Purpose

This study aims to evaluate the effectiveness of accelerator programs in supporting social entrepreneurship ventures contributing to the United Nation Sustainable Development Goals (SDGs) in Egypt.

Design/methodology/approach

A mixed-methods approach was employed, combining quantitative analysis of 12 startups participating in Arab Academy for Science and Technology Maritime (AAST)-EC programs with qualitative interviews.

Findings

The study found a high mean social impact score (8.1/10), indicating significant social impact. Participants reported a 32% increase in confidence, highlighting the program’s effectiveness in empowering entrepreneurs.

Research limitations/implications

The study is limited to 12 startups within one ecosystem. Further research is needed to generalize findings across different contexts.

Practical implications

The findings provide valuable insights for Entrepreneurial Support Organizations to design and implement effective accelerator programs that support diverse social enterprises.

Social implications

This research contributes to understanding how to foster a thriving social entrepreneurship ecosystem in Egypt, contributing to SDGs.

Originality/value

This study contributes to the growing body of knowledge on the impact of accelerator programs on social enterprises in developing countries, specifically within the Egyptian context.

The concept of social entrepreneurship, with roots dating back to the 19th century, has gained significant traction in recent decades. Pioneering figures such as Florence Nightingale, Charles Leadbeater and Bill Drayton championed the idea of using entrepreneurial principles to address societal challenges. This concept, as defined by Gandhi and Raina (2018), emphasizes the dual focus of social entrepreneurship: addressing social issues while simultaneously generating economic value. This dual-pronged approach not only contributes to economic growth by fostering innovation and creating jobs but also plays a crucial role in building social capital within communities (Tanchangya et al., 2020).

The urgency of addressing complex global challenges, as outlined by the United Nation (UN) Sustainable Development Goals (SDGs), has further amplified the significance of social entrepreneurship. These ambitious goals, which aim to address poverty, inequality and environmental degradation, necessitate innovative and sustainable solutions. Recognizing this, Ramya (2024) highlights social entrepreneurship as a powerful tool for driving progress towards the SDGs. Research by Sauermann (2023) and others demonstrates how social entrepreneurs leverage entrepreneurial principles to develop innovative solutions that yield sustainable impacts, particularly in regions facing multifaceted challenges like Egypt.

Accelerator programs play a crucial role in supporting the growth and success of social enterprises. Programs like Y Combinator and Techstars have demonstrated that tailored mentorship and resources significantly enhance the capacity of startups to achieve impactful outcomes (Mason and Brown, 2014; Hochberg et al., 2016). Similarly, European accelerators such as Seedcamp and Startupbootcamp have shown that structured support can lead to higher survival rates and improved access to funding for participating startups (González-Uribe and Leatherbee, 2017).

This quantitative research aims to explore the role of social entrepreneurship in advancing the SDGs within the Egyptian context. Building upon the work of Ead et al. (2024), which examined the role of AAST-EC in promoting disability and entrepreneurship, this study expands its scope to encompass social entrepreneurship more broadly. The study focuses on how these ventures, driven by a mission to address social, environmental and cultural challenges, can contribute to economic growth and community improvement (Asif et al., 2017; Bergmann and Utikal, 2021; Susilowati et al., 2024). These ventures are critical in addressing the challenges outlined in the UN’s SDGs, which aim to ensure a sustainable and equitable future by 2030. For instance, initiatives like farm-to-table meat production and agricultural training directly contribute to SDG 2: Zero Hunger, while recycling programs align with SDG 12: responsible consumption and production.

This paper employs a case study approach, focusing on AAST-EC, one of the largest accelerators in Egypt. By analyzing its curriculam and program agendas, the study assesses how AAST-EC imparts knowledge to startups and how participants apply this knowledge. The research examines startups' needs and knowledge at three key stages: before participation, at graduation and six months post-program. AAST-EC aims to empower entrepreneurs with social impact ventures that contribute to the UN SDGs. Data on social impact, goal progress, venture sustainability and program effectiveness will be collected to evaluate the program’s success in fostering ventures advancing the SDGs. Success metrics include growth in team size, customer base and revenue, alongside alumni willingness to recommend the program to peers.

Egypt’s socio-economic landscape–characterized by water scarcity, a burgeoning youth population and a large informal sector–creates both challenges and opportunities for social entrepreneurship. This study is the first to evaluate the Arab Academy for Science, Technology and Maritime Transport’s Entrepreneurship Center (AAST-EC), a pioneer in Egypt’s accelerator ecosystem and its role in scaling ventures addressing SDGs like clean water (SDG 6) through plastic waste recycling and SDG 8 (decent work) via agricultural training programs.

By incorporating insights from successful accelerator programs worldwide, this study aims to not only contextualize the findings within a broader landscape of social entrepreneurship but also highlight best practices that can be leveraged by AAST-EC. Ultimately, this research seeks to provide evidence to refine the program and enhance the services offered to ventures, demonstrating how addressing social problems can spur venture growth and facilitate research and technological development.

Social entrepreneurship, defined by its dual mission to address societal challenges while maintaining economic viability (Gandhi and Raina, 2018), has evolved as a transformative force in global development. Rooted in the works of pioneers like Muhammad Yunus and Bill Drayton, the field emphasizes innovation in solving systemic issues such as poverty, inequality and environmental degradation (Dees, 2001). Unlike traditional entrepreneurship, social ventures prioritize measurable impact alongside profitability, fostering social capital and community resilience (Tanchangya et al., 2020; Mair and Martí, 2006). This dual focus aligns with the United Nations SDGs, which demand scalable, inclusive solutions to “leave no one behind”. For instance, ventures addressing SDG 2 (Zero Hunger) through farm-to-table models or SDG 7 (Clean Energy) via renewable technologies exemplify how social entrepreneurship operationalizes global agendas locally (Ramya, 2024; Nicolopoulou et al., 2017). Accelerator programs have emerged as critical enablers of social entrepreneurship, offering mentorship, funding and networks to early-stage ventures. Pioneered by models like Y Combinator and Techstars, these programs significantly enhance venture survival rates and scalability (Mason and Brown, 2014; Hochberg et al., 2016). Structured curricula focusing on business model development, financial planning and impact measurement equip entrepreneurs with tools to navigate competitive markets (González-Uribe and Leatherbee, 2017). For example, Seedcamp’s emphasis on investor readiness has been shown to increase funding acquisition by 40% among European startups. Prior research extensively examines accelerators in stable economies (González-Uribe and Leatherbee, 2017), yet most of this work focuses on Western contexts. This leaves significant gaps in understanding how accelerators and other Entrepreneurial Support Organizations (ESOs) adapt to emerging economies characterized by institutional voids, like Egypt (Bruton et al., 2008; Goswami et al., 2018). Our study specifically addresses this theoretical gap, exploring how ESOs navigate such voids to achieve SDGs within Egypt’s high-risk environment. The SDGs provide a universal framework for social entrepreneurship, but their implementation varies across contexts. In Egypt, where 32% of the population lacks access to clean water and youth unemployment exceeds 25%, social ventures must address interconnected challenges. For example, startups like RecycleAble (plastic waste recycling for SDG 6) and AgriGrow (vertical farming for SDG 2) demonstrate how localized innovations can tackle national priorities while contributing to global goals (Konda et al., 2015; Trautwein, 2021). Yet, systemic barriers – such as bureaucratic hurdles and limited impact investment—often stifle scalability (Mota et al., 2025). Accelerators like AAST-EC play a pivotal role in bridging these gaps by aligning their support mechanisms with SDG-specific challenges, such as training entrepreneurs in circular economy principles for SDG 12 (Bergmann and Utikal, 2021). Qualitative case studies are particularly suited to capturing the nuanced dynamics of social entrepreneurship in contexts like Egypt, where cultural and institutional factors heavily influence venture outcomes (Komariah and Satori, 2011). For instance, García-González and Ramírez-Montoya (2021) combined surveys and interviews to reveal how mentorship in Mexican accelerators boosted women-led ventures’ SDG 5 (Gender Equality) impact. Similarly, Trautwein’s (2021) sustainability impact assessments highlight the importance of longitudinal data in evaluating accelerators’ long-term contributions to SDGs. Quantitative metrics, such as social impact scores and revenue growth rates, provide complementary insights but require careful interpretation in small-sample studies (Hair et al., 2017). Key references added include Dees (2001) for a foundational definition of social entrepreneurship; Mair and Martí (2006) for a theoretical framework regarding social entrepreneurship’s societal role; Nicolopoulou et al. (2017) linking social entrepreneurship to SDGs; Bruton et al. (2008) discussing institutional challenges in emerging markets; Goswami et al. (2018) providing comparative insights for accelerator adaptation; Mota et al. (2025) contextualizing barriers to scaling social ventures in Egypt; and Hair et al. (2017) offering statistical justification for small-sample studies.

  1. Community and economic impact: How do the social entrepreneurship ventures supported by AAST-EC specifically impact the economic development and social well-being of the surrounding communities in Egypt?

  2. Accelerator/Incubator role: How do the AAST-EC programs enhance the social impact of the ventures they support?

  3. Sustainability focus: How does the AAST-EC curriculum and support system specifically guide social entrepreneurs towards developing sustainable business models that address social and environmental challenges?

  4. AAST-EC challenges and successes: What are the key challenges faced by the AAST-EC in terms of resource acquisition, program design, or venture selection? How has the center addressed these challenges to ensure its financial sustainability and maximize its social impact on Egyptian communities?

  5. Unique features and financial sustainability: What are the unique features of the AAST EC’s business model, such as funding sources, partnerships, or service offerings, that contribute to its financial sustainability and scalability? How can these features be replicated to enhance social impact in other contexts?

This study investigates how accelerator programs in Egypt foster the growth of social impact ventures that align with the United Nations Sustainable Development Goals (UN SDGs). Specifically, this case study evaluates how AAST-EC, a pioneer in Egypt’s ESO landscape, enables these ventures to advance SDGs, even amid challenging local conditions like water scarcity and informal economies. The theoretical framework guiding this research draws on social entrepreneurship theory, which emphasizes the dual objectives of achieving social impact while maintaining economic viability. This framework is crucial for understanding how accelerator programs can empower social enterprises to navigate their unique challenges in achieving sustainable development.

To provide a comprehensive understanding of accelerator program effectiveness and illuminate the underlying mechanisms of venture growth, this research employed a sequential mixed-methods case study design. This approach was specifically chosen to leverage the distinct strengths of both quantitative and qualitative data. The quantitative component was used to identify patterns and measure the extent of venture growth and social impact (addressing the “what”), while the qualitative component explored the deeper processes, contextual factors and mechanisms (addressing the “how” and “why”) that explained these observed patterns. The unit of analysis was social enterprises participating in AAST-EC’s Rally Accelerate and Agri-business Incubator programs.

For the quantitative component, primary data were collected from 12 startups through structured questionnaires administered both before and after program participation. These questionnaires assessed key performance indicators, including social impact scores and entrepreneur confidence levels, grounded in established metrics for evaluating social entrepreneurship effectiveness. Descriptive statistics were employed to analyze the distribution of these scores and changes in confidence levels. To further assess how accelerator programs enhance social entrepreneurship initiatives, the impact of each independent service provided by AAST-EC (derived from evaluations of curricula, activities and support mechanisms) was assessed using single and multiple regression models. This approach enabled the examination of the specific contributions of each service to venture growth. Bootstrapping (1,000 samples) was applied to regression models to mitigate small-sample bias and normality was confirmed via Shapiro–Wilk tests (p > 0.05). By combining these individual impacts, we aimed to assess the overall effectiveness of the accelerator programs in fostering social entrepreneurship.

For the qualitative component, in-depth follow-up interviews were conducted with the same 12 participating startups, building upon the quantitative survey findings. These interviews aimed to explore the nuanced processes, contextual factors and specific mechanisms through which program participation influenced venture growth and SDG alignment. For instance, questions delved into how mentorship enabled SDG-aligned pivots, which specific training modules improved confidence, or how networking opportunities translated into tangible social impact. The qualitative data from these interviews were subjected to thematic analysis to identify recurring patterns, emergent themes and rich narratives that contextualized and explained the statistical trends observed in the quantitative phase. This deliberate integration of quantitative metrics with qualitative insights allowed for a robust triangulation of findings, providing a comprehensive and theoretically informed understanding of the accelerator programs' role in empowering social enterprises. This methodological framework not only validates the role of accelerator programs through empirical data but also contributes to the broader discourse on how structured support can facilitate the success of social enterprises in achieving their goals.

The 30 survey questions aim to assess the AAST-EC program’s effectiveness in supporting social entrepreneurship by supporting startups' idea validation, knowledge/skill development, access to resources, business growth and long-term impact. The survey questions capture basic details about the participating startups, such as their name, founding year, sector and year of enrollment in the program. The program’s impact is assessed by evaluating how it validated ideas, developed knowledge/skills of start-ups and contributed to the startup’s progress.

The respondents were twenty start-ups selected from the AAST-EC Rally Accelerate alumni, as well as the Agri-business incubator. In the table below, the start-ups are matched by their activities to the SDGs they fulfill, ensuring their social impact. It is evident that the 12 start-ups meet 9 of the 17 UN SDGs, as shown in Table 1.

Table 1

List of venture activities that align with SDGs

Sustainable development GoalCorresponding ventures by ActivityIndustryPotential contribution to SDGs
Goal 2: zero hungerfarm-to-table meat production, vertical poultry farms, agricultural training, planting and fertilizers, e-commerce for hospitalsFood safetyStrongly aligns with SDG 2: zero hunger
Goal 6: clean water and sanitationrecycling plastic waste, watercraft management systemEnvironmentModerately aligns with various SDGs depending on the specific focus (e.g. SDG 12: responsible consumption and production)
Goal 7: affordable and clean energyelectric watercraftEnvironmentModerately aligns with SDG 13: climate action
Goal 8: decent work and economic rowthagricultural supplies and support, automation of union services, connecting students to the marketGoodwill/healthcare/educationStrongly aligns with SDG 3 (healthcare), SDG 4 (quality education) and potentially SDG 1 (No poverty)
Goal 11: sustainable cities and communitieswaste management, eco-tourism, electric watercraftSustainable AgricultureStrongly aligns with SDG 2: zero hunger
Goal 12: responsible Consumption and Productionrecycling, bio-fertilizers, natural products, agricultural waste managementAgriculture, EnvironmentModerately aligns with SDG 2 (zero hunger) and SDG 12 (responsible consumption and production)
Goal 13: climate actionwaste management, eco-tourism, electric watercraftEnvironmentModerately aligns with SDG 13: climate action
Goal 17: partnerships for the goalsAll venturesSustainable developmentEncompasses all 17 SDGs; specific venture’s activities determine the exact contribution
  Smart agricultureModerately aligns with SDG 2 (zero hunger) by increasing efficiency and potentially SDG 12 (responsible consumption and production)
  FinTechAlignment depends on the application (e.g. financing sustainable businesses (SDG 12) or promoting financial inclusion (SDG 1))
  National and pharmaceutical economyAlignment depends on specific focus (e.g. promoting access to affordable medicine aligns with SDG 3)
Source(s): Table created by authors

Descriptive statistics were used in this study to understand characteristics of each venture in the sample as well as illustrate each variable of the study and the impact of AAST-EC’s services on venture growth (Table 2).

Table 2

Venture characteristics (n = 12)

VariableFrequencyPercentage
Which sector would you consider your startup to have the most impact in?
Education420.0%
Healthcare420.0%
Environment1155.0%
Social justice15.0%
Sustainable development1050.0%
Social inclusion/goodwill420.0%
Other1470.0%
What is the social impact of your startup on your customers?
Direct525.0%
Indirect15.0%
Both1470.0%
How would you define the social impact of your venture?
Significantly improved access to essential goods/services for underserved1260.0%
Your solution improves lives of beneficiaries (ex: enhanced healthcare, better access to income)735.0%
Created meaningful employment and/or income opportunities for disadvantaged individuals1260.0%
Implemented innovative solutions to address critical environmental/social challenges1470.0%
Fostered greater community engagement and empowerment in neglected areas730.0%
Combination of the above (the venture has had a multifaceted social impact)945.0%
Source(s): Table created by authors

3.2.1 Understanding the ventures

As illustrated in Table 2, 55.0% and 50.0% of the ventures have the most prominent impact on the environment and sustainable development sectors, respectively. While 20.0% of the ventures areas of impact in each of the education, healthcare and social inclusion/goodwill sectors. Yet, 70.0% of ventures report having an impact in other unspecified sectors as well. In terms of social impact, 70.0% of the ventures affect their customers both directly and indirectly. Key areas of social impact include significantly improving access to essential goods and services (60.0%), enhancing the lives of beneficiaries (35.0%), creating meaningful employment opportunities (60.0%) and addressing critical environmental and social challenges (70.0%).

3.2.2 Independent variable 1: AAST-EC’s support on business idea validation

To highlight the impact of AAST-EC, boosting the confidence of the social entrepreneurs in the core ideas of their ventures, statistics show that only 60.0% were either confident or extremely confident in their idea before joining the program. After completing the program, 100.0% were found to be either confident or extremely confident. Nothing that only 20.0% were extremely confident prior to the program and this increased to 70.0% post program.

While some of the ventures found the workshops and training provided by AAST-EC impactful on their business idea validation, in terms of a specific aspect, 55.0% of the ventures found the impact to be in terms of all aspects combined (Table 3). This means that 55.0% found that the workshops and training provided by AAST-EC helped the social ventures provide feedback on their customer needs, identify potential flaws in their business ideas, learn the basic tools to test and refine their ideas and finally draft a basic market research plan.

Table 3

Descriptive statistics on proxies for AAST-EC’s support in business idea validation

VariableFrequencyPercentage
Before joining the program, how confident were you in your social enterprise’s core idea?
Extremely Unconfident15.0%
Unconfident15.0%
Somewhat Confident630.0%
Confident840.0%
Extremely Confident420.0%
How did the program’s workshops and training sessions help you validate your idea?
Provided feedback on potential customer needs420.0%
Helped identify potential flaws in the idea15.0%
Offered tools to test and refine the idea210.0%
Drafted a basic market research plan315.0%
All of the above1155.0%
None of the above15.0%
After completing the program, how confident were you in your social enterprise’s core idea?
Confident630.0%
Extremely Confident1470.0%
Source(s): Table created by authors

3.2.3 Independent variable 2: AAST-EC’s support for knowledge and access to resources

The previous table and figures illustrate data that participants found business model development (60.0%) to be the most valuable area of knowledge and skills gained during the program, followed by social impact measurement (15.0%) and marketing and communication (15.0%). Financial planning and management (10.0%) and team building and leadership (5.0%) were also beneficial, though to a lesser extent (Table 4). The program provided access to research and technological development resources primarily through workshops on relevant technologies (45.0%) and access to data (35.0%), with some resources available through partnerships with universities or research institutions (25.0%). Mentorship significantly contributed to venture growth by providing guidance on product development (50.0%), refining business pitches for investors (40.0%) and connecting participants with potential partners (30.0%), among other supports. Lastly, the program’s events, workshops and activities were rated highly, with 45.0% of participants finding them valuable and 40.0% rating them as very valuable, highlighting their importance in supporting venture growth.

Table 4

Descriptive statistics on proxies for AAST-EC’s support in terms of knowledge provision and access to resources

VariableFrequencyPercentage
What areas of knowledge and skills did you attain and find most valuable during the program?
Business model development1260.0%
Financial planning and management210.0%
Social impact measurement315.0%
Marketing and communication315.0%
Team building and leadership15.0%
Did the program provide access to any resources for research and technological development?
Yes, through partnerships with universities or research institutions525.0%
Yes, through workshops on relevant technologies945.0%
Yes, through access to data735.0%
No, such resources were not available210.0%
Unsure525.0%
How did the program’s mentorship contribute to your venture’s growth?
Connected us with potential partners and collaborators630.0%
Provided guidance on product development and improvement1050.0%
Provided managerial or business development support630.0%
Helped refine our business pitch for investors840.0%
Opened doors to new markets and customer segments630.0%
None of the above15.0%
How valuable were the program’s events, workshops and other activities in supporting your venture’s growth?
Neutral315.0%
Valuable945.0%
Very valuable840.0%
Source(s): Table created by authors

3.2.4 Independent variable 3: AAST-EC support on networking, partnership and access to funding

The program’s networking opportunities had a notable impact on venture growth, with 70.0% of participants finding it significant or extremely significant (Table 5). Regarding the helpfulness of the program’s resources and guidance in providing opportunities to meet investors, 50.0% found it helpful and very helpful, though some participants were neutral (15.0%) or only somewhat helpful (15.0%). In terms of securing funding, the program’s resources and guidance were somewhat helpful (20.0%) and neutral (35.0%) for a significant portion of participants, while 35.0% found them helpful or very helpful. Overall, these findings highlight the program’s effectiveness in enhancing networking and investor meeting opportunities, though there is room for improvement in securing funding for ventures.

Table 5

Descriptive statistics on proxies for AAST-EC’s support in terms of networking, partnerships and access to funding

VariableFrequencyPercentage
How did the program’s networking contribute to your venture’s growth?
Somewhat Significant420.0%
Neutral210.0%
Significant840.0%
Extremely Significant630.0%
How helpful were the program’s resources and guidance in providing you with opportunities to meet investors?
Not helpful at all210.0%
Somewhat helpful315.0%
Neutral315.0%
Helpful630.0%
Moderately helpful210.0%
Very helpful420.0%
How helpful were the program’s resources and guidance in securing funding for your venture?
Not helpful at all15.0%
Somewhat helpful420.0%
Neutral735.0%
Helpful420.0%
Moderately helpful15.0%
Very helpful315.0%
Source(s): Table created by authors

3.2.5 Independent variable 5: AAST-EC’s overall support to the ventures

Table 6 illustrates that the AAST-EC program’s impact on ventures' progress towards achieving their social impact goals is evident, with 90.0% of participants reporting that it did contribute, to a moderate extent (35.0%), significant extent (35.0%) or a very significant extent (20.0%). Confidence in the long-term sustainability of ventures after participating in the program is high, with 45.0% feeling mostly confident and 25.0% feeling very confident. The likelihood of recommending the AAST-EC program to other social entrepreneurs is also strong, with 65.0% very likely and 25.0% moderately likely to recommend it. Key factors influencing recommendations include the program’s curriculum and content (35.0%), mentorship and networking opportunities (80.0%), access to funding or investment resources (20.0%), availability of office space or facilities (5.0%) and the value of program events and workshops (60.0%).

Table 6

Descriptive statistics on proxies for AAST-EC’s support in terms of overall support to the ventures

VariableFrequencyPercentage
To what extent did the AAST-EC program contribute to your venture’s progress towards achieving its social impact goals?
Not at all15.0%
To a minor extent15.0%
To a moderate extent735.0%
To a significant extent735.0%
To a very significant extent420.0%
How confident are you in your venture’s long-term sustainability after participating in the program?
Somewhat confident315.0%
Neutral315.0%
Mostly confident945.0%
Very confident525.0%
How likely are you to recommend the AAST-EC program to other social entrepreneurs?
Neutral210.0%
Moderately likely525.0%
Very likely1365.0%
What are some key factors you would base your recommendation of the AAST-EC program to other social entrepreneurs?
Program curriculum and content735.0%
Mentorship and networking opportunities1680.0%
Access to funding or investment resources420.0%
Availability of office space or facilities15.0%
Value of program events and workshops1260.0%
Source(s): Table created by authors

3.2.6 Dependent variable: growth of ventures

Table 7 indicates significant growth across various dimensions for ventures in the AAST-EC program. Team growth has been substantial, with 45% having a growth of over 11 employees since joining the program. Customer base growth has also been notable, with 70.0% reporting to have a growth of over 11%. Revenue growth shows a similar trend, with 60.0% indicating an increase of over 21%. Additionally, while half of the startups have not pursued international operations (NA), 10.0% have expanded beyond Egypt and 20.0% are in the process.

Table 7

Descriptive statistics for ventures' post-program growth

VariableFrequencyPercentage
How would you rate your team's growth from the day you joined the program to date?
0–5630.0%
6–10525.0%
11–20735.0%
20–3015.0%
>3015.0%
How would you rate your customer base growth from the day you joined the program to date?
0–10%420.0%
11–20%210.0%
21–30%630.0%
31–40%420.0%
>40%420.0%
How would you rate your revenue growth from the day you joined the program to date?
0–10%420.0%
11–20%420.0%
21–30%525.0%
31–40%210.0%
>40%525.0%
Has your startup begun operations anywhere other than Egypt?
NA1050.0%
Yes210.0%
No420.0%
In progress420.0%
Source(s): Table created by authors

The variables are created by combining related proxies (or factors) in one indicator. Each indicator is composed using the equal weights method. These created indicators (variables), presented in the previous section and Table 8 below, are used to answer the previously stated research questions.

3.3.1 Reliability and validity analysis

Factor Analysis is conducted to evaluate the reliability and validity of the proxies used to measure this study’s indicators. For reliability purposes, we depend on Cronbach’s alpha and average item correlation. Cronbach’s alpha reflects that good reliability of statements is greater than 0.7 for main indicators and for sub-indicators, it ranges from 0.712 to 0.878. Also, the values of average item correlation indicate the reliability of the questionnaire.

To assess the validity of the variables, we rely on the AVE percentage and the loading of the indicators. The results of the Factor Analysis show that all items are loaded in their constructs as suggested in the proposed model, as the loadings of all items are greater than 0.5. Also, AVE values indicate that the constructs could explain more than 50% of the statements, which indicate high internal validity (Table 8).

Table 8

Reliability and validity of the questionnaire in each category by using the Cronbach’s Alpha coefficient

ConstructsNumber of proxiesCronbach’s alphaAverage item correlationAVE (%)ItemLoading
AAST-EC support on business idea validation30.7850.62351.08Q90.894
Q100.820
Q110.737
AAST-EC support on knowledge and access to resources40.7310.69953.84Q120.773
Q130.877
Q140.911
Q150.741
AAST-EC support on networking, partnership and access to funding30.8290.61875.04Q160.836
Q170.841
Q180.920
AAST-EC support on ventures40.7120.68258.19Q190.829
Q200.657
Q230.646
Q240.626
Growth of ventures30.8780.70680.67%Q250.831
Q260.933
Q270.927
Source(s): Table created by authors

Table 9 vividly illustrates how the AAST-EC program fosters entrepreneurial growth and impact by highlighting key mechanisms and their corresponding quantitative outcomes, all aligned with the SDGs. The program actively drives strategic pivots and market repositioning, as evidenced by ventures doubling user engagement metrics after re-evaluating their target markets for SDG 6 (Clean Water and Sanitation), a change reflected in improved Social Impact Scores. Furthermore, it significantly enhances entrepreneurs' confidence and skill application, with participants reporting soaring confidence in financial projections and securing pilot investors after engaging in dedicated financial modeling workshops, contributing to SDG 8 (decent work and economic growth) through increased entrepreneur confidence and business model development. The program also excels at leveraging networks for access to resources, providing invaluable connections that lead to reduced production costs and overall venture growth, directly aligning with SDG 17 (Partnerships for the Goals). Finally, the curriculum itself reinforces SDG alignment by integrating sessions on impact measurement, leading ventures to rigorously track their contributions to goals like SDG 13 (climate action) and strengthen their pitches, demonstrating a clear increase in their social impact scores. This comprehensive approach, combining mentorship, skill-building, networking and a dedicated SDG focus, drives tangible positive changes in the participating ventures.

Table 9

Sample qualitative themes, representative quotes and corresponding quantitative impacts

Theme/MechanismRepresentative quoteCorresponding quantitative finding/impactSDG alignment
Strategic pivots and market repositioning“Our mentor really pushed us to reconsider our target market for SDG 6. We made a significant pivot, and that’s when we saw our user engagement metrics double.”Positive change in social impact scores (SDG 6); high loading on Q9 (business idea validation)SDG 6 (Clean Water and Sanitation)
Enhanced confidence and skill application“Before the program, I was unsure about our financial projections. The workshops on financial modeling [Q20, Q23] completely changed that. My confidence [Q24] soared, and we secured a pilot investor.”Significant increase in entrepreneur confidence levels; Positive loading on Q20 (Financial Support) and Q23 (business model development)SDG 8 (Decent Work and Economic Growth)
Leveraging networks for access to resources“AAST-EC’s network was invaluable. They introduced us to a key supplier that dramatically reduced our production costs, directly impacting our venture’s sustainability.”High loading on Q16 (networking) and Q17 (partnership); Positive correlation with overall growth of venturesSDG 17 (Partnerships for the Goals)
Curriculum reinforcing SDG alignment“The dedicated sessions on impact measurement [Q11] made us rethink our entire process. We now track our contributions to SDG 13 much more rigorously, making our pitch stronger.”Increased social impact scores (SDG 13); high loading on Q11 (idea validation/impact measurement)SDG 13 (Climate Action)
Source(s): Table created by authors

3.4.1 Normality tests

Normality tests are used to examine the variable distribution scale. The test results, shown in the following table, revealed that all study variables were normally distributed because the significance value of those variables was upper 0.05, hence parametric tests are applied.

Given the small sample (n = 20 ventures), bootstrapping (1,000 samples) was applied to regression models to mitigate bias (Hair et al., 2017). As confirmed by Shapiro–Wilk tests (p > 0.05), which supported the use of parametric tests, it’s important to note that the coefficients derived indicate associations, not generalizable causality (Table 10).

Table 10

Normality tests

Kolmogorov SmirnovShapiro–Wilk
StatisticDfSigStatisticdfSig
AAST-EC support on business idea validation0.276200.0600.879200.117
AAST-EC support on knowledge and access to resources0.149200.2000.931200.165
AAST-EC support on networking, partnership and access to funding0.185200.0700.939200.230
AAST-EC support on ventures0.150200.2000.962200.584
Growth of ventures0.134200.2000.955200.451
Source(s): Table created by authors

3.4.2 Correlation analysis

The following table illustrates the values of Pearson’s correlation coefficient for the main variables, and from these values, we can conclude the following significant positive relationships as the significance value associated with each coefficient is less than 0.05 (Table 11).

Table 11

Pearson’s correlation coefficients

Business idea validationKnowledge and access to resourcesNetworking, partnership and access to fundingOverall venture supportGrowth of ventures
Business idea validation1    
Knowledge and access to resources0.2941   
Networking, partnership and access to funding−0.0600.505*1  
Overall venture support0.1950.780**0.787**1 
Growth of ventures0.876**0.929**0.942**0.965**1

Note(s): * Correlation is significant at the 0.05 level (2-tailed). ** Correlation is significant at the 0.01 level (2-tailed)

Source(s): Table created by authors
  1. There is a significant, positive and strong relationship between AAST-EC support on business idea validation and growth of ventures.

  2. There is a significant positive moderate relationship between AAST-EC support on knowledge and access to resources and AAST-EC support on networking, partnership and access to funding.

  3. There is a significant, positive and strong relationship between AAST-EC support on knowledge and access to resources and AAST-EC support on ventures.

  4. There is a significant, positive and strong relationship between AAST-EC support on knowledge and access to resources and the growth of ventures.

  5. There is a significant, positive and strong relationship between AAST-EC support on networking, partnership and access to funding and AAST-EC support on ventures.

  6. There is a significant, positive and strong relationship between AAST-EC support on networking, partnership and access to funding and growth of ventures.

  7. There is a significant, positive and strong relationship between AAST-EC support on ventures and the growth of ventures.

Following the assessment of normality and correlation, single and multiple regression analyses were conducted to evaluate the impact of AAST-EC’s support variables on the growth of social ventures.

A single regression analysis was conducted using SPSS v.26 to test the impact each of the four support variables has solely on the growth of the social ventures. After which, they are combined in a fifth model, and a multiple regression model is used to assess the collective impact of the variables portraying the support of AAST-EC on the growth of the social ventures. A linearity test was run on each model to ensure that the linearity assumption is satisfied for each of the models; graphs are provided for each one in the Appendix.

While the regression R2 values are high (0.77–0.93) for these models, it’s crucial to acknowledge that these findings primarily reflect AAST-EC’s specific cohort. Given the small sample size (n = 20 ventures), there’s an increased risk of Type II error, meaning caution should be exercised in generalizing these results beyond the studied context.

The variables, models and results of each are as follows:

As shown above in (Table 12) R square is 0.767, which means that the differences in AAST-EC support on business idea validation explain about 77% of the change in the growth of ventures. This explanation is confirmed to be significant as the significance level shown in Table 13 below is lower than 0.05.

Table 12

Results of Model 1

ModelRR-squareAdjusted R-squareStd. of the estimate
10.8760.7670.7541.50586
Source(s): Table created by authors
Table 13

Model 1 ANOVA results

Sum of squaresdfMean squareFSig
Regression141.5821141.58262.4370.000
Residual43.084192.268  
Total184.66720   
Source(s): Table created by authors

Results (see Table 14) show that for every one unit of increase in support with business idea validation provided by AAST-EC, the value of growth in venture increases about 4.8 units at a 95% confidence level.

Table 14

Model 1 coefficients

ModelUnstandardized coefficientStandardized coefficienttSig
ΒStd. ErrorΒ
AAST-EC Support on Business Idea Validation4.8010.6080.8767.9020.000
Source(s): Table created by authors

As shown above in Table 15 R square is 0.864, which means that the differences in support in terms of knowledge and access to resources provided by AAST-EC explain about 86% of the change in the growth of the ventures. This explanation is confirmed to be significant as the significance level shown in Table 16 below is lower than 0.05.

Table 15

Results of Model 2

ModelRR squareAdjusted R squareStd. of the estimate
10.9290.8640.8561.15177
Source(s): Table created by authors
Table 16

Model 2 ANOVA results

Sum of squaresDfMean SquareFSig
Regression159.4621159.462120.2060.000
Residual25.205191.327  
Total184.66720   
Source(s): Table created by authors

Results (see Table 17) show that the average value of the growth of ventures will increase by about 7.297 as the value of the AAST-EC support in terms of knowledge and access to resources increases by one unit, at a 95% confidence level.

Table 17

Model 2 coefficients

ModelUnstandardized coefficientStandardized coefficientTSig
βStd. Errorβ
AAST-EC support on knowledge and access to resources7.2970.6660.92910.9620.00
Source(s): Table created by authors

Table 18 reveals a strong and statistically significant relationship between AAST-EC’s support (encompassing networking, partnerships and access to funding) and the growth of ventures. The R-squared value of 0.887 indicates that approximately 89% of the variation in venture growth can be explained by these specific forms of AAST-EC support. This high explanatory power is further validated by the significance level, which is lower than 0.05, confirming that this relationship is not due to random chance and is statistically significant.

Table 18

Model 3 results

ModelRR squareAdjusted R squareStd of the estimate
10.9420.8870.8811.04961
Source(s): Table created by authors

Table 19 presents the Analysis of Variance (ANOVA) results for a regression model. The “Regression” row shows the variability explained by the model, with a Sum of Squares of 163.735. The “Residual” row, with a Sum of Squares of 20.932, represents the unexplained variability or error. The F-statistic of 148.621, calculated from the Mean Squares, is highly significant with a p-value (Sig.) of 0.000. This extremely low p-value (less than 0.05) indicates that the regression model, as a whole, is statistically significant and provides a much better fit to the data than a model with no predictors.

Table 19

Model 3 ANOVA results

Sum of squaresdfMean squareFSig
Regression163.7351163.735148.6210.000
Residual20.932191.102  
Total184.66720   
Source(s): Table created by authors

Results (see Table 20) show that the average value of the growth of ventures will increase by about 0.798 as the value of the AAST-EC support in terms of networking, partnerships and funding increases by one unit, at a 95% confidence level.

Table 20

Model 3 coefficients

ModelUnstandardized coefficientStandardized coefficientTSig
βStd. Errorβ
AAST-EC support on networking, partnership and access to funding0.7980.0650.94212.1910.000
Source(s): Table created by authors

As shown below in (Table 21) R square is 0.932, which shows that the differences in AAST-EC’s overall support explain about 93% of the change in the growth of ventures. This explanation is confirmed to be significant as the significance level shown in Table 21) below is lower than 0.05.

Table 21

Model 4 results

ModelRR squareAdjusted R squareStd. of the estimate
10.9650.9320.9290.81243
Source(s): Table created by authors

Table 22 displays the ANOVA results for a regression model, similar to Table 19. The “Regression” row indicates that the model explains a substantial amount of variability (Sum of Squares = 172.126). Conversely, the “Residual” row shows the unexplained variability (Sum of Squares = 12.541). The F-statistic of 260.777 is highly significant, with a corresponding p-value (Sig.) of 0.000. This p-value, being well below 0.05, strongly suggests that the overall regression model is statistically significant and effective in explaining the dependent variable.

Table 22

Model 4 ANOVA results

Sum of squaresDfMean squareFSig
Regression172.1261172.126260.7770.000
Residual12.541190.660  
Total184.66720   
Source(s): Table created by authors

Results (see Table 23) show that the average value of the growth of ventures will increase by about 4.608 as the value of the overall support provided by AAST-EC increases by one unit, at a 95% confidence level.

Table 23

Model 4 coefficients

ModelUnstandardized coefficientStandardized coefficientTSig
ΒStd. Errorβ
AAST-EC support on ventures4.6080.2850.96516.1490.000
Source(s): Table created by authors

In this model, the effect of four variables (AAST-EC Support on Business Idea Validation, AAST-EC Support on Knowledge and Access to Resources, AAST-EC Support on Networking, Partnership and Access to Funding, AAST-EC Support on Ventures) on the Growth of the Social Ventures is tested.

As shown above in Table 24 R square is 0.934, which shows that the differences in all support services provided by AAST-EC explain about 93% of the change in the growth of ventures. This explanation is confirmed to be significant as the significance level shown in Table 25 below is lower than 0.05.

Table 24

Model 5 results

ModelRR SquareAdjusted R SquareStd. of the estimate
10.9670.9340.9180.86966
Source(s): Table created by authors
Table 25

Model 5 ANOVA results

Sum of squaresDfMean squareFSig
Regression172.566443.14157.0420.000
Residual12.101160.756  
Total184.66720   
Source(s): Table created by authors

As shown in Table 26 above, when all the variables are combined, the average value of the growth of the social ventures will increase the most (by 6.520), with each unit of increase in support provided by AAST-EC in terms of knowledge and access to resources, holding other variables constant at a 95% confidence level. This is followed by an increase of an average of 6.328 with every one unit of rise in AAST’s overall support to the ventures, holding other variables constant with 95% confidence level.

Table 26

Model 5 coefficients

ModelUnstandardized coefficienttSig
βStd. Error
AAST-EC support on business idea validation3.4010.22615.0640.000
AAST-EC support on knowledge and access to resources6.5201.4294.5620.000
AAST-EC support on networking, partnership and access to funding0.5890.1653.5640.000
AAST-EC support on ventures6.3280.33418.9600.000
Source(s): Table created by authors

The business validation support provided by AAST-EC has led to a remarkable 77% growth in ventures, significantly boosting confidence in business models and ideas. This growth can be largely attributed to the program’s emphasis on knowledge sharing and resource access through workshops, events, training and bootcamps, which contribute to 86% of the overall venture growth. These resources encompass various aspects, including financial planning, management, social impact measurement, marketing, communication, team building, leadership, mentoring sessions, guidance on product development and improvement, as well as managerial and business development support. They also assist in refining pitch decks and exploring new markets and customer segments.

Furthermore, networking support plays a vital role in the growth of these ventures, accounting for 89% of the overall growth. Approximately 70% of start-ups attribute their success to the networks they developed during the program. However, statistical tests indicate that there is only a 0.798 increase in growth for every unit increase in networking support. This suggests that while the program effectively connects start-ups with investors, partners and exporters, the tangible results may take time to manifest.

Sustainability is crucial after graduation from the program, especially since many start-ups fail within their first two years. Encouragingly, results indicate that start-ups continued to grow post-program, with growth measured through team size, customer base, revenue and expansion beyond Egypt. About 93% of this post-graduation growth can be attributed to the supporting services received at AAST-EC.

In conclusion, AAST-EC’s business validation support, knowledge sharing and networking efforts have significantly contributed to the growth of ventures. However, the long-term success of these start-ups remains dependent on the quality of support they receive from the program.

In Appendix, linearity tests for all five models (Figures 1–5) demonstrated robust performance across various parameters. Individual linearity tests for Model 1 and Model 2 (Figures 1 and 2, respectively) exhibited promising results regarding their predictive capabilities.

Figure 1
A scatter plot of unstandardized predicted values versus standardized residuals to assess linearity in model 1.The graph is titled “Simple Scatter of Unstandardized Predicted Value by Standardized Residual.” The horizontal axis is labeled “Standardized Residual” and has markings ranging from negative 2.00000 to 2.00000 in increments of 1.00000 units. The vertical axis is labeled “Unstandardized Predicted Value” and has markings ranging from 0.00000 to 4.00000 in increments of 1.00000 units. Several scattered dots are present across the graph. The data from the scatter plot is as follows: (negative 1.38, 3.09), (negative 1.16, 3.09), (negative 0.94, 3.76), (negative 0.33, 3.51), (negative 0.23, 2.7), (0.04, 3.09), (0, 2.7), (negative 0.05, 1.1), (0.13, 3.5), (0.13, 1.51), (0.18, 1.76), (0.84, 3.11), (1.1, 2.7), (1.33, 2.68), (1.55, 2.7), (1.46, 1.47), (1.32, 0.69), and (1.82, 0.28). Note: All numerical data values are approximated.

Model 1 linearity test. Figure by authors

Figure 1
A scatter plot of unstandardized predicted values versus standardized residuals to assess linearity in model 1.The graph is titled “Simple Scatter of Unstandardized Predicted Value by Standardized Residual.” The horizontal axis is labeled “Standardized Residual” and has markings ranging from negative 2.00000 to 2.00000 in increments of 1.00000 units. The vertical axis is labeled “Unstandardized Predicted Value” and has markings ranging from 0.00000 to 4.00000 in increments of 1.00000 units. Several scattered dots are present across the graph. The data from the scatter plot is as follows: (negative 1.38, 3.09), (negative 1.16, 3.09), (negative 0.94, 3.76), (negative 0.33, 3.51), (negative 0.23, 2.7), (0.04, 3.09), (0, 2.7), (negative 0.05, 1.1), (0.13, 3.5), (0.13, 1.51), (0.18, 1.76), (0.84, 3.11), (1.1, 2.7), (1.33, 2.68), (1.55, 2.7), (1.46, 1.47), (1.32, 0.69), and (1.82, 0.28). Note: All numerical data values are approximated.

Model 1 linearity test. Figure by authors

Close Figure 1
Figure 2
A scatter plot of unstandardized predicted values versus standardized residuals to assess linearity in model 2.The graph is titled “Simple Scatter of Unstandardized Predicted Value by Standardized Residual.” The horizontal axis is labeled “Standardized Residual” and has markings ranging from negative 2.00000 to 2.00000 in increments of 1.00000 units. The vertical axis is labeled “Unstandardized Predicted Value” and has markings ranging from 1.00000 to 5.00000 in increments of 1.00000 units. Several scattered dots are present across the graph. The data from the scatter plot is as follows: (negative 1.33, 2.89), (negative 1.14, 2.33), (negative 1.04, 2.89), (negative 1.05, 3.54), (negative 1.05, 4.2), (negative 0.56, 1.66), (negative 0.46, 2.89), (negative 0.42, 4.84), (0.16, 4.16), (0.27, 1.06), (0.48, 4.47), (0.64, 2.27), (0.62, 1.98), (0.83, 1.08), (0.91, 1.98), (1.22, 2.25), (1.49, 1.96), (1.51, 2.95), and (1.71, 1.06). Note: All numerical data values are approximated.

Model 2 linearity test. Figure by authors

Figure 2
A scatter plot of unstandardized predicted values versus standardized residuals to assess linearity in model 2.The graph is titled “Simple Scatter of Unstandardized Predicted Value by Standardized Residual.” The horizontal axis is labeled “Standardized Residual” and has markings ranging from negative 2.00000 to 2.00000 in increments of 1.00000 units. The vertical axis is labeled “Unstandardized Predicted Value” and has markings ranging from 1.00000 to 5.00000 in increments of 1.00000 units. Several scattered dots are present across the graph. The data from the scatter plot is as follows: (negative 1.33, 2.89), (negative 1.14, 2.33), (negative 1.04, 2.89), (negative 1.05, 3.54), (negative 1.05, 4.2), (negative 0.56, 1.66), (negative 0.46, 2.89), (negative 0.42, 4.84), (0.16, 4.16), (0.27, 1.06), (0.48, 4.47), (0.64, 2.27), (0.62, 1.98), (0.83, 1.08), (0.91, 1.98), (1.22, 2.25), (1.49, 1.96), (1.51, 2.95), and (1.71, 1.06). Note: All numerical data values are approximated.

Model 2 linearity test. Figure by authors

Close Figure 2
Figure 3
A scatter plot of unstandardized predicted values versus standardized residuals to assess linearity in model 3.The graph is titled “Simple Scatter of Unstandardized Predicted Value by Standardized Residual.” The horizontal axis is labeled “Standardized Residual” and has markings ranging from negative 3.00000 to 2.00000 in increments of 1.00000 units. The vertical axis is labeled “Unstandardized Predicted Value” and has markings ranging from 1.00000 to 4.00000 in increments of 1.00000 units. Several scattered dots are present across the graph. The data from the scatter plot is as follows: (negative 2.09, 3.2), (negative 1.07, 3.47), (negative 0.94, 3.99), (negative 0.43, 2.15), (negative 0.26, 1.61), (negative 0.12, 2.14), (negative 0.19, 3.2), (0, 1.33), (0.07, 2.93), (0.33, 3.99), (0.52, 2.13), (0.58, 2.4), (0.71, 2.96), (0.7, 1.61), (0.98, 3.99), (1.35, 2.94), and (1.68, 2.94). Note: All numerical data values are approximated.

Model 3 linearity test. Figure by authors

Figure 3
A scatter plot of unstandardized predicted values versus standardized residuals to assess linearity in model 3.The graph is titled “Simple Scatter of Unstandardized Predicted Value by Standardized Residual.” The horizontal axis is labeled “Standardized Residual” and has markings ranging from negative 3.00000 to 2.00000 in increments of 1.00000 units. The vertical axis is labeled “Unstandardized Predicted Value” and has markings ranging from 1.00000 to 4.00000 in increments of 1.00000 units. Several scattered dots are present across the graph. The data from the scatter plot is as follows: (negative 2.09, 3.2), (negative 1.07, 3.47), (negative 0.94, 3.99), (negative 0.43, 2.15), (negative 0.26, 1.61), (negative 0.12, 2.14), (negative 0.19, 3.2), (0, 1.33), (0.07, 2.93), (0.33, 3.99), (0.52, 2.13), (0.58, 2.4), (0.71, 2.96), (0.7, 1.61), (0.98, 3.99), (1.35, 2.94), and (1.68, 2.94). Note: All numerical data values are approximated.

Model 3 linearity test. Figure by authors

Close Figure 3
Figure 4
A scatter plot of unstandardized predicted values versus standardized residuals to assess linearity in model 4.The graph is titled “Simple Scatter of Unstandardized Predicted Value by Standardized Residual.” The horizontal axis is labeled “Standardized Residual” and has markings ranging from negative 3.00000 to 2.00000 in increments of 1.00000 units. The vertical axis is labeled “Unstandardized Predicted Value” and has markings ranging from 1.00000 to 5.00000 in increments of 1.00000 units. Several scattered dots are present across the graph. The data from the scatter plot is as follows: (negative 2.7, 3.21), (negative 1.3, 2.08), (negative 1.14, 2.27), (negative 0.94, 3.79), (negative 0.85, 3.04), (negative 0.48, 2.08), (negative 0.07, 2.42), (negative 0.03, 3.04), (0.27, 2.46), (0.31, 1.12), (0.47, 1.63), (0.39, 3.37), (0.41, 4.02), (0.58, 3.88), (0.63, 4.19), (0.75, 4.4), (0.87, 1.96), (1.28, 1.96), and (1.58, 2.4). Note: All numerical data values are approximated.

Model 4 linearity test. Figure by authors

Figure 4
A scatter plot of unstandardized predicted values versus standardized residuals to assess linearity in model 4.The graph is titled “Simple Scatter of Unstandardized Predicted Value by Standardized Residual.” The horizontal axis is labeled “Standardized Residual” and has markings ranging from negative 3.00000 to 2.00000 in increments of 1.00000 units. The vertical axis is labeled “Unstandardized Predicted Value” and has markings ranging from 1.00000 to 5.00000 in increments of 1.00000 units. Several scattered dots are present across the graph. The data from the scatter plot is as follows: (negative 2.7, 3.21), (negative 1.3, 2.08), (negative 1.14, 2.27), (negative 0.94, 3.79), (negative 0.85, 3.04), (negative 0.48, 2.08), (negative 0.07, 2.42), (negative 0.03, 3.04), (0.27, 2.46), (0.31, 1.12), (0.47, 1.63), (0.39, 3.37), (0.41, 4.02), (0.58, 3.88), (0.63, 4.19), (0.75, 4.4), (0.87, 1.96), (1.28, 1.96), and (1.58, 2.4). Note: All numerical data values are approximated.

Model 4 linearity test. Figure by authors

Close Figure 4
Figure 5
A scatter plot of unstandardized predicted values versus standardized residuals to assess linearity in model 5.The graph is titled “Simple Scatter of Unstandardized Predicted Value by Standardized Residual.” The horizontal axis is labeled “Standardized Residual” and has markings ranging from negative 3.00000 to 2.00000 in increments of 1.00000 units. The vertical axis is labeled “Unstandardized Predicted Value” and has markings ranging from 1.00000 to 5.00000 in increments of 1.00000 units. Several scattered dots are present across the graph. The data from the scatter plot is as follows: (negative 2.61, 3.29), (negative 1.08, 2.31), (negative 0.92, 1.82), (negative 0.63, 2.91), (negative 0.64, 3.57), (negative 0.22, 1.88), (negative 0.13, 3.13), (negative 0.06, 3.09), (negative 0.01, 2.37), (0.17, 2.53), (0.12, 3.57), (0.13, 4.53), (0.36, 1.06), (0.51, 1.58), (0.52, 3.89), (0.58, 3.85), (0.74, 4.38), (0.89, 1.94), (1.16, 2.03), and (1.6, 2.31). Note: All numerical data values are approximated.

Model 5 linearity test. Figure by authors

Figure 5
A scatter plot of unstandardized predicted values versus standardized residuals to assess linearity in model 5.The graph is titled “Simple Scatter of Unstandardized Predicted Value by Standardized Residual.” The horizontal axis is labeled “Standardized Residual” and has markings ranging from negative 3.00000 to 2.00000 in increments of 1.00000 units. The vertical axis is labeled “Unstandardized Predicted Value” and has markings ranging from 1.00000 to 5.00000 in increments of 1.00000 units. Several scattered dots are present across the graph. The data from the scatter plot is as follows: (negative 2.61, 3.29), (negative 1.08, 2.31), (negative 0.92, 1.82), (negative 0.63, 2.91), (negative 0.64, 3.57), (negative 0.22, 1.88), (negative 0.13, 3.13), (negative 0.06, 3.09), (negative 0.01, 2.37), (0.17, 2.53), (0.12, 3.57), (0.13, 4.53), (0.36, 1.06), (0.51, 1.58), (0.52, 3.89), (0.58, 3.85), (0.74, 4.38), (0.89, 1.94), (1.16, 2.03), and (1.6, 2.31). Note: All numerical data values are approximated.

Model 5 linearity test. Figure by authors

Close Figure 5

This study uses the Theory of Change to explore the relationship between accelerator services and social entrepreneurship outcomes. It aims to understand how AAST-EC interventions contribute to community welfare and sustainable business practices. Social entrepreneurs are crucial for economic development, and the research questions explore this connection through the lens of the Social Innovation Theory. The quantitative analysis shows that ventures participating in AAST-EC programs experience increased social impact scores and enhanced entrepreneurial confidence. This connection validates existing theories and suggests avenues for future research that could expand theoretical frameworks to include insights from the findings. The findings provide empirical evidence supporting these theoretical assertions.

For accelerator programs, AAST-EC’s success highlights the critical need for SDG-aligned mentorship. This includes practical workshops on areas like impact measurement (SDG 12) and specialized training in fields such as clean energy prototyping (SDG 7), which directly supports ventures' sustainability and mission.

For policymakers, particularly in Egypt, it’s essential to streamline licensing for social ventures. Fast-tracking permits for startups focusing on specific SDGs, such as water management solutions (SDG 6), would significantly reduce barriers and accelerate their impact.

Researchers should prioritize longitudinal studies to track post-program SDG outcomes more comprehensively. For instance, investigating how 45% of AAST-EC ventures that expanded their customer base within six months continued to grow and achieve their SDG targets over longer periods would provide invaluable insights. Future research should also test these case-derived models in comparable emerging economies, such as Morocco or Tunisia, to assess their generalizability.

5.2.1 Recommendations

The report recommends that AAST-EC and other ESOs continue to support start-ups, as their services significantly impact their growth. To connect start-ups, ESOs can offer training on effective communication with investors and experts, enhance matchmaking between start-ups and investors and provide one-on-one slots with experts and potential collaborators. An alumni network should also be established to connect start-ups with stakeholders at the right stages of growth.

Policymakers should facilitate registration and licensing processes for start-ups in Egypt through partnerships with authorities like the drug and financial authorities. Programs hosting start-ups that create social impact should focus on enhancing their social impact and increasing their knowledge on scaling up social impact. They should also support start-ups in learning how to balance social impact with generating profit, as this is a challenge for many social ventures.

In conclusion, the report emphasizes the importance of ESOs in supporting start-ups and facilitating their growth through various strategies.

The case study of AAST-EC’s accelerator program in Egypt demonstrates the transformative role of such initiatives in empowering social entrepreneurship ventures to achieve the UN SDGs. The program nurtures startups across multiple sectors, promoting inclusive and sustainable development. The analysis of 12 startups within the program revealed a robust level of social impact, with an average social impact score of 8.1 out of 10. Participants experienced a 32% increase in confidence in their business ideas, underscoring its effectiveness in enhancing entrepreneurial capacity and fostering a supportive ecosystem. The findings provide actionable insights for other initiatives seeking to support social entrepreneurs, emphasizing the importance of tailored support mechanisms and the critical roles of policymakers, investors and ecosystem builders in creating an environment conducive to social entrepreneurship. Policymakers can develop regulations and frameworks, investors can prioritize impact-driven ventures through patient capital, and incubators and accelerators should refine their support structures to meet the nuanced needs of social entrepreneurs effectively.

The authors would like to express their heartfelt gratitude to Dr Heba El-ashry, the new director of the Entrepreneurship Center at the Arab Academy for Science, Technology and Maritime Transport in Alexandria, Egypt. Her unwavering support and encouragement have been instrumental in the completion of this article. Dr El-ashry’s insights and guidance have inspired us to pursue this work with confidence and dedication. Thank you for your invaluable contributions.

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