically, the study seeks to answer the following research questions: What are the most relevant variables for an innovation management model in the specialty coffee sector? How can these variables be integrated into a conceptual model capable of supporting innovation processes across the specialty coffee supply chain?

By addressing these questions, this research contributes both theoretically and practically. From a theoretical perspective, it advances the understanding of how innovation management and OI approaches can be adapted to agri-food value chains characterized by fragmented production structures and strong quality differentiation. From a practical perspective, it provides a structured framework that can guide producers, cooperatives, policymakers, and other stakeholders in strengthening innovation capabilities within the specialty coffee sector, thereby supporting its long-term competitiveness and sustainability.

In addition to this Introduction, this article is divided into sections. Section 2 presents the theoretical framework, addressing key concepts related to OI, innovation management models, and their application in organizational contexts. Section 3 describes the materials and methods used in the research, including the literature review process, consultations with experts, and validation procedures with stakeholders in the sector. Section 4 presents the results and the proposed innovation management model for the specialty coffee sector, while Section 5 addresses the discussion. Meanwhile, Section 6 presents the conclusions, implications, limitations, and future research, and finally, Section 7 presents recommendations for the various stakeholders in the science, technology, and innovation system associated with the specialty coffee sector.

2. Theoretical Framework

2.1. OI Dynamics

The theory of bounded rationality [4], posits that human decision-making is constrained by the information available, the decision-maker’s cognitive limitations, and the time constraints under which decisions must be made. In the context of OI, this limitation is mirrored by the challenge’s companies face when collaborating with external actors to drive innovation. Decisions regarding what information to share or which innovations to adopt are shaped by the ability to process information and the limits of organizational capacity. Within OI dynamics, bounded rationality influences organizations’ ability to manage the flow of both external and internal information. An overload of information, differences in knowledge frameworks, and conflicting interests among collaborators can complicate effective decision-making.

OI requires efficient management of information flows, a task that becomes difficult when organizations, hindered by bounded rationality, struggle to interpret and leverage this information effectively [5]. In OI contexts, bounded rationality can lead to cognitive overload for individuals and teams responsible for managing innovation. Despite access to a wide array of resources, organizations may face challenges in identifying the most valuable ideas, especially when interacting with multiple external factors. This challenge is exacerbated by differences in the knowledge and capabilities of innovation partners [6].

Bounded rationality implies that, even when organizations engage in OI networks, their decisions are constrained by the context in which they operate, their organizational structures, and their ability to coordinate with other actors. While external networks offer access to vast amounts of knowledge, organizations limited by bounded rationality often struggle to adapt and filter out the information that adds the most value [7].

On the other hand, considering the importance of delivery platforms for OI—such as Uber, Airbnb, and Deliveroo—it is essential to note that these platforms facilitate the exchange of products and services by connecting suppliers with consumers. The business models of these platforms rely on the co-creation of value between different parties, using technology to optimize the supply and demand of services. The dynamics of OI on these platforms are characterized by continuous interaction and adaptation to emerging market needs, which makes them fertile ground for OI [8].

2.2. OI Dynamics on Delivery Platforms

OI dynamics on delivery platforms are shaped by the collaboration of various stakeholders within the digital ecosystem, including software developers, service providers, businesses, and consumers. Together, these actors contribute to the creation and delivery of innovative solutions. Delivery platforms enable continuous, real-time interaction, fostering the integration of external ideas into the development of new products and services [9]. These platforms act as digital infrastructures, where multiple participants exchange knowledge and co-create offerings. OI dynamics on such platforms are thus characterized by a constant flow of ideas and resources between the involved parties. This is not a centralized process but a collective one, where external contributions are actively encouraged [10].

In this context, OI facilitates the emergence of new business models grounded in continuous collaboration. Platforms become spaces for value exchange, where both consumers and providers can generate innovative ideas. This collaborative innovation is particularly crucial in digital and technology sectors, where platforms act as accelerators for new solutions [11]. Moreover, OI dynamics on delivery platforms can create network effects, increasing the platform’s value as more users and suppliers participate, fostering knowledge sharing among diverse actors, and accelerating innovation through open collaboration. In essence, these platforms transform into hubs for collective innovation [12].

2.3. Business Models with OI Dynamics

OI-driven business models are distinguished by their ability to integrate external knowledge into the value creation process. Leading companies like Tesla and Procter & Gamble have embraced this model, partnering with universities, startups, and suppliers to drive innovation. These models not only improve products and services but also enable companies to explore new markets by incorporating emerging technologies and leveraging collaborative networks [13]. By engaging with a broader ecosystem of innovators, these models help organizations transcend the limitations of internal capabilities, accelerating the development of novel products and services while offering competitive advantages. Such business models aim to harness collective creativity and foster synergies among a diverse range of stakeholders [14].

In an OI-driven business model, companies expand their focus beyond internal resources to seek strategic alliances, technology licensing, and collaborations with other entities (e.g., universities, startups, and consumers). This approach allows organizations to tap into ideas, technologies, and expertise that would otherwise be costly or impractical to develop in-house. Open business models are especially beneficial in industries where innovation speed is essential [15]. These models also place a strong emphasis on intellectual property management. To effectively leverage external innovations, companies must establish clear guidelines for sharing or licensing technologies and addressing potential conflicts over jointly developed intellectual property. Thus, business models must strike a balance between fostering collaboration and managing the risks associated with sharing knowledge [16].

An OI-driven open business model involves a shift in organizational structure, moving from a traditional, closed, hierarchical model to a more flexible and collaborative approach. Companies adopting this model must be capable of integrating external innovations into their internal processes while remaining adaptable to rapid changes. These dynamics require robust open innovation management, which combines the exploration of new ideas with the exploitation of existing ones in an ongoing cycle [17].

2.4. Social OI

Social OI refers to the integration of social platforms and networks into innovation processes, allowing various stakeholders—such as consumers, communities, and organizations—to collaboratively share and co-create knowledge. Digital technologies have enabled this collaboration by allowing real-time contribution, which facilitates the flow of ideas and experiences in innovation efforts. This is in line with the collaborative innovation paradigm of IMM., where external actors (including consumers, users, and other organizations) are critical contributors to the creation of new products and services [18,19].

The key idea in Social OI is that users provide valuable insights, creativity, and experiences that an organization may not have access to within its traditional boundaries. These interactions allow for better adaptation to social needs and create direct value for the community [20]. While Social OI is often discussed in the context of product and service innovation, it also plays a role in addressing social problems by fostering co-creation in developing solutions that benefit society. This inclusive approach to innovation emphasizes social and community well-being in addition to economic growth [21].

Social OI operates on the principle of “distributed innovation,” meaning that idea generation is decentralized, involving a global community of individuals with diverse backgrounds and perspectives. By adopting this OI approach, organizations tap into a wide variety of ideas and creative solutions that arise from an active, engaged community [22].

2.5. OI Engineering and Culture

OI engineering involves the systematic process of designing, managing, and optimizing knowledge flows both inside and outside an organization. It is crucial in creating tools and processes that facilitate the integration of external ideas and technologies into product development, while also safeguarding intellectual property rights. The role of OI engineering is to ensure the efficient management of complex information flows, helping sustain innovation over time [23].

In OI engineering, organizations develop processes, methodologies, and systems that allow them to effectively manage the integration of external knowledge and technologies into their development cycles. Tools used in OI engineering include intellectual property management systems, collaborative platforms, and open prototyping methods. These systems help companies integrate both internal and external innovations, making their development processes more flexible and efficient [24].

An essential component of OI engineering is the creation of organizational structures and processes that facilitate interaction with external partners. This can include open-source innovation systems, user networks, and strategic partnerships. The goal is to create a collaborative environment that maximizes value co-creation between the company and its external stakeholders, enhancing the overall innovation process. [21]. Technological platforms developed for OI engineering constitute critical convergence spaces where technological infrastructures and social collaboration intersect, giving rise to dynamic innovation ecosystems in which diverse stakeholders systematically interact to co-create products, services, and value.

OI engineering also involves structuring processes to enable the exploration of new ideas and the efficient use of existing ones. Rapid feedback loops are essential to this approach, allowing organizations to adjust products and services in real time based on contributions from users and external actors [25].

2.6. OI Culture and Innovation

OI culture refers to the organizational mindset, values, and behaviors that promote external collaboration and the sharing of knowledge. To successfully adopt OI, organizations must cultivate a culture that encourages the exchange of ideas and experimentation. Companies with an OI-oriented culture tend to be more flexible, risk-taking, and open to external knowledge, recognizing it as a vital source of competitive advantage [26].

For OI to be effectively integrated, an organization’s internal culture must prioritize external collaboration and knowledge sharing. This type of culture fosters innovation, as it encourages employees to view external contributions as essential to the company’s growth. A culture that supports OI is one that nurtures an environment where ideas can flow freely, experimentation is encouraged, and external collaborations are seen as opportunities for growth and learning.

In an OI-driven culture, organizations cultivate a mindset of continuous experimentation and learning. They value the ideas of employees and external partners, fostering an environment where innovation is viewed not as an isolated activity but as a collaborative process. This approach requires leadership that encourages open interaction and knowledge sharing with external stakeholders [27]. It also necessitates a shift in how failures and risks are perceived. Instead of seeing them as threats, organizations with this mindset view them as opportunities for learning and improvement. This perspective is vital for an environment that nurtures creativity and the ongoing generation of innovative ideas [28].

Such a culture also aligns with talent management, emphasizing the hiring and retention of individuals with collaborative, creative skills and a learning-oriented mindset. Organizations that embrace this culture create environments conducive to teamwork and interaction among diverse stakeholders, which facilitates the adoption of external ideas and the co-creation of innovative solutions [29].

2.7. Complexity and Limits of OI

The complexity of OI arises from the integration of various knowledge flows into organizational processes. Collaboration with multiple external stakeholders introduces uncertainty, leading to potential conflicts of interest, communication challenges, and difficulties in protecting intellectual property. Despite these challenges, complexity can provide significant advantages, such as diversifying ideas and accelerating innovation [20,21]. The diversity of stakeholders—ranging from companies and universities to consumers and startups—and the variety of knowledge they contribute can complicate the innovation process. Moreover, increased interaction among external actors introduces greater opportunities for conflict, uncertainty, and misalignment of goals, which organizations must navigate carefully [21].

OI’s complexity is further magnified by the need to integrate different types of knowledge into the innovation process. External knowledge is often highly specialized and diverse, requiring organizations to have the capability to identify, filter, and adapt the most valuable ideas to their context. This process depends on an organization’s absorptive capacity, which is critical for reducing complexity and enhancing OI effectiveness [30]. Additionally, OI is shaped by institutional, cultural, and regulatory factors. Local or global regulations impacting knowledge sharing, patents, and intellectual property can further complicate OI management. Companies operating in varied legal and cultural environments must adapt their OI strategies to meet regional specifics, adding another layer of complexity [31].

At the same time, the limits of OI refer to the challenges and restrictions organizations face when implementing OI models. These limits include issues related to intellectual property, the protection of confidential knowledge, and the difficulties of coordinating collaboration among disparate actors. While openness can enhance innovation opportunities, it also exposes organizations to risks related to the control and exploitation of knowledge [5]. Though OI offers great potential, it also has its limitations, including concerns about intellectual property, trust among collaborators, and the challenges of controlling the quality of external ideas. Companies must recognize these limits and develop strategies to effectively manage interactions with external actors while protecting their interests [32].

One critical limit of OI lies in the management of intellectual property. Organizations must strike a balance between openness in sharing ideas and protecting their intellectual assets. Excessive openness can lead to the loss of property rights over innovations, making it harder to monetize the outcomes. Furthermore, managing patents, licenses, and usage rights effectively is crucial for retaining control over the ownership of the innovations generated [17]. Another limitation of OI is the coordination of efforts among various stakeholders. Establishing an adequate organizational infrastructure to facilitate effective collaboration can be a significant barrier.

2.8. OI Management in Small and Medium-Sized Enterprises

OI management involves creating processes and structures that allow organizations to effectively capture and utilize external knowledge. This requires a strategic approach that defines how to interact with the innovation ecosystem, manage alliances, and protect organizational interests while sharing knowledge with external stakeholders [5,21]. It also involves the implementation of technological tools and platforms that facilitate the exchange of ideas and collaborative work between internal and external teams. These tools may include project management software, crowdsourcing platforms, and intellectual property management systems, all of which enable the efficient integration of external innovations [15].

Effective management requires organizations to handle contracts and agreements related to external collaborations, including intellectual property agreements, technology licenses, co-creation agreements, and distribution contracts. These agreements are essential for safeguarding the organization’s interests while ensuring the efficient use of external resources [17].

OI management also involves creating an organizational approach that fosters a culture of collaboration through leadership, multidisciplinary teams, and incentives that encourage active participation. Organizations must cultivate an environment that promotes creativity and collaborative innovation, ensuring internal and external collaborators work together toward shared goals [29].

In small and medium-sized enterprises (SMEs), OI management is particularly challenging due to limited resources. However, SMEs can leverage collaborations with universities, other SMEs, or even consumers to access new knowledge without significant investments in research and development. The key for SMEs lies in forming strategic alliances that enable them to access external ideas and resources, which are essential for competing in global markets [33]. OI in this context allows SMEs to overcome resource limitations by tapping into external knowledge and technologies. By collaborating with other companies, universities, and startups, SMEs can actively participate in innovation processes without the need for large internal R&D investments [5].

SMEs especially benefit from OI by gaining access to collaborative networks that enable them to co-create innovative solutions without incurring the high costs associated with internal technology development. Openness to new ideas and cooperation with other actors in the innovation ecosystem—such as universities or large corporations—can help strengthen SMEs’ competitiveness in the global market [34]. Furthermore, OI in SMEs can facilitate the creation of innovation ecosystems that promote knowledge sharing and ongoing collaboration. Collaborative platforms and social innovation initiatives enable small businesses to enhance their innovation capacity by working with customers, suppliers, and other stakeholders to develop new products and services [25].

Despite these advantages, SMEs face the challenge of managing the scale and complexity of OI, as they often lack the resources to oversee multiple external collaborations. However, this challenge can be mitigated through strategic alliances, the use of OI platforms, and leveraging government incentives that support participation in innovation processes [35].

2.9. Legal Aspects of OI in Smart Cities and Industry 4.0

The rapid integration of OI into public services, urban governance, and industrial systems has generated significant legal and ethical challenges. In European scholarship, recent open-access research emphasizes the need for regulatory frameworks that not only foster innovation but also safeguard fundamental rights, legal certainty, and public trust. This section draws primarily on the works of [36,37] to develop a contemporary legal-theoretical foundation for the use of OI in smart cities and Industry 4.0 contexts.

Ref. [36] conceptualize smart cities as socio-technical ecosystems in which digital technologies, particularly OI, are embedded into urban management, public administration, mobility systems, environmental monitoring, and service delivery. From a legal standpoint, they argue that the effectiveness of OI, driven urban services depends not only on technological capability but also on the coherence and adaptability of regulatory frameworks at the municipal, national, and European levels.

Moreover, they emphasize the importance of data protection and cybersecurity as foundational legal requirements in smart city infrastructures. Given the extensive collection and processing of personal and behavioral data, compliance with data protection principles, such as purpose limitation, proportionality, and user consent, becomes essential to maintaining public trust and preventing systemic risks. In this sense, legal governance is not merely a constraint but a structural condition for the sustainable development of smart cities.

Ref. [37] extends the legal analysis of OI beyond urban governance to the broader context of Industry 4.0, situating technological transformation within a human rights framework. The author argues that OI-driven automation, algorithmic management, and digital surveillance in industrial and labor environments pose significant risks to fundamental rights, including privacy, equality, human dignity, and access to justice. A key theoretical insight from [37] is that traditional regulatory instruments are insufficient to address the structural power imbalances created by OI systems. Automated decision-making in employment, production, and public administration can reinforce existing social inequalities, introduce opaque forms of discrimination, and weaken procedural safeguards. Consequently, ref. [37] advocates for a rights-based approach to OI governance that integrates human rights impact assessments into the design, deployment, and evaluation of OI systems.

Furthermore, ref. [37] underscores the importance of international and European legal harmonization in regulating OI. Given the transnational nature of digital technologies, fragmented national regulations risk creating legal uncertainty and regulatory arbitrage. Instead, the author calls for coordinated legal standards grounded in human rights law, labor law, and data protection law, ensuring that technological progress aligns with democratic values and social justice.

In smart cities, this means embedding legal safeguards into urban OI systems to ensure transparency, fairness, and democratic oversight. In Industry 4.0, it requires rethinking labor protections, liability regimes, and regulatory enforcement mechanisms to address algorithmic decision-making and automation. Ultimately, both strands of scholarship converge on the principle that OI should serve human well-being and social inclusion, rather than undermine legal protections or exacerbate structural inequalities.

Despite the increasing adoption of OI practices across sectors, the literature still shows fragmentation in conceptual clarity and empirical depth, particularly when applied to service-oriented and agribusiness value chains. Recent reviews highlight the need to synthesize OI studies to clarify underlying determinants, collaboration mechanisms, and theoretical constructs underpinning firm-level and ecosystem-level innovation processes [38]. Moreover, systematic analyses reveal that research on OI has largely concentrated on intra-organizational perspectives, with limited theoretical consolidation of external collaboration and ecosystem governance—an essential gap for understanding how firms integrate external knowledge into their innovation management practices.

While open innovation has been explored extensively in manufacturing and high-tech contexts, its application to service-centric and agro-industrial chains like specialty coffee remains underdeveloped. The literature on agrifood OI indicates that open practices are not yet well articulated across different links of value chains, and that barriers such as knowledge sharing, intellectual property risks, and communication challenges persist. Importantly, although agri-food contexts have been used to explore OI trends and collaborative barriers, these studies do not sufficiently integrate innovation management models with contextual factors such as supply chain complexity, stakeholder diversity, and market-driven sustainability requirements—thereby creating a distinct gap that this study aims to address.

The hypotheses of this research reflect not only the expected relationships but also the theoretical mechanisms that underpin them. Based on open innovation (OI) theory and studies demonstrating the impact of external collaboration on performance, two hypotheses were formulated:

H1: 

The degree of external collaboration (e.g., with partners, universities, and suppliers) is positively associated with innovation outcomes in specialty coffee value chains, which aligns with evidence that collaborative openness improves the performance of business innovation and its dynamic capabilities. Furthermore, given the specific barriers to OI in the agribusiness sector identified in the literature (e.g., skills gaps, governance challenges),

H2: 

Organizational and network governance structures moderate the relationship between external collaboration and innovation outcomes, such that better governance enhances the positive effect. This hypothesis directly addresses the demands for multilevel theoretical clarification and a systemic perspective in OI research.

3. Materials and Methods

The methodology is divided into phases. In each phase, a relevant aspect was addressed to compare the information obtained and thus obtain definitive findings.

This section explains the methodological process used to develop and validate the Innovation Management Model. It describes the research design, variable identification, expert validation, and model construction phases.

This study follows a four-phase model development methodology. Phase 1 identifies innovation variables through systematic literature review. Phase 2 prioritizes variables using expert evaluation. Phase 3 develops alternative conceptual models. Phase 4 validates and selects the final model (IMM-3).

The methodology developed in this paper related to the co-development of innovation management models has been previously used in studies related to agro-industrial sectors and companies such as [39,40,41,42].

Figure 1 details each of the research phases.

3.1. Phase 1

In this phase, a review of the specialized literature was conducted, focusing on IMMs, with an emphasis on the specialty coffee sector. To this end, searches were conducted in Scopus and Google Scholar.

Table 1. Search equations applied in Scopus.

Search Equations	Number of Articles	Related Articles
TITLE-ABS-KEY ((“innovation management model” OR “innovation strategies” AND coffee)	5	3
TITLE-ABS-KEY (“innovation management”) AND coffee	34	2
TITLE-ABS-KEY (“café”) y “gestión de la innovación”	2	2
TITLE-ABS-KEY (“coffee “) y “innovation management”	93	1
(TITLE-ABS-KEY (innovation AND strategies) AND TITLE-ABS-KEY (coffee) AND TITLE-ABS-KEY (innovation AND management))	29	3
TOTAL	163	11

Source: Prepared by the authors (2026).

lists the search equations applied in Scopus, and through the compilation of this information, the relevant variables for model formulation were identified.

Meanwhile, similar searches were conducted on Google Scholar, and 14 articles directly related to the research topic were found (see

Table 2. Google Scholar search equation.

Key Words	Related Articles
Innovation management models and innovation models.	14

Source: Prepared by the authors (2026).

).

Subsequently, a Systematic Literature Review (SLR) was conducted following the methodological framework proposed by [43]. This approach enabled the systematic identification, screening, and classification of relevant studies in order to determine key variables that could potentially influence the development of the pro-posed IMM (see Figure 2). The review process adhered to the PRISMA guidelines, a widely recognized standard for conducting and reporting systematic reviews. The adoption of this protocol ensured methodological rigor, transparency, and reproducibility throughout the study selection process.

Using the PRISMA (Preferred Reporting Elements for Systematic Reviews and Meta-Analyses) methodology, relevant articles were selected for further review. This protocol is used for the identification and selection of studies within Phase 1 and subsequently to select relevant variables that could influence the construction of the proposed information measurement models (see Figure 2) and can also be found at this link: https://docs.google.com/document/d/1YktCaz17Yu8eIOJXeTFqnQMtkGdbrinw/edit?usp=sharing&ouid=101792629319844010350&rtpof=true&sd=true (accessed on 19 December 2025).

3.1.1. Research Questions and Scope

The review was based on the following research question: What critical variables influence the design and implementation of Innovation Management Models (IMMs) in the department of Quindío, Colombia, and to what extent can these variables be generalized?

To operationalize this question, the following factors were considered: For the development and validation of the Innovation Model (IMM) in the specialty coffee sector, it is structured primarily around three dimensions or process components: input variables, which are initial factors representing the resources and conditions necessary to begin the innovation process; process transformation variables, which are factors that are modified or applied during the execution of the innovation strategy; and output variables, which are results and impacts representing the achievements obtained after the implementation of the initiatives. To prioritize these variables, the research used two fundamental indices validated by experts: (1) the relevance index (measured on a scale of 0 to 5 to determine the importance of each factor), and (2) the congruence index (measured on a scale of −1, 0, and 1 to ensure the coherence of the variables within the model).

3.1.2. Eligibility Criteria

Studies meeting the following criteria were included:

• Articles in peer-reviewed journals
• Indexed in Scopus
• Explicitly addressing innovation management and models for innovation management
• Written in English or Spanish
• Published without a time limit up to the final search date

Exclusion criteria included:

• Conference proceedings, editorials, book chapters, and documents unrelated to the research
• Duplicate records
• Studies lacking conceptual or analytical relevance

These criteria were defined a priori to reduce selection bias and improve replicability.

3.1.3. Information Sources and Search Strategy

The primary database used was Scopus, due to its broad multidisciplinary coverage and rigorous indexing standards.

3.1.4. Study Selection Process

All retrieved records were exported in CSV format and imported into VantagePoint V15.2 software for preprocessing and bibliometric structuring.

Duplicate entries were algorithmically identified and removed before selection.

The selection process was carried out in two sequential stages:

Stage 1: Selection of the title and abstract to assess thematic alignment with the Management Models for Innovation.

Stage 2: Evaluation of the full text to verify conceptual rigor, methodological clarity, and direct relevance to the analytical framework.

Discrepancies in eligibility decisions were resolved by consensus among the authors. The complete numerical progression between stages is detailed in the PRISMA 2020 flowchart (Figure 2).

3.1.5. Transparency and Replicability of Reports

To improve replicability, all search equations, database filters, screening logic, and extraction variables are documented in the Supplementary Materials. The dataset derived from the final corpus (n = 25 studies) is available upon reasonable request to the corresponding author.

The methodological rigor applied in this review ensures compliance with the PRISMA 2020 reporting standards and aligns with high-impact publishing practices in sustainability and related journals indexed in Scopus.

As mentioned in the systematic literature review conducted under the PRISMA guidelines, it was evident that most previous studies on the coffee sector focus on isolated factors—such as adaptation to climate change [44] or eco-innovation practices [45]—without proposing a comprehensive and systemic innovation management model. Furthermore, OI research has been primarily developed in manufacturing and high-tech sectors, leaving agro-industrial value chains relatively underexplored. To validate and contrast these approaches, Colombian studies applied to sectors such as livestock feed, aquaculture, construction, and the blackberry, flower, and sugarcane value chains were analyzed. These studies highlight the importance of knowledge management models, particularly the SECI model [46], to facilitate the effective conversion and transfer of knowledge within organizations. Likewise, the Innovation Management Models that have been published on the topic Foresight study using scenarios and the Delphi method in the leather agroindustrial chain to 2035—Alignment of results with OI [41,42] and Sustainable Innovation Management Model (MGI) for Agro-Industrial Citrus Chain [39].

3.2. Phase 2

In this phase, the variables identified for the Innovation Management Model (IMM) within the specialty coffee sector were prioritized using a Likert scale. Three types of variables were considered: input, transformation, and output. This approach facilitated the identification of key variables, particularly those deemed most significant by the participants, thereby defining the critical areas of innovation management.

The input-transformation-output approach was used following the guidelines of Check land’s soft systems methodology, which helps improve unstructured human activity problems. This methodology has been used in previous studies relating future studies to innovation management strategies and models, and it allows for the generation of conceptual models through the identification and prioritization of variables in the three stages of input, transformation, and output with experts.

3.3. Phase 3

Following the selection of articles with a high similarity index, a subsequent literature review was conducted to identify and prioritize the variables relevant to the development of the proposed IMM. This process resulted in the inclusion of 25 articles (11 from Scopus and 14 from Google Scholar), which were then subjected to evaluation. The objectives of this phase included assessing the relevance and consistency of the questions related to the key variables within the specialty coffee sector’s Innovation Management System (IMS), using specific indices. Additionally, three conceptual graphic models were developed to illustrate the relationships and outcomes associated with these variables at various performance levels (excellent, good, average, and poor).

3.4. Phase 4

In this phase, the most suitable Innovation Management Model (IMM) for the specialty coffee agri-chain was selected, guided by the prioritization of variables identified in previous phases. Workshops and discussion sessions were conducted with various focus groups to ensure alignment with the previously identified variables

4. Results

4.1. Phase 1 Analysis

The analysis of the specialized literature resulted in the selection of 11 articles for further evaluation. These articles are detailed in

Table 3. List of articles and variables in Scopus.

Author	Title	Variables o Characteristics of the Model	Journal
[47]	Spices Coffee: Innovation Strategy to Increase Quality on Powder Coffee Farmers.	Internal environment condition Coffee spice industry. External environment condition Coffee spice industry. Technological innovation strategy coffee spices.	Journal of Physics: Conference Series
[48]	Small-scale farmer access to international agri-food chains: A BOP-based reflection on the need for socially embedded innovation in the coffee and flower sector	Access to international markets. Technology and knowledge	Jstor.org.
[49]	Socio-Ecological and Green Innovation Management System Performance: An Approach Towards Green Enterprises	Performance of the green and socio-ecological innovation management system Socio-ecological system Green innovation Socio-ecological strategies	Socio-Ecological-and-Green-Innovation-Management-System-Performance-An-Approach-Towards-Green-Enterprises.pdf
[50]	Kanthal: Building bridges for increased innovation capability	Innovation Management System (IMS) Innovation Capacity Incremental Innovation Barriers in the Innovation Environment	World Scientific (Europe)ORLD SCIENTIFIC (EUROPE)
[51]	Innovation models in coffee production in the Sierra Norte of Puebla, Mexico	Productive and commercial innovation Innovations Innovation models Innovation index	Dialnet
[44]	Analysis of technology transfer policies in the coffee production chain in Antioquia	Technological Push Mode Localized Innovation System Policy Analysis Market Opportunity Distribution	Scielo
[52]	Implementation of green supply chain management for sustainable agroindustry in coffee processing unit, a case of Indonesia	Waste Management Ecological Impact Ecological Processing or Ecological Manufacturing Ecological Distribution Ecological Supply Chain Management (GSCM) Emission Reduction during Distribution Techno-ecological Feasibility Processing	Coffee Science, 17, 1–8.
[53]	Innovation Elements in the Sustainable Production of Indigenous Coffee in the Amazon	Elements of innovation Sustainable coffee development Institutional and economic development Innovation scenario Sustainable development Process innovation Technologies.	Springer Nature Singapore.
[45]	Foresight study: Application of Delphi method in specialty coffees in Colombia to 2025	Foresight in specialty coffees New businesses Dissemination strategies Marketing Eco-organic products New business lines in the specialty coffee sector	Espacios (Journal)
[54]	Maximizing sustainability of the Costa Rican coffee industry	Coffee production and processing Sustainability Cost reduction Risk reduction New opportunities Training Management Industry structure Cleaner Production (CP) Industrial Ecology (IE) Market niches Industrial performance Operational design Management attitudes Barriers to innovations	Sciencedirect

Source: Own elaboration (2026).

, which also highlights the most relevant variables for the specialty coffee sector. Critical aspects were identified, including the authors, titles, variables or characteristics of an Innovation Management System (IMM), and the journals in which each article was published.

The variables identified in the specialty coffee sector encompass a broad range, reflecting the complexity and diversity of factors that influence innovation management in this industry. Considering both internal and external environmental conditions, along with technological innovation strategies, access to international markets, and the essential role of technology and knowledge, underscores the need for a comprehensive approach to innovation management in the coffee sector.

The inclusion of variables related to the performance of the green and socio-ecological innovation management system highlights the growing importance of sustainable and socio-ecological practices within the industry. Green innovation management, socio-ecological strategies.

Innovation Management Systems (IMS) are presented as key elements for continuous improvement and sustainable coffee development. Furthermore, the inclusion of foresight in specialty coffees reflects a forward-looking vision, considering new business opportunities, dissemination strategies, marketing, and eco-organic products. This suggests the importance of anticipating emerging trends and adopting innovative approaches to maintain relevance in a constantly evolving market. In coffee production and processing, variables linked to sustainability, cost reduction, risk management, and the identification of new opportunities emphasize the need for agile and proactive management. The consideration of elements such as training, industry structure, cleaner production, and barriers to innovation provides a comprehensive overview of the challenges and opportunities in this sector. When these variables are integrated, they offer a comprehensive foundation for innovation management in the specialty coffee industry, emphasizing the importance of sustainability, adaptability, and strategic foresight for continued success in this dynamic market.

A search of articles on Google Scholar, using the chosen variables for the specialty coffee sector, yielded a total of 14 articles.

Table 4. List of Articles in Google Scholar.

Author	Title	Variables o Characteristics of Model	Journal
[55]	An innovation perspective to climate change adaptation in coffee systems.	Climate change adaptation Incremental vs. transformative adaptation Agricultural socio-technical systems Innovation management models	International Center for Tropical Agriculture (CIAT), Hanoi, Vietnam.
[56]	Analysis of MSME Innovation Strategies Before and After the COVID-19 Pandemic (Case Study of Omahan Kepanjen Coffee.	Culture of Innovation Financial Resources Human Resources Product/Service Development Process External Collaboration Idea Evaluation Triple Helix Model AI Model (Artificial Intelligence) Waterfall Innovation Model Agile Innovation Model Disruptive Innovation Model	International Journal of Humanities Education and Social Sciences (IJHESS)
[57]	Assessment of Innovation Potential of Gayo Coffee Agroindustry	Strategy and Planning Marketing Technological Processes Quality and Environment Logistics Human Resources Collaboration and Partnerships Sustainability and Environment Organizational Innovation Financing	Quality Innovation Prosperity, 21(3)
[58]	Eco-innovación: Estrategia de las empresas Agroindustriales de Colombia	Eco-innovation Industrial ecology Technological revolution	Fundación universitaria del Área Andina
[59]	Guatemalan coffee: a focus on the global market and its productivity	Coffee Production and Sustainability Innovation in Production International Competitiveness Coffee Value Chain	ZBW—Leibniz-Informationszentrum Wirtschaft/Leibniz Information Centre for Economics
[60]	Innovation Management in the Specialty Coffee Sector: A Case Study of the Association of Growers of Apía, Risaralda (Asoapia)	Innovation Social appropriation of environmental knowledge Solidarity economy Sustainability and territory Product quality Fair trade Agroecology	Sociedad y Economía, (25)
[46]	Innovation systems in developing countries: A top-down and bottom-up approach to studying the Colombian National System of Innovation and the coffee, flower and sugarcane production chains	Science, Technology, and Innovation (STI) Policies National Innovation System (NSI) Knowledge and knowledge generation Public, industrial, and academic organizations Research funding Global supply chains Governance and governability Culture and cultural factors Power and power dynamics Innovation based on public knowledge	Doctor of Philosophy Science and Technology Studies The University of Edinburgh 2015
[61]	The influence of innovation on the export performance of coffee exporting companies in Peru	Context and characteristics of the coffee industry Resources and capabilities Knowledge transfer Regularity over time and types of results	Universidad San Ignacio de Loyola. Facultad de Ciencias Empresariales ACULTAD DE CIENCIAS EMPRESARIALES Carrera de International Business
[62]	Technological innovation in the Cauca Coffee Growers Cooperative CAFICAUCA	Technological innovation Innovation management Competitiveness Competitive advantages Innovation culture Collaboration	Revista Científica Profundidad Construyendo Futuro, 12(12)
[63]	Proposal of a model to manage innovation in the Innovation Hubs of Cauca, Colombia	Innovation Hubs AI Trust between stakeholders Innovative products Collaboration structure Innovation processes Market	Revista Electrónica Gestión de las Personas y Tecnología

Source: Own elaboration (2026).

lists aspects such as authors, titles, variables or characteristics of Innovation Management Systems (IMS), and the journals in which these articles were published.

The detailed identification of variables in the specialty coffee sector reveals a wide range of crucial elements, ranging from climate change adaptation to management strategies and eco-innovation practices. In the agricultural sector, particular attention is paid to sociotechnical approaches, innovation management models, and innovative cultures. Furthermore, integrated management encompasses key aspects such as financial resources, human capital, and product and service development processes. The diversity of innovation models, strategies, and business practices highlights the complexity of this sector, where sustainability, quality, and economic and environmental impact are interconnected factors. OI, collaboration, and knowledge management emerge as catalysts for innovative processes, while certification and quality management are intrinsic elements for success in a globalized market. Ultimately, these variables encapsulate the richness and breadth of the elements influencing the dynamics and development of the specialty coffee sector.

4.2. Phase 2 Analysis

4.2.1. Analysis Based on Previous Studies

After identifying the most notable variables, they were grouped together to classify them according to the corresponding process component: input, transformation, or output. It is important to note that, prior to this classification, ref. [64] conducted previous research, including the development of an IMM for a company dedicated to the production of food supplements for the livestock sector, as well as another proposal for the fish farming sector in Colombia. These previous studies provided several variables relevant to the present research, which are detailed below in

Table 5. Grouped Variables for an Innovation Management Model (IMM) in a Feed Supplement Company for the Livestock Sector.

Components	Variables
Inputs	International competitiveness of agricultural products, knowledge engineering, key technologies for digital design of precision equipment, sustainable agricultural development, agricultural productivity, level of integration between crops and livestock, type of innovation, appropriate organizational definition, product portfolio, clients and consumers, innovation objectives, managerial functions, innovation process, market information, innovation model design, managerial functions.
Transformation	Innovation level, forage income and balance, innovation capacity, diversification, knowledge management, idea research/creativity, R&D activities, customer relationship management, customer and consumer information analysis, analysis and application of stages for formulating and implementing an innovation strategy, technological development, OI, technological monitoring,
Outputs	Innovation expenses, sustainable development, modern agriculture, livestock diversity, livestock innovations, conservation in mixed crop-livestock systems, alternative agriculture, outputs, integration of agricultural and livestock activities, conservation agriculture, mixed silages, innovation efficiency, technological innovation, agro-industrial company, innovation outcomes, innovation policies, innovation objectives, new product lines.

Source: Authors’ own creation (2026).

.

In this case, the input component includes 16 variables, the transformation component has 21 variables assigned, and the output component is associated with 18 variables.

Next, the existing variables from the proposed Innovation Management Model (IMM) for the aquaculture sector in Colombia are presented. These previous studies provided various relevant variables for the current study, which are detailed below in the corresponding

Table 6. Grouped Variables for an IMM in the Aquaculture Sector in Colombia.

Input	Technological policy, formalization, growth strategy, new concepts for business units, new skills, mastery of old technology, design, project management, integrated strategy, appropriate organizational definition, ideas/creativity, product innovation program, leadership, performance objectives, integration of clients and suppliers, knowledge exploration, nature of innovation, scope of innovation, type of organization, innovation management strategy, resources, entrepreneurship.
Transformation	Diversification, concentration of technological specialists, economic congruence, state of technology adoption, retraining, revolutionary changes in technology, project portfolio management, competency-based management, knowledge management, technological competitive intelligence activities, collective learning, R&D activities, customer relationship management, knowledge preservation, efficiency in innovation project management, vertical integration, business model, value chain.
Output	Innovation champions, breakthrough innovations, senior management is responsible for new product outcomes, successful commercialization of innovation, network management, effectiveness of the strategic plan, alliances with other organizations, new product lines, impact of innovations, registered patents, technological capacity, improvement opportunities, consumers, process management, prototyping, sustainable development, monetizing investment.
Input	Technological policy, formalization, growth strategy, new concepts for business units, new skills, mastery of old technology, design, project management, integrated strategy, appropriate organizational definition, ideas/creativity, product innovation program, leadership, performance objectives, integration of clients and suppliers, knowledge exploration, nature of innovation, scope of innovation, type of organization, innovation management strategy, resources, entrepreneurship.
Transformation	Diversification, concentration of technological specialists, economic congruence, state of technology adoption, retraining, revolutionary changes in technology, project portfolio management, competency-based management, knowledge management, technological competitive intelligence activities, collective learning, R&D activities, customer relationship management, knowledge preservation, efficiency in innovation project management, vertical integration, business model, value chain.
Output	Innovation champions, breakthrough innovations, senior management is responsible for new product outcomes, successful commercialization of innovation, network management, effectiveness of the strategic plan, alliances with other organizations, new product lines, impact of innovations, registered patents, technological capacity, improvement opportunities, consumers, process management, prototyping, sustainable development, monetizing investment.

Source: Author’s own creation (2026).

.

In this scenario, the input component encompasses a total of 22 variables, while the transformation component has 18 variables assigned, and the output component is linked to 17 variables.

4.2.2. Definition of Variables for an IMM

At this stage, following the previous research, the selected variables are presented, having undergone a rigorous filter by the authors and validated by experts in the agro-industrial field. The selected variables will play a crucial role in the creation of conceptual innovation management models for various agro-industrial chains, such as bananas, cold-climate fruits, dairy products, citrus, and leather. Their importance is reflected in their inclusion as fundamental variables in the process.

These elements, categorized as input, transformation, and output, are presented to experts during the proposed workshops. In this scenario, tabulation and calculation of key indicators, such as mode, modal frequency, and percentage of consensus, are carried out. These values form the basis for defining the selected variables, applying specific criteria (see

Table 7. Variable Scale for an Innovation Management Model (IMM).

Winning-Priority Variable	Non-Winning Variable	Variables Under Discussion
Mode 4 or 5 and consensus above the average of the thematic group.	Mode 0, 1, or 2 and consensus above the thematic group.	The other variables.

Source: Own elaboration (2026).

).

At this point, the variables that will be essential for constructing the model are identified. These findings represent a crucial turning point in the evolution of the process, marking a decisive phase in the development of the proposed model. Below, each variable is presented to facilitate the completion of the instrument.

4.2.3. Input, Transformation, and Output Variables

The variables listed below are classified as input variables, as they represent the factors, resources, and initial conditions necessary to begin the innovation management process. Below, these input variables are presented (see

Table 8. Input Variables for the Specialty Coffee Sector.

Variables

1. Creativity 2. Leadership 3. Financing, personnel, and facilities 4. Investment in equipment and infrastructure 5. Competitiveness 6. Types of innovation 7. R&D&I activities 8. Capacity development 9. Innovation Management System (IMS) 10. Innovation management models 11. Government policies and regulations 12. Technological development and key technologies 13. Innovation landscape 14. Management functions 15. Business skills 16. Knowledge engineering 17. Supply chain 18. Innovation capabilities 19. Knowledge generation 20. Customer and consumer demand 21. Benchmarking 22. Engagement 23. Product quality 24. Innovation objectives 25. Alternative innovation paths 26. Level of innovation 27. Organizational innovation 28. Innovation culture 29. Product innovation 30. Process/Technology Innovation 31. Monitoring Methods 32. Technological Monitoring 33. Competitive Monitoring 34. Strategic Intelligence 35. Sustainable Development Goals 36. Food Security 37. Agriculture 4.0 and 5.0 38. Sustainable Agriculture 39. Agroecology 40. Technological Roadmap 41. Technological Innovation Projects 42. Technological Innovation Center 43. Technological Changes 44. Agricultural Innovation Platforms 45. Information and Communication Technologies 46. New Production Technologies 47. Future Production Systems 48. Impact of Innovations 49. Circular Economy 50. Barriers to the Innovation Environment 51. Partnerships 52. University-Business-State Links 53. Conditions of the Business Environment and the International Market

Source: Own elaboration (2026).

).

The following variables correspond to transformation variables, as they contain elements that can be modified, developed, or applied during the innovation management process. These variables are not only initial inputs but also undergo changes or transformations as the organization progresses in its innovation strategy (see

Table 9. Transformation variables for the specialty coffee sector.

Variable

1. Innovation Management Strategy 2. Competency Management 3. Idea Generation and Evaluation for Experimental Development 4. Production Process 5. Value-Added Transformation 6. New Product Development through Testing and Prototyping 7. Training and Knowledge Transfer 8. Development of Innovative Products or Processes 9. Knowledge Management 10. Design Thinking, Lean Processes, and Agility 11. Intelligence Management 12. Innovation Flow Management 13. New Product and Service Strategy 14. Technology Management 15. Scaling Innovations 16. Diffusion of Innovations 17. Technology Adoption 18. Analysis of National, Local, and Territorial Innovation Systems 19. Business Model 20. Foresight 21. AI 22. Process and Technological Innovation 23. Technological Development 24. Innovation Capabilities 25. Value Chain 26. Knowledge Conservation 27. Innovation Strategy Growth 28. Product Innovation Program 29. Project Management 30. Sustainable Development 31. Market Innovation and Opportunity Analysis 32. Circular Economy 33. Market Validation 34. Knowledge Management 35. Strategic Innovation Projects 36. Knowledge Networks

Source: Own elaboration (2026).

).

Finally, the variables for the output component for the specialty coffee sector, considered as such because they represent the results, impacts, and achievements obtained as a result of the development and execution of innovative initiatives (see

Table 10. Variables for the output component for the specialty coffee sector.

Variable

1. Competitive advantages 2. Improved product portfolio 3. Know-how (acquired knowledge) 4. Value creation 5. Product portfolio 6. Radical innovation 7. Innovation for regenerativity 8. Effectiveness of the strategic plan in project management 9. Consumer engagement 10. Development of financial sustainability 11. Financial and business results 12. Strategies and alliances 13. Technological capacity and intellectual property 14. Profitability of innovation 15. Impact and efficiency on profitability 16. Adoption of innovative technologies and practices 17. Environmental impact and sustainability 18. Quality and performance 19. Economic and commercial impact 20. Strategic and organizational impact 21. Innovation in quality and sustainability 22. Result of innovation in registered patents 23. Improvement in operational efficiency 24. Customer satisfaction 25. Brand image 26. Resilience Organizational 27. Access to new markets 28. Supply chain improvements 29. Development of new capabilities and adaptation to environmental changes 30. Compliance with standards and regulations 31. Participation in innovation networks

Source: Own elaboration.

)

4.3. Phase 3 Analysis

Once the variables in the input components, transformation processes, and outputs were identified, prototypes of IMMs, specifically adapted to the specialty coffee agro-industrial chain were developed. These models were co-created with sector experts during a second workshop called “Co-development of the proposal for the design of management models”.

The validation and prioritization of these prototypes were carried out. Subsequently, each IMM was presented visually for expert evaluation, accompanied by a detailed description of the behavior of the variables in the input, transformation, and output components. It is important to highlight that, prior to presenting the IMMs, it was necessary to associate the variables obtained through the analysis of key variable definitions, collected during the first workshop, for the establishment of the IMM. This was due to the abundance of variables, and their integration was an essential step for coherent and effective understanding.

Additionally, it is worth noting that in this phase, Relevance indices (scale of 0 to 5) and Congruence indices (scale of −1, 0, and 1) were also applied.

Table 11. Unassociated vs. Associated Variables for IMM Graphing.

Unassociated Variables	Associated Variables
Input Variables
Idea management/creativity, investment in equipment and infrastructure, Agriculture 4.0 and/or 5.0, sustainable agriculture, agroecology, technological change, innovation management models, technological development and key technologies, knowledge engineering, knowledge generation, benchmarking, product innovation, process/technology innovation, technological and competitive surveillance and intelligence, achievement of sustainable development goals, information and communication technologies, circular economy, barriers in the innovation environment.	Technology in agriculture Sustainable approach in agriculture Innovation management Knowledge, technological surveillance, intelligence, and benchmarking Infrastructure and barriers in innovation Principio del formulario Final del formulario
Transformation variables
Strategy for innovation management, competency-based management (technological), generation and evaluation of ideas for experimental development, transformation with added value, development of new products through testing and prototyping, training and knowledge transfer, development of innovative products or processes, innovation flow management, technology management, scaling of innovations, diffusion of innovations, technology adoption, analysis of national, local, and territorial innovation systems, foresight, OI, innovation capabilities, technological development, knowledge preservation, product innovation program, sustainable development.	Innovation Capability Management Development of innovative products or processes and idea evaluation Technology management and adoption Knowledge transfer and preservation Large-scale innovation and diffusion Systems analysis and foresight Sustainable development
Output variables
Competitive advantages, know-how (acquired knowledge), value creation, development of financial sustainability, strategies and alliances, technological capability and intellectual property, economic and commercial impact, innovation in quality and sustainability, brand image, access to new markets, improvement in the supply chain, development of new capabilities and adaptation to changes in the environment, participation in innovation networks	Competitiveness and strategy Knowledge and financial development Value creation and capacity development Technological capability and intellectual property Economic and commercial impact Innovation and sustainability Brand and access to markets Supply chain and innovation networks

Source: Own elaboration (2026).

corresponds to the analysis of non-associated variables versus associated variables for the graphical representation of IMM.

A model is a structured guide to achieve specific objectives, providing activities and tools for its implementation. These models offer a simplified representation of reality and establish steps to obtain predictable results, allowing for experimentation and adjustment as needed. In our case, the SECI Model by [65] is employed, which classifies knowledge and describes processes for its conversion, essential for the implementation of the IMMs developed during this process.

Below is a summary of the analyzed IMMs (see

Table 12. Synthesis of the Generic-Iterative IMM.

IMM	Linearity Between the Input, Transformation, and Output Variables
Authors:	Luis Fernando Gutiérrez Cano, Jhon Wilder Zartha Sossa, John Fredy Moreno Sarta, Gina Lía Orozco Mendoza, Tatiana Álvarez Ríos and Juan Carlos Palacio Piedrahita.
Description	Information flows and linear relationship between input, transformation, and output variables.
Contribution of the model	Note: The contributions of the Innovation Management Model (IMM) will be presented in the Results Analysis section of the third survey. Outsourcing allows the knowledge to be transferred to be evidenced in different formats, not only from person to person but also from person to different formats, meaning that the knowledge must be understood in order to be captured in a format. Additionally, it is the step prior to combination, which allows for enhancing, contrasting, detailing, and discerning knowledge through interaction and contributions with other people, with different experiences and interpretations around the same topic.

Source: Own elaboration (2026).

,

Table 13. Iterations between varibales IMM.

IMM	Feedback/Iterations Between Variables
Authors:	Luis Fernando Gutiérrez Cano, Jhon Wilder Zartha Sossa, John Fredy Moreno Sarta, Gina Lía Orozco Mendoza, Tatiana Álvarez Ríos y Juan Carlos Palacio Piedrahita.
Description	Information flows and feedback and iteration between input, transformation, and output variables. Related to policies and the needs of the context
Contribution of the model	Note: The contributions of the Innovation Management Model (IMM) will be presented in the Results Analysis section of the third survey

Source: Own elaboration (2026).

and

Table 14. Synthesis of the IMM for strategic alignment and innovation.

IMM	Strategic Alignment and Innovation
Autores:	Luis Fernando Gutiérrez Cano, Jhon Wilder Zartha Sossa, John Fredy Moreno Sarta, Gina Lía Orozco Mendoza, Tatiana Álvarez Ríos and Juan Carlos Palacio Piedrahita.
Descripción	Iterations between input, transformation, and output variables.” Feedback between variables and bidirectional flows of information and knowledge” Related to policies and the needs of the context.
Contributions of the model	Note: The contributions of the Innovation Management Model (IMM) will be presented in the Results Analysis section of the third survey

Source: Own elaboration (2026).

).

The SECI model applied in the specialty coffee sector can foster knowledge exchange between farmers, the transformation of that knowledge into documented practices, the integration of various knowledge sources related to coffee cultivation, and the internalization of this knowledge into the organizational culture. This can drive innovation and sustainable development in the specialty coffee sector.

4.4. Phase 4 Analysis

4.4.1. IMM-1 or Linear

The IMM-1 is linear, meaning it directs the process in a sequential and organized manner, from input variables to desired outcomes. This approach makes it easier to understand how variables transform into ideas and improvements, delving progressively into the concept of significant variables. Each variable can be analyzed individually or together, depending on the stage of the innovation management process, and its relationship with the input, transformation, and output stages can also be explored (see Figure 3).

Among the contributions of IMM-1 are the input variables, ranging from idea/creativity management to barriers in the innovation environment. A wide range of initial factors impacting the innovation process are considered. This includes technological and sustainability aspects, as well as the consideration of obstacles present in the environment. The transformation variables in this model span from innovation management strategies to technological adoption, management of innovation flow, and prospective analysis.

Management capacity, idea evaluation, and the development of innovative products or processes are fundamental in this transformation process. The output variables range from competitive advantages, know-how, value creation, and financial sustainability development, among others, highlighting key results of innovative management. Competitiveness and strategy are intertwined with financial development, sustainability, brand, and access to markets, revealing a positive economic and commercial impact. This model provides a solid and structured framework. By delving into significant variables successively and allowing for their individual or group analysis, this approach contributes to a holistic understanding of the innovation process, from conception to the generation of tangible and sustainable results for the organization.

4.4.2. IMM-2 or Generic-Interactive

IMM-2, represented in Figure 4, introduces a generic-interactive approach to innovation management. In this model, variables are integrated simultaneously, without following a strict sequential order. The synergy of forces enables the dynamization of communication between variables, thus achieving a more robust integration of knowledge and information into the innovative process.

IMM-2 offers a dynamic and interactive model that not only integrates variables into a continuous process but also allows for constant feedback between the input, transformation, and output stages. This flexibility and adaptability are essential for adjusting to the specific policies and needs of each organization, enabling a progressive empowerment of the variables over time and within the applied context. This generic-interactive model positions itself as a versatile and adaptable approach to innovation management.

4.4.3. IMM-3 with Strategic Alignment and Innovation

IMM-3 or the winning model (see Figure 5) presents a model of strategic alignment and innovation that aims for a more complete integration of the phases of the innovative process. This model is based on the notion of gears, where each variable interacts synergistically across all processes inherent to innovation management. This structure facilitates continuous feedback between the input, transformation, and output variables.

Figure 5 (IMM3) synthesizes the study’s winning systemic model, structured into three analytical sets of variables according to their locus of control and level of determination: internal (I), external (E), and context (C). Internal variables (I) refer to capabilities and mechanisms that are manageable within the organizational system (e.g., the innovation management system, innovation capability, development processes, resources, eco-innovation practices, and innovation models). External variables (E) represent factors within the immediate ecosystem that enable or constrain organizational performance (e.g., market access, industrial structure, barriers in the innovation environment, value/supply chains, and collaboration and coordination with public, industrial, and academic organizations). Context variables (C) capture structural and socio-ecological conditions that frame the sector and territory (e.g., the socio-ecological system, sustainability, governance, climate adaptation, and characteristics of the specialty coffee sector).

To strengthen IMM3’s traceability, relevance, and explanatory coherence, a relationship matrix is formalized from the diagram. In this study, the term correlation is used in a systemic sense (i.e., association/influence among variables), and is operationalized as either a directed relationship (when the diagram indicates conditioning), or a bidirectional relationship (when the diagram indicates feedback). The overall structure represented by IMM3 is interpreted as a layered feedback system: the context layer (C) significantly conditions the external ecosystem (E), which in turn determines the performance of the internal subsystem (I). Conversely, the internal subsystem feeds back into the external ecosystem through outcomes (innovations, products, technology adoption, and practices), and contributes to reorienting context variables linked to socio-ecological impact and sustainability. This configuration—C→E→I, with feedback loops I→E and I→C—accounts for IMM3′s robustness as the winning model: it integrates structural determinants, ecosystem mechanisms, and endogenous capabilities into a coherent and auditable causal chain, enabling an understanding of how innovation and sustainability outcomes are generated in the specialty coffee industry.

The proposed or winning IMM-3 corresponds to a mixed model distinguished by its synergistic integration of variables across all phases of the innovation process. The feedback between the variables not only allows for development in diverse sequences but also empowers each variable according to the specific policies of the organization. This approach positions itself as a versatile and holistic tool for addressing challenges and seizing opportunities in the dynamic world of innovation.

5. Discussion

This section of the article is divided into two axes: Axis 1 corresponds to the analysis of the proposed or winning IMM-3, and its contrast with other IMM studies, while Axis 2 presents an analysis of IMM-3 alongside the opportunities offered by OI.

5.1. Axis 1

Currently, numerous IMMs have been proposed and designed for different chains, including agro-industrial ones, seeking greater competitiveness in today’s market.

After analyzing and contrasting the information obtained in this research, it is deduced that the winning IMM-3 for the specialty coffee agro-industrial chain does not show a significant number of directly related articles, except for the 11 from Scopus and 14 from Google Scholar. However, while there are some similarities, the comparison between the articles found in these databases and the findings in this study reveals a lack of references in the specialized literature.

For instance, an additional search in Scopus for IMM and specialty coffee yielded only one article. This article, by [66], highlights that current agro-industrial parad IMMs emphasize trade and sustainable, efficient agricultural practices, stressing the need for innovation in production, marketing, and distribution channels. The study aimed to identify the key innovation factors within coffee farm production processes, with its findings showing that economic, environmental, knowledge, technological, and change management factors are essential for strengthening the coffee industry.

Similarly, a search on Google Scholar yielded just one article [67]. These authors analyzed innovation activities in the production, distribution, and marketing processes of specialty coffee in Colombia, aiming to design an innovation model to identify key factors for improving the sector’s competitiveness. Their proposed model was based on logistic regression analysis, identifying critical variables for sustainable coffee production, including waste identification and management across all stages of the process, digital technology adoption, and flexibility in the face of change. The researchers hope that their model will contribute to the sector’s knowledge and promote greater efficiency and benefits for those involved in the specialty coffee supply chain in Colombia.

Research on IMMs with OI tends to repeat recommendations about good practices in terms of actors and their necessary interaction. However, the literature is sparse when it comes to the analysis of IMMs in the specialty coffee sector. For example, the works by [68,69] discuss OI in its various forms and manifestations, as well as internal or closed innovation as unique governance forms with varying benefits and costs, but their studies do not correspond to the agro-industrial chain selected for this article.

The IMM-1 or linear model has been explored from multiple perspectives. One such study by [70] emphasizes that innovation follows a unidirectional path, from basic research to commercial application. Their work serves as a reference for the development of technological innovations in various industries, but its application to agro-industrial sectors may be limited. In the case of the specialty coffee chain, there are external factors, such as climatic and social elements, which require greater flexibility and feedback between different actors in the process.

Among the research on IMMs with OI, and even with prospective scope, several studies have been developed for agro-industrial chains in Colombia. Notable examples include the prospective analyses by [71] on the agro-industrial chain of blackberries and [72], on sectorial prospective studies as inputs for training and research in agro-industrial process engineering.

It is evident that while the themes addressed by these previous studies are important for understanding IMMs, they do not align with the findings of this study, particularly because they were not applied to a specific agro-industrial chain with the proposal of an IMM based on a methodology involving actors linked to the chain.

A central contribution of this study lies in demonstrating that, despite the growing literature on innovation, sustainability, and OI in agriculture, there is still no consolidated Innovation Management Model (IMM) specifically designed for the specialty coffee agro-industrial chain. The structured searches revealed a fragmented evidence base, where studies related to innovation exist, but do not provide an integrated management architecture that can be implemented across the entire specialty coffee value chain.

Within the reviewed corpus, the scarcity of directly comparable models is evident. The manuscript’s screening process identified only a limited number of studies explicitly linking innovation management and coffee systems, and these papers mainly emphasize isolated innovation factors rather than an end-to-end management model. For example, one line of research identifies economic, environmental, knowledge-related, and technological factors shaping innovation in coffee production; however, these contributions remain at the level of determinants and do not articulate a systemic model that integrates inputs, transformation mechanisms, and outputs.

A broader comparison with related literature reinforces this gap. Several studies address innovation in coffee from partial perspectives: climate change adaptation frameworks treat innovation as an adaptive strategy; eco-innovation or green supply chain studies emphasize sustainability practices; and innovation potential assessments analyze organizational capabilities. Although these works contribute valuable insights, they do not converge toward a comprehensive innovation governance model for the specialty coffee chain.

Institutional and technical documents show a similar pattern. National innovation system analyses and policy-oriented studies focusing on coffee production chains provide macro-level interpretations related to governance, knowledge generation, or competitiveness, yet they do not translate these findings into actionable innovation management models tailored to specialty coffee actors. Cooperative and case-based studies, likewise, tend to focus on technological adoption, innovation culture, or competitiveness outcomes without proposing a structured, transferable IMM.

From an OI perspective, the absence of integrated models becomes even more explicit. The literature indicates that OI research has been predominantly developed in manufacturing and high-technology sectors, while applications to agro-industrial chains remain underdeveloped. Agrifood studies frequently discuss collaboration barriers, knowledge-sharing limitations, or intellectual property concerns, but they rarely integrate these elements into a coherent management framework capable of guiding innovation across all stages of a value chain.

The comparative analysis developed in this study suggests that previous contributions can be grouped into four categories: (i) thematic studies focused on sustainability, climate adaptation, or eco-innovation; (ii) organizational or cooperative case studies centered on innovation culture and competitiveness; (iii) policy and innovation-system analyses addressing governance and institutional dynamics; and (iv) conceptual or methodological contributions discussing innovation in agriculture at a generic level. None of these categories, however, proposes a validated innovation management model specifically structured for the specialty coffee agro-industrial chain.

Against this background, the proposed IMM-3 advances the state of the art by introducing a synergistic and feedback-oriented architecture connecting input, transformation, and output variables. Unlike prior works, IMM-3 integrates agricultural technologies, sustainability principles, open innovation practices, knowledge networks, and strategic intelligence into a unified managerial logic. This design allows the model to simultaneously address operational innovation (e.g., production and process improvements) and strategic innovation (e.g., alliances, competitiveness, and market positioning).

Moreover, the model responds directly to the fragmentation identified in the literature by converting dispersed innovation variables into an operational framework validated through expert workshops and stakeholder participation. This methodological strategy increases contextual relevance and ensures alignment with the real needs of specialty coffee actors, an aspect not commonly found in previous studies.

In summary, the evidence derived from the systematic review and comparative analysis supports the conclusion that no prior study has proposed a comprehensive and operational IMM tailored to the specialty coffee agro-industrial chain. The novelty of this research lies not only in identifying innovation variables but in structuring them into a strategic, adaptable, and ecosystem-oriented model capable of guiding innovation management decisions in a sector characterized by sustainability pressures, market differentiation, and multi-actor collaboration.

5.2. Axis 2

The application of OI typically focuses on identifying best practices and emphasizing solutions to challenges in specific thematic areas. However, the scientific literature on IMMs and the potential opportunities offered by OI in the specialty coffee agro-industrial sector remains limited. While research on OI is diverse and extensive, focusing on specific issues, there are few studies specifically addressing the specialty coffee chain. The works by [73,74] approach some topics regarding local OI initiatives as a means to improve public policies and foster greater collaboration for innovation in small and medium-sized enterprises (SMEs). The authors conclude that the application of various OI models is essential for strengthening regional development in different contexts. The work by [64] addresses internal or closed innovation models, which are characterized by differentiated governance structures, each with unique benefits and costs. Their work, situated in the field of comparative analysis for innovation management, identified four (4) different forms of governance for OI; however, their analysis does not extend to the specialty coffee agro-industrial sector.

Refs. [75,76], in a recent study, indicated that organizations implement OI processes to access external knowledge. They concluded that SMEs can improve their productivity by integrating OI practices into their operations.

Thus, it is clear that the proposed or winning IMM-3 for the specialty coffee agro-industrial chain, leveraging the opportunities offered by OI, constitutes a decisive approach for closing existing gaps in the sector.

While recent studies, such as those by [72,73] have contributed to the literature, it remains relatively sparse, given the importance of the topic of IMMs aligned with OI. However, this means that there are opportunities to improve the processes related to this chain, particularly those concerning innovation within it.

To expand on Axis 2 and corroborate various OI platforms, it is relevant to note that these platforms provide valuable data and report challenges and projects related to innovation in various agro-industrial sectors (see

Table 15. Platforms for Agro-industrial Chains. Source: Author’s Own Work (2026).

Platforms	Link	Challenges/Projects in the Agroindustry
Innocentive/Wazoku	https://www.innocentive.com/
2. NineSIMMa	https://www.ninesigma.com/
3. yet2.com	https://www.yet2.com/services/open-innovation-portals/
4. Itonics	https://www.itonics-innovation.com/open-innovation
5. Ennomotive	https://www.ennomotive.com/
6. Ruta N open innovation platforms	https://rutanmedellin.org/programas/retos-de-innovaci%C3%B3n-abierta-ofertas-ruta-n
7. I’mnovation	https://www.imnovation-hub.com/es

).

These platforms, particularly those from 1 to 6, present challenges, cases, and projects related to food from agro-industrial chains. Innocentive/Wazoku points out challenges in vegetable oils and legumes for diversity, agricultural intensification, and sustainability, as well as the optimal identification of crops for agrophotovoltaic applications and satellite remote sensing for small-scale farms. The NinesIMMa platform contributes to the analysis of biodegradable fertilizer production, corn, and salt in food products. Ruta N, in turn, works, in addition to livestock and tanning, on vegetable oils. The yet2 platform develops projects on new protein suppliers, as well as topics related to cellular aquaculture to isolate living fish cells, cultivate them, and later assemble them into fresh and frozen seafood products. Itonics showcases initiatives on the smart exploration of trends in the food industry. Finally, the Ennomotive platform tackles challenges and issues related to sustainable energy in cacao drying. On the I’mnovation platform, challenges are presented in sectors such as energy, construction, water, digital transformation, science and technology, and society. Only one reference was found to the food sector, specifically a challenge for an inflatable aeroponic lettuce farm in the desert.

From exploring each of these platforms, it is corroborated and demonstrated that there is little to no scientific evidence for the selected chain. This highlights the relevance of this research, given the existing gap, especially when addressing IMM with OI for specialty coffees.

5.3. Research Gap

To identify the research gap, three studies directly related to the coffee agroindustrial value chain were analyzed. First, the study by [51], which is included in our systematic review, examines innovation models in coffee production in the Sierra Norte de Puebla, Mexico. Additionally, two further studies were identified through Google Scholar using the search terms “Innovation Management Model* AND Coffee” in both Spanish and English: [77], addressing knowledge management and organizational innovation to reactivate the Robusta coffee chain in Ecuador; and [78], focusing on an innovation model for specialty coffee cultivation in the department of Cauca, Colombia. The search equation was re-applied in Scopus using the same terms, without obtaining additional results.

The research gap identified in relation to these three studies was incorporated into the final paragraphs of the discussion section of our manuscript, as detailed below.

The study conducted in the Sierra Norte de Puebla by [51] analyzes innovation in coffee production systems from the perspective of capacity measurement and determinants of technological adoption. The approach is oriented toward identifying factors influencing productive innovation and constructing performance indicators. However, several relevant gaps are identified in relation to the scope of the present study.

First, the study focuses primarily on productive and technological innovation without developing a comprehensive innovation management model integrating strategic, organizational, and collaborative dimensions. Second, it does not explicitly incorporate an open innovation framework nor does it analyze structured interaction mechanisms with external stakeholders as a formal component of a management system. Third, although it identifies variables associated with innovation, it does not conduct a systematic structural prioritization process to design a conceptual model; rather, variables are examined as explanatory factors. In contrast, the present paper proposes a structural innovation management model rather than merely a capacity diagnosis, integrates open innovation as a central dimension, develops a systematic process for identifying, hierarchizing, and selecting strategic variables, and focuses specifically on the specialty coffee value chain, whose competitive dynamics differ significantly from those of conventional coffee analyzed in the Mexican case.

Regarding the study by [77], which addresses knowledge management and organizational innovation based on a government–industry–academy–producers’ model, innovation is examined from a systemic and interinstitutional perspective. Their analysis emphasizes actor articulation and knowledge transfer processes as drivers of sectoral innovation. Nevertheless, three major limitations emerge in comparison with the proposal developed in the present article.

First, the study does not propose a structured and operationalizable innovation management model; rather, it analyzes collaborative relationships and dynamics from a descriptive and relational perspective. Strategic variables are neither identified nor prioritized using multicriteria methods or structured modeling techniques. Second, although the importance of cooperation among actors is acknowledged, the open innovation approach is not conceptualized as a formal management architecture, nor are specific tools for its implementation within agroindustrial value chains integrated. Third, the study does not focus on the specialty coffee value chain, nor does it address the territorial, productive, and quality differentiation characteristics specific to this subsector.

Therefore, our paper addresses this gap by: (i) explicitly integrating open innovation as the structural axis of the model; (ii) identifying and prioritizing variables through a systematic methodological process; and (iii) proposing and selecting an innovation management model specifically applicable to the specialty coffee value chain, with both territorial and sectoral orientation.

Finally, the study by [78], which proposes an innovation model for specialty coffee in Cauca, constitutes the closest antecedent to the present research due to its sectoral and territorial alignment. However, substantial differences remain that justify the scientific contribution of the newly proposed model.

First, the Cauca model is primarily oriented toward innovation in cultivation and primary production, with emphasis on agronomic practices, associativity, and productivity improvement. It does not develop a comprehensive innovation management system integrating strategic, organizational, and governance dimensions across the entire value chain. Second, although the institutional environment is recognized as relevant, the study does not formally structure open innovation as the architectural foundation of the model, nor does it establish methodological mechanisms for the identification and prioritization of strategic variables. Third, the existing model does not present an explicit comparative evaluation and selection procedure among alternative innovation management models, whereas the present paper incorporates a formal stage for proposing and selecting the most appropriate model.

Consequently, the gap addressed by this study consists of extending the focus from production-centered innovation toward comprehensive innovation management across the specialty coffee value chain, incorporating open innovation as a structuring dimension, and applying a systematic methodological process for model identification, prioritization, and selection.

6. Conclusions, Implications, Limitations and Future Research

This study aimed to identify and prioritize the key variables required to design an Innovation Management Model (IMM) for the specialty coffee sector in the Department of Quindío, Colombia, with the potential for application in other coffee-producing regions. The research sought to address the need for structured innovation management approaches capable of supporting collaborative and sustainable innovation processes within the specialty coffee value chain.

The results of the study allowed the identification and prioritization of a set of variables structured across three key stages: inputs, transformation processes, and outputs. Based on the analysis of the literature and expert validation, the proposed conceptual model (IMM-3) emerged as the most suitable framework for guiding innovation management processes in the specialty coffee sector. The model highlights the importance of integrating knowledge flows, technological capabilities, collaboration mechanisms, and innovation-oriented organizational practices throughout the value chain.

From a theoretical perspective, this research contributes to the literature on innovation management and OI by extending these concepts to the agri-food sector, specifically within specialty coffee value chains. While innovation management models have been widely explored in industrial and technological contexts, their application in agricultural systems remains relatively limited. This study contributes by proposing a structured conceptual framework that integrates innovation management principles with the specific dynamics of a specialty agricultural value chain characterized by small producers, quality differentiation, and strong market orientation.

From a practical perspective, the proposed model provides a useful reference framework for stakeholders across the specialty coffee ecosystem, including producers, associations, processors, research institutions, and policymakers. The prioritized variables and their associated flows can guide the design of innovation strategies, collaborative initiatives, and technology adoption processes. In particular, the model can support the formulation of innovation challenges on OI platforms, foresight studies, and sectoral development strategies. Additionally, it may serve as a reference for the development of innovation management certifications and standards applicable to organizations in the coffee sector.

The implementation of the proposed IMM-3 should consider several strategic actions. These include investing in advanced technologies for cultivation, harvesting, and processing; strengthening technological surveillance and benchmarking systems to monitor market trends; improving the physical and technological infrastructure required for innovation across the value chain; and promoting training programs focused on creativity, project management, and collaborative innovation. Furthermore, mechanisms that facilitate knowledge transfer and the dissemination of best practices among sector stakeholders should be strengthened to enhance collective learning and innovation capabilities.

Despite its contributions, this research presents several limitations. First, the set of variables used in the analysis was derived from a literature review and expert consultations conducted within a specific regional context. As innovation processes evolve and new technological, regulatory, and market conditions emerge, the relevance and prioritization of these variables may change over time. Second, the expert validation process involved a limited number of specialists, which may influence the prioritization outcomes. Future studies could expand the number and diversity of participants to obtain broader perspectives from different actors in the coffee value chain.

In addition, the conceptual model was developed primarily as a theoretical and analytical framework. Its practical implementation in real organizational contexts was beyond the scope of this study. Consequently, further empirical research is needed to test and validate the model in different coffee-producing regions and organizational settings.

Future research could focus on several directions. First, empirical studies could analyze the implementation and performance of the proposed innovation management model in coffee clusters or cooperatives to assess its effectiveness in improving innovation capacity and competitiveness. Second, comparative studies across different coffee-producing regions or countries could identify contextual factors that influence the adoption of innovation management practices. Third, future research could explore the integration of digital technologies, data-driven agriculture, and sustainability-oriented innovations within the framework of the proposed model. Finally, longitudinal studies could examine how innovation ecosystems evolve within specialty coffee regions and how collaborative innovation mechanisms contribute to long-term sectoral sustainability.

7. Recommendations

According to the variables prioritized within the input, transformation, and output stages, their relevance should be periodically reassessed, as well as the potential need to incorporate new variables. This process could be carried out through additional stakeholder workshops involving actors from the value chain at the Chamber of Commerce of Armenia and Quindío, ensuring a balanced participation of university, industry, government, society, and interface organizations such as technology development centers, productivity centers, research institutes, technology parks, and centers of excellence, among others.

Given the dynamic nature of innovation processes, innovation management practices, and the evolving context in which the specialty coffee sector operates, the conceptual models should be periodically reviewed. Emerging actors and changing contextual conditions may challenge the prioritized model and necessitate a reassessment of the other two models; moreover, this could lead to the development of a new conceptual model or to the adaptation of the prioritized one through the incorporation of new information and knowledge flows.

The implementation of the proposed or winning IMM-3 should consider the following recommendations:

• Invest in the acquisition and application of advanced technologies for coffee cultivation, harvesting, and processing, ensuring that producers have access to modern equipment and innovative methods. Develop and implement training and technical assistance programs to promote sustainable agricultural practices that protect the environment and ensure the long-term viability of coffee production.
• Establish a clear and structured strategy for innovation management that involves all levels of the organization and defines clear objectives, processes, and responsibilities.
• Implement robust technological surveillance systems and benchmarking analysis to monitor market trends and best practices in the coffee industry, identifying opportunities for innovation and continuous improvement.
• Invest in improving the physical and technological infrastructure necessary to support innovation at all stages of the coffee value chain, from production to commercialization.
• Provide training and skill development to staff involved in innovation, including project management, creativity techniques, problem-solving, and leadership and teamwork skills.
• Establish structured programs and processes to encourage the development of innovative ideas across the organization, fostering collaboration and creativity among employees.
• Propose strategies to facilitate the effective adoption of innovative technologies throughout the coffee value chain, including training and technical assistance programs for producers and creating incentives for adopting new technologies.
• Establish mechanisms and platforms to facilitate effective knowledge transfer and best practices among sector stakeholders, including experience exchange programs and collaboration between companies and organizations.
• Promote the dissemination of successful innovations throughout the coffee industry through outreach programs and events with research and development institutions.

The following table presents practical recommendations derived directly from the study’s empirical findings. The structure aligns the key findings identified with the strategic recommendations and their expected benefits and trade-offs (see

Table 16. Alignment between key findings, recommendations, and expected outcomes.

Key Finding (Empirical Evidence)	Strategic Recommendation	Expected Benefits	Potential Trade-Offs/Risks
Limited access to advanced technologies across the specialty coffee value chain.	Establish structured Open Innovation (OI) partnerships with universities, technology providers, and innovation hubs to facilitate access to Agriculture 4.0 and digital monitoring tools.	Improved productivity, enhanced traceability, increased product differentiation, and higher competitiveness in premium markets.	Dependence on external partners; need for governance mechanisms to manage intellectual property and data sharing.
Weak technological surveillance and benchmarking systems.	Implement a formalized technological and competitive intelligence system integrated into the Innovation Management Model (IMM-3).	Early identification of market trends, regulatory shifts, and technological disruptions; reduced strategic uncertainty.	Increased managerial workload; requirement for trained personnel in intelligence analysis.
Insufficient structured processes for idea generation and scaling of innovations.	Adopt stage-gate and prototyping mechanisms embedded in IMM-3 to systematize idea evaluation, pilot testing, and scaling processes.	Higher innovation efficiency, reduced failure rates, and improved commercialization success.	Longer development cycles; possible resistance to process formalization among small producers.
Fragmented collaboration among stakeholders in the specialty coffee chain.	Create formal innovation networks and multi-actor governance platforms under an Open Innovation framework (University–Business–State–Producers model).	Strengthened collective learning, accelerated knowledge transfer, improved systemic competitiveness.	Coordination complexity; potential conflicts of interest among actors.
Limited integration of sustainability and circular economy principles into innovation strategy.	Integrate sustainability KPIs and circular economy practices as mandatory components of innovation project evaluation within IMM-3.	Enhanced environmental performance, improved brand positioning, compliance with international sustainability standards.	Initial investment costs; need for monitoring systems and sustainability auditing.
Barriers related to infrastructure and institutional constraints.	Align IMM-3 implementation with regional public policies and leverage government incentives for innovation financing.	Improved access to funding, institutional support, and long-term innovation continuity.	Exposure to policy changes; administrative complexity.

Source: Own elaboration (2026).

).

Overall, the results of this research underline the strategic role that structured innovation management approaches can play in strengthening the sustainability and competitiveness of the specialty coffee sector. By identifying and prioritizing key variables across the stages of inputs, transformation, and outputs, the proposed innovation management model provides a systematic framework for guiding innovation processes and facilitating collaboration among stakeholders in the specialty coffee value chain. In particular, the model supports the integration of knowledge flows, technological capabilities, and organizational practices that are essential for fostering open and collaborative innovation environments. In this context, its adoption may contribute not only to improving innovation capabilities at the organizational level but also to strengthening regional innovation ecosystems associated with specialty coffee production. Ultimately, the implementation of this framework may support more resilient, knowledge-based, and sustainability-oriented development pathways for coffee-producing regions facing increasing global market pressures and environmental challenges.