Unlocking Innovation from Within: The Role of Internal Knowledge in Enhancing Firm Performance in Sub-Saharan Africa

Abstract

This paper examines the role of internal knowledge in driving innovation and firm performance in sub-Saharan Africa, using panel data from the World Bank Enterprise Surveys covering fifteen countries in the region. Specifically, the analysis assesses the extent to which internal knowledge, measured through employee educational attainment, stimulates innovation, and whether innovation, in turn, contributes to improved firm performance. The findings reveal that internal knowledge has a significant positive effect on innovation, and that both internal knowledge and innovation are key drivers of firm performance in developing country contexts. These results underscore the strategic importance of building firm-level knowledge capabilities to enhance competitiveness, particularly among manufacturing firms. The study offers valuable policy implications, emphasizing the need to strengthen internal learning systems, workforce skills, and innovation support mechanisms to foster inclusive industrial growth in sub-Saharan Africa.

1. Introduction

Innovation plays a crucial role in a firm’s performance and competitiveness and remains a critical driver of enhanced performance and economic growth of firms (Tellis et al., 2012). As an instrument that accelerates productivity, innovation is conducive to industrial growth (Adner & Kapoor, 2010), particularly for developing countries. Firms are driven to innovate in order to grow and obtain both stability and legitimacy in the market (Agarwal & Gort, 2002; Agarwal & Audretsch, 2001). Usually, as levels of competition intensify among firms, the rhythm of change tends to accelerate, thus prompting firms to renew themselves with new competences (Floyd & Lane, 2000). Firm performance could benefit from reduced financial constraints and limited research and development (Feenstra et al., 2014). Less financially constrained firms tend to be more innovative through their research and development (R&D) approaches and the knowledge spillovers from domestic knowledge pools. Although linkages between innovation spillover and performance have been established in relation to advanced economies, the same cannot be affirmed for less developed economies.

Innovation in developing countries tends to be slow and poorly understood, which makes manufacturing firms vulnerable in matching the standards of foreign firms in developed countries. According to Lee and Malerba (2017), late-comer economies in innovation must be prepared to pursue catch-up without wasting any available opportunities. In this context, R&D stock at the firm level is critical for industrial innovation, which also requires considerable absorptive capacity to guarantee performance toward the high end of the global value chain. However, the high failure rates for firms to innovate (Kuester et al., 2012) and the high external uncertainty of the business environment (Bingham et al., 2014) pose challenges for firms in translating innovation into performance. Further, for innovation to be more useful, literature increasingly shows that it must involve the sharing and application of knowledge (Kaur, 2019; Manniche & Testa, 2018). Hence, embedding knowledge in the strategies for firm innovation prompts firms to pursue competitive advantage (Cankurtaran et al., 2013; Shinkle & McCann, 2014).

Knowledge, be it internal or external, does not exist in a vacuum (Paavola et al., 2004), and correspondingly, innovation hardly exists in isolation (Rybnicek & Königsgruber, 2019). Knowledge and innovation are integral parts of a broader innovation ecosystem, where dynamic, recursive interactions occur among multi-level network members (Acemoglu et al., 2016; De Vasconcelos Gomes et al., 2018; Valkokari, 2015). Even though not regarded as interchangeable concepts, there are instances where knowledge and innovation co-exist to express mutual interaction, known as knowledge-based innovation (Carayannis & Campbell, 2009). Research findings show that capabilities to innovate and acclimate to new contexts through knowledge management provide competitive advantage, thus realizing increased performance (Peris-Ortiz et al., 2019). Whereas externally, the majority of firms have sourced knowledge and resources by opening their traditional organizational boundaries (Chesbrough, 2003; Laursen & Salter, 2006), internally, firms with strong organizational potentiality can leverage knowledge resources to achieve their anticipated performance (Sirmon et al., 2011). On the contrary, small and medium-sized enterprises (SMEs) face resource scarcity, unsystematic innovation activities, and inferior capabilities, which result in higher failure rates of innovations (e.g., Van de Vrande et al., 2009; Wynarczyk et al., 2013). Firms in developing countries have, in recent years, focused on investing money, time, and other resources in the search for innovative opportunities in order to be more competitive. This investment process increases the potential to create and use existing knowledge for possible new opportunities. Firms also acquire new knowledge from internal and external sources, which adds more value to their innovation process. In developing countries, knowledge transmission occurs through different channels such as direct trade relations, foreign suppliers, and partners, rendering decisive actions toward the adoption of effective technology by firms (Hoekman & Javorcik, 2006).

This study makes a significant contribution to the growing discourse on innovation and firm performance in developing economies by empirically demonstrating that internal knowledge serves as a foundational driver of innovation in sub-Saharan Africa’s manufacturing sector. This study responds to a research question: To what extent does internal knowledge influence innovation among manufacturing firms in sub-Saharan Africa, and how does this relationship affect firm performance? Whereas existing literature often emphasizes external sources of knowledge and technology transfer, this paper highlights that internal knowledge, captured through employee expertise and organizational learning, is equally, if not more, critical for driving firm-level innovation and enhancing productivity. This perspective brings to the fore the often-overlooked internal capabilities that firms in resource-constrained settings can leverage to build competitive advantages.

This paper aims to analyze the influence of internal knowledge on innovation among manufacturing firms in developing sub-Saharan African countries, while controlling for firm characteristics and country-specific effects. The study offers robust empirical evidence that innovation significantly boosts firm performance in terms of productivity, competitiveness, and growth potential, especially among SMEs that dominate Africa’s industrial landscape. The analysis also integrates firm characteristics and country-specific effects to ensure context-based findings, thereby offering actionable insights for both researchers and policymakers. Ultimately, the study reinforces the importance of cultivating firm-level knowledge assets and embedding innovation policies in broader strategies for industrialization and economic transformation across African countries.

To distinctly identify the role of internal knowledge, the paper utilizes an unbalanced panel dataset comprising 5462 manufacturing firms across two waves from fifteen sub-Saharan countries with similar firm characteristics. This study argues that internal knowledge serves as a key driver of innovation for manufacturing firms in the sub-Saharan region. The rest of the paper is organized as follows: Section 2 reviews the literature. Section 3 describes the materials and methods. Section 4 presents and discusses the results, while Section 5 concludes and presents some policy implications.

2. Theoretical Framework and Literature Review

2.1. Theoretical Framework

This study is grounded in the resource-based view (RBV) and the knowledge-based view (KBV) of the firm, which emphasize internal capabilities as critical drivers of sustainable competitive advantage. According to the RBV (Barney, 1991), firms achieve superior performance by leveraging valuable, rare, inimitable, and non-substitutable (VRIN) resources, among which internal knowledge stands out as a key strategic asset. The KBV extends this framework by positing that knowledge, rather than physical resources, forms the most strategically significant resource in a knowledge-intensive economy (Grant, 1996). In this view, firms that effectively accumulate, integrate, and apply internal knowledge through employee skills, experience, and organizational learning are better positioned to generate innovation. Internal knowledge thus forms the cognitive and experiential foundation upon which innovative capabilities are built, allowing firms to recognize opportunities, adapt technologies, and develop new processes and products (Zahra & George, 2002; Sirmon et al., 2011).

Building on this, innovation serves as both a direct output of internal knowledge and an intermediary mechanism linking knowledge to firm performance. The innovation–performance linkage is rooted in Schumpeterian theory, which views innovation as the engine of economic dynamism and firm growth (Schumpeter, 1934). Innovations derived from internal knowledge, whether product, process, or organizational, enhance productivity, market positioning, and value creation, ultimately leading to improved performance outcomes such as profitability, efficiency, and firm survival (Lööf & Heshmati, 2003; Darroch, 2005). This framework also recognizes the role of absorptive capacity (Cohen & Levinthal, 1990), whereby firms with rich internal knowledge bases are better able to assimilate external knowledge and convert it into commercially valuable innovation. Accordingly, the model proposed in this study postulates that internal knowledge contributes both directly and indirectly to firm performance, with innovation acting as a key transmission channel.

2.2. Literature Review

Internal knowledge remains critical in promoting innovation and improving firm performance, thus influencing competitive advantage and adaptability in dynamic markets. The organizational structures that promote collaboration, a culture of continuous learning, and effective knowledge exchange strategies significantly improve the use of internal resources. Through collective experience and ideas, firms can optimize their innovation processes, adapt to market changes, and, ultimately, achieve effective strategic objectives. Firms that effectively take advantage of internal knowledge resources exhibit higher innovation resources (Mardani et al., 2018; Ode & Ayavoo, 2020). Barasa et al. (2019) highlight the relationship between inputs of innovation and efficiency in manufacturing firms, suggesting that internal resources are fundamental to productivity. Further, Nyeadi and Adjasi (2020) show that foreign direct investment enhances innovation, which is dependent on the absorption capacity of a firm. The process of knowledge absorption involves the assessment, organization, interpretation, synthesis, integration, and ultimately exploitation of varied sources and types of knowledge (Gold et al., 2001). Knowledge absorption also often leads to new knowledge generation, which ultimately improves firms’ ability to sustain competitive advantage (Zahra & George, 2002). Knowledge absorption involves the consolidation of the newly created knowledge with existing internal knowledge stocks, as well as with the experimentation of past knowledge bases for innovative applications (Zheng et al., 2011). Knowledge absorption also facilitates the capability to collect and assimilate new knowledge gained through internal collaborations. This then heightens the working skills that lead to the appropriation of the new knowledge, which is a key necessity in collaborative innovation ecosystems (Carayannis & Campbell, 2009). Knowledge absorption thus supports the assimilation and application of newly created knowledge for commercial purposes, thereby increasing the capacity for innovation (Faccin et al., 2019). The path shows an agile innovation process to assimilate internal and external knowledge and technologies through collaborative relationships, in open innovation (Chesbrough, 2003; Najafi-Tavani et al., 2018). Zheng et al. (2011) proposed that since the level and form of dynamic prospects can be quite different across different settings, it would be economical to consider how this may lead to different levels of innovation performance.

Firms that balance internal research and development with external collaborations are shown to achieve better technological innovation results (Anzola-Román et al., 2018; Bustinza et al., 2019). In addition, the maturity of knowledge inputs significantly affects the value of innovation (Petruzzelli et al., 2018), suggesting that internal knowledge mechanisms are crucial for firms that seek to promote innovation and maintain performance in evolving markets (Flor et al., 2018; Najafi-Tavani et al., 2018). The mediating role of knowledge application also illustrates the need for robust knowledge management practices in obtaining successful innovation (Ghasemaghaei & Calic, 2020; McDowell et al., 2018). At the same time, research indicates that digital technologies and big data adoption can improve innovation performance, although they need to be effectively managed to achieve ideal results (Usai et al., 2021; Awan et al., 2021). The interaction of sources of internal and external knowledge remains vital in the navigation of post-global financial crisis challenges (Zouaghi et al., 2018), while knowledge-oriented leadership promotes the quality of innovation (Chaithanapat et al., 2022). A systematic review highlights the importance of internal capacities for the performance of echo-innovation in manufacturing firms (Salim et al., 2019), while emphasizing that firms should continually adapt their knowledge strategies to remain competitive (Rauter et al., 2019; Ferreira et al., 2019; Chatterjee et al., 2024).

Hussen and Çokgezen (2022) emphasize the importance of regional institutions in the mediation of innovation performance. In addition, Saka-Helmhout et al. (2020) illustrate the impact of informal institutions on the paths of innovation. On the other hand, challenges such as political instability (Krammer & Kafouros, 2022) and barriers to technology transfer (Casadella & Liu, 2019) make it difficult to disseminate knowledge. Strategies that promote sustainable innovation include environmental collaboration (Adomako, 2020) and modes of internationalization (Abubakar et al., 2019). Overall, internal knowledge remains a vital asset for firms to navigate the complexities of innovation in sub-Saharan Africa (Hussen & Çokgezen, 2021; Edeh et al., 2020; Vallejo et al., 2019; Li et al., 2022; Onu et al., 2022).

A substantial body of research has explored the role of innovation in enhancing firm performance, though much of this literature is concentrated in developed economies, where firms operate in environments with well-established innovation ecosystems and robust institutional support (Zahra & George, 2002). By contrast, relatively few studies have examined how internal knowledge, an essential yet often underutilized asset, drives innovation in resource-constrained settings like sub-Saharan Africa. The sub-Saharan Africa (SSA) geographical region as a focus of this study is well justified by both empirical gaps in the literature and the unique economic context of the region. Whereas substantial research has examined the innovation–performance nexus in advanced economies, much less attention has been paid to how firm-level internal capabilities, particularly the internal knowledge, drive innovation in resource-constrained settings like SSA. Hussen and Çokgezen (2021) argue that the institutional and infrastructural landscape in Africa significantly shapes firm-level innovation dynamics and thus requires context-specific analysis. Further, Vallejo et al. (2019) emphasize the rising relevance of endogenous innovation systems in African countries, where firms increasingly rely on internal capacities rather than imported technologies alone. Sub-Saharan Africa presents a compelling case due to its structural transformation agenda, growing manufacturing base, and widespread prevalence of SMEs. Moreover, empirical evidence linking internal knowledge, innovation, and firm performance in the African manufacturing context remains limited, fragmented, or inconclusive. This study addresses this gap by investigating how internal knowledge influences innovation and firm performance across multiple sub-Saharan countries, while accounting for firm heterogeneity and country context.

3. Materials and Methods

3.1. Data Sources

This paper utilizes firm-level panel data collected by the World Bank through the Enterprise Survey, covering fifteen developing countries1 in the sub-Saharan region. The study focuses on firms with similar characteristics across sub-Saharan Africa, and this similarity is primarily established through consistent selection criteria based on firm-level attributes captured in the World Bank Enterprise Survey. Specifically, only formal, registered firms operating in the manufacturing sector were included to ensure sectoral homogeneity. The unbalanced panel data were gathered in two waves, one in 2006 and another in 2013. The dataset includes firm-level information on employees, the innovation process, and country-specific characteristics. The sample consists of more than 5462 observations from small, medium, and large firms, primarily in the manufacturing sector. The data capture firm characteristics such as age and size, as well as individual-level attributes like employees’ education levels.

Table A1. Summary statistics.

		Wave 1					Wave 2
Variable	Mean	Std. Dev.	Min	Max	Observation	Mean	Std. Dev.	Min	Max	Observations
Innovation	0.54	0.49	0	1	918	0.51	0.49	0	1	5584
Sales Performance	2.37 × 107	4.24 × 108	−1.6	3.50 × 1010	7087	4.94 × 107	9.35 × 108	−2.6	6.00 × 1010	8304
Internal Knowledge	8.46	4.59	1	13	511	11.9	48.9	0	19	1636
Formal Training	0.26	0.44	0	1	4580	0.29	0.45	0	1	7972
Age	13.8	13.8	0	124	7123	16.5	14.0	0	166	9333
Age 2	383.7	950.1	0	15,376	7123	468.9	1075.6	0	27,556	9333
Size	1.42	0.65	1	3	5593	1.49	0.67	0	3	9065
Subsidiary Firm	0.14	0.35	0	1	6574	0.17	0.37	0	1	9560
Competition—Informal Firms	0.65	0.47	0	1	5017	0.69	0.46	0	1	9302
Licensed Technology Abroad	0.12	0.32	0	1	3741	0.16	0.37	0	1	4554

presents the summary statistics for both dependent and explanatory variables, showing that firms innovate at an average rate of 0.54 percentage points, while the mean level of employee education is 8.46 years.

The dependent variable in this paper is innovation, defined as the adoption of a new or significantly improved production or delivery method, including major changes in techniques, equipment, and software, as well as the introduction of a significantly new product. Minor modifications, increases in production or service capacity through the addition of similar manufacturing or logistical systems, discontinuation of a process, simple capital replacement or expansion, changes driven solely by factor price variations, customization, routine seasonal or cyclical changes, and the mere trading of new or improved products are not considered innovations2.

In this paper, innovation is presented as a dummy variable equal to 1 if a firm (i) introduced innovative manufacturing methods, (ii) implemented innovative logistics or delivery distribution methods for inputs, products, or services, (iii) introduced supporting activities for processes, such as maintenance systems or operational improvements in purchasing, accounting, or computing, or (iv) significantly launched a new product in the market. If a firm meets any one of these four criteria, it is classified as having engaged in innovation in our sample. The firm’s internal knowledge, the log-transformed main explanatory variable, is proxied by the average years of education of all permanent employees. This measure includes full-time employees with basic education, tertiary training, and university degrees. Control variables include formal training, a dummy variable equal to 1 if a firm provides formal training specifically aimed at developing or introducing innovative processes. Firm age is calculated as the difference between the year a firm began operating and the year of the survey. Firm size is measured by the total number of employees and is log-transformed. Additional control variables include degree of competition, a dummy variable equal to 1 if the firm competes against unregistered or informal firms, and technology, a dummy variable equal to 1 if a firm has previously used licensed technology from a foreign firm (excluding office software). Finally, the study includes subsidiary firm, a dummy variable equal to 1 if the firm is part of a larger corporate entity as highlighted in

Table 1. Summary table of variables, their definitions, and measurements.

Variable	Definition	Measurement	Source
Innovation	Adoption of a new or significantly improved product, process, or delivery method	Dummy variable = 1 if the firm (i) introduced a new product, (ii) applied a new manufacturing or logistics method, or (iii) improved processes	World Bank Enterprise Survey (WBES) database
Internal Knowledge	A firm’s accumulated knowledge based on employee education	Log of average years of schooling of all permanent employees (includes basic, tertiary, and university levels)
Firm Performance	Output efficiency and productivity	Sales per employee (Total annual sales/Number of employees)
Formal Training	Firm-sponsored training for innovation	Dummy variable = 1 if the firm offers formal training for innovation or process improvement
Firm Age	Years since the firm was established	Log of the number of years between the establishment year and the survey year
Firm Size	Size of the firm in terms of workforce	Log of the total number of employees
Subsidiary Firm	Whether the firm is part of a larger corporate entity	Dummy variable = 1 if the firm is a subsidiary
Competition—Informal Firms	Competitive pressure from unregistered businesses	Dummy variable = 1 if the firm reports facing competition from informal firms
Licensed Technology Abroad	Technology acquisition through international channels	Dummy variable = 1 if the firm has used licensed technology from a foreign firm (excluding office software)
Region/Country Effects	Country-specific and region–year fixed factors affecting innovation and performance	Dummy variables for country, region, and survey year fixed effects

.

Additionally, this paper examines the relationship between innovation and firm performance through a transmission mechanism, where performance is measured by firm sales, calculated as the ratio of total annual sales to the total number of employees. Beyond the link between innovation and firm performance, the analysis extends to exploring the role of knowledge in shaping firm performance, also measured by sales. This study builds on the work of Corrado et al. (2005) and Kotey and Folker (2007), who argue that knowledge accumulation enhances firms’ productivity and output. This paper extends this analysis to firms in developing countries within sub-Saharan Africa, providing empirical evidence on the role of knowledge in determining firm performance.

This study relies on unbalanced panel data from the World Bank Enterprise Survey (WBES), which, while comprehensive, presents certain limitations. The use of two survey waves introduces the possibility of attrition bias, as firms that dropped out between rounds may differ systematically from those that remained. Further, the survey covers only formal, registered firms, potentially introducing selection bias by excluding informal enterprises that make up a significant share of economic activity in many sub-Saharan African countries. Moreover, self-reported measures such as innovation, training, and internal knowledge (e.g., average employee education) are subject to measurement error and inconsistent reporting.

3.2. Empirical Strategy

The empirical method used is based on firms deciding whether or not to engage in innovation. As this paper seeks to analyze how a firm’s internal knowledge affects its innovation, we use a panel dataset with three levels of analysis: firms i, country j, at time t. The paper adapts and estimates an ordinary least squares (OLS), and the firm- and country-effects version of the equation is as follows:

$$Y_{i,j,t}=\alpha +{\beta K}_{i,j,t}+{\sum \gamma X}_{i,j,t}+{\pi }_{j,t}+{\mu }_{i,j,t}$$

(1)

$Y_{i,j,t}$ stands for product and service innovation, and $K_{i,j,t}$ is the internal knowledge measure for firm $i,\mathrm{c}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{y}j,\mathrm{a}\mathrm{n}\mathrm{d}\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{e}t.$ $X_{i,j,t}$ represents a vector of firm characteristics, such as age, firm size, and level of training of employees, firm being part of a large firm, degree of competition, and licensed technology. ${\pi }_{j,t}$ represents country effects, and ${\mu }_{i,j,t}$ captures unobservable firm and country characteristics. Similarly, a transmission model was developed to analyze the mechanisms through which innovation affects firm performance, using Equation (2), with innovation as an explanatory variable for firm performance.

$$P_{i,j,t}=\alpha +{\beta Y}_{i,j,t}+{\sum \gamma X}_{i,j,t}+{\pi }_{j,t}+{\mu }_{i,j,t}$$

(2)

where the variable $P_{i,j,t}$ is the measure of firm performance, and $Y_{i,j,t}$ is the variable representing product and process innovation. It is argued that innovation stimulates firm performance in developing countries (Rukundo, 2017). Lööf and Heshmati (2003) claim that knowledge production gives rise to innovation outputs, which contribute to the level of productivity. To further examine possible non-random attrition, we estimate the relation between internal knowledge and firm performance using Equation (3) below.

$$P_{i,j,t}=\alpha +{\beta K}_{i,j,t}+{\sum \gamma X}_{i,j,t}+{\pi }_{j,t}+{\mu }_{i,j,t}$$

(3)

where $P_{i,j,t}$ represents a measure of firm performance, and $K_{i,j,t}$ is the internal knowledge measure for firm $i,\mathrm{c}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{r}\mathrm{y}j,\mathrm{a}\mathrm{n}\mathrm{d}\mathrm{t}\mathrm{i}\mathrm{m}\mathrm{e}t,$ controlling for firm characteristics. It is assumed that knowledge production plays a significant role in firm performance. Knowledge production gives a causal relationship between innovation output and productivity (Crépon et al., 1998). There are various factors that have been identified as important for productivity growth through new knowledge. If we assume that they can be transformed in scope to generate new ideas, we expect a close relationship between knowledge and productivity.

4. Results and Discussion

The paper addresses whether internal knowledge affects firm innovation.

Table 2. Estimation results: internal knowledge on firm innovation.

	Innovation
Variables	(1)	(2)	(3)
Internal Knowledge (ln)	0.033 *	0.042 **	0.046 ***
	(0.017)	(0.017)	(0.017)
Formal Training	0.170 ***	0.165 ***	0.165 ***
	(0.020)	(0.020)	(0.020)
Age (ln)	0.031 *	0.034 **	0.031 *
	(0.017)	(0.017)	(0.017)
Age 2	−0.000 **	−0.000 **	−0.000
	(0.000)	(0.000)	(0.000)
Size	0.010	0.015	0.020
	(0.013)	(0.013)	(0.013)
Subsidiary Firm	0.062 ***	0.053 **	0.053 **
	(0.023)	(0.024)	(0.024)
Competition—Informal Firms	0.075 ***	0.065 ***	0.068 ***
	(0.020)	(0.020)	(0.020)
Licensed Technology Abroad	0.114 ***	0.116 ***	0.106 ***
	(0.024)	(0.024)	(0.024)
Constant	0.307 ***	0.280 ***	0.268 ***
	(0.059)	(0.059)	(0.059)
Country FE	YES	YES	YES
Regional FE	NO	YES	YES
Region-by-Year of Survey FE	NO	NO	YES
Observations	2619	2619	2619
R-squared	0.076	0.101	0.125

*** p < 0.01, ** p < 0.05, * p < 0.1; robust standard errors in parentheses.

shows the results of the estimates of internal knowledge effects on innovation for firms in developing countries. The three columns show the effect of a firm’s internal knowledge on innovation, first controlling for country effects, the second column controlling for country and regional fixed effects, and the last column controlling for country, regional, and region-by-year of survey effects.

The results in Table 2 examine the relationship between internal knowledge and firm innovation across the three model specifications. Results in all three models reveal that internal knowledge (log-transformed) has a positive and statistically significant effect on innovation. The coefficient increases from 0.33 * in model 1 to 0.046 *** in model 3, indicating that the inclusion of additional fixed effects strengthens the estimated impact. An increase of 1 percent in internal knowledge increases the levels of innovation by 0.033 percent. The results remain robust as the estimated coefficients are quantitatively similar while controlling for country, regional, and region-by-year of survey fixed effects. The results are consistent with prior literature (Zhou & Li, 2012), where internal knowledge significantly and positively increases firm innovation. The findings in this article concur with recent developments in research where scholars (Subramaniam & Youndt, 2005; Miller et al., 2007; Zhou & Wu, 2010) assert that a firm’s knowledge base represents its most unique resource for radical innovation development. We find in this study that internal knowledge of a firm induces innovation, similar to the findings of Zhou and Li (2012), who argue that a firm with a broad knowledge base in the presence of internal knowledge is more capable of undertaking product and service innovation. Internal knowledge, rather than external-focused market knowledge acquisition, forms the foundation for innovation. A firm endowed with a deep internal knowledge base may achieve innovation through enhanced internal knowledge sharing.

The results indicate positive and statistically significant coefficients for formal training of employees, the age of a firm, being a subsidiary of a large firm, competition from informal and non-registered firms, and licensed technology used by a firm from abroad. Competition from informal firms is positively associated with innovation, which suggests that external pressures can stimulate adaptive and innovative responses (Porter, 1991). Similarly, formal training also shows a strong and consistent positive association with innovation. Across all models, the coefficient remains around 0.165–0.170 and is highly significant at 1 percent. This underlines the critical importance of employee skills development in fostering innovative capacity within firms. Training of a firm’s employees stimulates internal flow of knowledge and provides relevant future action for firms to generate knowledge (Dierickx & Cool, 1989). This is an interesting finding, suggesting that training increases the likelihood of firms to innovate. On the other hand, we find that size as a firm characteristic does not have a significant effect on innovation, which may reflect that innovation is not exclusively driven by scale but by organizational learning and strategic investments in knowledge resources.

Firm age(log), which determines the level of experience of a firm, is positively associated with innovation, although the effect is only marginally significant in models 1 and 3, and slightly more robust in model 2. The negative and significant coefficient on the squared term of age in models 1 and 2 indicates a nonlinear relationship, an indication that innovation increases with firm age up to a point and then declines, suggesting diminishing returns to age in terms of innovation potential. The age of a firm is used as a proxy for firm experience in investigating innovation drivers by several empirical research studies (e.g., Nieto & Santamaría, 2007; Balasubramanian & Lee, 2008). As a firm becomes older over a period, the accumulated resources derived from previous business operations and experiences can be a form of increased competence to increase innovative capacity, thus increasing the production capacity (Nelson & Winter, 2002). The age of a firm in developing countries, which explains the firm’s experience, could be considered a signal for the level of market knowledge as well as accumulated resources (Hadjimanolis, 2000). Subsidiary firms exhibit a significant positive relationship with innovation, suggesting that being part of a large corporate structure or network may enhance access to resources and knowledge conducive to innovation. Finally, licensing foreign technology has a robust and positive effect on innovation in all models. This reinforces the view that access to advanced technology from abroad plays an important role in upgrading local innovation capabilities.

Table 3. Estimation results: firm innovation and firm performance.

	Firm Performance
Variables	(1)	(2)	(3)
Innovation	0.232 ***	0.228 ***	0.219 ***
	(0.062)	(0.061)	(0.063)
Formal Training	0.157 **	0.202 ***	0.219 ***
	(0.070)	(0.069)	(0.068)
Age (ln)	0.240 ***	0.232 ***	0.242 ***
	(0.052)	(0.051)	(0.050)
Age 2	0.000	0.000	0.000
	(0.000)	(0.000)	(0.000)
Size	0.286 ***	0.285 ***	0.303 ***
	(0.045)	(0.045)	(0.046)
Subsidiary Firm	0.273 ***	0.206 ***	0.175 **
	(0.075)	(0.076)	(0.077)
Competition—Informal Firms	−0.262 ***	−0.267 ***	−0.282 ***
	(0.063)	(0.063)	(0.063)
Licensed Technology Abroad	0.584 *** (0.094)	0.556 *** (0.094)	0.573 *** (0.094)
Constant	12.395 ***	12.433 ***	12.387 ***
	(0.149)	(0.146)	(0.147)
Country FE	YES	YES	YES
Regional	NO	YES	YES
Region-by-Year of Survey	NO	NO	YES
Observations	3370	3370	3370
R-squared	0.689	0.700	0.706

*** p < 0.01, ** p < 0.05; robust standard errors in parentheses.

presents the results of the effect of innovation on firm performance as a transmission mechanism. Across the three model specifications, innovation is positively and significantly associated with firm performance. The coefficient ranges from 0.219 to 0.232 and is highly significant at the 1 percent level. The result shows that innovation has a highly significant effect on firm performance, corroborating the Schumpeterian perspective that innovation is a primary engine of economic growth and firm-level success (Schumpeter, 1934). This result aligns with the empirical findings from Griliches (1998) and J. K. Hall et al. (2010), which show that innovation improves productivity, profitability, and long-term survival. This confirms that innovative firms tend to perform better, highlighting innovation as a key driver of firm success.

Formal training also plays a significant role in enhancing firm performance. Its coefficients increase from 0.157 * in model 1 to 0.219 *** in model 3, indicating that the effect becomes stronger when additional fixed effects are included. This emphasizes the value of investing in employee skills, not only for innovation but also for overall firm outcomes. Firm age (log) reveals a strong and consistently significant positive relationship with performance at the 1 percent level in all models. Interestingly, the squared term of age is statistically insignificant, implying that, unlike its effect on innovation, the relationship between age and firm performance is linear and does not exhibit diminishing returns. Firm size has a robust positive effect on performance with statistically significant coefficients around 0.286–0.303 across all models. This result suggests that larger firms benefit from economies of scale and resource availability that translate into better performance outcomes. The subsidiary firm is also positively associated with firm performance but shows a declining trend in its coefficients across the three models.

While the results portray significant results, the weakening effect may reflect the increasing role of location and time-specific factors once more granular fixed effects are added. The use of licensed technology from abroad shows a strong and highly significant positive effect on firm performance. This emphasizes the value of international technology transfer in enhancing firm competitiveness and productivity. Interestingly, competition from informal firms presents a negative and significant effect on firm performance across all models. The coefficients become slightly more negative with additional controls, ranging from −0.262 *** to −0.282 ***. This suggests that while informal competition may induce firms to innovate, it simultaneously corrodes profitability or productivity and creates regulatory disadvantages (Djankov et al., 2008). The significance of formal training, firm age, and firm size in enhancing performance further supports the resource-based view (RBV) (Barney, 1991).

We estimate the model using Equation (3) to examine the effect of internal knowledge on firm performance.

Table 4. Estimation results: internal knowledge and firm performance.

	Firm Performance
Variables	(1)	(2)	(3)
Internal Knowledge	0.211 ***	0.175 **	0.224 ***
	(0.078)	(0.076)	(0.076)
Formal Training	0.260 ***	0.298 ***	0.297 ***
	(0.082)	(0.080)	(0.079)
Age (ln)	0.274 ***	0.250 ***	0.255 ***
	(0.062)	(0.060)	(0.059)
Age 2	0.000	0.000	0.000
	(0.000)	(0.000)	(0.000)
Size	0.297 ***	0.313 ***	0.309 ***
	(0.053)	(0.053)	(0.053)
Subsidiary Firm	0.130	0.037	0.019
	(0.097)	(0.097)	(0.097)
Competition—Informal Firms	−0.229 ***	−0.218 ***	−0.250 ***
	(0.074)	(0.074)	(0.073)
Licensed Technology Abroad	0.722 ***	0.706 ***	0.711 ***
	(0.111)	(0.111)	(0.110)
Constant	12.299 ***	12.416 ***	12.325 ***
	(0.227)	(0.223)	(0.223)
Country FE	YES	YES	YES
Regional FE	NO	YES	YES
Region-by-Year of Survey	NO	NO	YES
Observations	2479	2479	2479
R-squared	0.647	0.660	0.670

*** p < 0.01, ** p < 0.05; robust standard errors in parentheses.

presents the results, which show a direct and significant relationship between internal knowledge and firm performance, with a positive and statistically significant association prevailing across the three models, underscoring that knowledge, even independent of innovation, enhances firm outcomes. The results suggest that firms investing in leveraging their internal knowledge base experience better performance, reinforcing the view that knowledge is a strategic asset in firm growth. Upon further reflection, firms may increase internal knowledge of employees and innovation to increase their level of performance through increased sales. These findings are in line with similar findings of Darroch (2005), who argues that knowledge management facilitates the efficient use of resources in firms, leading to innovation and better performance.

Formal training remains a robust and highly significant positive effect on firm performance, with coefficients 0.260 *** and 0.298 *** across all models. These results are consistent with other findings, emphasizing the role of human capital development in improving overall firm performance and outcomes. Firm age (log) shows a consistently strong and positive effect on firm performance. Similarly, firm size is positive and significantly associated with firm performance in all models, with coefficients around 0.297–0.313. The results are in line with economic theory, suggesting that larger firms benefit from economies of scale and broader resource availability. Licensed technology from abroad shows the highest positive effect among all the predictors, with significant coefficients around 0.711–0.722. The results emphasize the role of technology transfer from international sources in enhancing domestic firms’ performance, resonating with findings by Keller (2004) that international technology diffusion is an important contributor to domestic productivity. Competition from informal firms shows a negative and significant effect on firm performance across the models, with coefficients between −0.218 *** and −0.250 ***. This result shows that informal competition creates downward pressure on formal firms’ performance.

To control for unobserved heterogeneity across countries, all models include country fixed effects, which absorb time-invariant national characteristics that might influence innovation or performance, such as education systems, industrial policies, market size, regulatory environments, and historical institutional quality. This research modeling approach ensures that the estimated relationships between internal knowledge, innovation, and firm performance are not confounded by differences across countries. While the fixed effects themselves are not interpreted directly, we acknowledge that countries differ significantly in their structural environments for innovation. For example, countries like Kenya and Nigeria have relatively advanced innovation systems, stronger tertiary education infrastructure, and larger manufacturing bases, which may enhance the role of internal knowledge. In contrast, countries such as Niger, Zimbabwe, and Togo have weaker innovation ecosystems, lower R&D investment, and limited industrial capacity. These contextual factors may shape how internal knowledge is generated and converted into innovation or performance gains.

Robustness Analysis

Three sensitivity analyses based on knowledge interactions are conducted to explore the role of knowledge interacted with different levels of knowledge stocks, flows, and accumulation. First, we investigated whether an interaction of knowledge and formal training (

Table A2. Estimation results: internal knowledge with formal training and innovation.

	Innovation
Variables	(1)	(2)	(3)
Internal Knowledge × Formal Training	0.070 ***	0.067 ***	0.068 ***
	(0.008)	(0.008)	(0.008)
Age (ln)	0.033 **	0.037 **	0.034 **
	(0.017)	(0.017)	(0.017)
Age 2	−0.000 **	−0.000 **	−0.000 *
	(0.000)	(0.000)	(0.000)
Size	0.012	0.017	0.022 *
	(0.013)	(0.013)	(0.013)
Subsidiary Firm	0.064 ***	0.055 **	0.053 **
	(0.023)	(0.024)	(0.024)
Competition—Informal Firms	0.077 ***	0.067 ***	0.070 ***
	(0.020)	(0.020)	(0.020)
Licensed Technology Abroad	0.115 ***	0.118 ***	0.108 ***
	(0.024)	(0.024)	(0.024)
Constant	0.374 ***	0.363 ***	0.360 ***
	(0.049)	(0.049)	(0.049)
Country FE	YES	YES	YES
Regional	NO	YES	YES
Region-by-Year of Survey	NO	NO	YES
Observations	2619	2619	2619
R-squared	0.074	0.098	0.122

*** p < 0.01, ** p < 0.05, * p < 0.1; robust standard errors in parentheses.

), as direct effects of knowledge flows and knowledge sources, influences firm innovation. The central finding is that the interaction term (internal knowledge × formal training) is positive and highly significant across all model specifications. This result suggests that internal knowledge is more effective in promoting innovation when it is complemented by formal employee training programmes. In other words, the presence of formal training amplifies the innovation-enhancing effect of a firm’s internal knowledge resources. The result is consistent with knowledge-based views of the firm that emphasize knowledge as a strategic asset (Grant, 1996). Training not only enhances individual competencies but also fosters collective learning and knowledge sharing within firms. As Cohen and Levinthal (1990) reveal, the ability of a firm to exploit internal knowledge depends largely on the capabilities of its workforce, which are largely built through learning and training. Beyond the interaction term, other firm-level characteristics also present interesting patterns. Firm age continues to display an inverted U-shaped relationship with innovation, while firm size becomes marginally significant only in the third model, suggesting that size alone may not strongly determine innovation unless combined with regional and temporal contextual controls. Subsidiary firms and firms that license foreign technology transfer are consistently more innovative, reaffirming that external linkages and international technology transfer can augment internal capabilities (Keller, 2004). Further, the persistent positive effect of competition from informal firms reveals that such a competitive setting may act as a stimulus for innovation, as firms are forced to improve efficiency and differentiate themselves (Fu et al., 2018).

We also generate an interaction of knowledge and age of a firm (

Table A3. Estimation results: internal knowledge with age and innovation.

	Innovation
Variables	(1)	(2)	(3)
Internal Knowledge × Age	0.012 ***	0.015 ***	0.015 ***
	(0.005)	(0.005)	(0.005)
Formal Training	0.171 ***	0.166 ***	0.164 ***
	(0.020)	(0.020)	(0.020)
Age 2	−0.000 **	−0.000 **	−0.000 *
	(0.000)	(0.000)	(0.000)
Size	0.011	0.015	0.020
	(0.013)	(0.013)	(0.013)
Subsidiary Firm	0.063 ***	0.054 **	0.053 **
	(0.023)	(0.024)	(0.024)
Competition—Informal Firms	0.076 ***	0.066 ***	0.069 ***
	(0.020)	(0.020)	(0.020)
Licensed Technology Abroad	0.114 ***	0.116 ***	0.107 ***
	(0.024)	(0.024)	(0.024)
Constant	0.386 ***	0.375 ***	0.363 ***
	(0.036)	(0.036)	(0.036)
Country FE	YES	YES	YES
Regional	NO	YES	YES
Region-by-Year of Survey	NO	NO	YES
Observations	2619	2619	2619
R-squared	0.076	0.101	0.124

*** p < 0.01, ** p < 0.05, * p < 0.1; robust standard errors in parentheses.

) to investigate the relationship between internal knowledge and firm innovation moderated by firm age, which provides further insight into how firm maturity interacts with knowledge capabilities to influence innovation performance. The interaction term internal knowledge × age is consistently positive and highly significant (p < 0.01) across all model specifications. The result suggests that as firms grow older, the innovation returns from internal knowledge increase. This finding affirms that firms learn and adapt over time, improving their ability to process and apply knowledge (Argote & Miron-Spektor, 2011). Formal training remains significant and positively associated with innovation, echoing the major findings and underscoring the importance of continued employee development in maximizing innovation potential.

Finally, we interact knowledge with the size of the firm to explore whether the effect of internal knowledge on innovation depends on firm size, providing intuition into how organizational scale interacts with knowledge resources to influence innovation capacity. The results reveal that the interaction term is positive and statistically significant (p < 0.05) in models 2 and 3 (which include regional and temporal fixed effects). This result suggests that larger firms are better able to leverage internal knowledge to drive innovation. The results are consistent with Damanpour (1996), who finds that firm size positively affects innovation, not necessarily because size leads to innovation but because larger firms often have more structured research and development systems, more qualified personnel, and better knowledge management systems. This important finding can explain the fact that an array of internal knowledge affects innovation. Improved firm knowledge enhances the firm’s capacity to innovate (Yli-Renko et al., 2001). In general, internal knowledge appears to effectively support process and product innovation for firms in developing countries of sub-Saharan Africa.

5. Conclusions

The prime purpose of this paper is to investigate the role of internal knowledge on firm innovation in a developing country setting. The study estimates the relationship between internal knowledge and firm innovation using comprehensive data from manufacturing firms in fifteen African developing countries. Using robust empirical data, the paper presents compelling evidence that internal knowledge serves as a crucial determinant of innovation, which in turn significantly enhances firm performance. This relationship is especially salient in sub-Saharan Africa, where innovation and productivity gaps remain substantial. The findings show a virtuous cycle, where firms that invest in building and utilizing internal knowledge capabilities are more likely to innovate, and those that innovate exhibit higher performance outcomes, thereby improving their competitiveness and growth potential. These findings reinforce earlier work by Grant (1996) and Cirera and Maloney (2017), who emphasize the centrality of knowledge creation and internal learning processes in firm innovation and highlight the importance of firm-level capabilities as complements to external innovation support structures. This positive impact indicates the opportunity available for firms to exploit for competitiveness and growth. Increasing innovation would increase the firm’s capacity to produce goods and services that can compete with international products from other continents.

The results presented in this paper have relevant policy implications. First, the significant positive relationship between internal knowledge and innovation suggests that governments should prioritize human capital development within firms. Investments in technical and vocational education, on-the-job training programs, and partnerships between firms and higher education institutions can enhance employees’ skills and firm-level knowledge stocks, which are foundational elements for sustained innovation. Second, the results show that formal employee training strengthens the impact of internal knowledge on innovation. This calls for governments in sub-Saharan Africa to introduce targeted incentives to promote continuous learning within firms, such as tax credits for staff training, matching grants, or co-financing schemes through public–private partnerships. In particular, supporting SMEs, which often lack internal capacity to invest in training, may unlock innovation potential and improve performance outcomes.

Third, a positive link between innovation and firm performance highlights the need to embed innovation support within broader industrial and private sector development strategies. The findings align with studies by B. H. Hall and Mairesse (2006) and Crespi and Zuniga (2012), which show that innovation is a key channel through which firms in developing economies can enhance productivity and profitability. Policymakers in developing countries of sub-Saharan Africa should scale up innovation-focused business development services, including advisory support, incubation, and access to digital tools and advanced technologies. Special attention should be given to firms in the manufacturing sector, where productivity gains can contribute significantly to employment creation and structural transformation.

Fourth, since licensed foreign technology and internal knowledge complement each other, which highlights the dual importance of absorptive capacity (Cohen & Levinthal, 1990) and technology diffusion, governments should facilitate technology transfer mechanisms while strengthening firms’ absorptive capacities. This includes supporting firm-level R&D, providing access to quality infrastructure, and promoting regional knowledge-sharing platforms. Finally, to improve the competitiveness of “Made-in-Africa” products, national innovation policies need to recognize that innovation is not solely about new product creation, but also about process improvements, efficiency gains, and knowledge diffusion within firms. As such, industrial policies should move beyond large-scale innovation hubs and foster localized knowledge ecosystems that empower firms to innovate using their internal capabilities.

Ultimately, the findings in this research paper present the imperative for innovation policies in developing countries of sub-Saharan Africa to be holistic, context-specific, and firmly grounded at the firm level. Rather than relying solely on high-level policy frameworks or macroeconomic interventions, governments, private sector actors, and development partners should prioritize the development of internal knowledge systems within firms as a foundation for sustainable innovation. This means investing in employee skills development, fostering organizational learning, strengthening research and development capacities, and supporting internal knowledge management practices.

The robust link revealed in this study between internal knowledge, innovation, and firm performance provides a compelling framework for rethinking industrial and innovation policy. The link calls for integrated policy approaches that connect key domains such as education and skills training, industrial upgrading, technology adoption and diffusion, and enterprise development. Such an approach would not only enhance firms’ innovative capabilities but also improve their performance, competitiveness, and resilience in dynamic markets. As firms are the core engine of innovation, sub-Saharan African countries have the opportunity to design demand-driven, bottom-up strategies that are more likely to yield inclusive industrial growth. This could enable a greater number of local firms, especially SMEs, to participate in and benefit from regional and global value chains, ultimately accelerating structural transformation and reducing dependency on low-value economic activities.

Future Research

The study recommends that future research should explore the dynamic interactions between internal knowledge, innovation, and firm performance using longitudinal datasets that capture changes over time, particularly in the context of policy reforms or economic shocks. Further research should examine the sectoral heterogeneity of knowledge-driven innovation, especially across high-tech, agro-processing, and informal manufacturing segments, to understand differentiated innovation pathways. Additionally, future studies could incorporate qualitative case studies or mixed-method approaches to unpack how internal knowledge is created, shared, and applied within firms, especially SMEs. Exploring gender dynamics in knowledge accumulation and innovation leadership within firms could offer valuable insights for inclusive industrial policy in sub-Saharan Africa.