Competitor Orientation and Performance of Furniture Manufacturing SMEs in Dar es Salaam, Tanzania: The Mediating Effect of Customer Loyalty

Abstract

The effect of competitor orientation on the performance of furniture manufacturing Small and Medium Enterprises was examined, with a special focus on customer loyalty as a mediator. A sample of 364 firms was obtained from the Dar es Salaam Region using proportionate stratified sampling. Data were collected using structured questionnaires and analysed inferentially using covariance-based structural equation modelling. When the mediator was controlled, the results showed that responsiveness to competitor intelligence had a significant effect on FM-SME performance, suggesting partial mediation. However, competitor intelligence generation had no significant effect on FM-SME performance when the mediator was included, implying full mediation. The conclusion is that the mere generation of competitor intelligence does not guarantee improved performance unless firms deploy strategies to reinforce customer loyalty. FM-SMEs should monitor competitors and use competitor insights and customer feedback to improve product design and features, adjust prices, and enhance customer service.

1. Introduction

Within a competitive and dynamic market environment, competitor orientation is a well-known component of market orientation that enhances firm competitiveness and performance (Ferreira & Coelho, 2020). Competitor orientation refers to gathering competitor information and developing strategies to enhance a firm’s competitiveness (Ogundare & van der Merwe, 2024). From a behavioural perspective, Kohli and Jaworski (1990) conceptualise competitor orientation as encompassing three interrelated activities: the generation of competitor intelligence, the dissemination of such intelligence across organisational units, and the formulation of responsive actions based on that intelligence. It enables business organisations to discover opportunities for differentiation and makes it possible for them to gain a competitive advantage through the creation of new products as well as novel business models that are better than their rivals’ (Ogundare & van der Merwe, 2024).

The performance of Small and Medium Enterprises (SMEs) has become an area of interest around the world because they play an important role in sustainability and viability of economic development (Endris & Kassegn, 2022). Approximately 90 percent of businesses worldwide are SMEs, serving as a source of 50–60% of jobs (United Nations, 2023). This is also the case in Tanzania, where over 95% of the country’s enterprises are SMEs. They provide employment to approximately four million employees and contribute nearly 35% of the country’s gross domestic product (Tonya & Samwel, 2024; Lubawa et al., 2024). SMEs have the potential to create many jobs and promote industrialisation, innovation, social inclusion, and rural-urban linkage (Kazungu, 2023). In the SME context, furniture making represents a significant labour-intensive sector, constituting nearly 30% of SMEs and employing about 6% of the country’s manufacturing workforce (Nkwabi, 2020; Kumburu et al., 2019).

The demand for commercial and residential furniture continues to rise owing to urbanisation and increasing standards of living, leading to market growth on one side and the mushrooming of furniture businesses on the other (Frank Theodory & Julius Mbigili, 2025). Market forecasts indicate a promising trajectory for Tanzania’s furniture sector, with expectations of market value reaching USD 606.03 million by 2025. An annual growth rate of 1.37% is expected from 2025 to 2029 (Statista, 2024).

Despite the growth potential of the furniture market, SMEs in the furniture sector have not performed as expected (Mwagike, 2024; Israel, 2022). Fierce competition and ever-changing consumer preferences are some of the challenges impeding the performance of furniture manufacturing SMEs (FM-SMEs) (Kumburu & Kessy, 2021; Mwagike, 2024). By relying on outdated production techniques, limited resources, and a small customer base, FM-SMEs are handicapped in competing with larger domestic producers and importers (Mwagike, 2024).

The importation of foreign-produced furniture has been on the rise (Kumburu & Kessy, 2021; Mwagike, 2024). For example, in 2023, Tanzania imported about USD 24 million worth of furniture, with China’s share being around 52 percent of those imports (TrendEconomy, 2024). There is also increasing concern that consumers prefer imported furniture over domestic furniture. This is due to the belief that foreign furniture is superior in design and quality and cheaper than locally made furniture (Kumburu & Kessy, 2021; Mwagike, 2024).

To withstand competitive pressures, FM-SMEs need adaptive strategic orientations to achieve higher performance and competitiveness (Mwagike, 2024). Although the viability of competitor orientation as a strategic option is recognised (Ogundare & van der Merwe, 2024), the extent to which SMEs could possibly exploit this approach like large enterprises remains uncertain (Bamfo & Kraa, 2019). Furthermore, competitor orientation’s effect on SMEs’ performance in Tanzania remains inadequately researched. Previous studies have largely concentrated on large firms, especially in developed countries (Hassen & Singh, 2020), hence creating a contextual gap that this study sought to fill. In particular, this study addresses the lack of empirical evidence on how individual dimensions of competitor orientation, rather than the aggregate construct, affect the performance of SMEs operating in resource-constrained, highly competitive developing-country markets.

Although the market orientation framework considers competitor orientation a unidimensional construct (Sørensen, 2009), it is noteworthy that from a behavioural perspective, competitor orientation is a three-dimensional construct (Kohli & Jaworski, 1990). It encompasses “competitor intelligence generation (CoIG), intelligence dissemination across departments, and responsiveness to competitor intelligence (RCoI)” (Kohli & Jaworski, 1990). Recently, it has been advocated that the predictive power would be increased by examining the effects of these dimensions independently rather than considering competitor orientation as one aggregate construct (Abdulsamad et al., 2021; Alhakimi & Mahmoud, 2020). The logic behind this contention is that some dimensions may influence performance more strongly than others. However, owing to the small size and informal nature of most SMEs, which often lack departments, the dissemination of intelligence across departments is not relevant to this study. Hence, only two dimensions (CoIG and RCoI) were considered relevant.

Additionally, in an increasingly competitive environment where products are relatively homogeneous and customer preferences continuously change, competitor orientation may not fully translate into improved firm performance unless it results in a stable customer base (Sampaio et al., 2020). This means that its effectiveness is contingent on a firm’s ability to cultivate customer loyalty (LOY) (Rajagukguk et al., 2024; Ismail, 2023). Loyal customers consistently choose and remain committed to a firm and its products by making repeat purchases, spreading positive word-of-mouth, and resisting switching to competitors (Albarq, 2023), all of which boost sales and profitability (Sampaio et al., 2020; Boonmalert et al., 2020). Therefore, it is plausible that competitor orientation drives superior performance through LOY. However, empirical evidence from the Tanzanian context remains scarce, creating a contextual gap that this study sought to address. Therefore, this study examined the mediating role of LOY in the effect of competitor orientation on FM-SME performance in the Tanzanian context.

2. Theoretical Underpinnings

This study is grounded in the Dynamic Capabilities Theory (DCT) developed by Teece et al. (1997). DCT posits that “the performance of a firm is contingent upon the capability of a firm to respond to changes in business environment” (Teece et al., 1997). “Dynamic capabilities allow firms to sense, seize and reconfigure their resources in response to opportunities and threats within their business environment” (Teece et al., 1997).

In this study, competitor orientation is based on the extent to which FM-SMEs demonstrate their ability to sense and respond to competitive pressures. FM-SMEs can adjust their strategies based on market trends by actively tracking competitors’ moves, strengths, and weaknesses. In this case, CoIG represents the sensing capability, whereas RCoI represents the seizing capability.

Moreover, studies have established that dynamic capabilities are a significant determinant of LOY, which is a significant predictor of firm performance (Ashill et al., 2022). Loyal customers not only provide a stable revenue stream but also serve as a driver of competitive advantage, helping firms withstand competitive pressures (Mwang’amba & Matonya, 2023). Thus, FM-SMEs can enhance their performance by adapting their business practices to foster LOY in response to competitive pressure.

The effect of competitor orientation and SME performance through LOY draws attention in this study. It is anticipated that competitor-oriented firms are better positioned to leverage their capabilities to create a value proposition that drives LOY and superior performance. DCT offers a theoretical lens for examining the relationships among competitor orientation dimensions, LOY, and firm performance in the Tanzanian context.

3. Empirical Work and Hypotheses Development

3.1. Competitor Orientation and the Performance of SMEs

Competition in business is an inevitable reality that continues to intensify. Businesses that understand this basic truth and embrace competitor orientation can turn competitive pressure into a powerful strategic advantage (Fitri et al., 2022). Competitor-oriented firms tend to react well to competitive changes (Utami & Nuvriasari, 2023). Although competitor orientation is often less developed in SMEs than in larger firms, it serves as a critical driver of firm performance (Ogundare & van der Merwe, 2024).

Competitor orientation has been found to positively influence firm performance in many studies. For example, empirical evidence shows that competitor orientation positively affects the performance of manufacturing SMEs in Saudi Arabia (Saleh & Al-Hakimi, 2022). Similarly, competitor orientation had a positive and significant effect on the marketing performance of MSMEs in Indonesia (Yaskun et al., 2023). Reshid et al. (2022) also found the positive and significant impact of competitor orientation on business success among Ethiopian SMEs. The findings were similar in Kenya (Sigey et al., 2023) and Nigeria (Ogundare & van der Merwe, 2024).

However, the effect of competitor orientation on firm performance is not fully established, warranting further studies in different contexts. For instance, in Ethiopia, Hassen and Singh (2020) established that competitor orientation was not a performance determinant. Research on SMEs of expedition services in Surakarta City showed that competitor orientation does not significantly affect marketing performance (Utami & Nuvriasari, 2023). Likewise, a study of multi-industry SMEs in Indonesia did not find any significant influence of competitor orientation (D’souza et al., 2022). The inconsistent findings presented in prior studies have led scholars to suggest that the effect of competitor orientation on performance might be influenced by contingent factors or intervening variables (Hassen & Singh, 2020). Hence, it highlights the need to explore the impact of competitor orientation on performance in new contexts.

The mixed findings in the literature may partly reflect the tension between competitor and customer orientations as competing strategic emphases. Dev et al. (2009) argue that excessive focus on competitors can divert managerial attention from customer needs, potentially undermining performance gains. Their work on the hospitality industry demonstrates that the relative payoff of competitor versus customer orientation varies by market conditions, suggesting that context-specific investigations are essential. Furthermore, Botta (2019) highlights that SME performance is contingent upon financing decisions and financial leverage, variables that interact with strategic orientations in complex ways. These theoretical insights underscore the importance of examining competitor orientation dimensions individually and considering potential mediating mechanisms, such as customer loyalty, which may reconcile the competing demands of competitor-focused and customer-focused strategies.

Previous studies have predominantly conceptualised competitor orientation as a single-dimensional construct and investigated its aggregate effect on firm performance. Nevertheless, the complexity of this construct is believed to require us to take a component-wise approach to better understand how the different components influence (Schulze et al., 2022). Abdulsamad et al. (2021) and Alhakimi and Mahmoud (2020) affirm that the component-wise approach yields better predictive performance in comparison with its unidimensional counterpart. As competitor orientation is a multidimensional concept, this study examines the effect of each dimension on FM-SME performance in Dar es Salaam.

One of the dimensions of competitor orientation is CoIG, for which a firm strives to consistently monitor its competitors to enhance knowledge about their strategies, skills, strengths, and weaknesses (Crick, 2022). Previous observations by Hendar et al. (2020) indicate that firms with competitive intelligence generation can maximise their performance. Correspondingly, Puspaningrum (2020) stressed that market orientation as a driver of acquiring competitive information is critical to the marketing performance of SMEs. Therefore, for this study, it is hypothesised that:

H1. 

CoIG positively and significantly influences FM-SME performance.

While CoIG pertains to gathering information, the subsequent and equally critical dimension is responsiveness. RCoI encompasses a firm’s ability to creatively harness competitive insights in the marketplace to stay ahead of the competition (Science & Adewusi, 2024). It entails tactics such as reconfiguring marketing strategies in response to competitors, launching new or improved products, and enhancing the level of customer service (Abubakar et al., 2024). Ouma et al. (2022) found that the responsive capability to competitor intelligence significantly enhanced the competitiveness and overall performance of regulated microfinance banks in Nairobi, Kenya. This is particularly relevant for firms that operate in volatile and competitive environments. Hence, we hypothesise that:

H2. 

There is a positive significant relationship between RCoI and FM-SME performance.

3.2. Competitor Orientation and Customer Loyalty

The concept of LOY has been well-researched in marketing and is understood as an important variable influencing firm performance. LOY is manifested in the form of behaviour and attitude. Behavioural loyalty pertains to actual purchase behaviours, such as repeat purchases and brand advocacy (Ahmad & Dirbawanto, 2024). Attitudinal LOY, however, is defined as “the emotional and psychological attachment to a brand or business firm by a customer” (Watson et al., 2015). Empirical literature suggests that competitor orientation plays a significant role in driving LOY (Boonmalert et al., 2020; Khan & Ghouri, 2018). Boonmalert et al. (2020) established that CoIG allows firms to gauge their competitors and improve their products and services accordingly. This allows businesses to better meet customer needs, thereby fostering LOY. Additionally, Khan and Ghouri (2018) found CoIG to be positively related to LOY, as it helps firms anticipate and respond to rival actions.

LOY emanates from CoIG through the acquisition and utilisation of competitive intelligence to yield strategic value for customers. Firms that diligently monitor competitors can identify gaps in the market and develop unique value propositions that differentiate them from their rivals (Al-Hakimi et al., 2023). This differentiation can lead to increased customer satisfaction and, consequently, stronger brand loyalty. Additionally, competitor-oriented firms are better positioned to anticipate market trends and customer preferences, allowing them to proactively adapt their strategies and offerings (Mardiyono & Sugiyarti, 2024). This adaptability enhances the perceived value of a firm’s products or services in the eyes of customers, reinforcing their loyalty. It is, therefore, hypothesised that:

H3. 

CoIG has a significant positive effect on LOY.

Firm responsiveness to competitors is also a key aspect of competitor orientation. Schulze et al. (2022) established that responsiveness to competitive pressures allows firms to differentiate themselves from their competitors and achieve positional market advantages. Customers can easily compare product or service alternatives to make their purchase decisions. Therefore, it is plausible that firms that are responsive to competitors can adapt their offerings in response to competitive pressures, leading to enhanced LOY. Furthermore, building emotional connections with customers is a significant driver of LOY. This resonates with the findings of Rinaldi et al. (2023), who highlighted that responsiveness to competitive pressures can strengthen emotional connections with customers, fostering long-term loyalty. Therefore, the following hypothesis is proposed:

H4. 

RCoI has a significantly positive effect on LOY.

3.3. Customer Loyalty and Firm Performance

Extensive research has highlighted the significance of the LOY in driving business performance. Ismail (2023) conducted a related literature review and concluded that LOY is one of the strongest predictors of organisational performance, stimulating higher sales, profitability and market share. This is consistent with the findings of Sampaio et al. (2020), who conducted a study in the Western European hotel industry. This study hypothesises that:

H5. 

LOY significantly and positively influences FM-SME performance.

3.4. Mediating Role of LOY in the Competitor Orientation -Firm Performance Relationship

While the empirical literature recognises the mediating effect of LOY (Boonmalert et al., 2020), much prior research has focused on competitor orientation as a whole. This creates a gap in understanding the specific mechanisms by which different aspects of competitor orientation impact firm performance. This study hypothesises that:

H6. 

LOY mediates the effect of CoIG on FM-SME performance.

H7. 

LOY mediates the relationship between RCoI and FM-SME performance.

3.5. Conceptual Framework

The conceptual framework presented in Figure 1 depicts the hypothesised links between the predictor and criterion variables through the mediator. This study used the performance of the FM-SME construct as a criterion variable. CoIG and RCoI constructs were used as predictor variables, and LOY served as a mediating variable.

4. Research Methods

4.1. Data Descriptions

This was a correlational design study that utilised a quantitative approach. A correlational design was used to quantify and examine the relationships among variables without manipulating them (Gawali, 2023). The study was conducted in the Dar es Salaam Region, including all five districts. This region was selected because it has the highest concentration of FM-SMEs, whose market dynamics are more affected by competition from both local producers and imported goods (Kumburu et al., 2019). This diversity necessitates a focused study on how SMEs can navigate competitive pressures and leverage LOY to improve their market position.

A sample of 364 firms that have been in the furniture business for at least five years was selected using a proportionate stratified sampling method. According to the 2015 manufacturing survey conducted by the National Bureau of Statistics, Dar es Salaam Region hosts 1062 FM-SMEs: Ilala has 223, Kigamboni 53, Kindondoni 203, Temeke 427 and Ubungo 156. Although the survey data are nearly a decade old, they are the most recent comprehensive official source available. To mitigate potential discrepancies arising from this temporal gap, the researchers conducted a preliminary field verification exercise in each district to confirm the continued existence and operational status of the sampled enterprises before administering the questionnaires.

Based on Yamane’s (1967) formula for finite populations, the sample size was initially determined to be 291 with a 5% margin of error. A draft questionnaire was administered to a sample of 30 respondents as a pre-test, and revealed a non-response rate of 20%. To account for potential non-response bias, the sample size was adjusted to 364 based on the response rate, as recommended by best practices in survey methodology (Kish, 2005). Utilising the modified population size of 364, proportionate stratified random sampling was adopted to ensure that SMEs in each district were adequately represented. The final sample size for each district was as follows: Temeke (146), Ilala (76), Kindondoni (70), Ubungo (54) and Kigamboni (18).

4.2. Descriptions of Variables and Analytical Model

Four constructs (CoIG, RCoI, LOY, and FM-SME performance) were used to develop the measurement and structural models. All the constructs were operationalised through 5-point Likert scale items, with 1 = strongly disagree and 5 = strongly agree. The items of the measure (see

Table 1. Variables in the model under measurement.

Construct	Dimensions	Number of Items	Source
CoIG	Gathering of competitor information	Three	(Schulze et al., 2022)
	Monitoring of competitor actions	Four
RCoI	Product differentiation	One	(Schulze et al., 2022)
	Price adjustments	One
	Market adaptation and expansion	Three
	Strategic alliances and partnerships	One
LOY	Repeat purchase	One	(Ismail, 2023; Sampaio et al., 2020)
	Willingness to pay premium price	One
	Positive feedback and word of mouth	Two
	Customer forgiveness	One
FM-SME Performance	Sales growth	Three	(Kiyabo & Isaga, 2020a, 2020b)
	Growth in profit	Two
	Growth in asset	Four
	Growth in market share	Two
	Growth in employees’ number	One

) were adapted from different sources and tailored to suit this study’s context. Descriptions of the study constructs with corresponding observable items are provided in Appendix A.

The performance of FM-SMEs was measured using financial measures (profit, sales, and asset growth) and non-financial indicators (number of employees and market share). These indicators are commonly used to assess SME performance (Kiyabo & Isaga, 2020b, 2020a). To capture the multidimensionality of firm performance, this study utilised composite measures derived from the selected indicators. Composite measures are particularly relevant because a single measure cannot comprehensively reflect SME performance (Kiyabo & Isaga, 2020b).

It is important to note that all the constructs, including customer loyalty, were measured from the perspective of enterprise owners/managers, using self-reported assessments. While this approach is common in SME research, where direct customer surveys are logistically challenging (Vij & Bedi, 2016), it represents a methodological limitation. Owner-reported loyalty perceptions may not fully align with actual customer behaviours and attitudes. Nevertheless, prior studies have demonstrated that owner/manager assessments of business-related constructs exhibit acceptable convergent validity with objective measures (Vij & Bedi, 2016).

Based on the fact that many SMEs are often hesitant to maintain and disclose financial records (Duygulu et al., 2016; Jalali et al., 2020), this study opted for subjective metrics to measure firm performance. The practicality of subjective metrics is justified by Vij and Bedi (2016), who found that they are positively correlated with objective metrics. Data were collected by administering structured questionnaires to the owners/managers of the selected enterprises.

The questionnaire was originally developed in English and translated into Kiswahili by a bilingual expert to ensure accessibility for respondents. A back-translation procedure was employed to verify the semantic equivalence between the two versions. Of the 364 questionnaires distributed, 350 were returned, yielding a response rate of 96%. After screening for completeness, all 350 responses were retained for the analysis.

4.3. Data Analysis

This study employed covariance-based structural equation modelling (CB-SEM) to perform inferential data analysis. CB-SEM was considered suitable because of its strength in validating models and examining multiple variables simultaneously (Hair et al., 2019). The appropriateness of the dataset for factor analysis was tested using the Kaiser–Meyer–Olkin (KMO) measure. Bartlett’s test of sphericity was conducted for sampling adequacy and inter-item correlations. Exploratory factor analysis was conducted to identify correlated items and their underlying constructs. Eigenvalues greater than 1.0 were extracted by principal component analysis, and only items with factor loadings >0.5 in absolute value after varimax rotation were retained for further analyses. Internal consistency was tested using Cronbach’s alpha. Convergent validity was examined using the AVE. On the other hand, the discriminant validity test was carried out by comparing the AVE’s square root with the Pearson correlation coefficients.

CB-SEM was carried out using Analysis of Moment Structures (AMOS, version 23). This study acknowledges that firm-level characteristics such as firm size, age, and financial leverage were not included as control variables in the structural model. While these factors have been shown to influence SME performance (Botta, 2019; Forth & Bryson, 2019), the primary objective of the present study was to test the hypothesised direct and mediated pathways between competitor orientation dimensions, customer loyalty, and firm performance. Future research should incorporate these variables as controls to provide a more comprehensive understanding of the examined relationships. Path analyses were conducted to assess each component of the proposed models. The effect of LOY as a mediating variable was calculated using 5000 bootstrapping samples at a 95% confidence interval.

5. Results and Discussion

5.1. Exploratory Factor Analysis

The dataset’s suitability was confirmed with a KMO value of 0.954, confirming sampling adequacy. Bartlett’s test of sphericity was significant, confirming adequate correlation among items to justify factor analysis. The principal component analysis indicated five constructs with an Eigenvalue > 1.0, accounting for 66.506% of the total variance after rotation. The rotated component matrix, provided in

Table 2. Rotated component matrix.

Code	Component
	1	2	3	4	5
FP4	0.752
FP6	0.738
FP5	0.723
FP7	0.664
FP8	0.661
FP10	0.637
FP1	0.632
FP9	0.624
FP2	0.612
FP3	0.595
RCoI4		0.840
RCoI5		0.816
RCoI2		0.813
RCoI3		0.807
RCoI1		0.778
RCoI6		0.736
CoIG6			0.823
CoIG4			0.788
CoIG3			0.785
CoIG7			0.771
CoIG5			0.769
CoIG2			0.617
CoIG1			0.545
LOY3				0.731
LOY1				0.731
LOY4				0.685
LOY2				0.656
LOY5				0.598
FP11					0.715
FP12					−0.578

, shows distinct grouping of variables into five components, of which two items were excluded, resulting in 28 items being grouped into four components for further analysis.

5.2. Reliability and Validity Analysis

The Cronbach’s alpha coefficient of all the constructs was higher than the cut-off value of 0.70, as shown in

Table 3. Construct validity and reliability test results.

Construct	Item No.	Cronbach’s Alpha Coefficient	AVE
FM-SME performance	10	0.897	0.657
CoIG	7	0.905	0.563
RCoI	6	0.948	0.649
LOY	5	0.876	0.546

. This confirms that the items within each construct measure the same underlying constructs (Hussey et al., 2023). The AVE for each construct was above 0.5, signifying that the measurement items accurately measured the intended underlying constructs (Hair et al., 2019).

Table 4. Inter-correlation between constructs and square roots of AVE.

Construct	FP	CoIG	RCoI	Square Root of AVE
FM-SME performance	1			0.810
CoIG	0.542 **	1		0.750
RCoI	0.665 **	0.510 **	1	0.806
LOY	0.691 **	0.604 **	0.556 **	0.738

** indicates a statistically significant correlation at a 5% level.

shows that the square root of each construct’s AVE was higher than its inter-correlation coefficients with other constructs, thereby supporting divergent validity.

5.3. Measurement Models for the Study Constructs

Measurement models were developed to assess statistical validity and reliability. All the fit indices for firm performance were higher than the cut-off values, as presented in Appendix B. The model fit of LOY was within the acceptable range, as shown in Appendix C. For CoIG, the model fit perfectly with the recommended cut-offs, and similarly, the model for responsiveness indicated an excellent fit, as illustrated in Appendix D and Appendix E. The overall measurement models combining all the study constructs were developed, one without and another with the mediating variable, as shown in Appendix F and Appendix G. The measurement model without the mediator indicated strong relationships between the constructs, with the fit indices satisfying SEM requirements. With the inclusion of the mediator, the overall measurement model also showed an excellent fit, affirming the mediating role of LOY.

5.4. Assumptions of the SEM

Various assumptions, including linearity, normality of residuals, multivariate normality, and absence of multicollinearity, were tested and met. In particular, the skewness and kurtosis statistics were within the acceptable limits (±2 for skewness, ±7 for kurtosis), confirming that multivariate normality was met (Hair et al., 2019). There was no multicollinearity since the tolerance levels exceeded 0.1 and the VIF values were less than 10 (Hair et al., 2019). The model overall summary statistics (R-squared and F-tests) were examined, which indicated that the amount of variation in FM-SME performance accounted for by RCoI, CoIG, and LOY was approximately 60 per cent, as presented in

Table 5. Model summary statistics on R-squared values.

Model	R	R Square	Adjusted R Square	Std. Error of the Estimate
1	0.776 a	0.601	0.598	0.41699

^a Predictors: (Constant), LOY, RIC, CoIG.

.

5.5. Statistical Analysis of Hypotheses

Figure 2 depicts the structural model for the direct effects of predictor variables on FM-SME performance before considering the effect of LOY, while Figure 3 shows the direct effect when LOY is included. As depicted in Figure 3, the model fit indices met the recommended thresholds, signifying an acceptable fit of the data to the model. Thus, both the direct and mediated paths of CoIG and RCoI on FM-SME performance were statistically significant and reliable. This validates the significance of CoIG and RCoI as predictors of FM-SME performance and highlights the importance of considering mediation effects in future studies.

5.5.1. Direct Effects of CoIG and RCoI on FM-SME Performance Excluding Customer Loyalty

As shown in

Table 6. Analysis of the direct effects of CoIG and RCoI on FM-SME performance excluding LOY.

Path	Estimate	S.E.	C.R.	p-Value	Label
FP ← CoIG	0.204	0.047	4.365	<0.001	par_27
FP ← RCoI	0.437	0.043	10.078	<0.001	par_26

, CoIG had a substantial positive effect on FM-SME performance (β = 0.204; p < 0.001). The results revealed that SMEs’ awareness of competitors’ strategies, strengths, and weaknesses assists them in making informed strategic decisions to position themselves better in the market. RCoI was also positively associated with FM-SME performance (β = 0.437; p < 0.001). This indicates that the responsiveness of FM-SMEs to competitive pressure is a key predictor of their survival. This emphasises the need for not only collecting competitive information but also acting on it strategically to prevent loss or increase in market share.

5.5.2. Direct Effects Including Customer Loyalty

Table 7. Analysis of direct effect after including LOY.

Hypotheses	Paths			Estimate	S.E.	C.R.	p	Label
H3	LOY.	←	CoIG.	0.412	0.055	7.470	<0.001	par_25
H4	LOY.	←	RCoI.	0.274	0.043	6.440	<0.001	par_27
H2	FP.	←	RCoI.	0.280	0.038	7.416	<0.001	par_18
H1	FP.	←	CoIG.	0.000	0.046	0.009	0.993	par_19
H5	FP.	←	LOY.	0.513	0.067	7.686	<0.001	par_24

shows that CoIG had a non-significant direct effect on FM-SME performance when LOY was incorporated into the model (β = 0.000, p = 0.993). This indicates that the generation of competitor intelligence alone does not directly translate into firm performance. Therefore, H1 is not supported. The non-significant direct effect suggests that CoIG operates entirely through an indirect pathway; its effects on performance are fully realised only through the mediating influence of customer loyalty (LOY).

The results are in line with DCT, which emphasises capturing opportunities not only by sensing them but also by seizing them through the efficient development of both internal and external competencies. However, the results of this study differ from those of previous studies by Puspaningrum (2020) and Hendar et al. (2020), which established that CoIG has a direct positive influence on FM-SME performance. Thus, this study reveals a more complex impact of CoIG on FM-SME performance.

Nevertheless, the direct effect of RCoI on FM-SME performance was significant and positive, despite controlling for LOY (β = 0.280, p < 0.001). These findings support H₂, suggesting that the effective and creative use of competitor intelligence allows firms to adjust their strategies, thereby enhancing their performance. The alignment with H₂ enriches DCT by emphasising that responsiveness, as an essential capability, can exert its effect directly on performance. Contrary to the full mediation in H₁, this direct effect of RCoI implies that it could have an immediate impact on performance. These observations are in line with the study by Ouma et al. (2022), who found that a firm’s capability to make effective use of information about competitors is an important ability necessary to improve performance.

The results also revealed a significant positive association between CoIG and LOY (β = 0.412, p < 0.001). This implies that when FM-SMEs are more familiar with competitors, they have options to customise their offerings and eventually create loyalty. Similarly, RCoI was positively and significantly related to LOY (β = 0.274, p < 0.001). In other words, competitive intelligence can lead to adaptive behaviours against competitors, which provide firms with a competitive advantage through price adjustment and marketing strategies to build customer satisfaction and loyalty.

The results provide strong support for H3 and H4 regarding the effects of CoIG and RCoI on LOY. The results also reinforce the dynamic capabilities view that LOY are built and sustained by both sensing (in intelligence generation) and seizing activities (i.e., responsive actions). The observations are in accordance with those of Boonmalert et al. (2020) and Khan and Ghouri (2018), which established that competitor intelligence works towards improving LOY. H₅ proposed that LOY has a significant effect on FM-SME performance. This was further supported, as the study indicated that FM-SME performance is significantly positive to LOY (β = 0.513, p < 0.001). This implies that LOY not only keeps customers coming back but also drives stronger performance. These results are consistent with those of studies by Ismail (2023) and Sampaio et al. (2020), which confirmed that LOY is not only good for customer retention but also for business success.

5.5.3. Analysis of the Mediating Effect of Customer Loyalty

The results in

Table 8. Effect of RCoI and CoIG on LOY.

Model Summary
R	R-sq	MSE	F	df1	df2	p
0.6692	0.4478	0.3683	140.7121	2.0000	347.0000	<0.001
Model
	Coeff.	se	t	p	LLCI	ULCI
Constant	1.2866	0.1556	8.2670	<0.001	0.9805	1.5927
RCoI	0.2764	0.0382	7.2288	<0.001	0.2012	0.3517
CoIG	0.4064	0.0435	9.3337	<0.001	0.3208	0.4921

show that both CoIG and RCoI significantly influenced LOY, explaining 44.78% of its variance. Both effects were statistically significant, suggesting that intelligence generation and responsive practices are key methods for increasing LOY. However, the coefficients of 0.4064 for CoIG and 0.2764 for RCoI indicate that firms’ efforts to generate competitor intelligence are more effective in driving LOY.

Table 9. Effect of RCoI, CoIG and LOY on FM-SME performance.

Model Summary
R	R-sq	MSE	F	df1	df2	p
0.7755	0.6014	0.1739	174.0342	3.0000	346.0000	< 0.001
Model
	Coeff.	se	t	p	LLCI	ULCI
Constant	1.2576	0.1170	10.749	<0.001	1.0275	1.4877
RCoI	0.2526	0.0282	8.963	<0.001	0.1972	0.3081
LOY	0.3498	0.0369	9.484	<0.001	0.2773	0.4224
CoIG	0.0662	0.0339	1.9528	0.0514	0.0004	0.1320

presents the results of the direct effect analysis of RCoI, CoIG, and LOY on FM-SME performance. The model’s R-squared value indicates that the three variables together explain approximately 60% of the variance in FM-SME performance. The effects of RCoI and LOY on FM-SME performance were statistically significant at the 1% level. Specifically, a one-unit increase in RCoI is associated with a 0.2526 increase in FM-SME performance, while for every one-unit increase in LOY, there is a 0.3498 increase in FM-SME performance. However, CoIG did not achieve statistical significance at the conventional 5% level (p = 0.0514, coefficient = 0.0662). This confirms that CoIG has no significant direct effect on FM-SME performance, reinforcing the conclusion that its influence operates indirectly through LOY rather than directly on performance.

Finally, the direct and indirect effects were tested to determine how CoIG and RCoI affected the performance of FM-SMEs through the mediation of LOY, as shown in

Table 10. Analysis of direct and indirect effects of CoIG on FM-SME performance.

Direct Effect
Effect	SE	t	p	LLCI	ULCI
0.0648	0.0332	1.949	0.0521	−0.0006	0.1301
Indirect Effect(s) via Mediator
Mediator	Effect	BootSE	BootLLCI	BootULCI
LOY	0.1436	0.0193	0.1063	0.1827

and

Table 11. Analysis of direct and indirect effects of RCoI on FM-SME performance.

Direct Effect
Effect	SE	t	p	LLCI	ULCI
0.2526	0.0282	8.9631	<0.001	0.1972	0.3081
Indirect Effect(s) via Mediator
Mediator	Effect	BootSE	BootLLCI	BootULCI
LOY	0.0967	0.0192	0.0627	0.1380

, respectively.

Bootstrapping analysis confirmed that LOY mediated the effect of both CoIG and RCoI on FM-SME performance. The indirect effect of CoIG through LOY was significant at 0.1436, with a bootstrap standard error of 0.0193. The confidence interval ranged from 0.1063 to 0.1827. The indirect effect of RCoI through LOY was significant at 0.0967, with a bootstrap standard error of 0.0192. The bootstrap confidence intervals ranged from 0.0627 to 0.1380 and did not cross zero, confirming the significance of the mediation.

The results revealed that LOY fully mediated the link between CoIG and FM-SME performance. This was indicated by the significant mediating effect through LOY, whereas the direct impact of CoIG on FM-SME performance dropped to non-significant when considering loyalty. This full mediation implies that the CoIG benefit is captured mainly via the firm’s ability to nurture LOY. In addition, LOY partially mediated the relationship between RCoI and FM-SME performance. This seems to indicate that RCoI has a direct impact on performance and an indirect impact on performance through strengthening loyalty to provide a competitive advantage via an alternative path.

6. Conclusions, Implications and Limitations

6.1. Conclusions

Taken together, it is concluded that the mere generation of competitor intelligence does not result in better performance unless firms deploy strategies to enhance LOY. This study highlights the significance of sensing and seizing competitive opportunities in the market.

6.2. Theoretical Contributions

This study makes several theoretical contributions to the literature on competitor orientation and SME performance. First, by decomposing competitor orientation into its constituent dimensions (CoIG and RCoI) rather than treating it as a unidimensional construct, this study reveals differential effects that would otherwise be masked in an aggregate analysis. Specifically, the finding that CoIG has no significant direct effect on FM-SME performance while RCoI exerts a significant direct effect advances our understanding of how different facets of competitor orientation operate through distinct causal pathways. Second, this study extends the Dynamic Capabilities Theory by demonstrating that sensing capabilities (CoIG) require the complementary mechanism of customer loyalty to translate into performance outcomes, whereas seizing capabilities (RCoI) can influence performance both directly and indirectly. Third, by situating the investigation within the context of Tanzanian FM-SMEs, this study provides empirical evidence from a developing country, resource-constrained setting where competitive dynamics differ markedly from those in developed economies.

6.3. Practical Implications

The findings carry important practical implications for FM-SME owners, managers, and policymakers. First, FM-SMEs should not merely collect competitor intelligence but also translate it into customer-centric strategies that build and sustain loyalty. This means using competitive insights to improve product design and features, adjust pricing, and enhance customer service. Second, given the significant direct effect of RCoI on performance, FM-SMEs should develop responsive capabilities that enable them to act swiftly on competitor intelligence, for example, by adapting their product offerings, entering new market segments, or forming strategic partnerships. Third, FM-SMEs should invest in customer relationship management to foster repeat purchases, positive word-of-mouth, and customer forgiveness, which collectively strengthen loyalty and drive sustainable performance. Furthermore, FM-SMEs should engage in collaborative networks to share insights on market trends and coordinate resources to enhance customer satisfaction. The Ministry of Industry and Trade should support FM-SMEs by offering subsidies for technology adoption and facilitating workshops on competitor-oriented practices and customer relationship management.

6.4. Limitations and Future Research Directions

Several limitations should be acknowledged when interpreting these findings. First, the study employed a cross-sectional design, which precludes causal inferences regarding the relationship between competitor orientation, customer loyalty, and firm performance. Longitudinal studies would provide stronger evidence of causality and reveal how these relationships evolve over time. Second, all the constructs were measured using the self-reported perceptions of enterprise owners/managers, which may introduce common method bias. In particular, customer loyalty was assessed from the owner’s perspective rather than from actual customers, which may not fully capture the true extent of customer loyalty behaviour. Future research should collect customer loyalty data directly from customers to enhance construct validity. Third, the study relied on a 2015 sampling frame from the National Bureau of Statistics, which, despite field verification, may not perfectly reflect the current FM-SME population. Fourth, the study did not include control variables such as firm size, age, or financial leverage, which have been shown to influence SME performance (Botta, 2019). Future studies should incorporate these variables to provide a more nuanced understanding of the relationships. Fifth, the findings are contextually bounded to furniture manufacturing SMEs in Dar es Salaam, Tanzania, and may not be generalisable to other sectors, regions, or countries. Future research should replicate this study in other industries and geographical contexts to enhance the findings’ external validity. Finally, future studies could examine additional mediating or moderating variables, such as innovation capability, digital technology adoption, and environmental dynamism, to further unpack the competitor orientation–performance nexus.