The Impact of Logistical Competences on Organizational Performance in Small and Medium Enterprises Moderated by Competitive Advantages in Social Media Campaigns

Abstract

Organizational performance defines how well an organization achieves its goals and objectives. To fulfill these, the organization should improve its logistical competencies including delivery speed, order accuracy, and returns handling. At the same time, social media plays an important role. Therefore, the main objective of this study is to examine and research the influence of logistical competence on the performance of small and medium enterprises (SMEs) with the moderating effect of the competitive advantages of social media. We used a quantitative, descriptive, cause–effect, and cross-sectional approach to actualize this research. A non-probability convenience sampling method was used as it is cost-effective, practical, easy to access, and time-efficient. The main variables, such as delivery speed, order accuracy, and returns handling, were analyzed to determine their influence on organizational performance. A total of 163 respondents participated, ranging from middle to top management employees in SMEs, specifically in Jordan, who completed a structured Google form. Simple, multiple, and hierarchical regression were used to check the hypotheses in this research. The conclusion shows that logistical competence positively affects organizational performance, with competitive advantages in social media campaigns enhancing this effect significantly; this was evident as social media campaigns emerged as an essential platform for marketing logistical strengths and boosting customer engagement. This study and research give recommendations for SMEs to integrate logistics and E-marketing strategies properly. Regarding the study limitations, we see that the regional focus and the small sample size are acknowledged. In the future, research is highly encouraged which looks into industry-specific dynamics, advancing technologies, and cross-cultural contexts. This research bridges the gap between logistics and marketing, thus showcasing a framework promoting logistical competence to gain a competitive advantage in the SME market.

1. Introduction

This study and research seek to investigate how logistical competence, including essential variables such as delivery speed, order accuracy, and returns handling, affects organizational performance in small and medium enterprises (SMEs), focusing on the moderating variable of social media campaigns in strengthening the competitive advantage of these logistics’ competencies. Most usually require further resources and reach to compete with larger companies. Although logistical competency is integral to organizational success, SMEs need to utilize this as a marketing tool. As mentioned earlier, they may need more successful and focused strategies to communicate and show their logistical strengths to customers [1,2]. This research revolved around the broad problem of how SMEs can use logistical competencies to enhance and encourage organizational performance, including a sub-problem of understanding how social media campaigns can be used to enhance these logistical strengths. Using social media for marketing logistics in SMEs still needs to be fully utilized, as not doing so may lead to missing out on potential competitive advantages [3,4]. Also, looking at the moderate variable, we see that [5] demonstrated that social media moderates the relationship between operational competency and positive organizational outcomes, thus highlighting the strategic significance of marketing logistics competencies. Social media has replaced human resources when it comes to improving organizational performance to reach organizational goals and objectives.

In addition, Ref. [6] emphasized that social media campaigns help enhance brand visibility and gain trust, which is very important for SMEs that work in resource-limited markets. Despite this, SMEs face many challenges, such as little technical expertise and poor digital infrastructure, impeding the ability to leverage social media as a competitive resource [7]. Addressing these challenges, this study provides a complete framework for SMEs to integrate logistical competence with social media strategies. This research seeks to respond to the following questions:

• What are the integral parts of logistical competence in SMEs, and how do they affect organizational performance?
• In what ways can social media campaigns that highlight logistical strengths serve as a competitive advantage for SMEs?
• How does the emphasis on logistical competence in social media campaigns moderate the impact of logistical competence on organizational performance in SMEs?
• What strategies can SMEs adopt using social media to emphasize logistical competency to affect organizational performance positively?

The Research Objectives are as follows:

• Identify the parts of logistical competence in SMEs regarding specific delivery speed, order accuracy, and returns handling as important elements that contribute to organizational performance.
• To investigate how social media campaigns that highlight logistical strengths are a moderating factor that improves logistical competence in SMEs’ organizational performance.
• To include recommendations for managers in SMEs on leveraging logistical competence in social media to improve organizational performance.

This research is important as it discusses a critical gap in understanding how logistical competence can be marketed as a competitive advantage to improve organizational performance in small and medium enterprises (SMEs). In a competitive and customer-centric market, SMEs require assistance in matching more prominent entities’ resources. On the other hand, they can promote value by focusing on logistical strengths such as delivery speed, order accuracy, and return handling. Although this can be utilized and has potential, many SMEs need better and more effective strategies to communicate these logistics competencies to their customers. Social media is becoming more of an accessible and impactful platform for marketing these strengths; the strategic use of content focusing on logistics in social media campaigns remains underexplored. The theoretical importance of this research lies in its attempt to bridge logistics and marketing (in specific social media campaigns) disciplines. It offers a framework for grasping how logistics can improve competitive positioning when marketed appropriately. This has the potential of expanding on the theoretical understanding between logistical competence and digital marketing in the SME context, and this paper serves as an introduction to how logistics can be used as a marketing tool in social media campaigns and positively affect organizational performance. From a practical perspective, this research brings forward actionable strategies for SME managers and marketers to apply logistical competencies integrated with digital marketing efforts that equip SMEs with practical methods to use social media to gain a competitive advantage and encourage growth.

2. Literature Review and Hypotheses Development

This literature review investigates the impact of logistical competence on organizational performance in SMEs, focusing on the moderating role of social media in amplifying the visibility and competitive advantage of these logistics’ capabilities.

Logistical Competence and Organizational Performance in SMEs

From an organizational perspective, logistical competence is usually defined as the strength to carry out logistics operations aligned with strategic business goals. It comprises diverse transportation, inventory, distribution, and warehousing systems. A study of the [8] shows its basis in regulating materials management strategies with business objectives; this is very important for the growth of sales and increasing customer satisfaction. This also helps with easier decision-making across the supply chain.

Adaptability is a crucial aspect of logistical competence, for example, through advanced logistics technologies. The work of [9] indicates that technologies like real-time tracking and automated inventory systems play an important role in increasing operational efficiency. Integrating the previously mentioned tools assists firms in improving delivery speed, order accuracy, and responsiveness in supply chains. This is, therefore, especially important for small and medium enterprises (SMEs) with tied resources [10].

Organizational performance is usually considered the organization’s ability to accomplish its goals effectively and efficiently [11]. It has been measured through financial metrics, such as profitability, asset return, and market share [12]. Until now, more modern frameworks have updated their definitions to include non-financial metrics in aspects such as customer satisfaction and operational efficiency [13].

From the customer experience perspective, improving interactions at various customer touchpoints has been crucial for maintaining competitive advantage and boosting organizational performance, contributing to value [11]. Aligning logistics processes with customer expectations ensures timely delivery, accurate orders, and better services. Moreover, integrating logistics into organizational goals improves competitive advantage and strengthens performance over the long term [14].

This is also very important in SMEs, as successful SMEs depend on modern advanced technologies to remain competitive. For example, with resources like data analytics and digital solutions, SMEs can position their logistical competence with performance goals [15].

H01: 

There is no statistically significant impact of logistical competence on the organizational performance of SMEs at α ≤ 0.05.

Delivery speed, order accuracy, and returns handling

Delivery speed is a crucial element of logistical competence, significantly having an impact on organizational performance. Studies by [2] have shown that faster delivery times meet customer expectations and increase customer loyalty, an integral part of SMEs’ success. Also, [16] discovered that the promised delivery speed shapes customer expectations and has a strong influence on purchasing decisions. This shows the need for SMEs to have a balance in speedy deliveries. Ref. [17] found that these logistic elements improve supply chain responsiveness in manufacturing firms, especially in developing markets.

Delivery speed is an important part of logistical competence, greatly influencing organizational performance by addressing customer demands for punctual services. Research [18] shows that faster delivery fulfills customer expectations and enhances organizational competitiveness and customer loyalty. The Scania case study sheds light on the order-to-delivery process, which finds that shorter lead times enhance customer satisfaction by aligning logistics with market demands while reducing capital tied up in the supply chain [19].

Order accuracy is integral in improving operational efficiency and customer satisfaction. It is determined as the percentage of orders fulfilled without errors, having the correct products, quantities, and specifications. Better order accuracy rates build customer trust and work on reducing costly mistakes such as refunds, negative reviews, and returns [20].

“Strategies like employee training and implementing robust quality control systems during order fulfillment have proven effective in maintaining accuracy rates above the industry standard of 95%” [21]. Keeping such vast accuracy increases customer loyalty and positions organizations to achieve a competitive advantage in their respective markets.

Akram also agrees that in the context of e-commerce and logistics, accurate order fulfillment ensures that the delivered products match the customer’s expectations regarding description, condition, and quantity. Discrepancies, such as incorrect items or damaged goods, significantly reduce customer trust and lead to dissatisfaction, harming long-term customer relationships and organizational performance [22].

Furthermore, accurate fulfillment “fosters a perception of reliability, encouraging repeat purchases and positive word-of-mouth, both critical for sustained growth” [22]. The integration of advanced technologies, including automated picking systems and predictive analytics, is identified as a best practice for improving order accuracy. These technologies enable proactive error detection and enhance coordination across supply chain stakeholders, ensuring seamless delivery experiences and increased operational resilience [22].

Order accuracy is a crucial component of logistical competence, directly influencing organizational performance by ensuring customer expectations are consistently met. Accurate order fulfillment reduces operational inefficiencies, such as returns, refunds, and corrective actions, which are costly and detrimental to performance. Ref. [23] emphasize that maintaining high order accuracy fosters customer satisfaction and loyalty by building trust and reliability in service delivery, which is essential for SMEs operating in competitive markets [24]. Accurate orders improve organizational performance by aligning supply chain operations and reducing disruptions due to order errors, increasing financial outcomes, and customer retention.

Handling returns effectively is key for developing customer loyalty and significantly impacting organizational performance. Ref. [25] have shown that returns management, when grouped with marketing and operational goals, brings about significant customer value through improving customer satisfaction and retention. It is also emphasized that correct return processes can turn a potentially harmful experience into a positive one, thus gaining strategic significance, primarily for companies that aim to distinguish themselves through service quality. Ref. [26] also highlights that return processes improve customer satisfaction and reduce churn rates, especially for SMEs aiming to build long-term customer relationships and offer excellent customer service, integrating reverse logistics (RL) with returns strategies supported by good inspection, collection, and redistribution, to ensure remanufactured, reused, or recycled products. These activities reduce waste and improve environmental and economic performance, which are important for a sustainability-focused market [27,28]. Also, using updated technologies, such as IoT and RFID, ensures quality control, helps with the accurate tracking of returned products, and increases customer satisfaction [27].

Accurate returns handling within reverse logistics improves organizational performance. Reverse logistics includes handling returned products, remanufacturing, recycling, and reducing costs [29]. SMEs usually have resource limitations, but efficient returns control lowers costs and enhances customer satisfaction, therefore improving organizational performance. Supply chain partner cooperation is a key to achieving planned outcomes. As [30] stated, partnerships improve the planning and execution of activities, minimize disruptions, and create a competitive advantage.

H01.1: 

There is no statistically significant impact of delivery speed on the organizational performance of SMEs at α ≤ 0.05.

H01.2: 

There is no statistically significant impact of order accuracy on the organizational performance of SMEs at α ≤ 0.05.

H01.3: 

There is no statistically significant impact of effective returns handling on the organizational performance of SMEs at α ≤ 0.05.

Logistical Competence, Organizational Performance, and Competitive Advantages in Social Media Campaigns

Social media platforms affect behavior due to information printed on products/services [31], and social media can moderate the effect of logistical practices on organizational performance. According to [4], social media plays a dual role in the promotion mix, supporting communication and enhancing engagement. Social media campaigns improve logistical competencies by speeding delivery and returns to create a competitive advantage and also improve customer perceptions and engagement. Refs. [3,32] said that social media improves brand loyalty and customer satisfaction through logistics.

In their research, Ref. [33] clarify that social media plays a crucial role in the relationship between logistics suppliers with customers, which improves engagement and creates a competitive advantage. It offers an opportunity to communicate with consumers and is a company’s socialization.

According to [34], social media helps SMEs to use customer feedback to enhance service quality. Ref. [35] concluded that customer feedback improves customer loyalty and brand image. Social media helps to localize the company’s campaigns, which consider local culture, and it personalizes messages according to customers’ preferences [36]. Finally, social media is an important tool for improving the relationship between logistical competence and performance results. Social media has positively moderated the correlation between operational competency and organizational performance [5].

H02: 

There is no statistically significant moderating role of competitive advantages in social media campaigns in the impact of logistical competence on the organizational performance of SMEs at α ≤ 0.05.

The researcher developed this model based on the following studies: [5,7,37]. As shown in Figure 1.

3. Methodology

This paper uses a quantitative, descriptive, and cause–effect approach to investigate the effect of logistical competence on the organizational performance of SMEs and examines the moderating role of social media campaigns on this effect to create a competitive advantage to improve the organizational performance of SMEs [38]. This research uses a descriptive research design to describe population characteristics [39], which involves 163 respondents (cross-sectional).

The independent variable (Logistical Competence) involves delivery speed, order accuracy, and returns handling, which is measured in 12 paragraphs (four for each one); the dependent variable (Organizational Performance) is measured in 5 paragraphs, while the moderating variable (Social Media) is measured in 4 paragraphs. This study utilized the five-point Likert scale, with one indicating ‘strongly disagree’ and five indicating ‘strongly agree,’ following all studies that used the same questionnaire to measure the variables. This is advantageous in social science research because it allows credibility, flexibility, and ease of analysis. Most studies (90%) prefer using a Likert scale with an odd number of response options, such as 5, 7, 9, or 11, with the five-point scale being the most used among researchers [40].

The population of this study consists of 163 managers in SMEs. A non-probability convenience sampling approach was employed; it was chosen due to the cost-effectiveness, the practical ease of reaching many respondents with the available resources, and the limited time constraints, engaging 163 respondents who completed a structured Google form. Determining the size of the sample is essential to ensuring that the outcomes reflect the population under investigation. According to [41,42], there is a widely used table for calculating the sample size for a population of 10 million (which is approximately the population of Jordan). Jordan has been selected because the study is directed to investigate the effect of logistical competence on the organizational performance of SMEs and examines the moderating role of social media campaigns on this effect to create a competitive advantage to improve the organizational performance of SMEs in Jordan. A sample size of at least 384 must be selected for a 95% confidence level and 5% error. Data analysis was conducted using the statistical package SPSS version 29.0.1 for Windows (IBM SPSS Statistics Inc., Chicago, IL, USA) [43].

Quantitative data is statistically summarized using the following descriptive statistics: mean, standard deviation, and categorical data displayed in terms of frequencies and percentages [38]. Concerning the inferential statistics, comparisons between numerical variables were made by analysis of variance (ANOVA) or the Independent Samples. The t-test is typically employed for data that follows a normal distribution, whereas the Kruskal–Wallis H test or Mann–Whitney U test is utilized for non-normally distributed data. Comparing categorical variables was done using graphical charts, like bar charts. Furthermore, correlations between study variables were evaluated for variables. In this study, the p-value is considered significant when it is lower than the adopted significance level (α ≤ 0.05).

4. Analysis and Results

Table 1. The mean of the research variables.

Variables	Mean	Std. Deviation	Rank
Delivery Speed	3.9969	0.58531	6
Order Accuracy	4.0782	0.55313	2
Returns Handling	4.0307	0.59885	5
Logistical Competence	4.0353	0.47619	4
Social Media Campaigns	4.0629	0.59534	3
Organizational Performance	4.0834	0.54152	1
Valid N (listwise)

demonstrates the applied descriptive statistics to the data collected from respondents.

The analysis of the variables reveals valuable insights into the respondents’ perceptions of logistical competence and its impact on organizational performance. Organizational Performance achieved the highest mean score (Mean = 4.08, Std. Dev = 0.54), indicating that respondents strongly agree on the significant role played by logistical factors and social media campaigns in enhancing overall performance. This is closely followed by Order Accuracy (Mean = 4.07, Std. Dev = 0.52), reflecting its critical importance as a component of logistical competence. The low standard deviation suggests a high level of consistency among respondents in recognizing the order accuracy’s contribution to organizational outcomes. Social Media Campaigns (Mean = 4.06, Std. Dev = 0.59) also emerged as a key factor, highlighting its role in amplifying logistical strengths and fostering competitive advantages. Returns Handling (Mean = 4.05, Std. Dev = 0.47) similarly received high agreement, with uniform responses emphasizing its influence on operational success. Lastly, Delivery Speed (Mean = 3.99, Std. Dev = 0.58), while slightly lower in comparison to the other variables, still reflects a relatively high level of agreement on its importance for enhancing organizational performance. The slightly lower mean for delivery speed may suggest that while it is critical, it is perceived as less impactful compared to the precision of order accuracy and the role of social media in logistics visibility. Overall, the data underscores the collective importance of these variables in driving organizational success.

Reliability Test:

Table 2. Reliability statistics (Cronbach’s Alpha).

Variable	Items	Cronbach’s Alpha
Logistical Competence	12	0.791
Social Media	4	0.606
Organizational Performance	5	0.720

shows that the reliability analysis reveals that all Cronbach’s Alpha values are above the acceptable threshold of 0.60, indicating consistent and reliable measures across the variables. The Logistical Competence scale achieved the highest reliability (α = 0.791, 12 items), demonstrating good internal consistency when all logistical sub-variables (delivery speed, order accuracy, and returns handling) are combined. This suggests that the overall construct of logistical competence is well measured and provides a reliable basis for further analysis. Organizational Performance (α = 0.720, 5 items) also displayed acceptable reliability, indicating that the items effectively capture the construct without significant variability. This reliability ensures confidence in the measurement of organizational performance outcomes as impacted by logistical competence. On the other hand, Social Media Campaigns (α = 0.606, 4 items) showed moderate reliability. While this is acceptable in exploratory research, the consistency of the scale could benefit from further refinement or the inclusion of additional items to enhance its reliability. Collectively, the results confirm that the scales used in the study are sufficiently reliable for assessing the constructs of interest, with logistical competence and organizational performance demonstrating particularly robust measurement consistency.

Validity Test:

Factor analysis (Principal Component Analysis) has been used to confirm the validity;

Table 3. Factor analysis: Principal Component Analysis.

Item	1	2	3	4	5
DeliverySpeed1	0.745**
DeliverySpeed2	0.692**
DeliverySpeed3	0.463**
DeliverySpeed4	0.720**
OrderAccuracy1		0.646**
OrderAccuracy2		0.810**
OrderAccuracy3		0.758**
OrderAccuracy4		0.443**
ReturnsHandling1			0.721**
ReturnsHandling2			0.800**
ReturnsHandling3			0.542**
ReturnsHandling4			0.645**
SocialMedia1				0.684**
SocialMedia2				0.695**
SocialMedia3				0.685**
SocialMedia4				0.654**
OrganizationalPerformance1					0.540**
OrganizationalPerformance2					0.755**
OrganizationalPerformance3					0.649**
OrganizationalPerformance4					0.663**
OrganizationalPerformance5					0.725**

**. Correlation is significant at the 0.01 level (2-tailed).

shows that factor loading for all items is above 0.50, except DeliverySpeed3 item 0.463 and OrderAccuracy4 item 0.443, which are accepted according to [41,42].

In addition, the Pearson correlation coefficient has been used to ensure the validity of variables.

Table 4. Pearson Correlation.

		1	2	3	4	5	6
1	Delivery Speed
2	Order Accuracy	0.573 **
3	Returns Handling	0.484 **	0.491 **
4	Logistical Competence	0.834 **	0.827 **	0.807 **
5	Social Media Campaigns	0.409 **	0.465 **	0.443 **	0.533 **
6	Organizational Performance	0.437 **	0.460 **	0.576 **	0.898 **	0.618 **	1

**. Correlation is significant at the 0.01 level (2-tailed).

shows the correlation analysis, which highlights several key insights regarding the relationships between logistical variables, social media campaigns, and organizational performance. Delivery Speed demonstrated a medium but statistically significant positive relationship with organizational performance (r = 0.437, p = 0.008), suggesting that faster delivery has a medium yet meaningful impact on organizational outcomes. Additionally, delivery speed showed a strong positive correlation with logistical competence (r = 0.834, p < 0.01), emphasizing its importance as a core component of logistical competence. Order Accuracy displayed a slightly stronger positive correlation with organizational performance (r = 0.460, p = 0.002), indicating that accurate order processing enhances organizational efficiency and customer satisfaction. Similarly, its strong correlation with logistical competence (r = 0.834, p < 0.01) reaffirms its role as a vital contributor to overall logistical competence. Returns Handling revealed a moderate positive relationship with organizational performance (r = 0.576, p = 0.001), showing its importance in maintaining customer trust and operational efficiency. Its strong correlation with logistical competence (r = 0.807, p < 0.01) highlights its strategic significance in comprehensive logistics management. Competitive Advantages in Social Media Campaigns emerged as a moderately strong factor, with its relationship with organizational performance (r = 0.618, p < 0.01) enhancing those of the logistical sub-variables. This indicates that leveraging social media for logistical visibility significantly enhances performance metrics. Lastly, logistical competence showed a strong positive relationship with organizational performance (r = 0.898, p = 0.001), reinforcing the combined impact of logistical competence on driving organizational success.

Hypothesis Testing:

H01: 

There is no statistically significant impact of logistical competence on the organizational performance of SMEs at α ≤ 0.05.

Table 5. Model summary H01.

Model	R	R2	Adjusted R2	Std. Error of the Estimate
1	0.285 a	0.081	0.075	0.52068

^a. Predictors: (Constant), Logistical Competence.

,

Table 6. ANOVA^a H01.

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	3.857	1	3.857	14.228	0.001 b
	Residual	43.648	161	0.271
	Total	47.505	162

^a. Dependent Variable: Organizational Performance, ^b. Predictors: (Constant), Logistical Competence.

and

Table 7. Coefficients^a H01.

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	2.776	0.349		7.953	0.000
	Logistical Competence	0.324	0.086	0.285	3.772	0.000

^a. Dependent Variable: Organizational Performance.

show the regression analysis, which reveals a significant correlation between logistical competence and organizational performance. The model summary shows an R-value of 0.285, indicating a weak positive correlation between these two variables. The R² value of 0.081 suggests that logistical competence accounts for 8.1% of the variance in organizational performance, while the Adjusted R² of 0.075 confirms this explanatory power after using several predictors. The ANOVA findings (F (1, 161) = 14.228, p < 0.001) demonstrate that the model is statistically significant, indicating that logistical competence has a meaningful impact on organizational performance. From the coefficients table, the Unstandardized Coefficient (B) of 0.324 signifies that for each unit increase in logistical competence, organizational performance is enhanced by 0.324 units. Additionally, the Standardized Coefficient (Beta) of 0.285 reflects a moderate positive influence of logistical competence. The p-value (<0.001) is significantly below the threshold of 0.05, driving the rejection of the null hypothesis (H01) and confirming that logistical competence is a crucial influence on organizational performance. These findings underestimate the importance of enhancing logistical competence to achieve improved outcomes in SMEs. The analysis reveals that logistical competence has a statistically significant positive impact on the organizational performance of SMEs. Although the effect size (R² = 0.081) suggests that logistical competence is not the sole driver of organizational performance, its contribution is meaningful and cannot be ignored. Based on the regression analysis, H01 is rejected. This confirms that logistical competence significantly influences the organizational performance of SMEs, supporting the research model.

H01.1: 

There is no statistically significant impact of delivery speed on the organizational performance of SMEs at α ≤ 0.05.

Table 8. Model summary for H01.1.

Model	R	R2	Adjusted R2	Std. Error of the Estimate
1	0.208 a	0.043	0.037	0.53129

^a. Predictors: (Constant), Delivery Speed.

,

Table 9. ANOVA^a H01.1.

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	2.060	1	2.060	7.297	0.008 b
	Residual	45.446	161	0.282
	Total	47.505	162

^a. Dependent Variable: Organizational Performance; ^b. Predictors: (Constant), Delivery Speed.

and

Table 10. Coefficients^a H01.1.

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	3.313	0.288		11.502	0.000
	Delivery Speed	0.193	0.071	0.208	2.701	0.008

^a. Dependent Variable: Organizational Performance.

indicate that the statistical interpretation provides key insights into the correlation between delivery speed and organizational performance. The model summary reveals an R-value of 0.208, showing a weak positive relationship between delivery speed and organizational performance. The R² value of 0.043 suggests that delivery speed accounts for 4.3% of the variance in organizational performance, while the Adjusted R² of 0.037 shows a slight reduction due to the small number of predictors but still indicates meaningful explanatory power. The ANOVA results (F (1, 161) = 7.297, p = 0.008) confirm that the model is statistically significant, demonstrating that delivery speed has a notable effect on organizational performance. Furthermore, the coefficients table highlights an Unstandardized Coefficient (B) of 0.193, indicating that for each unit increase in delivery speed, organizational performance improves by 0.193 units. These findings emphasize the importance of optimizing delivery speed to enhance organizational outcomes. The results show that delivery speed has a statistically significant positive impact on organizational performance. While the effect size (R² = 0.043) suggests that delivery speed alone does not explain a large proportion of the variance in organizational performance, its contribution is still meaningful and important for SMEs. Based on the regression analysis, H01.1 is rejected. This confirms that delivery speed significantly influences the organizational performance of SMEs, aligning with the hypothesis that logistical factors, such as delivery speed, play a crucial role in enhancing organizational outcomes.

H01.2: 

There is no statistically significant impact of order accuracy on the organizational performance of SMEs at α ≤ 0.05.

Table 11. Model summary for H01.2.

Model	R	R2	Adjusted R2	Std. Error of the Estimate
1	0.245 a	0.060	0.054	0.52665

^a. Predictors: (Constant), Order Accuracy.

,

Table 12. ANOVA^a H01.2.

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	2.850	1	2.850	10.277	0.002 b
	Residual	44.655	161	0.277
	Total	47.505	162

^a. Dependent Variable: Organizational Performance; ^b. Predictors: (Constant), Order Accuracy.

and

Table 13. Coefficients^a H01.2.

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	3.105	0.308		10.088	0.000
	Order Accuracy	0.240	0.075	0.245	3.206	0.002

^a. Dependent Variable: Organizational Performance.

demonstrate that the statistical interpretation highlights the relationship between order accuracy and organizational performance. The model summary demonstrates an R-value of 0.245, indicating a weak positive relationship between the two variables. The R² value of 0.060 reveals that order accuracy explains 6% of the variance in organizational performance, while the Adjusted R² of 0.054 reflects a slight adjustment due to the small predictor set but remains meaningful. The ANOVA results (F (1, 161) = 10.277, p = 0.002) confirm that the model is statistically significant, indicating that order accuracy has a notable impact on organizational performance. The coefficients table further supports this with an Unstandardized Coefficient (B) of 0.240, showing that for every unit increase in order accuracy, organizational performance is enhanced by 0.240 units. The Standardized Coefficient (Beta) of 0.245 reflects a weak to moderate positive effect, while the p-value (Sig.) of 0.002 is well below the significance threshold of 0.05, showing rejection of the null hypothesis (H01.2). These findings show that order accuracy has a statistically significant positive impact on organizational performance. Although the explained variance is modest, it demonstrates that order accuracy plays a meaningful role in enhancing organizational outcomes in SMEs. Based on the regression analysis, H01.2 is rejected. This supports the hypothesis that improving order accuracy significantly enhances the organizational performance of SMEs, aligning with the importance of logistical competence as a driver of performance.

H01.3: 

There is no statistically significant impact of effective returns handling on the organizational performance of SMEs at α ≤ 0.05.

Table 14. Model summary for H01.3.

Model	R	R2	Adjusted R2	Std. Error of the Estimate
1	0.250 a	0.062	0.057	0.52595

^a. Predictors: (Constant), Returns.

,

Table 15. ANOVA^a H01.3.

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	2.969	1	2.969	10.732	0.001 b
	Residual	44.537	161	0.277
	Total	47.505	162

^a. Dependent Variable: Organizational Performance Total; ^b. Predictors: (Constant), Returns.

and

Table 16. Coefficients^a H01.3.

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	3.172	0.281		11.283	0.000
	Returns	0.226	0.069	0.250	3.276	0.001

^a. Dependent Variable: Organizational Performance.

indicate that the statistical interpretation highlights the relationship between returns handling and organizational performance. The model summary shows an R-value of 0.250, showing a weak positive relationship between returns handling and organizational performance. The R² value of 0.062 reveals that returns handling explains 6.2% of the variance in organizational performance, while the Adjusted R² of 0.057 reflects a slight reduction due to the adjustment for the predictor, but remains meaningful. The ANOVA findings (F (1, 161) = 10.732, p = 0.001) confirm that the model is statistically significant, demonstrating that effective returns handling significantly impacts organizational performance. The coefficients table further supports this with an Unstandardized Coefficient (B) of 0.226, showing that for each unit increase in returns handling, organizational performance is enhanced by 0.226 units. The Standardized Coefficient (Beta) of 0.250 reflects a weak to moderate positive effect, while the p-value (Sig.) of 0.001 is well below the significance threshold of 0.05, indicating the rejection of the null hypothesis (H01.3). These results demonstrate that effective returns handling has a statistically significant positive impact on organizational performance. While the explained variance is modest, it confirms that efficient management of returns contributes meaningfully to the performance outcomes of SMEs. Based on the regression analysis, H01.3 is rejected. This validates that effective returns handling significantly improves the organizational performance of SMEs, emphasizing its critical role as a component of logistical competence.

Finally, R and R² can be low, but the relationships can be meaningful due to a single sub-variable; this can explain a small part of the relationship and effect, and it also solidifies the model.

H02: 

There is no statistically significant moderating role of competitive advantages in social media campaigns in the impact of logistical competence on the organizational performance of SMEs at α ≤ 0.05.

Table 17. Model summary for H02.

Model	R	R2	Adjusted R2	Std. Error of the Estimate
1	0.285 a	0.081	0.075	0.52068
2	0.371 b	0.138	0.127	0.50590

^a. Predictors: (Constant), Logistical Competence; ^b. Predictors: (Constant), Logistical Competence, Social Media Campaigns.

and

Table 18. ANOVA^a H02.

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	3.857	1	3.857	14.228	<0.001 b
	Residual	43.648	161	0.271
	Total	47.505	162
2	Regression	6.556	2	3.278	12.809	<0.001 c
	Residual	40.949	160	0.256
	Total	47.505	162

^a. Dependent Variable: Organizational Performance; ^b. Predictors: (Constant), Logistical Competence; ^c. Predictors: (Constant), Logistical Competence, Social Media Campaigns.

show that the hierarchical multiple regression analysis reveals the combined effects of logistical competence and competitive advantages in social media campaigns on organizational performance. In Model 1, logistical competence explains 8.1% of the variance in organizational performance, with a statistically significant model (F (1, 161) = 14.228, p < 0.001). When social media campaigns are introduced in Model 2, the variance explained increases to 13.9%, highlighting the significant moderating role of social media campaigns (ΔR² = 0.058, p = 0.002). Social media campaigns emerge as a critical factor, with a significant standardized coefficient (Beta = 0.278, p = 0.002), suggesting that leveraging social media to promote logistical strengths amplifies their impact on organizational performance. While logistical competence remains a positive predictor, its standalone significance diminishes in the presence of social media campaigns, underscoring the synergistic effect of integrating both elements. The results confirm the rejection of the null hypothesis (H02) by demonstrating that competitive advantages in social media campaigns significantly moderate the relationship between logistical competence and organizational performance.

Combined Impact of Logistical Competence and Social Media Campaigns on Organizational Performance

Table 19. Model summary for H02.

Model	R	R2	Adjusted R2	Std. Error of the Estimate	Change Statistics
					R2 Change	F Change	df1	df2	Sig. F Change
1	0.290 a	0.084	0.067	0.52313	0.084	40.862	3	159	0.003
2	0.373 b	0.139	0.117	0.50872	0.055	100.137	1	158	0.002

^a. Predictors: (Constant), Returns Handling, Delivery Speed, Order Accuracy; ^b. Predictors: (Constant), Returns Handling, Delivery Speed, Order Accuracy, Social Media Campaigns.

,

Table 20. ANOVA^a H02.

Model		Sum of Squares	df	Mean Square	F	Sig.
1	Regression	3.992	3	1.331	4.862	0.003 b
	Residual	43.513	159	0.274
	Total	47.505	162
2	Regression	6.615	4	1.654	6.391	0.000 c
	Residual	40.890	158	0.259
	Total	47.505	162

^a. Dependent Variable: Organizational Performance; ^b. Predictors: (Constant), Returns Handling, Delivery Speed, Order Accuracy; ^c. Predictors: (Constant), Returns, Delivery Speed, Order, Social media total.

and

Table 21. Coefficients^a H02.

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	20.765	0.352		70.858	0.000
	Delivery Speed	0.050	0.089	0.054	0.560	0.576
	Order Accuracy	0.134	0.095	0.137	10.418	0.158
	Returns Handling	0.142	0.082	0.157	10.724	0.087
2	(Constant)	20.419	0.359		60.740	0.000
	Delivery Speed	0.016	0.087	0.017	0.178	0.859
	Order Accuracy	0.063	0.095	0.064	0.661	0.510
	Returns Handling	0.079	0.082	0.087	0.961	0.338
	Social Media Campaigns	0.253	0.079	0.278	30.184	0.002

^a. Dependent Variable: Organizational Performance.

show the model summaries. In model 1, the independent variables (delivery speed, order accuracy, and returns handling) were included in the model. The results show an R² value of 0.084, indicating that these variables collectively explain 8.4% of the variance in organizational performance. The Adjusted R² value of 0.067 accounts for several predictors, confirming the explanatory power of the independent variables. The ANOVA results (F (3, 159) = 4.862, p = 0.003) demonstrate the statistical significance of the model. Among the independent variables, order accuracy (B = 0.134, Beta = 0.137, p = 0.158) and returns handling (B = 0.142, Beta = 0.157, p = 0.087) display slightly higher coefficients compared to delivery speed (B = 0.050, Beta = 0.054, p = 0.576), though none of the variables are individually statistically significant.

In model 2, the moderating variable, Social Media Campaigns, was added. The addition of this variable led to an R² value of 0.139, suggesting that the model now explains 13.9% of the variance in organizational performance, with an Adjusted R² of 0.117. The change in R² (ΔR² = 0.055, p = 0.002) indicates that social media campaigns significantly contribute to the model. The ANOVA results (F (4, 158) = 6.391, p < 0.001) confirm the statistical significance of the enhanced model. Social media campaigns emerge as a significant predictor (B = 0.253, Beta = 0.278, p = 0.002), demonstrating that leveraging competitive advantages in social media amplifies the impact of logistical competence on organizational performance.

5. Discussion and Conclusions

This study has shown that logistical capability is a crucial determinant of the organizational performance of small and medium enterprises (SMEs). Using the three logistical dimensions of delivery speed, order accuracy, and returns handling, the study shows how these logistical dimensions affect operational performance, customer satisfaction, and organizational performance. The findings show that logistical competence is not only good for organizational performance but is also enhanced when the competence is in line with the social media campaigns.

The hierarchical regression analysis has shown that in social media, the campaign company’s logistical strengths are more visible and effective. The synergy of logical and virtual operations assists SMEs in overcoming resource constraints and competing in the dynamic market. This integrated approach gives the organization transparency, builds confidence with the customers, and creates brand loyalty, which is vital for the growth of the organization.

However, the research also revealed that the role of the logistical dimensions is not new and straightforward. All the factors (delivery speed, order accuracy, and returns handling) are positive in their impact on performance, but they differ in strength. Order accuracy and returns handling are slightly stronger than delivery speed as predictors of performance, which implies that proper management of operations is crucial. Social media campaigns as a moderating variable have the highest incremental impact, and this supports their usefulness in the current marketing environment.

This research has also contributed to the development of theory in the area of logistics and digital marketing by suggesting a model that SMEs can use to derive benefits from logical competence in the market. Building on the findings of this work, SME managers are advised on how to organize their logistics and marketing departments to gain a competitive advantage and achieve sustainable growth in the market.

While the study provides valuable insights, many limitations have to be acknowledged:

Sample Size and Scope: The study was conducted with a non-probability convenience sample of 163 respondents, limiting the generalizability of outcomes to the broader SME population.

Regional Focus: The research is specific to SMEs in Jordan, and the findings may not fully apply to SMEs operating in different cultural or economic contexts.

Self-Reported Data: Reliance on self-reported survey data lowers the potential for bias, as respondents may overestimate or underestimate their practices and perceptions.

Finally, the study recommends investigating the effect of logistical competence and social media campaigns on the performance of different industries. The ways in which technologies like blockchain, artificial intelligence, and IoT improve logistical competence should be examined. Other variables should be investigated, such as sustainability in logistics, to improve organizational performance.

Using fast delivery, real-time tracking, and predictive analytics can create a competitive advantage, as can using quality control systems to ensure accurate order fulfillment, enhance customer trust, and improve operational efficiency. Clear return policies should be developed to support reverse logistics. Different social media platforms such as Facebook and Instagram should be used to reach customers and track customer engagement.