Analysis of the Relationship between Organizational Performance, Marketing Orientation and Learning Orientation ()
1. Introduction
The central objective for both emerging and established nations is to bolster their economies and accelerate progress. Achieving this requires the optimal use of available resources and the adoption of strategies to maximize employment opportunities. Small and medium-sized enterprises (SMEs) are widely regarded as the cornerstone of global economic advancement, and their growth through well-designed policies remains a top priority for fostering national prosperity.
Investing in SME development significantly enhances a country’s competitiveness, spurs investment, drives innovation, facilitates technology integration, generates employment, and ultimately helps alleviate unemployment and poverty. These position SMEs as pivotal contributors to socio-economic advancement. However, in the current fast-paced and competitive business landscape, SMEs often grapple with constraints stemming from their limited scale and resources. These challenges impede their capacity to devise effective strategies, manage resources efficiently, and respond to shifting market dynamics. Additionally, restricted access to critical information hampers their decision-making processes and limits growth potential.
SMEs constitute a critical segment of the business ecosystem, prompting many governments to embed policies for their support and expansion within national frameworks (Bank of Mongolia, 2011). Data from the World Trade Organization highlights that SMEs represent 90% of businesses worldwide, account for 60% - 70% of jobs, and contribute 55% to GDP in advanced economies. In the Asia-Pacific region, they comprise approximately 95% of enterprises, provide 20% - 75% of employment, and generate 16% - 60% of GDP. Globally, SMEs are celebrated as engines of job creation and economic vitality, significantly shaping international economic growth.
In Mongolia, SMEs represent 75.7% of all businesses and supply 72% of employment, yet their contribution to GDP stands at just 5.5% (NSO, 2023). This disparity underscores the urgent need to strengthen the SME sector in the country. The World Bank projects that by 2030, global labor demand will necessitate 600 million new jobs, a target that nations can address by prioritizing SME growth.
Aligned with this global outlook, Mongolia’s long-term roadmap—Vision 2050—emphasizes the promotion of value-added industries, large-scale strategic projects, SMEs, and knowledge-driven creative sectors as key pillars of economic development (Parliament of Mongolia, 2020). As such, SMEs are instrumental in boosting economic expansion, reducing poverty, fostering innovation, and creating jobs (NSO, 2023).
Given this context, examining the connection between SME performance and their marketing orientation and learning orientation (reflected in training activities) is essential for enhancing organizational competitiveness and outcomes. This study aims to examine the relationship between the performance of SMEs in Mongolia and their marketing and learning orientations.
2. Conceptual Approach
2.1. Organizational Performance
The Fourth Industrial Revolution has placed immense pressure on top executives, SME entrepreneurs, and strategists to pinpoint critical elements that enable organizations to navigate rapid transformations, reforms, and obstacles. Strategic orientation is widely acknowledged as a cornerstone for securing a competitive edge and driving superior organizational performance (Davis & Aggarwal, 2020). According to the resource-based theory, this orientation serves as an intangible resource that bolsters organizational outcomes by improving access to essential assets, expertise, markets, and cutting-edge digital tools. Contemporary studies on strategy emphasize that intangible resources and knowledge stimulate proactive thinking, enabling firms to assess competitors’ approaches and build lasting competitive strengths. In times of crisis, organizations with strong collaboration, access to reliable information, and advanced technological capabilities demonstrate enhanced learning orientation and adaptability (Powell et al., 1996). The COVID-19 pandemic, for instance, inflicted severe setbacks on the global economy, with SMEs facing significant difficulties in sustaining operations due to constrained financial reserves (Kurniawan et al., 2023; Charoensukmongkol, 2022). Worldwide, policymakers now view strategic orientation and innovation as vital for meeting developmental targets (Lückenbach et al., 2019). To boost organizational performance, strategies such as innovation catch-up and innovation-driven growth have been recommended (Dombrowski et al., 2018). Orientations like entrepreneurial, market, and learning orientation equip firms with access to innovation-related insights and the ability to shape governmental policies in their favor (Alkahtani et al., 2020; Moreira et al., 2022). Moreover, the use of financial incentives and state-of-the-art technologies fosters creativity and strengthens organizational capabilities (Vaitoonkiat & Charoensukmongkol, 2020). In essence, strategic orientation is indispensable for overcoming market hurdles, achieving a competitive advantage, and elevating performance (Seran & Bez, 2021). However, this hinges on policymakers prioritizing innovation and providing the information needed to cultivate open-mindedness and capacity-building. Scholars have tied organizational performance measurement to various dimensions: strategy Clement et al., (2021), Maisel (2001), Gates (2000), Morgan (2004), Ittner, Larcker, & Randal (2003), Neely et al., (1997), Tangen (2004) operational efficiency Otley (2002), Bititci, Carrie & McDevitt (1997), Forza & Salvador (2000), Bourne et al. (2003) and planning, control, decision-making, and performance oversight. Extensive research also highlights a clear connection between organizational growth, performance, and the alignment of entrepreneurial focus with success (Clement et al., 2021; Adam et al., 2022; Mintah et al., 2022) According to the research conducted by Inthavong et al. (2023) use the non-financial indicators of performance are internal customer/employee satisfaction, external customer satisfaction, environmental performance, and social performance. Their research argued that non-financial indicators are as important as financial performance, they stand for financial performance (Inthavong et al., 2023). Our research only considers the non-financial performance of the organizations.
2.2. Marketing Orientation
Marketing orientation is characterized as a strategic approach rooted in an organization’s culture, geared toward addressing customer demands (Kohli & Jaworski, 1990; Narver & Slater, 1990). Research has demonstrated its positive impact on organizations with evidence showing it enhances performance at both industry and firm levels (Narver & Slater, 1990). The foundational models by Kohli & Jaworski, (1990) and Narver and Slater (1990) remain the most prominent in outlining its core traits. Narver and Slater (1990) devised the MKTOR scale to measure marketing orientation, focusing on its psychometric evaluation and identifying three key elements: customer orientation, competitor orientation, and inter-functional coordination. Narver and Slater (1990) devised the MKTOR scale to measure marketing orientation, focusing on its psychometric evaluation and identifying three key elements: customer orientation, competitor orientation, and inter-functional coordination.
Customer orientation involves grasping the present and future needs of target markets, allowing firms to consistently deliver exceptional products and services.
Competitor orientation entails analyzing competitors’ immediate strengths and weaknesses, as well as their long-term plans and capabilities.
Inter-functional coordination refers to harmonizing resources across departments to provide outstanding value to customers (Narver & Slater, 1990).
Effective coordination across functions fosters knowledge exchange, organizational learning, and improved teamwork between units, all of which amplify marketing orientation.
According to the research conducted by Hollensen & Saeidi (2023) entrepreneurial marketing and business performance. Their study supported that given the high level of uncertainty in the current market, entrepreneurial marketing has been shown to have the potential to enhance organizational performance (Hollensen & Saeidi, 2023).
2.3. Learning Orientation
Research in strategic management increasingly underscores learning orientation as a critical factor for gaining a competitive edge and improving organizational performance (Omar et al., 2019). It accelerates knowledge generation within firms and serves as a mechanism for boosting long-term financial results by uncovering new insights and opportunities (Santos-Vijande et al., 2022). It accelerates knowledge generation within firms and serves as a mechanism for boosting long-term financial results by uncovering new insights and opportunities. Companies embracing learning orientation cultivate a culture that refines existing knowledge and values, encouraging employees to integrate their beliefs, frameworks, and assumptions to devise innovative solutions (Al-Shami et al., 2022). This orientation spans multiple levels—individual employees, teams, the organization, its engagement with a dynamic external context, and the results staff are tasked to achieve (Cheun et al., 2012). For learning orientation to thrive, systems must supply and share accurate, supportive data (Migdadi, 2021). Studies highlight that firms prioritizing this approach shape cognitive frameworks, promote a cohesive organizational vision, emphasize commitment to learning, and encourage creative problem-solving (Sinkula, Baker, & Noordewier, 1997; Özşahin et al., 2011). Organizational learning positively influences organizational non-financial performance as collective organizational learning is disseminated among employees to cope with the customers’ rapidly changing environment and demands (Inthavong et al., 2023).
3. Research Methodology
This study adopted the framework by Narver and Slater (1990) to evaluate marketing orientation, utilizing 15 questions divided into three categories: customer orientation, competitor orientation, and inter-functional coordination.
To assess training activities, reflecting learning orientation, the methodology from Sinkula, Baker, & Noordewier (1997) was employed, featuring 14 questions organized into three dimensions: commitment to learning, open-mindedness, and shared vision.
Organizational performance was measured using four questions adapted from Agarwal et al. (2003), focusing on indicators such as customer and employee satisfaction, profit growth, and market share.
As shown in Figure 1, a research model combining these factors was developed.
Figure 1. Research model.
3.1. Research Sample
In Mongolia, the total number of small and medium-sized enterprises (SMEs) with up to 199 employees stands at 76,561. Using a 95.0% confidence level and a 5.0% margin of error, the ideal sample size was determined to be 383. However, the survey included responses from 390 organizations, and this sample was used for the subsequent data analysis.
3.2. Research Methodology and Design
The survey comprised 33 questions, drawing on established frameworks: Sinkula, Baker, & Noordewier (1997) for assessing learning orientation, Agarwal et al. (2003) for evaluating organizational performance, and Narver & Slater (1990) for examining marketing orientation. Responses were measured on a 5-point Likert scale, ranging from 1 (“strongly disagree”) to 5 (“strongly agree”). The selection of variables relied on well-established and internationally validated measurement items. Data analysis was performed using the statistical software SPSS 24 and SmartPLS 4.1.0.1.
4. Results of the Research
4.1. Participant Statistics
The majority of the surveyed organizations were funded domestically. In terms of operational experience, those active for over six years represented the largest portion, accounting for 65.4% of the sample. Regarding financial performance, most participants reported annual revenues surpassing 1.0 billion MNT, as detailed in Table 1.
Table 1. SME characteristics.
Factor |
Frequency |
(%) |
Factor |
Frequency |
(%) |
Investment |
Foreign |
8 |
2.1 |
Annual Income (mm.mnt) |
<300.0 |
92 |
23.6 |
Domestic |
328 |
84.1 |
300.0 - 999.9 |
57 |
14.6 |
Joint Venture |
54 |
13.8 |
>1000.0 |
241 |
61.8 |
Position |
Chairman of the Board |
48 |
12.3 |
Years of
Operations |
Up to 1 year |
25 |
6.4 |
CEO |
90 |
23.1 |
1 - 5 years |
110 |
28.2 |
Middle Management |
86 |
22.1 |
6 - 10 years |
122 |
31.3 |
Lower Management |
166 |
42.6 |
Over 10 years |
133 |
34.1 |
Firm size |
1 - 9 employees |
70 |
17.6 |
Type |
LLC |
170 |
42.7 |
10 - 49 employees |
55 |
13.8 |
Stock company |
130 |
32.7 |
50 - 199 employees |
67 |
16.8 |
Cooperative |
12 |
3.0 |
More than 200 |
206 |
51.8 |
Others |
86 |
21.6 |
Source: From the results of a questionnaire survey.
The bulk of the study’s respondents belonged to the lower and middle management tiers, comprising 64.7% of the total. When grouped by industry, the trade sector dominated with 33.8% of the participating organizations, followed by other service sectors at 21.8%, and manufacturing at 13.6%. The hotel, accommodation, and food service sector had the smallest representation, making up just 2.6% of the sample. 31.4 percent of the organizations included in the study were small businesses with up to 50 employees, while 68.6 percent were medium businesses with more than 51 employees.
4.2. Reliability Analysis
To explore the relationships among variables, a factor loading analysis was performed. Furthermore, reliability analysis such as Cronbach’s alpha, Composite Reliability (CR) and Average Variance Extracted (AVE) were computed. The outcomes of this analysis are summarized in Table 2.
Table 2. Results of the reliability analysis of the factors.
N˚ |
Variables |
Measurements |
Factor loading |
Cronbach’sα |
AVE |
CR |
Mean |
1 |
Learning orientation |
14 |
0.872 - 0.939 |
0.902 |
0.836 |
0.939 |
3.903 |
2 |
Marketing orientation |
15 |
0.874 - 0.925 |
0.892 |
0.823 |
0.933 |
3.887 |
3 |
Organizational performance |
4 |
0.852 - 0.881 |
0.892 |
0.755 |
0.925 |
3.786 |
Sources: Authors’ Elaboration.
The reliability analysis results indicate that the survey questions used to assess the factors exhibit consistency within an acceptable range. Cronbach’s alpha, employed to measure the internal consistency of the factors, ranged from 0.892 to 0.902. These values surpass the recommended threshold of 0.70 (Nunnally, 1978), reflecting a high degree of reliability across all factors.
The Composite Reliability (CR) scores, varying between 0.925 and 0.939, also exceed the minimum acceptable level of 0.70 (Hair et al., 2019), reinforcing the robust reliability of the factors.
Moreover, the Average Variance Extracted (AVE) values, which fell between 0.755 and 0.836, surpassed the benchmark of 0.50 for each latent variable. This confirms both practical significance and discriminator validity (Hair et al., 2019).
4.3. Correlation Analysis of the Factors
A correlation analysis was carried out to examine statistical relationships among the variables, with the findings as detailed in the subsequent (Table 3).
Table 3. Correlation analysis of the factors.
N˚ |
Variables |
Learning orientation |
Marketing orientation |
Organizational performance |
1 |
Learning orientation |
1.000 |
|
|
2 |
Marketing orientation |
0.861 |
1.000 |
|
3 |
Organizational performance |
0.788 |
0.839 |
1.000 |
**. Correlation is significant at the 0.01 level (2-tailed) (Sources: Authors’ Elaboration).
The correlation analysis demonstrated that the organizational performance of the surveyed enterprises is strongly and positively related to both marketing orientation (r = 0.839, p < 0.01) and learning orientation (r = 0.788, p < 0.01). This suggests that enhancements in marketing orientation led to improved performance, and similarly, greater engagement in learning orientation is associated with better performance outcomes.
Additionally, a strong positive correlation was observed between learning orientation and marketing orientation (r = 0.861, p < 0.01). This indicates that as organizations adopt a more learning-focused approach, their marketing efforts become increasingly dynamic and effective.
4.4. Structural Equation Modeling (SEM)
To assess the structural model and the significance of the variables, R-squared (R2) values were computed using Partial Least Squares (PLS) analysis. The analysis explored the connections between independent and dependent variables, confirming all proposed hypotheses. The regression coefficients exhibited statistically significant positive values, with the findings as detailed in Table 4.
Table 4. Results of PLS-SEM.
N˚ |
Hypothesis |
Path |
Standardized Coefficients Beta |
Std.
Error |
t |
Sig. |
Remarks |
1 |
H1 |
LO-OP |
0.405 |
0.060 |
7.310 |
0.000 |
Supported |
2 |
H2 |
MO-OP |
0.436 |
0.057 |
7.948 |
0.000 |
Supported |
3 |
Н3 |
МO-LO |
0.532 |
0.0125 |
8.321 |
0.000 |
Supported |
Note: ***: p < 0.001; **: p < 0.01; *: p < 0.05, significance levels are two-tailed Variable definition: LO, Learning orientation; MO, marketing orientation; OP, Organization performance (Sources: Authors’ Elaboration).
After confirming the accuracy and reliability of the model, the PLS and Bootstrapping algorithms were executed to test the hypothesized relationships.
As illustrated in Figure 2, the study’s results validated all proposed hypotheses. The findings reveal that both marketing orientation and learning orientation exert a significant positive impact on organizational performance, with path coefficients of β = 0.788 and β = 0.839, respectively. The coefficient determination (R2) for organizational performance stands at 0.721, indicating that these factors account for 72.1% of the variance in performance, with the remaining portion attributed to variables outside the model.
Figure 2. PLS-SEM estimates of the research model (Sources: Authors’ Elaboration).
Additionally, marketing orientation strongly influences learning orientation, evidenced by a path coefficient of β = 0.861. The R2 value for learning orientation is 0.740, suggesting that 74.0% of its variance is explained by the model, while the rest is due to unexamined factors.
5. Conclusion
This research examined the relationship between organizational performance, marketing and learning operations. The study collected data through a questionnaire from a random sample of 390 small and medium-size enterprises in Mongolia. Factor analyses, variables’ reliability, correlation analysis between factors conducted, and the results generated using Structural Equation Modeling (SEM). The questionnaire survey was conducted online between January 17, 2025, and March 10, 2025. In this research, organizational performance was measured using four questions developed by Agarwal et al. (2003). Marketing orientations were assessed with fifteen questions created by Narver and Slater (1990) while learning orientations were evaluated using fourteen questions from Sinkula, Baker, & Noordewier (1997). The results confirmed all the proposed hypotheses. Correlation analysis indicated that the performance of the participating organizations had strong positive correlations with both marketing orientation (r = 0.839) and learning orientation (r = 0.788, p < 0.01). In other words, as an organization’s marketing orientations improve, its performance also improves. The results demonstrate that as organizations engage in learning, their performance increases. Furthermore, learning orientation strongly and positively correlates with marketing orientation (r = 0.861), indicating that their marketing activities become more dynamic as organizations learn. The research findings reveal that marketing and learning orientations strongly correlate with organizational performance and are important factors in enhancing it.
The research findings show that marketing and learning orientations strongly correlate positively with organizational performance. Therefore, improving marketing orientation while also cultivating a learning organization culture will improve organizational performance. In our case, we focused on organizations’ non-financial performance improvements, and the execution of strategic plans increased, making a substantial contribution to organizational development.