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.

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.

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 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.

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:

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

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:

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

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:

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:

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:

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.

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:

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

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

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:

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

The following data collection procedures were implemented:

Analysis were done by way of:

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 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.

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.

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

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

Analytical network process cluster structure:

Early modelling from FGD shows influential interdependencies:

This will be quantified in the full ANP computation.

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.

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

This study has several limitations that need to be acknowledged:

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