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.

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.

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 ).

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 ).

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.

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.

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 .

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.

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 .

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 ).

A correlation analysis was carried out to examine statistical relationships among the variables, with the findings as detailed in the subsequent ( Table 3 ).

**. 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.

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 .

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.

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.