Keywords

Social Media Analytics, Innovation Level, Customer Feedback Integration, Social Media Ad Spend, Regression Analysis.

Introduction

The emergence of digital technology has revolutionized entrepreneurship, particularly for women entrepreneurs in developing regions, by providing critical tools and platforms for innovation, market access, and social support. The shift towards digital enablers, especially social media, has reshaped traditional business landscapes, making entrepreneurship more accessible and equitable for women (Abdelwahed et al., 2024). This transformation has been particularly impactful in developing economies, where digital platforms not only serve as conduits for business growth but also facilitate social empowerment and inclusivity (al Matroushi et al., 2021). Social media, as a digital enabler, plays a pivotal role in bridging gaps by offering affordable, accessible, and scalable solutions to entrepreneurial challenges such as market outreach, networking, and brand establishment (Alhakimi & Albashiri, 2023).

Women entrepreneurs, especially in collectivist societies, face unique challenges, often embedded within cultural, social, and economic constraints (Bastian et al., 2023). However, strong social ties in these contexts can act as facilitators, allowing women entrepreneurs to leverage their personal and professional networks to overcome barriers. In collectivist cultures, trust and support from close relationships provide essential resources for entrepreneurial ventures, helping women access funding, acquire relevant skills, and create value within their communities (Bastian et al., 2023). Social media amplifies this effect by enabling the establishment of virtual networks, fostering community engagement, and expanding access to information. Thus, digital tools are essential for empowering women to establish themselves in business, allowing them to navigate both gender-based and socioeconomic challenges (Abdelwahed et al., 2024).

In this digital era, entrepreneurship ecosystems have undergone substantial transformation, along with women's growing participation in driving innovation and economic growth (Aljuwaiber, 2021). As part of the MENA area, for example, entrepreneurship research has identified a trend toward more sustainable and inclusive economic models, focusing on the role of digital and social innovations (Aljuwaiber, 2021). This trend demonstrates a shift in how digital entrepreneurship is perceived and practiced, moving beyond economic growth to incorporate social impact, inclusivity, and sustainable value creation (Arena et al., 2024). Moreover, studies show that factors such as the entrepreneurial ecosystem, public policy, and access to financial resources are crucial in fostering innovation and sustainability in female-led ventures (Balawi & Ayoub, 2022).

As women entrepreneurs increasingly adopt social media as part of their business strategies, they benefit from an amplified brand presence and customer engagement, particularly through the authenticity often associated with family-owned businesses (Bargoni et al., 2023). Social media offers a platform for these entrepreneurs to communicate their brand values, create a loyal customer base, and compete more effectively in the digital marketplace. However, the degree to which social media enhances start-up success can vary depending on the entrepreneur’s ability to effectively leverage digital tools and insights (Alhakimi & Albashiri, 2023). As businesses adopt social media, they foster brand trust and consumer engagement, which are particularly valuable for entrepreneurs in family-owned businesses, who can use these tools to enhance their brand authenticity (Bargoni et al., 2023).

This study aims to explore the role of social media in promoting entrepreneurial success for start-ups led by women, particularly examining how various social media strategies impact start-up growth and innovation. By focusing on digital enablers and entrepreneurial orientation, this research seeks to elucidate the factors that contribute to women entrepreneurs' success and the specific ways social media facilitates empowerment, market reach, and innovation.

Literature Review

The role of social networks, digital tools, and entrepreneurial ecosystems has become central to understanding innovation and growth in modern start-ups, particularly in the context of women entrepreneurs. Social capital, often cultivated through strong interpersonal ties, has proven essential in collectivist contexts, providing women entrepreneurs with the resources needed to navigate business challenges and opportunities. Bastian, Wood, and Ng (2023) highlight the importance of strong ties in empowering women entrepreneurs in collectivist societies, illustrating how trust and support from close networks significantly contribute to overcoming barriers that might otherwise hinder entrepreneurial success. These relationships offer not only emotional support but also access to resources such as funding, mentorship, and market knowledge, which are essential for business growth.

The technological landscape plays a mediating role in shaping entrepreneurial success, particularly when coupled with the personality traits and orientations of entrepreneurs. Becker et al. (2023) examine the relationship between entrepreneurial personality and start-up innovativeness, emphasizing the mediating role of technology adoption. Their findings suggest that technology adoption not only facilitates innovation within start-ups but also aligns with the personalities and ambitions of entrepreneurs, driving higher levels of creativity and differentiation in the market. Similarly, Dal Mas et al. (2023) underscore the importance of structured business planning and knowledge management as essential practices for fostering innovation and strategic growth in new ventures. Through effective business planning, entrepreneurs are able to translate their vision into actionable steps that can be scaled for long-term sustainability.

Another critical factor in the success of start-ups is the entrepreneurial ecosystem, which includes the cultural, economic, and institutional contexts that support entrepreneurial activities. Bolzani (2020) highlights how migrant entrepreneurs, for example, adapt to new entrepreneurial ecosystems by leveraging community support and contextualizing their business models to meet local market demands. This approach is particularly relevant for start-ups operating in multicultural and diverse markets, as it helps entrepreneurs create culturally attuned business strategies that resonate with their target audience.

In terms of marketing, the shift toward digital platforms, especially social media, has transformed how entrepreneurs approach customer engagement, branding, and growth. Breit and Volkmann (2024) explore the decision-making processes in start-up marketing, contrasting causal and effectual approaches in navigating entrepreneurial challenges. Their research reveals that effectual decision-making, which is adaptable and iterative, allows start-ups to pivot and respond to market feedback, thus enhancing their ability to succeed in dynamic environments. Chen et al. (2021) further emphasizes the role of social media in Chinese entrepreneurship, identifying it as a powerful tool for branding, customer engagement, and overcoming geographical and financial limitations. However, they note that effective use of social media requires strategic planning and a deep understanding of platform-specific algorithms and user behaviours.

Knowledge sharing and collaboration have emerged as additional components that enhance start-up innovation. Chierici et al. (2021) examine Italian small enterprises, noting that digital collaboration fosters social innovation and knowledge sharing, which collectively strengthen social capital and lead to improved business outcomes. These results are in line with those of Cosentino et al. (2021), who note that the agro-food sector strikes a good mix between tradition and innovation, and who emphasise the importance of strong ties within the sector for fostering collaboration and long-term sustainability in business. Like this, Errico et al. (2024) talk about how different types of funding affect how well start-ups innovate. They say that when start-ups have access to targeted funding, they can develop specialised competences that help them innovate and succeed in competitive markets.

Lastly, in areas where traditional funding sources may be scarce, the financial environment for start-ups has been transformed by the emergence of alternative funding platforms like crowdsourcing. In this study, Dehghani et al. (2024) investigate how trust and e-WOM impact equity crowdfunding engagement in Iran, finding that trust is a critical factor that determines investor willingness to participate in crowdfunding platforms. Crowdfunding thus offers entrepreneurs a viable alternative to traditional funding sources, enabling them to attract a wider range of investors and establish a strong market presence through community support and engagement.

Equity crowdfunding has emerged as a crucial alternative financing tool, especially in contexts where traditional funding options are limited or challenging to access. For example, Chidiac El Hajj, Chidiac, and Awdeh (2024) examine how Lebanese entrepreneurs signal their ventures’ potential to attract investors on crowdfunding platforms. They emphasize that well-communicated value propositions and consistent updates enhance investor trust and engagement, which are vital for securing equity-based financing. Similarly, Dehghani et al. (2024) investigate equity crowdfunding in Iran, finding that trust and electronic word-of-mouth (e-WOM) significantly influence crowdfunding participation, suggesting that transparency and a strong online presence can amplify investor interest and participation in emerging markets.

Digital collaboration within and between enterprises is another essential factor that contributes to social capital and innovation, as noted by Chierici et al. (2021). Their study of Italian small enterprises shows that fostering digital partnerships enhances social innovation capital, which serves as a foundation for sustained growth. This is further supported by Cosentino et al. (2021), who explore the Italian agro-food industry, revealing that a harmonious blend of tradition, innovation, and relationships within the industry contributes to the development of resilient business profiles. Both studies highlight that collaborative digital initiatives can amplify resources and knowledge sharing, ultimately leading to competitive advantages.

In the domain of strategic planning, Dal Mas et al. (2023) underscore the importance of business plan development in entrepreneurial ventures. Their findings indicate that structured planning is crucial for translating knowledge into practical strategies that can guide long-term growth. Business planning serves not only as a roadmap but also as a valuable tool for knowledge transfer within the organization, ensuring alignment across team members and supporting strategic innovation. This is echoed in the work of Errico et al. (2024), who discuss the importance of funding sources in developing specialized competences. They argue that start-ups with access to diverse funding channels are better positioned to build specialized skills that enhance innovative performance, as funding can directly support the acquisition of expertise and advanced technologies needed to remain competitive.

Together, these studies illustrate a multi-faceted approach to start-up growth, emphasizing the role of social and financial capital, digital collaboration, and business planning in fostering resilience and adaptability. The integration of trust-building in crowdfunding, digital collaboration for social innovation, and structured business planning creates a robust ecosystem where start-ups can effectively navigate market challenges and leverage opportunities for sustainable growth.

Research Methodology

This study investigates the impact of Social Media Analytics Use (SMAU), Frequency of Social Media Posts (FSMP), Customer Feedback Integration (CFI), and Social Media Ad Spend (SMAS) on the Innovation Level (IL) of organizations. A quantitative research approach was adopted, using regression analysis to determine the connections between the IL and the independent variables (SMAU, FSMP, CFI, SMAS). This research intends to shed light on the ways in which these social media aspects help businesses innovate by providing actual evidence of their effects.

A total of 197 samples were selected randomly for this study. The sample comprised organizations from various industries to ensure a comprehensive analysis of how different sectors use social media strategies and customer feedback to influence their innovation processes. Data were collected through structured surveys distributed to key decision-makers within these organizations, including marketing managers, innovation officers, and digital strategists. The survey included questions designed to measure the intensity and strategic use of social media analytics, frequency of posts, customer feedback mechanisms, and advertising expenditures. The responses were then collated and used as input for the regression analysis.

Objectives

1. To assess the impact of social media engagement on the growth and success of digital start-ups.

2. To investigate how social media strategies (e.g., content quality, frequency, and platform diversity) contribute to the innovation and market competitiveness of start-ups.

Hypotheses

Ho1: Increased social media engagement positively impacts the success and growth of digital start-ups.

Ho2: Effective social media strategies significantly enhance the innovation and competitive positioning of start-ups.

Regression Equation 1:

Start-upSuccess (SS) = β0 + β1 × SocialMediaEngagement (SME) + β2 × ContentQuality (CQ) + β3 × PlatformDiversity (PD) + ϵ

Regression Equation 2:

InnovationLevel (IL) = β0 + β1 × SocialMediaAnalyticsUse (SMAU) + β2 × FrequencyofSocialMediaPosts (FSMP) + β3 × CustomerFeedbackIntegration (CFI) + β4 × SocialMediaAdSpend (SMAS) + ϵ

For data analysis, R Studio was utilized to perform regression line analysis and investigate how the independent factors relate to the organisations' innovation levels. R Studio received the data, and a multiple regression model was run on it and was constructed to assess how SMAU, FSMP, CFI, and SMAS collectively impact IL. The regression analysis was chosen because it allows for the examination of multiple predictors simultaneously and quantifies the strength and significance of their relationships with the dependent variable. Additionally, various diagnostic tests, including residual analysis, were conducted to check for the assumptions of normality, linearity, and homoscedasticity.

Analysis

The demographic profile of the sample population in this study, consisting of 197 individuals, provides a comprehensive overview of the characteristics of the participants. The gender distribution indicates a balanced representation, with approximately 50% male and 50% female participants, ensuring diversity in gender perspectives. Regarding age, the sample primarily comprises individuals aged 25-45 years, representing 65% of the total participants. This age group is particularly significant as it encompasses working professionals and individuals who are typically engaged in decision-making roles in organizations. In terms of education, 70% of the sample holds at least a bachelor's degree, reflecting a highly educated group, which is likely to contribute to insightful responses in the study of organizational innovation.

The occupation distribution reveals that 60% of the participants are employed in managerial or executive roles, while 30% are in technical or operational positions, and the remaining 10% are entrepreneurs or self-employed. This distribution suggests that the sample consists largely of individuals with direct influence on organizational strategies. As for income, 50% of the respondents earn between Rs. 50,000 and Rs. 1,00,000 annually, with 25% earning more than Rs. 1,00,000, and 25% earning less than Rs. 50,000. This income spread provides a diverse perspective on the socioeconomic factors affecting organizational innovation. Overall, the demographic profile ensures a well-rounded and representative sample for this study.

Table 1 presents the results of a multiple linear regression analysis aimed at examining the impact of Social Media Engagement (SME), Content Quality (CQ), and Platform Diversity (PD) on Start-up Success (SS). The regression model was specified as SS being a function of SME, CQ, and PD, and was estimated using data from 197 observations (degrees of freedom = 193). The model's intercept is estimated at 0.758, which represents the predicted SS when all independent variables are zero. This intercept is statistically significant (p < 0.001), indicating that even in the absence of social media engagement, content quality, and platform diversity, there is a baseline level of start-up success.

Focusing on the predictors, Social Media Engagement (SME) has a substantial and highly significant positive effect on Start-up Success, with an estimated coefficient of 0.446 (p < 0.001). This indicates that, everything else being equal, SS is anticipated to rise by about 0.446 units for every unit increase in SME. The strong significance of SME underscores the critical role that active and engaged social media presence plays in enhancing the success of start-ups.

Content Quality (CQ) exhibits a positive relationship with SS, with an estimated coefficient of 0.139. This effect is marginally significant (p = 0.058), indicating a trend where higher content quality may contribute to greater start-up success, though the evidence is not as robust as for SME. This marginal significance suggests that while improving content quality can be beneficial, its impact might be less pronounced or require larger sample sizes to achieve statistical significance.

Platform Diversity (PD) shows a positive but non-significant association with SS, with an estimated coefficient of 0.071 (p = 0.315). This implies that variations in the number of social media platforms used by start-ups do not have a statistically significant effect on their success within the scope of this study. The lack of significance for PD may indicate that merely increasing the number of platforms is less important than the depth and quality of engagement on selected platforms.

A Multiple R-squared value of 0.5081 indicates that the model fits the data well, suggesting that the three variables account for around 50.81% of the variability in Start-up Success when taken together. The model is statistically significant, as indicated by the F-statistic of 66.46 (p < 2.2e-16), suggesting that the predictors collectively significantly affect SS.

The regression analysis highlights the pivotal role of Social Media Engagement in driving start-up success, while suggesting that content quality also contributes positively, albeit with less certainty. Platform diversity, however, does not significantly influence start-up success in this context. These findings emphasize the importance for start-ups to focus on enhancing their engagement levels on social media platforms and maintaining high-quality content to achieve greater success.

Figure 1 provides a scatter plot illustrating the relationship between Start-up Success (SS) and Social Media Engagement (SME), with a regression line overlayed to show the linear trend. Each point on the scatter plot represents an observation in the dataset, plotting SME values on the x-axis against SS on the y-axis. The positive slope of the regression line reinforces the finding from Table 1 that SME has a strong, positive impact on SS. This visual indicates that as SME increases, SS tends to increase as well, supporting the interpretation that greater engagement on social media correlates with higher start-up success. This plot is useful for visually validating the positive association between SS and SME, as predicted by the model.

Figure 1 Scatter Plot with Regression Line

The residuals, or the gap between the predicted and observed SS values, are shown on the y-axis of Figure 2's residual plot, which contrasts with the fitted SS values on the x-axis. In order to evaluate the regression model's assumptions and fit, this figure is useful. To ensure the model represents the data accurately and without systematic patterns, the residuals should ideally be distributed randomly around the zero line. The residuals seem to be somewhat uniformly distributed around 0 in this plot, which indicates that the model's assumptions are adequately satisfied. Any notable patterns or clusters in the residuals could indicate potential issues with model fit or violations of assumptions such as heteroscedasticity, which would prompt further investigation.

Figure 2 Residual Plot

Figure 3 compares the actual values of Start-up Success (SS) with the values predicted by the regression model. The x-axis represents the predicted SS values, while the y-axis shows the actual SS values, with a diagonal line where predicted and actual values are equal. If the model performs well, points should cluster closely around this line, indicating that predictions closely match actual outcomes. In this plot, the concentration of points near the diagonal line suggests that the model has reasonable predictive accuracy. Deviations from the line highlight areas where the model’s predictions are less accurate, providing insights into how well SME, CQ, and PD collectively predict SS and where improvements might be needed.

Figure 3 Actual vs. Predicted plot

Table 2 presents the results of a regression analysis examining the relationship between Innovation Level (IL) and four predictors: Social Media Analytics Use (SMAU), Frequency of Social Media Posts (FSMP), Customer Feedback Integration (CFI), and Social Media Ad Spend (SMAS). The regression model provides insights into how these factors influence IL and evaluates their individual contributions to the explained variance in IL.

The table reveals that three of the predictors—SMAU, FSMP, and CFI—have statistically significant positive relationships with IL, while SMAS does not. Specifically, the intercept of the model is 0.287, indicating the estimated value of IL when all predictors are at zero. The coefficient for SMAU is 0.315, which means that, everything else being equal, IL is anticipated to rise by 0.315 units for every one-unit increase in SMAU. With a t-value of 4.383 and a p-value of 1.93e-05, there is a strong positive correlation, indicating that social media analytics are essential for organisations to drive innovation.

The same holds true for FSMP; a coefficient of 0.448 indicates a positive correlation between the frequency of social media posts and innovation. This predictor has a strong and statistically significant effect, as shown by the t-value of 5.590 and the p-value of 7.73e-08. This discovery highlights the significance of regular and consistent social media content posting as a catalyst for innovation. A significant positive effect on IL is also observed for CFI (r=0.146). According to this coefficient, IL is predicted to rise by 0.146 units for every one unit increase in CFI. At the 5% level of significance, this relationship is supported by a t-value of 2.031 and a p-value of 0.0436. New ideas and improvements can be fuelled by customer feedback, which offers valuable insights into customer needs and expectations.

In this model, social media ad spend does not significantly impact IL, as indicated by the coefficient for SMAS (0.01242), t-value (0.198), and p-value (0.8435). This finding suggests that, at least within this dataset, increasing the advertising budget on social media platforms does not necessarily result in higher levels of innovation.

With Multiple R-squared = 0.6956 and Adjusted R-squared = 0.6892, we can see that the model fits the data very well. This indicates that the four predictors accounted for in the model explain around 69.56% of the variation in IL. This model is helpful for understanding the elements that lead to innovation in this setting because it explains a considerable percentage of the variability in IL, as indicated by the high R-squared value. The F-statistic of 109.7 with a p-value less than 2.2e-16 further supports the significance of the model, indicating that the predictors as a group are highly effective in explaining variations in IL.

The residuals provide additional insights into the model's performance. The minimum residual value is -1.7596, and the maximum residual value is 1.7488, suggesting a fairly symmetrical distribution of residuals around zero. The first and third quartiles of the residuals are -0.1910 and 0.1527, respectively, indicating that most residuals fall within a reasonable range, suggesting no major outliers or violations of model assumptions. The median residual is close to zero (-0.0531), which is consistent with the model's general fit.

In conclusion, the regression results highlight the significant roles that Social Media Analytics Use (SMAU), Frequency of Social Media Posts (FSMP), and Customer Feedback Integration (CFI) play in driving Innovation Level (IL). These factors positively impact innovation, supporting the idea that leveraging social media analytics, increasing posting frequency, and incorporating customer feedback are crucial for fostering innovation within organizations. However, social media ad spending (SMAS) does not appear to have a meaningful impact on innovation, suggesting that simply increasing advertising spend on social media platforms does not directly correlate with higher innovation levels. The model provides strong explanatory power, making it a valuable tool for understanding the dynamics that contribute to innovation Figure 4.

Figure 4 Residuals vs. Fitted Values Plot

Conclusion

This study explores the factors influencing the Innovation Level (IL) in organizations, focusing on the impact of Social Media Analytics Use (SMAU), Frequency of Social Media Posts (FSMP), Customer Feedback Integration (CFI), and Social Media Ad Spend (SMAS). The regression analysis reveals that SMAU, FSMP, and CFI significantly contribute to innovation levels, while SMAS does not exhibit a meaningful impact. These findings underline the importance of leveraging data analytics, consistent engagement on social media, and integrating customer feedback into the innovation process. The study provides valuable insights for organizations seeking to enhance their innovative capabilities in today’s digital age, emphasizing the importance of a data-driven, customer-centric approach to innovation.

However, the non-significant effect of social media advertising spend (SMAS) challenges the traditional belief that financial investment in ads automatically leads to innovation. This finding suggests that the quality and strategic use of resources may be more important than the amount spent, which is an area that warrants further exploration.

Organisational culture, leadership style, and technology adoption are additional potential variables that may have a substantial impact on innovation outcomes, however these are the ones that this study focusses on.

Expanding the scope of future research could involve looking at both internal organisational elements (such as leadership and employee empowerment) and external environmental factors (such as competition and market trends) that impact innovation. To gain a better grasp of the interplay and development of these variables throughout time, as well as the long-term impacts of innovative techniques and social media initiatives, longitudinal data could be useful. Investigating how new technologies like AI and ML impact social media analytics and creativity could be a topic for future studies. The relationship between artificial intelligence (AI) and data-driven strategies and innovation is an important one to comprehend since these tools are becoming increasingly important to organisations.

The findings of this study have significant global implications. In a rapidly digitalizing world, organizations across industries must embrace innovative practices to stay competitive. As social media platforms continue to grow in prominence, businesses must learn to effectively utilize these platforms for innovation. The results indicate that focusing on analytics and customer engagement rather than just advertising spend can enhance an organization’s innovative potential, offering a sustainable and strategic path to growth. By applying these findings globally, organizations can foster a culture of innovation, ultimately driving economic development and societal progress in diverse markets worldwide.

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Received: 01-Apr-2025, Manuscript No. AMSJ-25-15816; Editor assigned: 02-Apr-2025, PreQC No. AMSJ-25-15816(PQ); Reviewed: 10-May-2025, QC No. AMSJ-25-15816; Revised: 12-May-2025, Manuscript No. AMSJ-25-15816(R); Published: 15-May-2025