Strategic Sensitivity and Performance of Manufacturing Firms in Nairobi County, Kenya

Keywords: Strategic Sensitivity and Performance

Paper Type: Research Article

Recommended citation: Cherotic J. (2025). Strategic Sensitivity and performance of the manufacturing firms in Nairobi County, Kenya. Journal of Economics, Management Sciences and Procurement, 5(1), pp. 210–225.

1. Introduction

Global manufacturing has undergone substantial transformation, driven by technological advancement, globalization, and supply chain restructuring. Between 2020 and 2023, global output rebounded to over USD 16.5 trillion, with Asia accounting for 52% of total production (UNIDO, 2023). Strategic sensitivity the ability of firms to anticipate and respond to market changes has emerged as a key driver of competitiveness. Firms that integrate agile decision-making, data-driven operations, and rapid market responsiveness consistently achieve superior performance (Ciampi et al., 2021).

In Sub-Saharan Africa, manufacturing growth remains constrained by infrastructural deficits, high energy costs, and limited innovation capacity. Kenya’s sector contributed only 7.8% to GDP in 2022, far below the Vision 2030 target of 20% (KNBS, 2023). Despite modest output growth and increased SME employment, most firms lack strategic sensitivity and performance-oriented practices (Ngila & Mutua, 2023; Okumu, 2025). Structural barriers, including aging transport systems and high electricity expenses, exacerbate these challenges.

Within East Africa, Kenya leads in manufacturing, primarily in vehicle assembly, metal fabrication, agro-processing, and consumer goods. Yet, firms face persistent operational constraints, while informal enterprises remain fragmented and lack strategic alignment. Empirical studies indicate that heightened strategic sensitivity enhances operational efficiency, innovation, and adaptive management. For example, Ngila and Mutua (2023) demonstrated improved performance among cement manufacturers with responsive strategies, while Okumu (2025) reported strong links between responsiveness and firm outcomes in steel firms (B = 0.393, p < .05).

Nairobi, as Kenya’s industrial hub, hosts large, medium, and small manufacturers across multiple sectors. These firms face rapid market shifts, energy unreliability, and global competition. Understanding how strategic sensitivity drives operational and financial performance is therefore critical. Insights from Nairobi-based firms can inform strategies for enhancing competitiveness and scaling manufacturing performance in Kenya and the wider East African region, supporting industrial revival and sustainable growth.

2. Theoretical Review

2.1 Resource-Based View Theory

The Resource-Based View (RBV) theory provides a framework for understanding how a firm’s internal resources determine performance outcomes. It argues that unique, valuable, rare, and inimitable resources create competitive advantage and superior performance (Wernerfelt, 1984; Barney, 1991). In this study, RBV informs the performance variable by emphasizing strategic sensitivity the firm’s ability to anticipate and respond to market changes as a critical capability. Firms with strong strategic sensitivity can effectively deploy tangible resources, such as technology and production systems, alongside intangible assets, including human capital, relational networks, and innovation capabilities, to enhance customer satisfaction, profitability, and organizational growth (Al-Ali et al., 2022; Rashidirad et al., 2022). Performance is thus viewed as the outcome of efficiently integrating and leveraging these resources. RBV provides a theoretical basis for examining how strategic sensitivity drives operational and financial performance in manufacturing firms.

2.2 Empirical Review (Hypotheses Development)

2.2.1 Strategic Sensitivity and Performance

In Kenya, Kimenye and Wanyoike (2021) examined strategic sensitivity’s role in the performance of state-owned manufacturing enterprises. Using a descriptive survey approach, they gathered primary data via questionnaires from managers across multiple firms and conducted inferential statistical analysis. The study revealed a significant positive effect of strategic sensitivity on organizational performance, suggesting that firms with robust environmental scanning and market awareness realize better results

In the same national context, Okumu (2023) conducted a study on steel manufacturing firms in Kenya. Employing a correlational research design and surveying 132 top managers through structured questionnaires, regression analysis showed that strategic sensitivity had a statistically significant positive influence on performance (β = .168, p < .05), accounting for 53.8% of performance variance

In the cement industry in Kenya, Ngila and Mutua (2024) provided a desktop review analysis where secondary data from reports and journals were used to explore the impact of strategic agility including strategic sensitivity on firm performance. Their findings indicated that firms with high sensitivity toward market and technological trends experienced marked improvements in revenue, profitability, and market share

In South-South Nigeria, Bassey, Uwa, and Okurebia (2024) investigated how strategic sensitivity a dimension of strategic agility affects performance in selected manufacturing firms. Adopting a survey design, they sampled 177 respondents across 319 employees and utilized structured questionnaires analyzed via simple linear regression. Findings reveal that strategic sensitivity alone accounts for 61.1% (R² = 0.611) of performance variance (F = 62.587, p < .001), indicating a significant positive influence on firm performance (k = 2024). This underscores the importance of environmental scanning and market awareness in manufacturing contexts

H1: Strategic sensitivity has no significant effect on performance

3. Research Methodology

The study employed an explanatory research design, enabling the assessment of causal links between the variables that is strategic sensitivity and performance. The units of observation were the 105 manufacturing firms, while the units of analysis were 1217 managers of the selected firms. A stratified random sampling approach was used to categorize the projects into distinct strata, ensuring that each group was adequately represented in the sample.

Sample Size and Data Collection

This study targeted senior management employees that is (one senior manager, 5 middle managers, 5 lower managers) in all the 105 firms. The total target population was therefore 1217 Managers. All data was obtained from responses to a field questionnaire that employed a five-point Likert scale to obtain responses. Relying on the Taro Yamane (1967) formula to derive a suitable sample from the population, 245 managers were selected out of the total target population. Of all the participants surveyed, 25 formed the study's pilot cohort, accounting for 10% of the total sample size. The evaluation of reliability confirmed that all the instruments had a Cronbach's alpha value of greater than 0.7, indicating that the items of the questionnaires were consistently reliable for the subsequent data analysis.

Data Analysis and Model Specification

This study employed a quantitative approach to data analysis, utilizing both descriptive and inferential techniques via SPSS version 25. Descriptive statistics, including means, standard deviations, percentages, and the minimum and maximum values, were used to summarize demographic and primary variables. Relationships between variables were examined using inferential methods, specifically correlation and regression analyses, to assess the strength and direction of associations.

Regression Model

The study employed the following regression model to evaluate and test the stated hypotheses:

Model 1: Tested for the effect of control variables on performance.

Y= β₀ +C

Model 2: Tested for the effect of independent variable on performance.

Y= β₀ +C+ B₁X₁ + ε………………………………1

Y= Performance

X₁= Strategic Sensitivity

4. Findings

A sample of 301 respondents from Nairobi's large and medium-sized businesses provided 251 surveys for the study. An above-average 82% response rate, typical of entrepreneurial research, was obtained from 245 replies that satisfied the predetermined criteria (Anseel et al., 2010). This response rate provided sufficient information to determine the connection between organizational culture, strategic agility, and the manufacturing enterprises' performance in Nairobi County, Kenya. Van Waeyenberg et al. (2015) and Roberts et al. (2020).

4.1 Firm Characteristics

The descriptive statistics of Firm age and firm size are important control variables that help explain organizational dynamics, resource capabilities, and their influence on performance outcomes. Examining their descriptive statistics provides insights into how respondents perceived their relevance within the study context. Forn FA1, the firm attempted to determine the duration of operations of the businesses surveyed as a metric of age. The mean score was 2.09 with a standard deviation of 0.577 which represents a moderate score with much lesser variation. This suggests that many businesses tended to fall within the same age brackets. For FA2, the evaluation focused on the ascertainment on the age of the firm which was distributed across the sample. The mean was 2.11 with a standard deviation of 0.575 which supports the above claims on the distributions demonstrating the same age across the sample being analyzed. For FA3, age was given to organizational experience and routines which suggests a mean of 2.08 with a standard deviation of 0.564 which indicates agreement, though slightly lower. For FA4, the firm attempted to find out if the age of the firm is associated with having developed certain practices. The age of the firm was given was 2.09 with a standard deviation of 0.590. This supports the previous metrics which suggest that the age of the firm has a moderate influence on internal organizational processes. Lastly, For FA5 which sought to answer the question does age of the firm increases its resilience and adaptability, the mean was 2.07 with a standard deviation of 0.562. This indicates moderate agreement which is somewhat weaker than the other dimensions of age. In the case of FS1, the analysis considered operational scale as a defined aspect of firm size. The value of the mean is equal to 2.20 on a 5-point scale, with a standard deviation of 0.562, showing a moderate level of perception with regard to firm age. In the case of FS2, which operationalized firm size as the employee count, the mean value of perception is equal to 2.12 with a standard deviation 0.571, showing moderate consensus with regard to the perception of firm size as well as low variation. FS3 examined the extent to which firm size determines resource accessibility, the mean value being 2.16 with a standard deviation of 0.571. Such values suggest that the respondents have fairly negative perceptions, which is, however, stabilizing, meaning that it is widely accepted that large firms could possess more abundant resources. In FS4, which examined the extent to which a firm enjoys operational scale cost advantages, the mean value is 2.16 accompanied by a standard deviation of 0.599, indicating moderate consensus with a bit more variation. Ultimately, FS5 examined the degree to which a firm enjoys market power as a result of its size, with the mean being 2.16, accompanied by standard deviation of 0.573. This shows moderate consensus whereby the respondents reinforce the perception that structural scale advantages result in increased market power. In general, indicators for the age of firms had an overall average of around 2.09, while indicators for the size of firms had an average mean of about 2.16. These results indicate that the respondents had a relatively stronger impression about the influence of the size of a firm on the workings of an organization in comparison to its age. Tim age and size of the organization also had relatively small standard deviations which emphasizes the fact that the responses given by the firms were in agreement, thus reinforcing their utility as control variables for the study.

4.2 Descriptive Statistics for Performance

Performance variable was measured using 15 items each rated on the Likert scale; 5 = Strongly Agree, 4 = Agree, 3 = undecided, 2 = Disagree, 1=Strongly Disagree. These descriptive statistics provide insight on how to understand data distribution, identify outliers, and support informed decision-making through simplified analysis. For PP1, the company wished to assess whether the company meets its financial objectives such as revenue growth and profit margin targets. The mean score was 2.80 and the standard deviation was 0.78 meaning that respondents were most likely neutral to some mild level of disagreement. This points to the fact that many employees accepted the fact that the company was able to accomplish its financial objectives, however, a large number of employees were either indifferent or cynical, as to whether, those accomplishments were achieved on a consistent basis. As for PP2, the emphasis was whether the company’s profitability is due to proper balancing of cost control and value added to its customers. The mean score for the case was 2.81 and standard deviation, 0.79. The case shows that there were some who held the view that the firm managed to combine customer value and efficiency, however, there were some who held a contrary view and thus, there was a gap as to how this was experienced or communicated in the organization.

Similar to all other Phases of the inquiry, PP3 had the mean score reflect on to what extent the firm manages to continuously identify and exploit the most profitable products, services, and customer segments, if any, for the respondent representative samples in the PPP survey. The mean score was 2.83 and the standard deviation was 0.79. The data is indicative of the same observations of neutrality with slight disagreement. It resonates with the idea that employees recognize some action is taken in the captured profitable opportunities, but there are gaps in the across the firm in the perceived direction and extent of the strategy’s deployment. PP4 had the participants focus on whether the firm manages to retain profit when there are changes to the market through changes to price, activities, or products. The mean score was 2.78 with a standard deviation of 0.81, the measure is lower in confidence relative to other indicators. It demonstrates that there is a lower sensed positive correlation with the firm’s adaptability resilience in context of the volatility of the market suggesting the firm finds difficulty sustaining profit. PP5 was utilized to ascertain the extent to which the firm’s leaders undertake regular analysis of the financial data in respect to strategic actions the firm can take to enhance profit. The mean in this case was 2.79 with a standard deviation of 0.79, indicative of some neutral positions with lower-than-average data confidence. It signals the data supports the case that data-driven decision making is recognized, but it is a weak sense, absent of unanimous or strong confidence.

For CS1, the company wanted to find out if the customer satisfaction index is utilized to assess the effectiveness of services and products offered. The mean score was 2.81 and the standard deviation was 0.78, indicating that respondents on the whole had neutral opinions, and to a slight degree disagreed. This points to the conclusion that while some level of Satisfier measurement is made, employees strongly do not perceive it as a joke or an organization-wide standard implementation. For CS2, the company wanted to see if customer feedback is used to enhance the products as well as the services and the entire customer experience. The mean score was 2.77 and the standard deviation was 0.79. This shows neutral opinions with, to a very slight extent, a degree of disagreed. This suggests that mechanisms may exist but from the employees’ viewpoint, these mechanisms are not strongly visible and not consistently acted upon. For CS3, the question was the extent to which the organization possess an understanding of customer needs and expectations. The mean score was 2.77 while the standard deviation was 0.79. Both these indicators point to a neutral position among the respondents. This suggests that the organization shows some level of understanding of customer needs, however the level and consistency of this understanding across the organization is in doubt. In CS4, the firm assessed the maintenance of customer long-term relationships by determining the percentage of expectations met or exceeded and the mean score was 2.78 with a standard deviation of 0.82. This demonstrates mild perception which means the Organization’s efforts of building long lasting relationships with the customers were recognized, but the employees had little faith in their ability to consistently meet or exceed expectations. In CS5, the emphasis was placed on customer satisfaction and the likelihood of subsequent purchases and positive referrals. The mean score was 2.82 and the standard deviation was 0.77. This once again demonstrates a neutral tendency, deriving the notion that employees acknowledge the positive advocacy and repeat purchase correlation but do not believe it is a prominent enough factor to be fully optimized within the business.

For RG1, the firm aimed to determine whether it effectively identifies and capitalizes on new revenue streams to drive growth. The mean score was 2.76 with a standard deviation of 0.82, indicating a generally neutral perception with a slight leaning toward disagreement. This suggests that while some initiatives exist to tap into new revenue sources, employees did not strongly view them as consistent or effective.

For RG2, the focus was on whether revenue generation is supported by a clear and differentiated value proposition for target markets. The mean score stood at 2.78 with a standard deviation of 0.81. These results reflect a neutral perception, showing that while there is some recognition of a value proposition, employees did not see it as highly distinct or strongly emphasized in driving revenues. For RG3, the company examined if it regularly reviews and adapts its pricing strategies to maximize revenue potential. The mean score was 2.78 with a standard deviation of 0.79. This again shows neutrality among respondents, suggesting that while pricing strategies are considered, they may not be frequently or visibly adjusted in ways that maximize revenue opportunities. For RG4, the assessment was on whether the company leverages digital channels and technologies to expand market reach and boost revenue. The mean response recorded a mean of 2.82 with a standard deviation of 0.79. This indicates slightly stronger perceptions compared to other indicators, though still neutral overall, implying that digital tools are being used but may not be fully optimized for maximum revenue impact. For RG5, the company sought to establish if revenue growth is driven by both customer acquisition and retention strategies. The mean score was 2.79 with a standard deviation of 0.78, suggesting neutral responses from employees. This means that while both acquisition and retention are acknowledged, they may not be systematically emphasized or clearly visible as key drivers of revenue growth within the firm.

The average attributed to the performance construct (PF) is 2.79; standard deviation (SD) is 0.73. Such average scores suggest that respondents have perceptions with a slight tendency to disagree regarding the overall performance of the firm. Based on these scores, employees recognized performance to a certain degree, however, the firm was not viewed as performing highly effective across all operations. The SD of 0.73 indicates that there is a moderate degree of agreement among respondents. Participants, however, viewed the perception quite differently, and so there appears to be performance distortion at the organizational level. This raises an important concern for the management with regard to delivering organizational outcomes, both operational and strategic, to strengthen the linkage in performance disparity at the organizational level.

Source: Surveyed data, 2025

4.3 Descriptive Statistics for Strategic Sensitivity

Strategic sensitivity was measured using 5 items each rated on the Likert scale; 5 = Strongly Agree, 4 = Agree, 3 = undecided, 2 = Disagree, 1=Strongly Disagree. These descriptive statistics helped the organization to have the ability to foresee changes within and beyond the market and proffer necessary responses to changes in the environment pertains to strategic sensitivity. It focuses on anticipation, shift, and change or choice. In SS1, the study sought to find out whether the firm reviews its past experiences and strategic direction for future planning. Mean score was 2.57 with standard deviation of 0.69 suggesting some level of neutral perception among responders. respondents. This neutral perception suggests that some level of reflection is in the organization, even though it is not practiced enough to be considered a tool for designing future strategies. In SS2, the study sought to know if the firm focuses on understanding and foreseeing the emergence of market trends and customer needs. Mean score of 2.54 with standard deviation of 0.68 once more suggests neutrality. respondents in this sample perceived that the organization was not adept enough in anticipating changes and shifts in the market, hence the organization’s response to the customer needs was also neglected. In SS3, the firm wanted to find out if it is likely to implement a new business model which is to some degree innovative and likely to coincide with relevant changes in the market. Mean score was 2.55 with standard deviation of 0.67. once more was neutral, meaning some efforts in innovation exists, but the efforts are not well known or communicated within the firm. For SS4, the assessment gauged if the company takes notes of competitors’ moves to mitigate threats as well as gain opportunities. The average response of 2.53 and the standard deviation of 0.70 suggests some nonalignment in the agreement as well as discord in the replies which results in a more generalized numbness. This implies that although the company checks on competitors, feedbacks tend to be incorporated in a more general versus tactical manner. For SS5, the employees were appraised on the extent to which participation from all tiers and levels was possible in the identification of changes in the external environment. The average score of 2.53 with a standard deviation of 0.66 reflects nonalignment as well as a general numbness; therefore, employees are not active in the scanning of the environment as well as discouraged from bringing approaches for the empowerment of the company.

4.4 Correlation Analysis

The direction of the linear relationship and the degree of the correlation between research variables are assessed using the Pearson correlation coefficient method. (Schober, Boer, & Schwarte, 2018). Correlation is a phrase used to describe the relationship between two or more quantitative variables, according to Gogtay and Thatte (2017). It also assesses the amount and intensity of the relationship between the variables as well as the relationship's direction. The coefficient's value, which indicates whether there is a positive or negative association, can range from -1 to +1. In this study, Pearson's correlation was utilized to assess the relationship between the study variables.

Table 3 show a high positive significant correlation between performance and strategic sensitivity (ρ = .661, p = .000)

Source: Survey, 2025

Where PF=Performance

SS=Strategic sensitivity

4.5 Regression Analysis (Hypothesis Testing)

Before testing for the direct effect hypotheses, the study examined the effect of the control variables in this study. This was done by adopting hierarchical regression analysis as presented by Table 4.13. In the first model of the hierarchical model, only the control variable (CV) was entered to predict performance (PF). The results showed that CV did not significantly explain variance in PF, with an R² of .004 and an adjusted R² of .000, meaning its predictive contribution was negligible. The regression coefficient for CV was negative (B = -.140, β = -.065), but statistically insignificant (p = .312). This indicates that CV had no meaningful relationship with PF. Therefore, at this stage, the model was weak, explaining virtually none of the performance variation among the sampled firms.

In the second model only the independent variables (SS) were added alongside CV, the explanatory power of the model improved substantially. The R² increased to .490, meaning the predictors together explained 49% of the variance in PF, which is a significant improvement compared to the covariate-only model. The ANOVA results confirmed model significance (F (4,240) = 57.569, p < .001). The predictor, SS (β = .408) was significantly positive to performance, while CV remained insignificant (p = .360). This suggests that firm-specific strategies, learning, and resource frameworks are stronger drivers of performance than the control variable.

Where SS=strategic sensitivity

CV =Control Variable (Age and Size of the firm)

a. Dependent Variable: PF

Source: survey data, 2025

a. Predictors: (Constant), SS, CV

b. Dependent Variable: PF

Source: survey data, 2025

5. Discussion

The model summary shows that strategic sensitivity (SS) and the control variable (CV) together explain 49% of the variance in performance (PF), with an R² of 0.490 and an adjusted R² of 0.481, indicating a strong model fit. The F-test (F = 57.569, p < 0.001) confirms statistical significance. Specifically, SS positively influences performance, suggesting that firms with higher strategic sensitivity achieve better operational and financial outcomes. The Durbin-Watson value of 1.992 indicates no autocorrelation in residuals.

6. Conclusion

To conclude this study, it established how important strategic sensitivity is regarding the performance of manufacturing firms. Construction firms that predict, understand, and act to changes in the market are more capable of attaining a greater level of operational performance, profit, and customer satisfaction. The model captures 49% of the performance variance, suggesting that internal and core assets of the form, such as strategic sensitivity, are important to the success of the organization. The performance of the firm, particularly in highly dynamic environments, illustrate the importance of managers complementing strategic sensitivity for the performance of the firm to be improved. In closing, the outstanding findings continue to add value and reflect the Resource-Based View theory. It demonstrates the need for the firm to utilize and acquire different forms of resources that are unique and valuable, and that are difficult to replicate in the market to achieve a sustained competitive advantage.

7. Implications

7.1 Managerial Recommendation

This study greatly impacts administration of manufacturing companies functioning in the fast-changing competitive world as well. Strengthening strategic sensitivity through managerial investment in market and competitive data analytics and environmental scanning systems is recommended. The ability to detect shifts in customer expectations, technology, and competitive forces is essential. Instead, the Threats and Opportunities framework should be streamlined through cross-functional participative management. Furthermore, managers need to adjust both strategic sensitivity and human resources as well as adaptable processes to enhance value of outcomes. Operational resilience, efficiency, and competitive advantage, especially in the long term, enhance sensitivity of strategy in the management of a company.

7.2 Policymakers’ Recommendation

The study provides valuable insights for policymakers that aim to improve the performance of the manufacturing sector. Appropriate policies must be established to be able to assist firms improve their strategic sensitivity by providing access to timely market information and providing the relevant digital and innovation-influenced infrastructures. Policies that influence the use of technologies, the development of skills, and the use of data for decision making, particularly for beginners, can help firms to adapt to the dynamically changing market. Furthermore, improved energy and transport infrastructures along with reduced regulatory uncertainty will help establish the environment for effective strategic pivots. Public–private partnerships, innovation ecosystems, and policies that foster the developing of knowledge freely available to all, will improve the performance of firms and the enhancing the manufacturing sector.

7.3 Theoretical Implications

This research adds to the conversation around the Resource-Based View (RBV) and its usage for understanding firm performance in flexible and new markets. Investigating the role of performance influenced by strategic sensitivity for the first time revises RBV’s focus on granular resource ownership to resource responsiveness by shifting the focus on milieu to resource capability. Strategic sensitivity can thus be considered a valuable, rare and unique resource in supplementing the organization’s internal resource deployment for restoring organizational performance. Also, the contribution adds to the literature on strategic management in the developing world, noting the disused developing economies manufacturers. It further argues espouses the differential performance, at a minimum, internal capabilities, and not external factors, should be the focus.

References