The connection between innovation and firm performance is based on established theoretical frameworks, notably the Resource-Based View (RBV) and dynamic capabilities theory. The RBV posits that organisations attain enduring competitive advantage through the development and utilisation of valuable, rare, inimitable, and non-substitutable resources ([1]). In this context, innovation signifies a strategic capability that allows firms to distinguish themselves and enhance performance outcomes. Nevertheless, the Resource-Based View (RBV) presumes relatively stable environments and inadequately elucidates how firms adjust to swift market fluctuations. The Dynamic Capabilities theory enhances this viewpoint by highlighting the organization’s capacity to integrate, develop, and reorganise internal and external competencies in reaction to evolving environments ([23]). Innovation serves as a means by which companies consistently modify their processes, products, and organisational frameworks. In unstable and competitive markets, particularly in developing economies, the capacity for innovation is intrinsically linked to survival and expansion.

Innovation is a multi-dimensional construct rather than a singular concept. The Oslo Manual delineates a widely recognised framework that classifies innovation into four primary categories: product, process, organisational, and marketing innovation ([17]). Product innovation denotes the introduction of novel or substantially enhanced goods or services. Process innovation encompasses enhancements in production or delivery techniques that increase efficiency. Organisational innovation pertains to modifications in business practices, workplace structure, or external relationships, whereas marketing innovation emphasises novel strategies in product promotion, pricing, or market positioning.

These dimensions are conceptually separate yet frequently interconnected. For example, enhancements in processes may facilitate product innovation, whereas organisational modifications may enable both advancements. Considering innovation as a multifaceted concept facilitates a more refined comprehension of the ways various strategies impact organisational performance.

Empirical research consistently indicates a positive correlation between innovation and firm performance, though the intensity and characteristics of this correlation differ across contexts. Preliminary research indicates that companies involved in innovation activities typically attain greater productivity, profitability, and market share ([21]; [7]). Nonetheless, these conclusions predominantly rely on evidence from developed economies, where enterprises function within more organised innovation frameworks. In developing economies, the evidence is more ambiguous. Research indicates that innovation markedly enhances firm performance, especially when companies implement various forms of innovation concurrently ([12]; [15]). Some emphasise that constrained resources, inadequate institutional backing, and market limitations can diminish the efficacy of innovation strategies ([18]; [3]). This indicates that the effect of innovation is contingent upon context and shaped by organisational competencies and external factors. Furthermore, research that dissects innovation into its constituent elements demonstrates that various forms of innovation yield disparate effects on performance outcomes. Marketing innovation is frequently linked to enhanced market access and competitiveness, whereas process innovation improves operational efficiency. Product innovation typically impacts revenue growth, while organisational innovation facilitates internal coordination and long-term adaptability ([19]; [7]).

The reviewed literature leads to three broad conclusions. First, innovation is generally associated with improved firm outcomes, but the strength of that relationship varies across innovation types and across contexts ([21]; [7]). Second, evidence from developing economies suggests that innovation does not operate uniformly, since firm capabilities, institutional support, and market conditions influence whether innovation translates into stronger performance ([12]). Third, studies that separate innovation into product, process, organizational, and marketing dimensions suggest that these dimensions do not contribute equally to firm outcomes, yet the comparative importance of each dimension remains unsettled ([19]).

Despite these insights, important gaps remain. Much of the literature examines innovation either as a single aggregate construct or as isolated dimensions, which limits the ability to compare how different innovation strategies relate to firm performance within the same empirical setting. In addition, a large share of the evidence is drawn from developed economies, while studies from Sub-Saharan Africa remain relatively limited and often do not provide an integrated assessment of the four major innovation dimensions ([12]; [15]). In the Kenyan context specifically, existing studies acknowledge the importance of innovation for manufacturing firms, but there is still insufficient evidence on the relative performance implications of product, process, organizational, and marketing innovation when examined together in one model.

This study responds to that gap by examining the four dimensions of innovation simultaneously in a Kenyan manufacturing context. Rather than treating innovation as a single undifferentiated capability, the study tests whether each innovation dimension is positively associated with firm performance and allows comparison of their relative contributions within the same empirical framework. On that basis, the following hypotheses are proposed:

H1: Product innovation is positively associated with firm performance.

H2: Process innovation is positively associated with firm performance.

H3: Organizational innovation is positively associated with firm performance.

H4: Marketing innovation is positively associated with firm performance.

The conceptual framework guiding this study positions the four dimensions of innovation (product, process, organizational, and marketing innovation) as independent variables influencing firm performance as the dependent variable. Figure 1, the model assumes that each innovation dimension exerts a distinct direct relationship with firm performance competitiveness.

Figure 1. Conceptual framework of the study.

Figure 1 presents the direct relationships examined in this study. Product innovation, process innovation, organizational innovation, and marketing innovation are specified as the core independent variables, while firm performance is treated as the dependent variable. Modeling the four innovation dimensions separately, rather than collapsing them into a single composite innovation index, makes it possible to compare their relative contributions and determine which dimension has the strongest association with firm performance in the Kenyan manufacturing context.

This framework provides the basis for the empirical model tested in the methodology section.

The overarching paper utilized a mixed-methods approach, incorporating both survey and interview data; however, this paper concentrates on the quantitative aspect, as the primary aim is to evaluate the proposed correlations between innovation strategies and firm performance through firm-level survey data. In line with this goal, the paper uses a quantitative, cross-sectional survey design ([5]; [4]). This approach was suitable as the study aimed to quantify the relationships between distinctly defined innovation variables and firm performance through standardized responses and statistical analysis. The cross-sectional design entailed gathering data from manufacturing companies at a specific moment. It is good for finding patterns of association, but it doesn’t help with strong causal inference.

The target population consisted of registered manufacturing companies operating in Kenya. The paper delineates the sector as comprising industries such as food and beverage, textiles and apparel, chemicals, and machinery, noting that the country had approximately 6000 registered manufacturing firms ([11]; [22]). The firm served as the unit of analysis, with data gathered from respondents anticipated to possess adequate knowledge of the firm’s innovation practices and performance. A purposive sampling method was employed to create an initial sample of 500 manufacturing companies ([6]). The sampling frame was constructed from registered manufacturing firms identified in official and sectoral sources, aiming to encompass key subsectors including food and beverage, textiles and apparel, chemicals, and machinery. Questionnaires were administered to informed managerial or supervisory individuals, including general managers, operations managers, production managers, or comparable respondents responsible for overseeing firm operations and performance. The document delineates three primary selection criteria: the firm must be registered and functioning within the Kenyan manufacturing sector, must have been operational for a minimum of five years, and must exhibit some form of innovative activity. The final criterion enhanced relevance to the innovation-centric research question, yet it may have also constrained variation in the explanatory variables by diminishing the representation of distinctly non-innovative firms. The results should be interpreted as relationships within an already innovation-active segment of Kenyan manufacturing, rather than across the entire population of firms. The original target sample included 500 firms, but the final dataset for the empirical analysis contained 350 valid responses, yielding an approximate response rate of 70%. This sample constituted the foundation of the quantitative analysis.

Primary data for the quantitative component were obtained through a structured questionnaire distributed to the selected manufacturing firms ([13]; [4]). The instrument was developed to collect data on product, process, organizational, and marketing innovation, in addition to firm performance metrics. It also gathered pertinent background information regarding firm characteristics.

The questionnaire method was suitable, as it facilitated the acquisition of standardized data from a considerable number of firms. Respondents were chosen based on their familiarity with firm operations, strategic initiatives, and performance results, indicating that responses were anticipated from informed managerial or supervisory personnel rather than unknowledgeable staff. This enhanced response quality, although the utilization of self-reported firm-level data still introduces the potential for response bias. The overarching paper includes interviews and other qualitative inputs; however, the analysis presented in this paper relies solely on survey data.

The overarching paper defined firm performance as a multidimensional construct, utilizing indicators such as profitability, sales growth, market share, and return on investment. Nonetheless, the regression output included in the article draft utilizes ROI as the sole reported dependent variable. This paper thus regards the empirical analysis as a financial performance specification based on ROI, while recognizing that this is more limited than the broader concept encompassed in the questionnaire. The independent variables consisted of the four dimensions of innovation strategy: product innovation, process innovation, organizational innovation, and marketing innovation. Product innovation encompasses the launch of new or substantially enhanced products. Process innovation indicated enhancements in production or delivery techniques aimed at increasing efficiency. Organizational innovation pertains to modifications in organizational structures, managerial practices, or workplace systems, whereas marketing innovation encompasses novel marketing techniques, promotional strategies, and market positioning approaches. The measures were derived from the questionnaire utilized in the paper and corresponded with the Oslo Manual classification of innovation types ([17]). The questionnaire employed multi-item metrics for the dimensions of innovation and the overarching performance construct, while the article-level analysis is based on the composite variables detailed in the paper. This article is an extraction from the approved paper, not a reconstruction from the raw dataset and complete appendix; therefore, it summarizes the construct definitions and reported composites without reproducing the entire item inventory. Table 1 indicates that the study designates ROI as the dependent variable in the presented regression model, with product, process, organizational, and marketing innovation serving as the primary explanatory variables.

Table 1. Measurement of variables.

The quantitative data were analyzed using SPSS ([8]). Consistent with the paper structure, the analysis proceeded in three stages. First, descriptive statistics were used to summarize respondent and firm characteristics and to provide an overview of the main study variables. Second, correlation analysis was used to assess the direction and strength of the bivariate relationships among the variables. Third, multiple regression analysis was used to estimate the relationship between innovation strategies and firm performance ([13]). Prior to regression estimation, standard diagnostic checks were considered for the underlying assumptions of the model, including linearity, normality, homoscedasticity, and multicollinearity, in line with common practice in survey-based innovation studies. In the regression model, firm performance was specified as the dependent variable, while product, process, organizational, and marketing innovation were entered as explanatory variables. This approach made it possible to assess whether each innovation dimension was positively associated with firm performance while holding the others constant. It also allowed comparison of the relative strength of the four innovation dimensions within a single empirical model. As specified in Equation (1), firm performance is modeled as a function of the four innovation dimensions.

where F P i denotes firm performance for firm i , β 0 is the intercept term, P I i denotes product innovation, P r I i denotes process innovation, O I i denotes organizational innovation, M I i denotes marketing innovation, β 1 to β 4 are the corresponding coefficients, and ε i is the error term. Due to the cross-sectional nature of the data and the reliance on self-reported metrics for key constructs, the analysis is understood in associational rather than causal terms. The results indicate a correlation between innovation strategies and enhanced firm performance but do not establish a definitive causal relationship for performance improvements.

The study observed standard research ethics in the collection and handling of data. Participation was voluntary, and respondents were assured that the information provided would be treated confidentially and used strictly for academic purposes. No firm-specific information was disclosed in the reporting of results, and the analysis was presented in aggregate form to protect respondent anonymity.

The quantitative analysis is based on 350 valid responses from the targeted sample of manufacturing firms, which is about 70% of the total number of responses. The survey data collected information about the firms’ characteristics, performance metrics, and the level of innovation in their products, processes, organizations, and marketing that they used. The attained response level constituted an adequate foundation for both descriptive and inferential analysis.

Descriptive statistics were used to summarize the achieved sample and the distribution of the main study variables. As shown in Table 2, the study reports summary statistics for firm size, annual revenue, ROI, and the four innovation dimensions. Figure 2 and Figure 3 complement these statistics by providing visual summaries of the innovation measures and the performance-related indicator retained in the descriptive analysis.

Table 2. Summary of key descriptive statistics.

Figure 2. Distribution of innovation dimensions and ROI.

Figure 3. Distribution of innovation strategy scores.

Table 2 illustrates significant variability in the size and financial scale of the responding firms. The typical firm employed 75 workers; however, the significant disparity between the minimum and maximum values suggests substantial heterogeneity among the sampled firms. The average annual revenue was USD 1.2 million, and the mean ROI was 12.5%. Within the dimensions of innovation, product innovation achieved the highest average score, succeeded by process and organizational innovation, whereas marketing innovation exhibited the lowest average level. Figure 2 enhances these statistics by illustrating the distribution, central tendency, and anomalous observations across the innovation dimensions and the performance-related indicator. Figure 3 offers an alternative perspective on the distribution of innovation scores, indicating that the reported innovation levels were predominantly clustered around the mid-range rather than uniformly distributed across all response options.

Collectively, these descriptive patterns suggest that innovation practices were inconsistently reported among the sampled firms. Specifically, product-related innovation seems to have been prioritized over other dimensions of innovation, aligning with the perspective that manufacturing firms frequently emphasize visible product differentiation in response to competitive pressures.

Correlation analysis was used to examine the bivariate relationships between the innovation dimensions and firm performance. The paper indicates that product innovation had the strongest reported positive correlation with ROI, while process innovation also showed a positive moderate relationship. Organizational and marketing innovation were likewise described as positively related to ROI, although more weakly than product and process innovation.

These patterns provide preliminary evidence that higher reported levels of innovation tend to be associated with stronger financial outcomes. However, bivariate correlations do not show whether each innovation dimension retains an independent relationship with firm performance once the others are considered jointly. For that reason, the study proceeded to multiple regression analysis.

The paper employed multiple linear regression to assess the individual association between each innovation component and financial performance, with ROI as the dependent variable. Table 3 reports the regression coefficients presented in the paper, while Figure 4 provides a visual summary of the standardized coefficients associated with the innovation dimensions.

Table 3. Regression analysis summary.

Figure 4. Standardized coefficients for innovation dimensions and firm performance.

Table 3 shows that product innovation had the biggest positive and statistically significant coefficient of all the reported predictors. Process innovation had a positive and significant effect on ROI, and marketing innovation had the same effect, but with a smaller coefficient. On the other hand, the reported regression results did not show a statistically significant link between organizational innovation and ROI. Figure 4 enhances Table 3 by illustrating the comparative magnitude of the standardized coefficients across the four aspects of innovation. The figure facilitates visual comparison; nevertheless, it must be analyzed in conjunction with the regression table, as statistical significance and coefficient magnitude are determined by the regression output, not alone by the chart. The regression results indicate that the most significant financial correlations were associated with product innovation, followed by process innovation, while marketing innovation also had a beneficial impact. Organizational innovation, albeit favorable, could not demonstrate statistical significance in the presented regression model. A single caution is warranted in this context. The paper presents an OLS summary including a markedly low R-squared and a non-significant model-level F-statistic, which does not quite correspond with the comprehensive coefficient table provided earlier. The internal discrepancy necessitates that the regression data in the current paper be read as the pattern presented in the paper, rather than as a fully reconciled inferential result. Consequently, robust assertions on overall model fit or validated statistical accuracy would be unsuitable in the current iteration.

The findings indicate a varied relationship between innovation dimensions and firm performance, rather than a consistent effect across all types of innovation. The robust positive correlation between product innovation and ROI indicates that the launch of new or enhanced products may be the most direct means by which manufacturing firms in Kenya improve financial performance. This interpretation aligns with previous research that associates product innovation with market revitalization, revenue growth, and competitive differentiation ([19]; [21]; [7]).

The positive and significant correlation between process innovation and ROI suggests that companies can enhance performance by optimizing production and delivery techniques. Second, the positive and significant association between process innovation and ROI indicates that firms may also improve performance by refining production and delivery methods. This finding aligns with the argument that process innovation contributes to operational efficiency and cost reduction, which can ultimately improve financial returns ([7]).

Third, marketing innovation also appears to matter, although its reported effect is smaller than that of product and process innovation. This suggests that changes in promotion, pricing, packaging, or market positioning may help firms improve market reach and sales outcomes, even if these effects are not as strong as those associated with product-oriented innovation ([10]). The non-significant result for organizational innovation is noteworthy because it differs from the stronger expectations suggested by part of the literature review. One possible interpretation is that organizational changes may contribute more indirectly to performance by enabling other innovation activities rather than generating an immediate direct effect on ROI. Another possibility is that the measurement of organizational innovation in the paper captured broad internal changes whose financial implications take longer to materialize. This interpretation is still broadly consistent with studies that view organizational innovation as an enabling capability rather than a purely immediate financial driver ([23]; [2]; [16]).

Overall, the results support the argument that innovation matters for firm performance, but they also indicate that not all innovation dimensions contribute equally. In the Kenyan manufacturing context represented by the paper data, product and process innovation appear to be the most consequential dimensions for financial performance, while marketing innovation plays a supportive role and organizational innovation shows a weaker immediate relationship.

From a managerial perspective, the results imply that manufacturing firms seeking immediate financial improvements may benefit most from prioritizing product and process innovation, while still treating marketing and organizational innovation as complementary rather than irrelevant. For policymakers, the findings reinforce the value of supporting innovation systems that improve firms’ capacity to develop products, upgrade processes, and commercialize innovations effectively.

At the same time, the interpretation of the findings must remain cautious. The study relies on cross-sectional and self-reported data, which limits causal inference. In addition, the internal inconsistency between parts of the reported paper regression output indicates that the final paper should avoid overstating the precision of the empirical results. The evidence is still useful for identifying broad patterns, but it should be framed carefully as reported associations rather than definitive causal estimates.