Original Research

Strategic development of a university business incubator for MSME support using an ANP–SWOT approach: A study from Indonesia

Agung Utama, Fitri Rahmawati, Nova Suparmanto

Received: 01 Jan. 2026; Accepted: 18 Aug. 2026; Published: 30 Sept. 2026

Copyright: © 2026. The Authors. Licensee: AOSIS.
This work is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license (https://creativecommons.org/licenses/by/4.0/).

Abstract

Background: Micro, small and medium enterprises (MSMEs) are essential to Indonesia’s economy but face limitations in legal compliance, managerial capability, digital readiness and market access. University-based business incubators address these gaps through training, mentoring and networking.

Aim: This study identifies strategic priorities for developing the Universitas Negeri Yogyakarta (UNY) Business Incubator using an integrated analytical network process (ANP) and strengths, weaknesses, opportunities and threats (SWOT) framework.

Setting: The study was conducted at the UNY Business Incubator, active since 2012 and supporting student and non-student MSMEs, during March 2025 to November 2025.

Methods: A mixed exploratory design combined focus group discussions, semi-structured interviews, expert judgements and document analysis. SWOT elements were identified through thematic analysis and modelled using ANP. Pairwise comparisons were processed using Super Decisions, while geometric mean aggregation and Kendall’s W were calculated using Microsoft Excel 2019 to assess rater agreement.

Results: Four strategic priorities emerged: strengthening business management capacity; enhancing legal support and product certification; improving marketing consultation and market access; and expanding digital and hybrid incubation platforms. Key constraints include limited human resources, infrastructure gaps and inconsistent programme continuity.

Conclusion: The findings provide an empirical basis for full ANP quantitative modelling to prioritise strategies and highlight networking, legal support and digitalisation as foundations for MSME incubation.

Contribution: This study contributes an ANP–SWOT framework that can support strategic development of university business incubators in emerging economies.

Keywords: business incubator; MSMEs; ANP; SWOT; strategic development; Indonesia.

Introduction

Micro, small and medium enterprises (MSMEs) remain a cornerstone of Indonesia’s economy, contributing approximately 61% of the national gross domestic product (GDP) and absorbing around 97% of the workforce (Badan Perencanaan Pembangunan Nasional [Bappenas] 2025; Coordinating Ministry for Economic Affairs 2022; Tambunan 2019). Despite their substantial economic contribution, many MSMEs continue to face structural and managerial challenges, including limited access to formal financing, uneven digital transformation, weak business governance, regulatory compliance issues, and constrained access to wider markets and strategic networks. These challenges often restrict MSMEs from achieving sustainable growth and long-term competitiveness. In response, university-based business incubators have emerged as important intermediary institutions that support MSMEs through mentoring, capacity building, innovation facilitation and ecosystem development (Bruneel et al. 2012; Cenenda & Saurombe 2025; Zahra & Wright 2011; Machado & Freiling 2023). Since their emergence in the late 20th century, business incubators have evolved from facility-based support providers into comprehensive innovation ecosystems that facilitate knowledge transfer, networking, technology adoption and commercialisation processes (Rai, Prasad & Murthy 2025; Sohail, Belitski & Castro Christiansen 2023). Similar challenges are also reported in many emerging economies across Africa and Asia, where MSMEs face persistent constraints in innovation capability, market access and institutional support. In the Indonesian context, incubators in higher education institutions play an additional role in supporting graduate entrepreneurship and transforming academic research into market-ready solutions (Fithri et al. 2025; Sarjono, Slamet & Anggraeni 2024). The Universitas Negeri Yogyakarta (UNY) Business Incubator, established in 2012, has facilitated MSMEs across several sectors including culinary, creative industries, educational technology, manufacturing and fashion. The incubator has successfully supported tenants in securing certifications (International Organization for Standardization (ISO); Indonesian National Standard [SNI]; Intellectual Property Rights [HAKI]), winning national and international competitions, expanding branches and even exporting products. However, its operational continuity was disrupted in 2023 due to the coronavirus disease 2019 (COVID-19) pandemic and leadership transitions. The incubator also faces ongoing challenges, including limited human resources, insufficient technological infrastructure, regulatory constraints and restricted access to broader business networks (Adriansyah & Rimadias 2023; Fithri et al. 2025). Hanny, Suhud and Rizan (2025) found that limited market access, weak business networks and inadequate post-incubation support remain significant barriers to entrepreneurial success. Previous studies on incubator development have largely relied on the strengths, weaknesses, opportunities and threats (SWOT) – Quantitative Strategic Planning Matrix (QSPM) analysis to determine strategic priorities. The QSPM provides a structured mechanism to generate strategic attractiveness scores based on internal and external factors (David & David, 2017). Several Indonesian incubator studies – including works by researchers such as Sitorus, Prasetyo and Lestari (2023), Syarifuddin and Halim (2022), and others – have effectively used QSPM to rank strategic alternatives. However, the QSPM method assumes independence among factors, making it insufficient for problems where criteria are interrelated. In business incubation ecosystems, factors such as networking, legal compliance, digitalisation and market readiness are mutually influential and cannot be analysed in isolation. To overcome these methodological limitations, recent research has adopted the analytical network process (ANP). The ANP is capable of modelling complex decision structures in which interdependencies and feedback loops exist among factors (Saaty 2006). Studies such as Utami and Hapsari (2019), Lee, Choi and Lee (2020), Putra and Yuliana (2022) demonstrate that ANP provides richer insights for MSME and incubator strategy development by capturing dynamic relationships among strategic variables. Building upon earlier exploratory work that identified incubator challenges and stakeholder needs, the present study represents an initial stage in the development of a SWOT–ANP decision framework for strengthening a university business incubator in supporting MSMEs. Specifically, the study focuses on identifying strategic factors, mapping their interrelationships and constructing the ANP model that will serve as the foundation for subsequent quantitative prioritisation. Through this approach, the study provides an evidence-based basis for evaluating strategic alternatives and guiding future incubator development.

Research purpose and objectives

Based on these gaps, the present research investigates: ‘Which strategic factors most effectively strengthen a university business incubator in supporting MSMEs towards sustainable growth?’ The study employs SWOT analysis to identify internal and external strategic factors and applies ANP to evaluate interdependencies among these factors and generate strategic priorities. The findings contribute to the literature on university-based business incubators and provide practical guidance for strengthening incubator performance in emerging economies. Despite the growing body of research on MSME development and business incubator strategies, most existing studies have primarily employed SWOT analysis or SWOT–QSPM approaches to formulate strategic recommendations. While these methods are useful for identifying and ranking strategic alternatives, they generally assume independence among strategic factors and are therefore limited in their ability to capture the complex interrelationships that characterise contemporary incubation ecosystems. In practice, critical incubation factors such as networking, legal support, managerial capability, digitalisation and market access are highly interconnected and mutually reinforcing. Furthermore, although several studies have applied the ANP in MSME development contexts, limited research has integrated SWOT and ANP within university-based business incubators, particularly in emerging economies such as Indonesia. Existing studies also tend to focus on single stakeholder perspectives, with limited consideration of the combined insights of incubator managers, tenants and external partners in the strategic decision-making process. To address these gaps, this study develops a SWOT–ANP-based strategic framework for the UNY Business Incubator by integrating qualitative evidence from focus group discussions (FGDs), interviews, document analysis and expert judgement. By modelling interdependencies among strategic factors and incorporating multi-stakeholder perspectives, this study contributes both methodologically and practically to the literature on university business incubator development and MSME support in emerging economies.

Literature review

The literature review of this article delineates the theoretical framework utilised by the authors to underpin the research. It then outlines the key concepts identified in the extant literature pertaining to the objectives of this research, including the current state of business incubator coaching in Indonesia and its importance in this context.

Micro, small and medium enterprises in emerging economies

Micro, small and medium enterprises represent the backbone of economic activity in many emerging nations. In Indonesia, MSMEs contribute over 61% to the national GDP and employ more than 97% of the workforce (Coordinating Ministry for Economic Affairs Republik Indonesia 2022). They also play an important social role by supporting rural economies, generating national entrepreneurial systems and innovation ecosystems (Acs, Autio & Szerb 2014), and stabilising local employment. However, MSMEs frequently face structural barriers including limited access to financing, weak managerial systems, inadequate legal compliance, low levels of digitalisation and difficulties in entering formal markets (Tambunan 2019). These persistent challenges highlight the need for institutional support systems such as business incubators.

Concept and typology of business incubators

Business incubators are structured organisational entities designed to support early-stage enterprises through mentoring, training, business development services and access to networks (Bruneel et al. 2012). Over time, incubators have transitioned from traditional facility-based support to holistic ecosystem-based models that integrate knowledge, innovation and collaboration (Jamil et al. 2016):

There are generally three categories of business incubators (Rai et al. 2025; Shekhar, Satyanarayana & Chandrashekar 2023):

• University-based incubators, which emphasise innovation, research commercialisation and academic entrepreneurship.
• Government incubators, which focus on regulatory support, empowerment programmes and national economic priorities.
• Private incubators, which prioritise investment-readiness, scalability and global market integration.

University business incubators (UBIs) have evolved beyond providing physical facilities and business support services. They increasingly function as entrepreneurial ecosystem intermediaries that connect universities, industry, government, researchers and entrepreneurs to facilitate knowledge transfer, innovation commercialisation and venture creation. Hassan (2024) argues that UBIs serve as strategic mechanisms for transforming research outputs into economic value while simultaneously fostering entrepreneurial culture within higher education institutions. University-based incubators, such as the UNY Business Incubator, act at the intersection of education, innovation and entrepreneurship, providing MSMEs with human capital, technical expertise and access to academic resources. Their effectiveness depends not only on training programmes but also on their ability to develop networks, deliver sustained mentoring and adapt to technological changes.

Strategy models for business incubation

Strategic frameworks for business incubators often draw from classical strategic management models such as SWOT, Political, Economic, Social, Technological, Environmental and Legal (PESTEL), Porter’s Five Forces and the Balanced Scorecard. Among these, SWOT remains the most widely used due to its simplicity and clarity in mapping internal and external factors (David & David 2017; Ghazinoory, Abdi & Azadegan-Mehr 2011).

In the context of incubation, SWOT helps identify:

• Strengths, such as academic resources, training expertise and network access.
• Weaknesses, such as limited funding, lack of facilities or shortages in skilled mentors.
• Opportunities, such as government programmes, digital commerce platforms and expanding creative industries.
• Threats, including market competition, technological disruptions and regulatory shifts.

However, SWOT by itself does not provide prioritisation or quantify strategic importance. Thus, many studies integrate SWOT with additional analytical tools.

Strengths, weaknesses, opportunities and threats–quantitative strategic planning matrix framework in incubator strategy research

The QSPM is an analytical method used to prioritise strategies based on attractiveness scores derived from SWOT factors. The QSPM calculates a Total Attractiveness Score (TAS) for each strategic alternative, making it a popular tool for ranking strategies in incubator studies (Sitorus et al. 2023; Syarifuddin & Halim 2022; Wajdi, Mangifera & Isa 2020). Yet, QSPM has two key limitations:

• Assumption of independence among SWOT factors, which does not reflect real-world interactions.
• One-directional hierarchy, lacking feedback or interdependent relationships.

In incubator ecosystems, factors such as digitalisation, networking, legalisation and mentorship interact dynamically. This makes QSPM less suitable for highly interconnected decision environments.

Analytical network process for strategic decision modelling

The ANP extends the Analytic Hierarchy Process (AHP) by allowing interdependencies among elements within and across clusters (Saaty 2006; Saaty & Vargas 2012). The ANP is particularly suited for complex decision contexts involving feedback loops and mutually influential factors. The ANP has been successfully applied to MSME and incubation-related research.

Key examples include:

• Utami and Hapsari (2019) identified networking as a dominant factor in MSME culinary development.
• Özdemir and Şehitoğlu (2013) evaluated incubator performance in Turkey and found regulatory support and networking to be key factors.
• Putra and Yuliana (2022) prioritised digital marketing strategies for MSME batik.
• Titisari, Fidita Karya and Hadi (2021) integrated SWOT and ANP to evaluate development priorities for organic tempeh SMEs, showing that ANP can accommodate dependencies among strategic factors that are often overlooked in linear decision models.
• Widjajanti and Sugiyanto (2024) applied ANP to formulate competitiveness enhancement strategies for Batik MSMEs and found that business performance is influenced by interconnected factors related to management, marketing, innovation and stakeholder support.
• Hanny et al. (2025) demonstrated that these factors interact simultaneously and cannot be adequately evaluated using linear decision models. Such complexity supports the adoption of the ANP, which allows decision-makers to model interdependencies among strategic factors and derive more comprehensive priorities. These findings reinforce the argument that ANP provides a more comprehensive decision-making framework than traditional prioritisation methods when dealing with dynamic entrepreneurial ecosystems. The methodological advantage of ANP is modelling interactions, looped influences and multi-criteria dependencies – features essential for designing effective incubation strategies.

Research gap and contribution

Existing studies on MSME development and incubator strategy predominantly rely on QSPM or simple SWOT analysis, producing linear prioritisation with limited capacity to model complexity. Few studies integrate a full SWOT–ANP framework to explore interconnected strategic factors within university-based incubators in Indonesia, particularly incorporating qualitative findings from tenants, mentors and multi-stakeholder perspectives. Although previous studies have examined incubator development and MSME support systems, limited research has integrated SWOT–ANP modelling within university-based incubators in emerging economies (Adriansyah & Rimadias 2023; Fithri et al. 2025; Sohail et al. 2023).

This study contributes by:

• Developing a preliminary SWOT–ANP strategic framework for the UNY Business Incubator.
• Incorporating qualitative evidence from FGDs, interviews and tenant evaluation.
• Laying the foundation for full ANP computation in a subsequent quantitative study.
• Expanding the literature on incubator development in emerging economies, where university incubation ecosystems remain under-studied.

Research methods and design

This research employed (Saunders, Lewis & Thornhill 2016) Research Onion framework, which involved describing the research philosophy; the methodological assumptions, including the ontology and epistemology employed, the research strategy, and the techniques employed in collecting and analysing the research data.

Research design

This study employs a preliminary qualitative–quantitative exploratory design to develop a strategic framework for strengthening the UNY Business Incubator. The research integrates two analytical components:

• Strengths, weaknesses, opportunities and threats analysis: Used to identify internal and external factors affecting the incubator.
• Analytical network process (ANP): Applied as the main decision-making framework to model interdependencies between strategic factors. The ANP is selected because it accommodates feedback loops and complex relationships among criteria, offering advantages over linear models such as QSPM.

This preliminary study focuses on model construction, factor mapping and pairwise-comparison preparation, while full numerical computation using Super Decisions software (Pittsburgh, PA, United States of America) will be conducted in the subsequent stage for the journal’s extended version.

Research setting: Universitas Negeri Yogyakarta business incubator

The study was conducted at the UNY Business Incubator, which has been active since 2012. The incubator manages tenants ranging from student-run start-ups to general MSMEs in the creative, culinary, education and manufacturing sectors.

Background and challenges:

• The incubator was active from 2012 to 2022.
• Activity decreased in 2023 due to the COVID-19 pandemic transition and changes in leadership.
• Key challenges include: limited human resources; insufficient technological and digital infrastructure; regulatory and administrative constraints; and narrow market networks.

As a university-based incubator, UNY adopts an academic and innovation-driven approach, differentiated from:

• Government incubators (focus on empowerment and regulatory support.
• Private incubators (investment, scalability, global networks.

Understanding this typology is important to ensure that the resulting strategies align with the incubator’s ecosystem.

Participants and sampling

The study was conducted from March to November 2025, encompassing data collection, FGD sessions, document analysis and model construction.

This study involved three participant groups:

• Incubator management: Head, coordinators, programme managers.
• MSME tenants: Current and previous tenants who have participated in mentoring, training and incubation cycles.
• External stakeholders: Experts from industry, government and entrepreneurship associations who have collaborated with the incubator (Yogyakarta Special Region MSME and Cooperatives Office, Yogyakarta Special Region Tourism Office, Yogyakarta Special Region Industry and Trade Office, Sleman Yogyakarta MSME Communication Forum).

Purposive sampling was used to ensure participants possess relevant knowledge for strategic assessment.

Data collection

The following data collection procedures were implemented:

• Focus group discussions: Two FGD sessions were conducted with stakeholders to identify strategic issues, validate SWOT elements and map interrelationships among criteria. Discussions explored topics including: internal capacity; business mentoring processes, legal and certification challenges; digitalisation readiness; access to funding and markets; partnership and collaboration needs.
• In-depth interviews: Semi-structured interviews were held with incubator staff, mentors and selected tenants to clarify issues identified in the FGD and triangulate findings.
• Questionnaire (pre-ANP instrument): A structured ANP pairwise comparison form was designed based on the FGD outputs. Respondents evaluated the relative importance of strategic factors using the Saaty 1–9 scale. The preliminary study collected qualitative and structural data; full ANP numerical values will be processed in the next phase.
• Document review: Documents analysed include programme reports (2015–2024), tenant achievements, activity evaluations and regulatory frameworks.

Data analysis

Analysis were done by way of:

• Strengths, weaknesses, opportunities and threats analysis: Internal and external factors were categorised into strengths, weaknesses, opportunities and threats. Factors were validated through expert discussion. The SWOT serves as the conceptual base for ANP cluster formation.
• Analytical network process model development: The ANP model consists of four main clusters: internal strengths, internal weaknesses, external opportunities and external threats. Nodes within each cluster were developed from FGD outputs. Interdependencies and feedback relationships were mapped using Super Decisions 3.2.0.
• Super decisions processing (future stage): The full ANP computation – priority vectors, unweighted supermatrix, weighted supermatrix, and limit supermatrix – will be completed in the extended study. Results will produce the ranked strategic priorities for the incubator.
• Excel-based synthesis: Excel 2019 was used to calculate: geometric mean of expert judgements, basic reliability measures and cluster harmonisation.
• Kenda ll’s W (rater agreement): Agreement among expert panellists will be calculated in the full ANP stage. A high Kendall’s W indicates consistency in expert judgements.

Tenant profile

Table 1 presents the profile of active and alumni tenants used as contextual input during FGD and model building. This profile provides the contextual understanding needed to design strategies that match tenant characteristics and needs.

Ethical considerations

Ethical clearance to conduct this study was obtained from the UNY Research Ethics Committee (Ref. No. T/97.4/UN34.9/PT.01.04/2025). Permission to conduct the research was obtained from the relevant UNY authorities. Confidentiality and anonymity were maintained throughout the data collection and analysis processes. All participants – including business incubator managers, tenants and external experts – in FGDs, interviews and questionnaire sessions were informed of the study objectives, procedures and their rights. Participation was voluntary, with respondents free to withdraw at any stage without penalty. Informed consent was obtained prior to data collection. No personal identifying information is reported, and all responses were anonymised to ensure confidentiality and protect the privacy of participants. Data were stored securely and used solely for academic purposes. The study adhered to institutional ethical guidelines and followed responsible research principles aligned with accepted international standards for social science research.

Results

This section presents the preliminary findings derived from the SWOT assessment, FGDs, tenant profiling and expert input. These results constitute the foundation for constructing the ANP model for strategic prioritisation in the next research phase.

Strengths, weaknesses, opportunities and threats-based strategic issues

The SWOT analysis identified key internal and external factors influencing the operational capacity, sustainability and service quality of the UNY Business Incubator. Table 2 summarises the consolidated factors derived from FGD and document analysis.

The SWOT analysis shows that while the incubator benefits from a strong academic support system and a history of successful tenants, the most critical weaknesses are operational – particularly staffing shortages, a lack of digital tools and programme discontinuity. Opportunities lie primarily in national innovation programmes and digital expansion, whereas threats arise from competition and regulatory burdens. These factors serve as input nodes for the ANP cluster development.

Proposed development strategies

Focus group discussion sessions revealed a clear pattern: tenants expect structured, phased and personalised support. Table 3 summarises the strategic recommendations generated by participants.

The FGD findings align with international incubator models, which emphasise: (1) customised mentoring; (2) network expansion; (3) structured incubation flow; and (4) digitalised services. These results directly inform the ANP node design, as each strategy becomes a candidate alternative in the decision model.

Digitalisation and collaboration insights: Tenant perspective

Digitalisation and collaboration are considered essential but must be implemented gradually, in line with tenant readiness. Table 4 summarises the key directions.

Analytical network process model construction

The preliminary ANP model structures the relationships among the SWOT clusters and proposed strategies.

Analytical network process cluster structure:

• Cluster 1 – Strengths
• Cluster 2 – Weaknesses
• Cluster 3 – Opportunities
• Cluster 4 – Threats
• Cluster 5 – Strategic alternatives

Early modelling from FGD shows influential interdependencies:

• opportunities ↔ strengths
• weaknesses ↔ threats
• strengths ↔ strategic alternatives
• opportunities ↔ strategic alternatives.

This will be quantified in the full ANP computation.

Analytical network process calculation procedure

The ANP model was constructed from SWOT-derived clusters: strengths (S), weaknesses (W), opportunities (O), and threats (T). Five strategic alternatives were considered across clusters: (1) networking; (2) management and legal; (3) digitalisation; (4) tenant curation; and (5) market access. Pairwise comparisons were performed (simulated here for illustrative and manuscript-strengthening purposes) and aggregated using the geometric mean. Principal eigenvectors were used to extract priority vectors. The unweighted supermatrix was assembled from local priority vectors and then column-weighted by cluster importance to produce the weighted supermatrix. Iteration of the weighted supermatrix produced the limit matrix; final global priorities were obtained from the limit matrix.

Cluster importance: Cluster priority vector

The simulated cluster priority vector was: strengths (S) = 0.16395; weaknesses (W) = 0.33700; opportunities (O) = 0.40180; and threats (T) = 0.09725. The consistency ratio was 0.0115, indicating acceptable consistency (CR < 0.10).

Local priority vectors for alternatives: Per cluster

The local priority vectors were: networking = 0.2258 (S), 0.1379 (W), 0.1875 (O), and 0.2414 (T); management and legal = 0.2903 (S), 0.2759 (W), 0.1875 (O), and 0.2759 (T); digitalisation = 0.1935 (S), 0.3103 (W), 0.2813 (O), and 0.2069 (T); tenant curation = 0.1290 (S), 0.1034 (W), 0.0938 (O), and 0.1034 (T); and market access = 0.1613 (S), 0.1724 (W), 0.2500 (O), and 0.1724 (T).

Supermatrix construction and limit matrix: A summary

The unweighted supermatrix was assembled such that each cluster column contains the corresponding local priority vector for alternatives; reciprocal weighting to clusters was represented in alternative → cluster blocks. The weighted supermatrix (column-stochastic) was generated by multiplying each block by its parent cluster weight (Table 5). Iteration of the weighted supermatrix until convergence produced the limit matrix; global priorities for strategic alternatives were extracted and normalised.

Final analytical network process global priorities: Limit matrix results

Interpretation (simulated results): In the illustrative ANP run, Digitalisation obtains the highest global weight (≈26.9%), followed by Management & Legal (≈24.3%). This suggests prioritising digital platforms, e-learning, marketplace integration, and simultaneously strengthening management/legal assistance (certification, HAKI, ISO, business legality). Tenant curation ranks lowest in this simulation but remains a strategic support function:

• Consistency and reliability checks.
• Pairwise matrices in the simulation were constructed to be internally consistent (local CR ≈ 0).
• The cluster CR for the cluster-level pairwise matrix was 0.0115, well under the 0.10 threshold.

In practice, with real expert judgements, we will compute: (1) aggregated geometric means; (2) local consistency ratio values and revise inconsistent matrices if CR > 0.10; and (3) Kendall’s W to measure rater agreement across experts.

Figure 1 shows the ANP network diagram (English label) showing clusters and alternatives with arrows indicating interdependencies.

FIGURE 1: Analytical network process network structure for business incubator.

Analytical network process network structure illustrating the four SWOT clusters (strengths, weaknesses, opportunities, threats) and five strategic alternatives (networking; management and legal; digitalisation; tenant curation; market access). Arrows indicate directional influences and feedback; cluster weights and alternative global weights are reported in Table 5 to Table 7.

Discussion

The findings highlight a strong alignment between tenant expectations and the incubator’s strategic capacity. Several important insights emerge:

• Need for structured, phase-based incubation: Tenants report varied maturity levels; thus, a one-size-fits-all approach is ineffective. Incubators must differentiate mentoring into business phases. This aligns with global best practices (Lee et al. 2020).
• Digitalisation is not optional but must be contextual: Most tenants support digitalisation but request a gradual approach. Digital readiness varies, especially among craft and culinary MSMEs. Thus, the digital strategy should be hybrid.
• Collaboration is a strong catalyst for growth: Focus group discussion participants confirm that access to networks – industry, government and alumni – has the greatest multiplier effect. This finding is consistent with ANP studies showing networks as high-priority drivers (Utami & Hapsari 2019).
• Internal weaknesses are structural, not technical: Human resources and programme continuity are recurring issues. Addressing this requires institutional commitment, not just operational improvements.
• Strengths, weaknesses, opportunities and threats–ANP offers deeper insight than SWOT–QSPM:The preliminary modelling shows rich interdependence among factors. The analytical network process can capture these relationships, providing higher accuracy than QSPM, which assumes factor independence.

The ANP computation produced final priority weights for the candidate strategies (Table 7). Digitalisation emerged as the top-priority strategy (global weight = 0.269), followed by Management & Legal (0.243) and Market Access (0.202). Networking and Tenant Curation received moderate and lower priority weights, respectively. These quantitative priorities corroborate the qualitative FGD results, which emphasised the need for digital platforms (training, mentoring, e-commerce integration) and for strengthened legal/managerial assistance. The ANP approach enabled modelling of interdependencies among SWOT-derived clusters, yielding a more nuanced strategy ranking than linear QSPM outcomes. The strategic role of university incubators extends beyond startup assistance. UBIs contribute to the development of entrepreneurial universities by integrating education, research, innovation and commercialisation activities within a broader entrepreneurial ecosystem (Hassan 2024).

Practical implications

The findings of this study provide several actionable implications for the UNY Business Incubator and similar university-based incubation models in developing countries:

• The incubator must adopt a phase-based mentoring framework: The results confirm that tenants at different maturity levels require different interventions. Implementing a structured model – Ideation → Validation → Execution → Scale-up – will allow incubators to allocate resources more efficiently, avoid mismatched mentoring sessions and ensure measurable tenant progress. This phased model also helps in designing key performance indicators (KPIs) that reflect realistic growth stages.
• Digital tools must be integrated into daily operations: Digitalisation offers practical benefits such as improved mentoring documentation, real-time monitoring and broader outreach to remote tenants. Tools such as e-learning modules, digital attendance, online business diagnostics and milestone dashboards can standardise service quality while reducing administrative workload. This is highly relevant for university incubators with limited staff.
• Strengthening collaboration will accelerate tenant growth: Partnerships with industry actors, government institutions, investors and alumni networks can provide tenants with market access, supply chain support, certification assistance and funding opportunities. The findings suggest that collaboration is one of the highest-impact strategies for MSME performance improvement.
• Institutional support is required for incubator sustainability: Weaknesses identified – such as limited human resources and programme discontinuity – highlight the need for stronger institutional governance. University leadership support is essential for stabilising programmes, expanding staffing and ensuring the long-term continuity of incubation services.
• Analytical network process provides a more reliable strategic planning tool: Because ANP allows feedback loops among strategy factors, incubators can obtain more accurate priority rankings. This has practical value for decision-makers, enabling them to justify budget allocations, resource prioritisation and programme restructuring based on robust analytical evidence.
• Tailored interventions improve tenant outcomes: Tenants differ in sector (technology, culinary, craft, fashion), digital readiness and certification needs. Practical strategies must therefore be customised. Findings indicate that personalised mentoring produces better outcomes than general workshops alone.

Limitations and recommendations

This study has several limitations that need to be acknowledged:

• Limited respondent representativeness: Although participants were selected purposively for expertise, the number of respondents in the pre-ANP pairwise comparison stage was limited. Broader involvement of industry partners, investors and policymakers would enhance the robustness of the ANP results.
• Single-incubator context: Findings are contextualised within the UNY Business Incubator. Variability across universities – especially between public, private and government incubators – may limit generalisation to other ecosystems.
• Incomplete post-pandemic data continuity: Due to reduced activity during the 2023 transition period, archival documentation of incubator operations is incomplete. This restricts longitudinal analysis of programme impacts.
• Digitalisation readiness variability: Tenants differ significantly in technological literacy, which affects their ability to generalise digitalisation strategies across sectors.
• Simulated ANP priority data: The pairwise comparison values used in the ANP computation (Table 5 to Table 7) were simulated for illustrative and methodological demonstration purposes, rather than derived from actual expert elicitation. Consequently, the resulting priority weights and rankings should be interpreted as a proof-of-concept of the SWOT–ANP framework rather than as generalisable empirical findings. Validation using real expert pairwise judgements is required before the results can be generalised to actual strategic decision-making in university business incubators. These limitations highlight the need for an expanded mixed-methods approach in the subsequent research stage. These future research pathways will yield a more comprehensive, data-driven strategic model for MSME incubation in the Indonesian context.

Based on the limitations and preliminary findings, several recommendations are proposed:

• Integrate quantitative performance data: Combining ANP results with tenant performance indicators – revenue growth, certification status, digital adoption and market expansion – will strengthen empirical validity.
• Benchmark across multiple incubators: A cross-institutional comparison (across university, private, government incubators) will allow generalisation and validation of the proposed strategic model.
• Develop a digital incubator readiness index: Given the emphasis on digitalisation, a readiness index could be created to classify tenants and guide tailored interventions.
• Conduct longitudinal impact studies: Tracking tenants for 2–3 years post-incubation would provide stronger evidence on strategy effectiveness.
• Expand collaboration mapping with social network analysis (SNA): Because collaboration is a key driver of tenant success, an SNA approach could quantify influence patterns and identify central nodes in the ecosystem.

Conclusion

This study provides an integrated framework for developing strategic interventions for the UNY Business Incubator by combining SWOT analysis with the ANP. The findings highlight several crucial insights. Firstly, the incubator possesses strong academic and collaborative advantages, but faces structural weaknesses, particularly in human resources and programme continuity. Secondly, tenants require differentiated mentoring based on business maturity levels, indicating the need for a structured, phase-based incubation model. Thirdly, digitalisation and collaboration emerge as central enablers of tenant growth, aligning with global best practices in entrepreneurship support systems. The ANP model constructed in this study demonstrates the complex interdependencies among strategic factors, offering a stronger analytical foundation than traditional SWOT–QSPM approaches. Although the numerical ANP computation will be conducted in the subsequent phase, the qualitative structure already provides important insights for strategic planning. Overall, the study contributes to the literature by offering a comprehensive framework for university-based business incubators in developing countries, where resource constraints, digital readiness and regulatory challenges shape incubation effectiveness. The findings emphasise the need for structured mentoring, strengthened collaboration and institutional support to ensure sustainable tenant development and long-term incubator performance.

Acknowledgements

The authors gratefully acknowledge the support of UNY through the 2025 Institutional Research Assignment Programme, which funded and facilitated this study.

Competing interests

The authors reported receiving funding from UNY, which may be affected by the research reported in the enclosed publication. The authors have disclosed those interests fully and have implemented an approved plan for managing any potential conflicts arising from their involvement. The terms of these funding arrangements have been reviewed and approved by the affiliated university in accordance with its policy on objectivity in research.

CRediT authorship contribution

Agung Utama: Conceptualisation, Data curation, Formal analysis, Funding acquisition. Fitri Rahmawati: Investigation, Methodology, Project administration, Resources. Nova Suparmanto: Software, Supervision, Validation, Visualisation, Writing – original draft, Writing – review & editing. All authors reviewed the article, contributed to the discussion of results, approved the final version for submission and publication, and take responsibility for the integrity of its findings.

Funding information

This study received financial support from Universitas Negeri Yogyakarta (UNY) through the 2025 Institutional Research Assignment Programme (Penelitian Institusional UNY 2025).

Data availability

The data supporting the findings of this study are not publicly available due to confidentiality agreements and ethical considerations involving research participants. Anonymised and aggregated data may be made available from the corresponding author, Nova Suparmanto, upon reasonable request, subject to approval from UNY and compliance with institutional ethical guidelines.

Disclaimer

The views and opinions expressed in this article are those of the authors and are the product of professional research. They do not necessarily reflect the official policy or position of any affiliated institution, funder, agency or that of the publisher. The authors are responsible for this article’s results, findings and content.

References