The Effect of Corporate Social Responsibility on Corporate Performance in the Food Industry in Saudi Arabia: A Partial Least Squares Structural Equation Modeling Approach

Abstract

Although there has been a growing interest in understanding the influence of corporate social responsibility (CSR) on business outcomes, the specific mechanisms through which CSR impacts financial performance (FP) and competitive advantage (CA) remain underexplored, particularly in the context of the food industry. This study contributes to the literature by looking into the mediating effect of innovation and the moderating role of CA in the relationship between CSR and FP in Saudi Arabian food industry companies. Employing Partial Least Squares Structural Equation Modeling (PLS-SEM), data were collected from executive managers of these companies to assess how CSR practices foster innovation and, consequently, enhance financial outcomes and competitive positioning. The results reveal that CSR significantly improves FP and CA, primarily when focused on innovation. Moreover, CA does not influence the strength or direction of the relationship between CSR and FP. This research offers essential guidance for food industry managers to integrate CSR into core business strategies, foster innovation, and strengthen stakeholder relationships, thereby achieving sustainable growth and profitability.

1. Introduction

The term corporate social responsibility (CSR) encompasses the practices and policies implemented by corporations to create a positive impact on society. It embodies a company’s commitment to conduct its business ethically, to take responsibility for its actions, and to contribute to the welfare of its employees, the community, and the environment. CSR began in the 1950s and has grown significantly in recent years, becoming an integral part of modern business strategy [1]. Companies increasingly recognize that their responsibilities extend beyond shareholders to a broader set of stakeholders, including employees, customers, suppliers, communities, and the environment [2].

The scope of CSR includes a broad array of activities, such as environmental management, eco-efficiency, responsible sourcing, stakeholder engagement, labor standards, working conditions, employee and community relations, social equity, gender balance, human rights, good governance, and anti-corruption measures [3]. These practices are designed not only to enhance a company’s reputation and ensure compliance with regulatory requirements but also to contribute positively to the society and environment in which the company operates [4].

In Saudi Arabia, the foundation of CSR is deeply embedded in traditional philanthropy and Islamic principles, especially the practice of Zakat [5]. Initially focused on charitable donations, CSR has evolved with the introduction of Vision 2030 into a strategic practice aligned with national goals of sustainability and economic diversification [6]. In the food industry, CSR is particularly vital, as it significantly contributes to public health, food security, and environmental sustainability. Saudi Arabian food companies are increasingly adopting CSR practices to enhance their competitive advantage (CA), improve product quality, and build consumer trust while also contributing to the broader societal and economic objectives [7]. Given the food industry’s significant role in the national economy, effective CSR practices are essential for promoting long-term sustainability and improving corporate performance in this sector [8].

There is a notable gap in research concerning the association between CSR and financial performance (FP). This study seeks to address this gap by exploring the mediating role of innovation and the moderating role of CA in the CSR-FP relationship. Previous research has rarely examined CA as a moderating factor in this context. Moreover, these variables have not been thoroughly explored within the Saudi Arabian food industry, highlighting the need for a more comprehensive understanding of how different CSR initiatives impact FP in this sector, providing an opportunity to contribute substantially to the existing literature.

The objective of this study is to examine the impact of CSR on companies’ financial performance and CA through the mediating role of innovation and the moderating role of CA. By employing Partial Least Squares Structural Equation Modeling (PLS-SEM), this study investigates the interrelationships, including mediation and moderation effects, between CSR practices, FP, CA, and innovation. The study aims to provide empirical evidence on the role of CSR in enhancing both FP and CA within the food industry in Saudi Arabia and to offer insights into how responsible corporate practices can be improved to achieve better financial outcomes. This study will contribute to detecting these relationships and provide valuable information to the food industry, aiming to improve social responsibility practices, enhance companies’ financial performance and CA, promote academic study, and encourage future studies on the topic.

The article is organized as follows: The introduction Section 1 provides an overview of the study’s objectives and the significance of CSR in the food industry in Saudi Arabia. Section 2, covering the literature review and hypothesis development, outlines the proposed relationships between the study variables. The methodology Section 3 covers how data was collected and analyzed using PLS-SEM. The analysis and results Section 4 explores the findings. The discussion Section 5 interprets the results in the context of existing literature and the study’s objectives. Finally, the conclusion Section 6 summarizes the main points and offers suggestions for future research directions.

2. Review of Literature and Hypothesis Development

CSR has significantly evolved from its initial focus on philanthropic activities to encompass economic, environmental, and ethical dimensions, integrating these aspects into core business strategies [9]; this broader approach reflects a commitment to sustainable and ethical business practices [10]. Several theories provide a framework for understanding CSR and its impact on business performance [11]. Carroll’s Pyramid, a foundational CSR model, proposes a hierarchy of responsibilities starting with economic, followed by legal, ethical, and philanthropic responsibilities [12]. The Triple Bottom Line (TBL) theory, developed by John Elkington, expands business performance metrics to include social and environmental dimensions alongside economic performance, promoting sustainability [13].

Stakeholder Theory, proposed by Edward Freeman, stresses the importance of addressing the interests of all stakeholders, such as employees, customers, suppliers, communities, and shareholders, highlighting the importance of transparency and balancing diverse interests for long-term success [2]. Modern CSR concepts emphasize sustainability, ethical governance, and stakeholder engagement, highlighting the increasing awareness of the interdependence between businesses, society, and the environment [14].

Recently, there has been increased interest in CSR, prompting global organizations to develop CSR-oriented standards like ISO 26000 [15] and the UN Global Compact to promote socially responsible business practices [16]. CSR in education has also evolved, with many business schools now offering dedicated courses on social responsibility. Additionally, accreditation systems like AACSB require schools to demonstrate a commitment to ethical, responsible, and sustainable business practices [17].

The relationship between CSR and FP is complicated and often moderated or mediated by other variables such as CA and innovation. This literature review aims to explore these relationships, focusing on how CA and innovation act as mediators or moderators.

2.1. CSR and Financial Performance

The association between CSR and FP has been extensively researched, yielding mixed results. Some study shows a positive association, while others report negative or inconsistent effects, indicating that the impact of CSR on FP varies depending on several factors. For example, a study by Ma et al. [18] highlighted that CSR plays a significant role in fostering environmentally sustainable growth, demonstrating an exceptional impact on FP. Giannarakis et al. [19] conducted a study across various industries in the United States, concluding that CSR activities positively influence FP. Similarly, Javed et al. [5] found that CSR initiatives in Pakistan’s manufacturing sector enhance both corporate reputation and FP. Also, Marakova [20] deduced that combining CSR with innovative strategies significantly boosts FP.

In the context of small and medium-sized companies in Zambia, Choongo [21] revealed that CSR activities have a significant positive effect on FP. In the food industry, Omidi and Shafiee [22] found that CSR activities improve FP by enhancing social performance and customer reactions, leading to increased loyalty and satisfaction. Similarly, CSR practices in Saudi Arabia’s service sector positively affect company performance, reputation and customer satisfaction, indirectly boosting financial success [23].

In emerging markets, localized CSR strategies have proven particularly effective, CSR activities tailored to local cultural and economic contexts significantly enhance FP in Saudi Arabia by aligning with stakeholder expectations, as emphasized by [24]. CSR activities in emerging markets enhance FP by improving corporate reputation and customer satisfaction, leading to intangible assets like brand loyalty and trust [25].

Although CSR may not have a significant short-term impact on FP, it offers substantial long-term fiscal benefits when integrated with study and development (R&D), as observed in Taiwanese businesses [26]. Additionally, it has been confirmed that CSR does not directly affect FP, with the social dimension of CSR, in particular, negatively impacting performance [27]. Consequently, the present study puts forth the following hypothesis:

Hypothesis 1 (H1).

Corporate social responsibility positively and significantly affects financial performance.

Note that testing this hypothesis should be conducted without a moderator, as the moderator is assigned in Hypothesis 5. This is because the direct (main) effect of CSR on FP in Hypothesis 1 is treated as a simple effect when CA moderates the relationship between CSR and FP in Hypothesis 5 (see [28]).

2.2. CSR and Competitive Advantage

CSR is essential for enhancing CA. Research indicates that CSR capabilities positively impact customer orientation and price premium, leading to sustained CA by improving reputation [29]. CSR activities build intangible assets such as brand reputation and customer loyalty, which help companies differentiate themselves and retain market share [30]. Marketing activities, innovation activities, and the application of CSR are vital sources of CA in large companies operating in the market [31]. Additionally, companies actively engaging in CSR are better positioned to attract and retain talented employees, further solidifying their competitive edge [32].

In the food industry, CSR has positively impacted CA, corporate reputation, and customer satisfaction [33]. This is further supported by findings from [34], illustrating how Polpharma’s engagement in CSR initiatives has increased customer trust and a stronger competitive position. In the banking sector, CSR initiatives enhance customer trust and satisfaction, resulting in competitive advantages such as customer loyalty and reduced churn rates [35]. Consequently, this study puts forward the ensuing hypothesis:

Hypothesis 2 (H2).

Corporate social responsibility positively and significantly affects competitive advantage.

2.3. CSR and Financial Performance: The Mediation Role of Innovation

CSR plays a significant role in fostering innovation within companies. Studies demonstrate that CSR activities improve a company’s public image and stimulate innovation capabilities. For instance, Yan et al. [36] indicated that CSR initiatives in heavily polluting industries in China enhance sustainable innovation, with companies in favorable macroeconomic environments demonstrating stronger CSR-innovation relationships. Additionally, CSR positively impacts the innovative performance of companies by building trust and good relationships with stakeholders, creating an environment conducive to innovation [37].

In the context of the European Union, CSR activities have been associated with heightened green innovation and improved resource utilization, thereby supporting sustainable development goals [38]. Furthermore, Ratajczak and Szutowski [39] emphasized that CSR practices foster innovation by leveraging stakeholder engagement and ethical business practices.

Studies examining the association between CSR and FP with innovation as a mediator indicate that innovation plays an essential role. Homayoun et al. [40] found that CSR positively impacts FP, with green innovation playing a significant mediating role. Similarly, a study by Al-Shuaib [41] examining 197 Saudi companies revealed that CSR positively influences FP through innovation and productivity improvements. In Germany, a study has established a positive relationship between CSR and innovation, leading to financial benefits [42]. Similarly, in the United States, studies across various industries found that innovation plays a moderating role, further enhancing the positive impact of CSR on FP [43].

Bahta et al. [44] revealed that CSR positively affects both FP and innovation capability in small and medium-sized companies, with innovation capability partially mediating this relationship. CSR directed towards the natural environment and customers has been shown to positively impact innovation, while CSR towards the local community can have a negative effect [45].

Zahid et al. [46] corroborated the positive effect of CSR on FP but argued that innovation’s mediating role is less significant compared to other variables. This partial influence suggests that while innovation is a key mediator, its impact varies in intensity. Conversely, Bocquet et al. [47] pointed out that the type of CSR strategy is crucial responsive CSR hinders innovation, whereas strategic CSR promotes it, benefiting both process and product innovations.

Research indicates that higher levels of innovation are linked to improved FP, as confirmed by Bigliardi [48]. However, De Oliveira et al. [49] suggested that while innovation efforts can have an impact, they do not always result in immediate financial gains, highlighting the inherent risks and costs associated with innovation. In this context, the ensuing hypothesis is formulated:

Hypothesis 3 (H3).

Innovation acts as a mediator in the relationship between corporate social responsibility and financial performance.

This hypothesis is divided into three sub-hypotheses.

Hypothesis 3a (H3a).

Innovation is positively and significantly influenced by corporate social responsibility practice.

Hypothesis 3b (H3b).

Financial performance is positively and significantly influenced by innovation.

Hypothesis 3c (H3c).

Corporate social responsibility positively and significantly indirectly affects financial performance through Innovation.

2.4. CSR and Competitive Advantage: The Mediation Role of Innovation

Research indicates that innovation significantly enhances manufacturing competitiveness, particularly in developing economies [50]. In the frozen food industry in Thailand, innovation has been shown to strengthen CA by leveraging external factors, categorized into micro-oriented and macro-oriented elements [51]. Additionally, innovation directly impacts CA through crucial dimensions such as time, quality, cost, and flexibility, suggesting that sectors like banking should support innovation across all business and operational aspects to maintain their competitive edge [8].

The association between CSR and CA with innovation as a mediator is limited. Vilanova et al. [52] highlighted that the learning and innovation cycle mediates CSR strategies and CA, emphasizing the importance of managerial values and strategic updates. Furthermore, responsible innovation is pivotal to the success of CSR strategies in enhancing companies’ competitive positioning [7].

An empirical study by Marakova et al. [31] demonstrated that marketing activities, innovation, and CSR are essential sources of CA. Their research found that integrating CSR initiatives with innovation efforts significantly strengthens a company’s competitive positioning. In the hotel and casino industry, Zhao et al. [53] demonstrated that while CSR can sometimes negatively impact innovation, strategic CSR behavior fosters innovative activities, improving both process and product innovations and enhancing CA. Additionally, Chang et al. [54] confirmed that green product innovation mediates the positive relationship between corporate environmental ethics and CA, whereas green process innovation does not.

Research by Padilla-Lozano and Collazzo [50] emphasized that in the food industry in Ecuador, CSR positively impacts corporate reputation and customer satisfaction, leading to improved CA. INN enables companies to develop new products and processes that meet stakeholder expectations and differentiate them from competitors. Therefore, the current study puts forward the ensuing hypothesis:

Hypothesis 4 (H4).

Innovation mediates the relationship between corporate social responsibility and competitive advantage.

This hypothesis is further broken down into two sub-hypotheses.

Hypothesis 4a (H4a).

Competitive advantage is positively and significantly influenced by innovation.

Hypothesis 4b (H4b).

Corporate social responsibility positively and significantly indirectly affects competitive advantage through innovation.

2.5. CSR and Financial Performance: The Moderate Role of Competitive Advantage

Moderation occurs when a third variable, known as the moderator, influences the relationship between two other variables. This moderator can alter both the strength and direction of the relationship between the primary variables [55]. In the context of CSR and FP, CA plays a critical role as a moderator. Understanding this moderating effect can offer deeper insights into how companies can strategically leverage CSR initiatives to enhance their financial outcomes. This relationship emphasizes the importance of integrating CA into CSR strategies to optimize FP, as highlighted by various studies in the field. To our knowledge, no study has considered this specific relationship. The existing studies have primarily focused on other variables that moderate this relationship. For example, a study by Wang et al. [56] examined the moderating effect of environmental context, revealing that CSR’s influence on financial performance is more pronounced in developed economies with mature institutional systems compared to developing economies. Similarly, Tang et al. [57] investigated how a CSR engagement strategy moderates this relationship, revealing that companies benefit more from consistent and strategically aligned CSR efforts, though the pace of implementation does not significantly moderate the CSR-FP relationship. Additionally, Liu et al. [58] examined the role of fintech technology as a moderator between CSR and FP, concluding that combining governance and social disclosures does not significantly influence FP. These studies underscore the lack of research explicitly examining CA as a moderating factor in the association between CSR and FP. Consequently, the present study puts forward the following hypothesis:

Hypothesis 5 (H5).

Competitive advantage serves as a moderator in the relationship between corporate social responsibility and financial performance.

The theoretical model of this study explains the mediating role of innovation (INN) in the association between CSR and FP, as well as between CSR and CA. Additionally, the model proposes that CA moderates the association between CSR and FP. The hypotheses presented within this study’s framework are depicted in Figure 1.

3. Methodology

3.1. Sample and Data Collection

The current study examined the impact of CSR on FP and CA through the mediating role of INN and the moderating role of CA in moderating the relationship between CSR and FP. This study’s population consists of all companies within the Food sector in Saudi Arabia, encompassing a total of 1320 companies [59]. Companies were classified based on size (small, medium, and large). The size of the company is measured based on the number of employees. The classification provided by the Small and Medium Enterprises General Authority [60] has been followed, defining a small company in Saudi Arabia as one with fewer than 50 employees, a medium-sized company as having between 51 and 250 employees, and any company with over 250 employees as a large company. This stratified approach ensures that the sample accurately represents the different company sizes within the food industry.

A total of 600 questionnaires were distributed, with 15% allocated to executive managers from small companies (90 questionnaires), 40% to those from medium-sized companies (240 questionnaires), and 45% to those from large companies (270 questionnaires).

The gathered data were analyzed utilizing the PLS-SEM technique through the RStudio statistical programming environment to assess the proposed hypotheses.

3.2. Mathematical Formulation of the Model

Based on the study model provided in Figure 1 and the associated hypotheses, we can represent the model in a mathematical form suitable for analysis using PLS-SEM. The model includes direct relationships, mediating effects of innovation, and a moderating effect on the relationship between CSR and FP by CA.

The proposed model can be mathematically represented as follows:

-

Direct relationships:

$$FP={\gamma }_1.CSR+{\zeta }_1$$

(1)

$$CA={\gamma }_2.CSR+{\zeta }_2$$

(2)

$$INN={\gamma }_3.CSR+{\zeta }_3$$

(3)

-

Mediation by innovation:

$$FP={\gamma }_4.INN+{\gamma }_1^{\prime }.CSR+{\zeta }_4$$

(4)

$$CA={\gamma }_5.INN+{\gamma }_2^{\prime }.CSR+{\zeta }_5$$

(5)

-

Moderation by competitive advantage:

$$FP={\gamma }_6.(CSR.CA)+{\gamma }_1^{\prime \prime }.CSR+{\gamma }_2^{\prime \prime }.CA+{\zeta }_6$$

(6)

In this model:

• - $CSR$: Corporate social responsibility;
• - $INN$: Innovation;
• - $CA$: Competitive advantage;
• - $FP$: Financial performance.

The coefficients ${\gamma }_1,{\gamma }_2,\dots ,{\gamma }_6$, denote the effects of the independent variables on the dependent variables. The coefficients ${\gamma }_1^{\prime },{\gamma }_2^{\prime }$ represent the mediated effects of CSR on FP through INN and the mediated effects of CSR on CA through INN, respectively. The coefficient ${\gamma }_1^{\prime \prime }$ represents the simple effect of CSR on FP that is moderated by CA, while ${\gamma }_2^{\prime \prime }$ represents the direct effect of CA on FP, accounting for the interaction with CSR. The error terms are denoted by ${\zeta }_1,{\zeta }_2,\dots ,{\zeta }_6$.

The interaction term (CSR. CA) captures the moderation effect.

The idea of an interaction term was introduced to simplify the integration of a moderator variable into a PLS path model. The two-stage approach is a widely adopted method for generating interaction terms and demonstrates superior performance in parameter recovery and statistical power compared to other methodologies [61,62]. If the interaction term’s effect on the endogenous construct is significant, the next step is determining the moderating effect’s strength. The $f^2$ effect size is used to measure the contribution of moderation to the explanation of the endogenous construct. It can be calculated as follows:

$$f^2={\displaystyle \frac{R_{\mathrm{included}}^2-R_{\mathrm{excluded}}^2}{1-R_{\mathrm{included}}^2}}$$

where $R_{\mathrm{included}}^2$ and $R_{\mathrm{excluded}}^2$ are the $R^2$ values of the endogenous construct when the interaction term of the moderator model is included in or excluded from the PLS path model. The $f^2$ values of 0.02, 0.15, and 0.35 represent small, medium, and large effect sizes, respectively, [63].

The mathematical model, derived from the conceptual framework shown in Figure 1, is consequently represented in Figure 2.

3.3. Questionnaire Development

The data for this study were collected using a questionnaire method. We used subjective questionnaires because direct access to quantitative data was challenging and some quantitative information was not available in Saudi Arabia. This research seeks to understand managers’ views on CSR practices, making subjective assessments especially relevant. The questionnaire was designed based on the contributions of previous researchers, specifically references [7,10,21,25,41,46,50,53,64,65,66]. It is composed of two parts: the first part includes demographic questions that cover gender, age, education level, employment status (Job title), and years of professional experience. It also collects information about the company, such as the company name, date of establishment, and company size. The second part involves seventeen items designed to measure CSR, across four principal dimensions: environmental, ethical, economic, and philanthropic, based on the studies cited in [21,25,45,46]. The CA component includes seven items adopted from [29,31,32,33,50]. The FP consists of seven items, as reported in [5,18,22,34]. Additionally, the seven items assessing innovation were incorporated from [7,38,41,46,53,54]. Each item is evaluated on a 5-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).

Table A1. Survey statements used in the study.

CSR	Statement
CSR.1	The company is keen on preserving environmental resources.
CSR.2	The company has a waste management system that protects the environment
CSR.3	The company selects raw materials for production and marketing that result in minimal pollution.
CSR.4	The company works to reduce the negative environmental impacts of its activities.
CSR.5	Reducing the use of single-use products is a top priority in the company
CSR.6	The company is committed to providing its employees a safe and healthy work environment.
CSR.7	The company conducts all its activities with honesty and transparency.
CSR.8	The company always respects its contractual commitments.
CSR.9	The company has a dedicated department to handle and resolve customer complaints within 48 hours.
CSR.10	Our company plays an active role in supporting the local economy by providing job opportunities annually.
CSR.11	The company practices business in a way that balances profitability with social responsibility.
CSR.12	The company adheres to paying taxes and fees on time, in accordance with local regulations.
CSR.13	The company prioritizes monitoring and continuously improving productivity to ensure efficient use of resources.
CSR.14	The company supports small and medium-sized businesses through partnership programs or financial support.
CSR.15	The company allocates part of the budget for charitable donations and funding social events.
CSR.16	The company encourages employees to participate in volunteer activities and charitable initiatives.
CSR.17	The company participates in training and continuous education programs in the local community.
CA	Statement
CA.1	The company’s products are considered to be of better quality than those of competitors.
CA.2	The company’s customer service is superior to that of competitors.
CA.3	The company has more robust relationships with suppliers compared to competitors.
CA.4	The company is more committed to developing employee skills compared to competitors.
CA.5	The company offers its products at competitive prices.
CA.6	The company meets the needs and desires of specific market segments.
CA.7	The company adopts effective marketing strategies that distinguish it from competitors.
INN	Statement
INN.1	The company allocates a significant portion of its budget to research and development.
INN.2	The company always looks for new ways to innovate and improve its work processes.
INN.3	The company uses the latest technologies to develop its products.
INN.4	The company continually launches new products.
INN.5	The company develops its products based on customer needs.
INN.6	The company motivates innovative employees both financially and morally.
INN.7	The company markets its products with innovative ideas.
FP	Statement
FP.1	The company has continuously grown in return on assets over the past three years.
FP.2	The company’s market share has increased in recent years.
FP.3	The company has strong cash liquidity, enabling it to meet its financial obligations.
FP.4	The company’s financial statements show consistent growth in shareholder profitability.
FP.5	The company ensures efficient cost management, which positively affects profitability.
FP.6	The company has achieved continuous sales growth in recent years.
FP.7	The company is committed to reducing production costs.

displays the scale items included in the final version of the questionnaire.

After thoroughly filtering, 486 out of the 600 distributed questionnaires were deemed valid, forming the final sample. This yielded a response rate of over 81%, which is notably high.

Table 1. Demographic profiles and company information (n = 486).

Demographic Characteristics		Frequency	Percent
Gender	Male	285	58.6%
	Female	201	41.4%
Age	Less than 30	65	13.4%
	30 and less than 45	245	50.4%
	45 and less than 60	169	34.8%
	60 years or more	7	1.4%
Education	Secondary (High School)	23	4.7%
	Diploma	193	39.7%
	Bachelor’s Degree	137	28.2%
	Master’s Degree	119	24.5%
	Doctorate Degree	14	2.9%
Job Title	Chief Executive Officer (CEO)	21	4.3%
	Deputy CEO	82	16.9%
	Branch Manager	241	49.6%
	Department Manager	142	29.2%
Company Information
Establish Date	Before 2000 AD	142	29.2%
	Between 2000 and before 2010 AD	255	52.5%
	Between 2010 and 2020 AD	79	16.3%
	After 2020 AD	10	2.1%
Company Size	Less Than 50 employees	67	11.2%
	Between 51 and 250 employees	196	32.7%
	More than 250 employees	223	37.1%

summarizes the sample information, including participant demographics and company details.

3.4. Data Analysis Using PLS-SEM

Due to the high correlation among the indicators constituting each construct or latent variable, the model consists of four composite reflective constructs, including CSR, INN, FP, and CA, which are estimated in mode A [67,68].

The PLS-SEM, a variance-based structural equation modeling technique, is used to estimate the proposed model. This technique facilitates estimating complex models encompassing numerous latent and observed variables if they contain mediation [67]. Other reasons for choosing this technique include the suitability of PLS-SEM for estimating multiple relationships between latent constructs, particularly when moderation effects are involved. Additionally, PLS-SEM provides robust solutions even with not very large sample sizes, mitigating issues related to model identification and convergence. It is also suitable for use when the data do not meet the assumption of multivariate normality [69,70].

The proposed model in this study has been analyzed using the SEMinR package within the RStudio statistical programming environment (CRAN: Package seminr). As suggested by Henseler, a bootstrapping method with 10000 subsamples was used to test the hypotheses [71].

Specifying and estimating a structural equation model using SEMinR involves four steps: collecting and cleaning data, defining the measurement model, establishing the structural model, and then estimating, bootstrapping, and summarizing the model.

4. Analysis and Results

The main steps in the data analysis include addressing common method bias, assessing the measurement model, evaluating the structural model, conducting path analysis, and testing the hypotheses outlined in this section and visualized in Figure 3.

4.1. Common Method Bias

The common method bias (CMB) can occur in questionnaires when the variance is due to the measurement method rather than the constructs being measured [72]. Harman’s single-factor test was conducted on the questionnaire data to assess CMB. The test results indicated no significant CMB in the data, as the total variance extracted by a single factor was only 13.82%, which was below the recommended threshold of 50%.

4.2. Assessment of the Measurement Model

In the measurement model, we assume that all latent variables serve as causes for their indicators, indicating that they are reflective of the latent variables. Consequently, we conducted analyses of indicators and construct reliability, convergent validity, and discriminant validity to evaluate this.

4.2.1. Indicator Reliability

The initial step in assessing a reflective measurement model involves determining the extent to which each indicator’s (item’s) variance is accounted for by its construct. This is achieved by squaring the indicator loading, representing the correlation between the indicator and the construct. The resulting value, referred to as indicator reliability, indicates the commonality of the indicator. The results presented in

Table A2. Indicators reliability.

CSR	${\mathit{loading}}^2$	INN	${\mathit{loading}}^2$	FP	${\mathit{loading}}^2$
CSR_Q1	0.5585	INN_Q1	0.5560	FP_Q1	0.6557
CSR_Q2	0.5409	INN_Q2	0.5280	FP_Q2	0.6068
CSR_Q3	0.5635	INN_Q3	0.5792	FP_Q3	0.6099
CSR_Q4	0.6666	INN_Q4	0.5954	FP_Q4	0.6423
CSR_Q5	0.6261	INN_Q5	0.5852	FP_Q5	0.5201
CSR_Q6	0.6825	INN_Q6	0.5750	FP_Q6	0.5618
CSR_Q7	0.6617	INN_Q7	0.5017	FP_Q7	0.5355
CSR_Q8	0.6460	CA
CSR_Q9	0.6858	CA_Q1	0.6554
CSR_Q10	0.6726	CA_Q2	0.5101
CSR_Q11	0.6681	CA_Q3	0.5573
CSR_Q12	0.6773	CA_Q4	0.5913
CSR_Q13	0.5048	CA_Q5	0.5669
CSR_Q14	0.6586	CA_Q6	0.5431
CSR_Q15	0.4999	CA_Q7	0.5378
CSR_Q16	0.5161
CSR_Q17	0.6641

in the Appendix A demonstrate that the indicator reliability for all items exceeds 0.5, indicating that the construct accounts for more than 50% of each indicator’s variance, thus ensuring acceptable indicator reliability.

4.2.2. Internal Consistency Reliability and Convergent Validity

To evaluate the internal consistency reliability of the questionnaire, we calculated Cronbach’s alpha (Alpha), Composite Reliability (rhoc), Average Variance Extracted (AVE), and the exact reliability coefficient (rhoA). Based on the results in

Table 2. Reliability and validity for the questionnaire.

Construct	Alpha	rhoc	AVE	rhoA
CSR	0.950	0.955	0.617	0.951
INN	0.855	0.888	0.560	0.868
CA	0.865	0.890	0.566	0867
FP	0.878	0.905	0.590	0.887

, we conclude that all constructs in this study have Cronbach’s alpha values exceeding 0.7, indicating that the chosen scale is reliable for all constructs. Moreover, the values of rhoc for the constructs ranged from 0.888 to 0.955, which are significantly above the threshold level of 0.70. The AVE values for the constructs ranged from 0.532 to 0.576, substantially above the threshold level of 0.5, indicating that all variables fulfil the conditions for rhoc and construct validity. Additionally, the values of the reliability coefficient rhoA lie between the alpha and rhoc, as expected. Overall, we conclude that the current variables exhibit both reliability and validity (see Figure 4).

4.2.3. Discriminant Validity

We used the Fornell and Larcker test to assess the discriminant validity of the constructs. Since the values of fact-loadings for all constructs are greater than the cross-loadings, the Fornell–Larcker Criterion for discriminant validity is met, indicating that all constructs are distinct from each other (see

Table 3. Discriminant validity measures.

Constructs	CSR	FP	CA	INN
CSR	0.747
INN	0.247	0.730
CA	0.247	0.259	0.732
FP	0.263	0.329	0.329	0.759

).

4.3. Evaluation of the Structural Model

After confirming the reliability and validity of the measurement model, the results of the structural model need to be assessed. This systematic approach includes: assessing collinearity issues within the model, evaluating the significance and relevance of the model’s relationships, examining the model’s explanatory power, and determining its predictive accuracy. Each of these steps ensures a comprehensive evaluation of the structural model, allowing for a robust analysis of its efficacy and applicability.

4.3.1. Assess Collinearity Issues within the Model

The construct scores of the predictor constructs in each regression within the structural model are utilized to calculate the variance inflation factor (VIF) values. VIF values above 5 indicate the presence of potential collinearity problems among predictor constructs. However, collinearity can also arise at lower VIF values, notably within the range of 3 to 5. Conversely, VIF values less than 3 suggest the absence of multicollinearity [73,74]. The results in

Table 4. The structural model collinearity VIF.

Model		VIF
Endogenous	Predictor
CA	CSR	1.072
CA	INN	1.072
FP	CSR	1.113
FP	INN	1.143
FP	CA	1.159
FP	CSR*CA	1.054

reveal that the VIF values for each construct are below this threshold, indicating no multicollinearity issues.

4.3.2. The Significance and Relevance of the Model Relationships

In this step, the significance and relevance of the path coefficients are evaluated. The bootstrapping (10,000 resamples) procedure was carried out to calculate t-values of path coefficients and confidence intervals [75]. A path coefficient is considered significant at the 5% level if the value zero does not fall within the 95% confidence interval. In terms of relevance, path coefficients typically range between −1 and +1. Coefficients closer to −1 represent strong negative relationships, whereas coefficients closer to +1 indicate strong positive relationships. The results presented in

Table 5. Path coefficient estimates, significance, and confidence intervals.

Path	Original Est.	Bootstrap Mean	Bootstrap SD	T Stat.	2.5% CI	97.5% CI
CSR → INN	0.2592	0.2652	0.0396	6.5514	0.1864	0.3427
CSR → CA	0.1856	0.1879	0.0477	3.9079	0.0886	0.2751
CSR → FP	0.1834	0.1859	0.0466	3.9383	0.0958	0.2790
INN → CA	0.2145	0.2219	0.0425	5.0509	0.1338	0.2991
INN → FP	0.2943	0.2996	0.0374	7.8702	0.2271	0.3753
CA → FP	0.2366	0.2373	0.0482	4.9072	0.1427	0.3352
CSR*CA → FP	0.0197	0.0214	0.0462	0.4257	−0.0666	0.1145

indicate that all path coefficients are significant at the 5% level, as none of the confidence intervals includes zero except the interaction term CSR.CA, which is insignificant and zero includes in the confidence interval. These coefficients demonstrate positive relationships between the constructs, suggesting that the model possesses strong and significant predictive relationships among the constructs evaluated.

4.3.3. Examine the Model’s Explanatory Power

In PLS-SEM, the primary metrics for examining the model’s explanatory power are the coefficient of determination ($R^2$) and the effect size ($f^2$). Generally, $R^2$ values of 0.75, 0.50, and 0.25 are regarded as substantial, moderate, and weak, respectively, within many social science fields [76]. Nonetheless, the acceptability of $R^2$ values depends on the specific study context, with values as low as 0.10 deemed satisfactory in certain scenarios [77]. The effect size ($f^2$) is interpreted as follows: 0.02 indicates a small effect, 0.15 a medium effect, and 0.35 a large effect [28].

The $R^2$ values for the endogenous constructs CA and FP are 0.502 and 0.648, respectively, which can be considered moderate. In contrast, the $R^2$ value for the endogenous construct INN is 0.272, considered weak.

Table 6. The effect sizes $f^2$.

	CSR	INN	CA	FP
CSR	-	0.172	0.136	0.137
INN	-	-	0.147	0.193
CA	-	-	-	-
FP	-	-	-	-

displays the $f^2$ values for each combination of endogenous constructs (columns) and their corresponding predictor constructs (rows). It is observed that CSR exhibits a medium effect size of 0.172 on INN, 0.136 on CA, and 0.137 on FP. Conversely, INN demonstrates a medium effect size of 0.147 on CA and 0.193 on FP.

Based on the results of $R^2$ and $f^2$, we conclude that the model moderately explains CA and FP but has limited explanatory power for INN. The analysis confirms that CSR and INN significantly influence the other constructs, highlighting their importance in the model. These findings provide a clear understanding of the model’s explanatory power and its implications for the studied constructs.

4.3.4. Assess the Model’s Predictive Power

The principal method for examining the predictive power of a PLS-SEM model is PLS-Predict. This method entails estimating the model using a training sample and subsequently evaluating its predictive performance on a holdout sample. Additionally, k-fold cross-validation can be employed, involving the division of the dataset into k equal subsets. The model is iteratively trained on $k-1$ subsets and validated on the remaining subset, with this process repeated k times so that each subset serves as the test set once. Predictive performance metric RMSE is calculated for each iteration and averaged to assess overall model accuracy [78,79]. To interpret this metric, we compare each indicator’s RMSE values with a naïve linear regression model (LM) benchmark. The LM benchmark values are obtained by performing a linear regression of each endogenous construct’s indicators on the indicators of the exogenous constructs in the PLS path model [80].

To compare RMSE values with LM values, we follow these guidelines: if all PLS-SEM indicators have lower RMSE than the LM, the model has high predictive power; if most indicators have lower prediction errors than the LM, the model has medium predictive power; if only a few indicators have lower prediction errors than the LM, the model has low predictive power; and if none of the indicators have lower prediction errors than the LM, the model lacks predictive power [79]. The PLS-Predict procedure was executed with $k=10$ folds, and the RMSE values for the PLS path model, along with the naïve LM model benchmark, were calculated. The results are presented in

Table 7. RMSE values for the PLS path model and the naïve LM model benchmark.

CA	PLS	LM	FP	PLS	LM	INN	PLS	LM
CA_Q1	0.7944	0.7951	FP_Q1	0.7749	0.7927	INN_Q1	0.8105	0.8173
CA_Q2	0.7901	0.8156	FP_Q2	0.7642	0.7984	INN_Q2	0.8182	0.8420
CA_Q3	0.8795	0.9079	FP_Q3	0.8138	0.8484	INN_Q3	0.8485	0.8699
CA_Q4	0.8055	0.8339	FP_Q4	0.7760	0.7804	INN_Q4	0.8717	0.8728
CA_Q5	0.8645	0.8851	FP_Q5	0.7989	0.8435	INN_Q5	0.8393	0.8530
CA_Q6	0.8172	0.8350	FP_Q6	0.7940	0.8245	INN_Q6	0.8277	0.8579
CA_Q7	0.8070	0.8142	FP_Q7	0.7819	0.8017	INN_Q7	0.8230	0.8544

.

According to the results presented in Table 7, the PLS path model consistently shows lower out-of-sample predictive errors (RMSE) compared to the naïve LM model benchmark for all indicators. This indicates that our model possesses high predictive power.

4.4. Path Analysis and Hypotheses Testing

After the structural model results have passed most of the assessment criteria, confirming that the model has a proper and acceptable fit, the next step involves conducting path analysis and testing the study hypotheses based on the analysis of the study data.

Table 8. Path coefficients and achievement of the hypotheses.

Effects Type	Estimate	2.5% CI	97.5% CI	Hypothesis	Result
Direct effects
CSR → FP	0.1859 ***	0.0958	0.2790	H1	Supported
CSR → CA	0.1879 ***	0.0886	0.2751	H2	Supported
CSR → INN	0.2652 ***	0.1864	0.3427	H3a	Supported
INN → FP	0.2219 ***	0.1338	0.2991	H3b	Supported
INN → CA	0.2995 ***	0.2270	0.3753	H4a	Supported
Indirect effects
CSR → INN → FP	0.0588 ***	0.0325	0.0896	H3c	Supported
CSR → INN → CA	0.0795 ***	0.0512	0.1127	H4b	Supported
interaction term’s effect
CSR*CA → FP	0.0197	−0.0666	0.1145	H5	Not supported

Significance codes: less than 0.01 ‘***’.

summarizes the path coefficients and achievement of the study hypotheses (see Figure 5).

The findings in Table 8 indicate that the direct effect of CSR on FP is (${\gamma }_1$ = 0.1859), with a 95% confidence interval ranging from 0.0958 to 0.2790. Since this interval does not encompass zero, the direct effect is both positive and significant. Consequently, the first Hypothesis H1, asserting that corporate social responsibility positively and significantly influences financial performance, is confirmed. Similarly, a positive and significant influence of CSR on CA has been found (${\gamma }_2$ = 0.1879**), thereby verifying H2. Additionally, the results reveal a positive and significant effect of CSR on INN (${\gamma }_3$ = 0.2652), confirming H3a. Moreover, Hypothesis H3b, which states that financial performance is positively and significantly influenced by innovation, is supported. Similarly, Hypothesis H4a, which suggests that CA is positively and significantly influenced by INN, is also confirmed.

4.4.1. Mediating Effects

As can be seen from the results presented in Table 8, the indirect effects of CSR on FP and CA through INN are both positive and significant (${\gamma }_1^{\prime }$ = 0.0588, ${\gamma }_2^{\prime }$ = 0.0795, respectively), and the corresponding direct effects of CSR on FP and CSR on CA have already been found to be significant. Thus, it can be concluded that INN fully mediates the relationship between CSR and FP, as well as the relationship between CSR and CA, confirming H3c and H4b, respectively. On the other hand, since both the direct and indirect effects are positive, INN serves as a complementary mediator for the impact of CSR on both FP and CA.

4.4.2. Moderating Effect

To test the moderator effect using PL-SEM, the model includes two new paths, one from CA to FP and another from the interaction term (CSR*CA) to FP, along with the simple effect of CSR on FP when CA moderates the association between CSR and FP (see Table 5, Figure 5). As evidenced by the results in Table 8, the interaction term’s effect (${\gamma }_6$ = 0.0197) of CSR.CA on FP is insignificant. Consequently, it can be concluded that CA does not moderate the association between CSR and FP. Hence, Hypothesis H5 is not supported.

5. Discussion of Results

This study employed PLS-SEM to investigate the impact of CSR on companies’ FP and CA through the mediating role of INN and the moderating role of CA. The findings from the analysis reveal several critical insights into how CSR influences FP and CA in the food industry in Saudi Arabia.

The results of this study strongly support the hypothesis that CSR positively impacts FP, aligning with previous study conducted by [19,21,23,81,82], These studies collectively demonstrate that CSR activities significantly enhance FP by building trust, ensuring product quality, and strengthening a company’s reputation. Such improvements are critical for maintaining customer loyalty, meeting regulatory standards, and aligning with societal values—factors that are essential for achieving long-term success. However, these findings contrast with those of [26] who found no significant relationship between CSR and FP in Saudi companies, as well as [27] which reported a negative impact.

The study also confirms that CSR plays a pivotal role in enhancing CA, consistent with previous study [10,30,31,32,34] emphasizing CSR’s contribution to strengthening a company’s market position. By implementing CSR practices, companies can differentiate themselves from competitors, build stronger relationships with stakeholders, and ensure product quality, all of which are crucial for bolstering competitive positioning, particularly within Saudi Arabia’s food industry.

Furthermore, the study validates the positive influence of CSR on INN, highlighting its role in driving innovative capabilities within companies. This finding aligns with previous studies [36,37,39,83] that illustrate how CSR fosters an environment of trust and engagement with stakeholders, encouraging companies to explore new ideas, adopt sustainable practices, and develop innovative products and processes.

In addition, the study reaffirms that INN has a significant positive impact on FP, demonstrating that innovation activities are crucial for driving financial success within companies. This conclusion is supported by previous study [42,48], which emphasizes the importance of INN in enhancing a company’s financial outcomes. Through INN, businesses can develop new products, optimize processes, and improve efficiency, all of which contribute to stronger FP.

The study also emphasizes the mediating role of INN in the relationship between CSR and FP. This finding is in line with previous study [40,41,44], which underscores the significant role that innovation plays in enhancing the impact of CSR on FP. However, Zahid et al. [46] suggest that while CSR positively affects FP, the mediating role of innovation may be less significant compared to other variables, indicating that its influence can vary. Conversely, Bocquet et al. [47] argue that the type of CSR strategy employed is crucial, with responsive CSR potentially hindering INN, while strategic CSR promotes it, benefiting both process and product innovations. This highlights the nuanced role of innovation in the CSR-FP relationship, depending on the approach and context in which CSR is implemented.

The study further supports the notion that INN positively influences CA, in agreement with previous study [8,50,51,52,54]. Innovation enables companies to develop new products and processes that meet stakeholder expectations, allowing them to differentiate themselves from competitors and secure a stronger market position.

Interestingly, the study did not find evidence supporting the hypothesis that CA moderates the association between CSR and FP. The lack of a significant moderating effect in this study may be due to the specific context of the Saudi food industry, where other factors such as market conditions, regulatory environment, and consumer preferences might play a more dominant role.

6. Conclusion Remarks

This study comprehensively explores the influence of CSR on FP and CA within the food industry in Saudi Arabia. Utilizing PLS-SEM, the study confirms that CSR significantly enhances both FP and CA. Additionally, INN plays a critical mediating role in these relationships, underscoring the importance of fostering innovative practices through CSR initiatives.

The key findings from this study reveal that CSR activities not only directly improve FP but also provide a competitive edge by differentiating companies from their competitors and building stronger stakeholder relationships. The mediating role of INN highlights that CSR-driven innovation can lead to new product developments, improved processes, and operational efficiencies, further enhancing a company’s market position and financial performance.

However, the anticipated moderating effect of CA on the relationship between CSR and FP was not supported. This suggests that other factors may play a more significant role in moderating this relationship in the Saudi food industry.

The findings of this study offer important advice for managers in the food industry. First, companies should include CSR practices in their main business strategies to boost FP and gain a competitive edge. This not only benefits shareholders but also customers, employees, and the community. Second, promoting innovation through CSR is essential. Innovative practices can improve efficiency, product offerings, and market position. Lastly, using CSR to build trust and enhance the company’s reputation is crucial, especially in the food industry, where consumer trust is key to success. By focusing on these areas, companies can stand out from competitors and build stronger relationships with stakeholders, leading to sustainable growth and profitability.

While this study provides valuable insights, it has some limitations. It focused solely on the food industry in Saudi Arabia, which may not apply to other sectors or regions. Additionally, we relied on subjective responses from companies’ managers, which may reflect biases inherent to individual perceptions. Furthermore, this study is based on data from a single point in time. Long-term studies could offer a deeper understanding of how CSR impacts FP and CA over time. Further research could also investigate other factors that might influence the relationship between CSR and FP.