Does Supply Chain Resilience Mediate the Relationship Between CRM Dimensions and Customer Satisfaction?

Customer Orientation and Customer Satisfaction

Customer satisfaction is the priority of any business because customers are the primary source of revenue for any company (Yussoff & Nayan, 2020). Customer satisfaction is complex to evaluate because it is difficult to quantify the level of satisfaction. The contemporary methods for quality in organizations and enterprises are demonstrated through customer satisfaction and it performs its function in creating a customer-oriented culture and management (Shamsudin et al., 2019). It is strongly proven that stronger customer-oriented behaviours in companies have a favourable impact on the performance of enterprises. Measurement of customer satisfaction suggests instantaneous, substantial and purposeful feedback about the expectations of customers and their preferences that leads enterprises to optimize their value and thus yield higher profits (Alshourah et al., 2018). Subsequently, the customer-oriented behaviour of enterprises not only facilitates the development of a long-term relationship with the customers that strengthens the performance of the organization but it helps retain the potential customer which leads to an increase in the satisfaction level of the customer and also has a direct impact on marketing (Domi et al., 2020).

On the other hand, customer orientation is a cluster of task-oriented attributes like services for customer support. The business not merely benefits themselves by developing distinguish worth for their enterprises but also their consumers with the help of an appropriate understanding and proficient knowledge about the demands of customers. In other words, the goal of customer orientation is to enhance and sustain the profitability of enterprises by generating long-term associations with the customers and thus optimizing the satisfaction of customers (Gonu et al., 2023). Moreover, CRM has changed the customer mindset and behaviour towards purchasing anything which provides an opportunity in which customer gets a variety of products and services at the moment with the help of a better SC management system (Daud & Aziz, 2019). Due to advanced information technology used in CRM, it has become more customer-oriented which provides all the advanced features to help customers have a better experience (Bhatnagar & Saxena, 2013). Moreover, the studies of Usman et al. (2012) provided comprehensive insights into the major features of CRM which enable the system to be more efficient and effective in terms of comfortable interactions with the customer, prompt processing of transactions, security and privacy, trust and convenience, customized products and services, and so on.

A study by Ismail (2023) suggested that customer orientation is a key factor in developing customer loyalty and satisfaction through functional and relational customer orientations. Functional customer orientation is centred on task-oriented behaviours that guide customers in making informed purchasing decisions. In contrast, relational customer orientation aims to establish strong personal connections with customers. Similarly, a study by Singh et al. (2023) highlighted that companies that embrace a customer-oriented strategy may benefit from increased customer satisfaction, loyalty and retention, which in turn leads to a better understanding of what customers want and can help them create goods and services that satisfy those demands. Therefore, the following hypothesis is proposed:

H₁: Customer orientation has a direct impact on customer satisfaction.

Knowledge Management and Customer Satisfaction

To create a marketing strategic plan, the customers’ diversified tastes and preferences make complicated to categorize customers in a sheer homogeneous population. Every customer has different choices concerning his/her personal and individual needs. Characteristics of customers can be understood through assessing customer knowledge, customer deviation, customer acquisition and customer retention (Alani et al., 2023). Managing the knowledge regarding the preferences of customers can help companies in understanding their customers’ needs and thus serve them in a better way which leads to stronger relationships with customers that will be reflected in customer satisfaction and better business outcomes. Knowledge management enables communication to be more effective in terms of providing two-way communication with customers; it also helps to gather customer information after having better interaction with customers. Knowledge of customers regarding their habits, interests and behaviour towards products and services facilitates enterprises in making quick decisions. Knowledge management is quite useful in quick and accurate customer interaction with the help of gathering customer information to manage the queries related to customers (Mahawrah et al., 2016). In addition, previous research has shown that firms can enhance their performance by adopting knowledge management strategies (Fidel et al., 2018; Taherparvar et al., 2014). Yusr et al. (2022) argued that processes of knowledge management provide support to businesses by directing them on how to satisfy the needs of customers. Similarly, Hamzah and Shamsudin (2020) pointed out that to satisfy customers, companies tend to provide superior products and excellent services, and continuously search for ways to improve, differentiate themselves from their competitors and exceed customer expectations. Furthermore, the study by Chi (2022) noted that promoting knowledge management improves an organization’s chances of achieving customer satisfaction because enterprises tend to use collected knowledge in manufacturing products according to the needs of customers. Therefore, the following hypothesis is proposed:

H₂: Knowledge management has a direct impact on customer satisfaction.

Technology-based CRM and Customer Satisfaction

In the era of technology, companies enable to collect customer information, communicate with them in a better way and keep track of key customers, which leads to a better understanding of customers and strong relationships between them (Pozza et al., 2018; Tian & Wang, 2017). Information technology enhanced the worth of available data. It enables the enterprises to access extensive data to develop more worth of their company. A significant progression has been made by modern information technology in the domain of interactivity and it transferred the marketing strategy from mass to individual which information regarding the needs of every customer (Demirel, 2022). Moreover, various IT tools can be used to attract customers and improve customer retention and loyalty. In addition to managing customer data, technology-based CRM practices become more effective and accurate which improves the performance and business relationships among suppliers, vendors, brokers, middlemen and customers (Aloqool & Alsmairat, 2022). Blockchain technology can be considered a helpful tool in business transactions (Mbaidin et al., 2023). The implementation of blockchain technology in SC management will affect future business growth and it will affect the large businesses of organizations for whom blockchain technology is more relevant and a trustworthy business partner (Durach et al., 2021). Technology has a disruptive impact on CRM practices which affects the customer’s mind to understand organizational value and its business strategy (Magatef et al., 2023).

Customer experience is one of the main reasons for customer satisfaction which can be achieved by using appropriate CRM tools (Mulyono & Situmorang, 2018). Perceived rewards and privileged transactions are the two main dimensions of e-CRM that affect the relationship with customers. The quality of the relationship with customers defines the customer’s loyalty to the products and services (Sokmen & Bas, 2019). The technology-based CRM has importantly played its role in the data-driven era where data science evolved as a revolutionary system to understand the customer tendency of buying behaviour. This behaviour helps to make more effective and accurate strategies for customer satisfaction and customer retention. Now advanced artificial intelligence-enabled e-CRM tracks the customer needs and the customers who are potential to buy products and their habits of purchase and purchasing process (Alam et al., 2022).

Based on previous arguments, the following hypothesis is proposed:

H₃: Technology-based CRM has a direct impact on customer satisfaction.

Supply Chain Resilience and Customer Satisfaction

The extant literature has introduced many approaches to optimize the SC design, such as flexibility, agility and resilience (Linton & Vakil, 2020; Remko, 2020). The roots of SCR can be traced back to the late 1950s. Scholars emphasize that many uncertainties and risks exist along with the various SC activities such as supply, demand and control risks that may disrupt the SC (Pettit et al., 2019). To remain relevant and meaningful, SCs have to continuously change and evolve, which necessitates managers to thrive for adaptability and transformability, allowing them to anticipate and foresee events outside the SC. SCR is an emergent feature that makes SC management more effective (Al-Shboul & Alsmairat, 2023). SCR is more cost effective which helps to reduce the expenses (Benjamin et al., 2015). Linton and Vakil (2020) emphasized the importance of companies having a higher level of resilience in their SC to mitigate future disruptions and uncertainties. Likewise, Gu et al. (2023) acknowledged that developing a resilient SC is a difficult endeavour that necessitates firms to allocate extra resources and reconfigure their SC. It is evident that organizations with resilient capabilities can endure unstable and volatile conditions and can be more competitive in the market, which may enable them to survive for a longer period (Karl et al., 2018). To build a resilient SC, comprehensive information regarding the determinants of service quality is essential to ensure the satisfaction that customers get through the delivery of products (Das et al., 2021). As a result, companies with excellent SCR can enhance the quality of their services and attain high levels of customer satisfaction (Ridwan et al., 2021). Based on previous arguments, the following hypothesis is proposed:

H₄: SCR has a direct significant impact on customer satisfaction.

The Mediation Role of SCR

Previous research has emphasized the significant and direct impact of SCR on achieving various objectives such as mitigating risks, enhancing operational performance, gaining competitive advantages, improving customer services and achieving higher levels of customer satisfaction (Pimenta et al., 2022).

Cardoso et al. (2015) argue that business with SCR will better adjust their operations in response to customer preferences and satisfy changing demands. Understanding and addressing customer needs and preferences can improve a company’s responsiveness and agility, both of which are critical components of a resilient SC (Lozada-Contreras et al., 2022). The dimensions of CRM can enhance information flow and communication among SC partners, which can reduce uncertainties and risks. The use of information and communication systems enables companies to maintain business strategies relationally, allowing for the processing and storage of consumer data to better understand individual demands interactively (Morrish & Jones, 2020). This understanding of customer demands can have a significant impact on the resilience of SC networks.

The mediating effect of SCR on different dimensions of CRM and customer satisfaction has not gained much attention in the literature whereas the study of Pirani et al. (2020) has explored the mediation effect of SCR on the dimensions of SC integration and the SC performance. Furthermore, Zhao et al. (2023) investigated the mediating effect of SCR between SC digitalization and SC performance. Hence, previous studies have focused on the direct impact of SCR on customer satisfaction, as well as the relationship between CRM and SCR. However, the mediation role of SCR in the relationship between CRM dimensions and customer satisfaction has not been extensively explored. It can be argued that a more resilient SC network leads to higher customer satisfaction, as it requires a high level of technology, knowledge and customer orientation. So, the hypotheses stated below are formed following the above discussion:

H₅: SCR mediates the relationship between customer orientation and customer satisfaction.

Figure 1 depicts the conceptual framework of SCR, CRM dimensions and customer satisfaction. The direct relationship between the construct is represented through solid arrows while the mediating effect of SCR between the dimensions of CRM and customer satisfaction is illustrated through a dotted line.

OPEN IN VIEWER Figure 1.

Conceptual Framework.

Method and Sample

This study aims to examine the mediation role of SCR between CRM dimensions and customer satisfaction in SMEs. A quantitative approach was used in this study to obtain quantitative results of research constructs. A survey-based questionnaire designed based on a points Likert scale was used to gather data. Furthermore, an online questionnaire was distributed to different customers of SMEs through Google Forms. The questionnaire has been distributed to the customers of SMEs which were selected through the medium of social media. The focus of the study was the customers of SMEs in the Middle East, specifically in Jordan, Qatar, the United Arab Emirates, Saudi Arabia and Bahrain. The sample for this study was drawn from SMEs operating in these countries. The reason for targeting these countries from the Middle East was that the SMEs of these countries had a significant share in their gross domestic product (GDP). The economic contribution of SMEs in selected countries is presented in Table 1. We tracked the customers of SMEs in these five countries through social media and a questionnaire was distributed. A technique of convenient sampling was used. Initially, we sent a questionnaire to 630 customers of SMEs of which 60 customers did not respond and the rest 41 responses were omitted due to response bias. So, in total 529 observations with a response rate of 83.9% were collected and analysed, which was deemed an appropriate sample size for the data analysis of the study. Because the minimum requirement for sample size is 100–200 which was suggested by Wong (2013). The research data were analysed using structural equation modelling (SEM) with Smart PLS as the analytical tool. PLS-SEM is an efficient technique for exploring the interrelationships among multi-item constructs (Hair et al., 2019) and this technique is widely employed among social sciences and business scholars to analyse multivariate data. The wide acceptance of the PLS-SEM technique among students and scholars is because it can facilitate researchers to examine the simultaneous association between the latent and study variables in a complicated model. Furthermore, it can be equally helpful for small and larger sample sizes (Memon et al., 2021). Moreover, the SEM technique has two sub-models; an inner model which reflects the association between the explanatory and outcome latent variable. On the other hand, an outer model depicts an association between the latent variable and their perceived measure.

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Table 1.

Economic Contribution of SMEs in Selected Countries.

Selected Countries	SMEs Operating (%)	Contribution in GDP
UAE	94%	63.5%
Qatar	97%	<16%
Bahrain	93.3%	30%
Saudi Arabia	44%	33%
Jordan	98%	<50%

Source: General Authority for SMEs, Authority for Small and Medium Development, and Ministry of Industry & Commerce.

Instrument Design

The survey items used in this study were adopted from previous research studies. The items measuring SCR were adopted from the study conducted by Asamoa et al. (2021). The items used to measure customer focus and knowledge management were taken from the research conducted by Sin et al. (2005). The items assessing customer satisfaction were adopted from the study conducted by Le et al. (2020), and the items measuring technology-based CRM were taken from the studies conducted by Lin et al. (2010) and Sin et al. (2005). By using previously validated survey items, the current study was able to build upon existing research and ensure the validity and reliability of the survey instrument.

Demographic Variables

The data highlight the variation in the respondents’ gender, age and income. The sample includes 529 individuals, of which 54.3% are males and 45.7% are females. Moreover, the majority of the sample falls into the 18 to 26 years age bracket, with 49.1% of the individuals, followed by 21.4% of respondents in the age bracket of 36 to 44 years. The respondents in age brackets between 27 and 35 years and 45 years and above make up only 17.8% and 11.7% of the sample, respectively. The income of respondents is converted into US dollars; the majority of the sample earns more than $500, with 51.2% of individuals falling into this category. The next largest group, at 28.2%, earns between $1,000 to $1,500. A smaller proportion, 9.5%, earns more than $2,000, and only 11.2% earns more than $2,500. Overall, these data provide insight into the demographic composition of the sample population. The sample is predominantly male and young, with the majority earning a relatively low income (more than $500). Table 2 summarizes these characteristics.

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Table 2.

Sample Characteristics.

Demographic Characteristics		Count	%
Sex	Male	287	54.3
	Female	242	45.7
Age	18 to 26 years	260	49.1
	27 to 35 years	94	17.8
	36 to 44 years	113	21.4
	45 years and above	62	11.7
Income	More than $500	271	51.2
	Between $1,000 and $1,400	149	28.2
	More than $2,000	50	9.5
	More than $2,500	59	11.2
Total		529	100%

Measurement Model Assessment

Measurement models are assessed based on the constructs’ validity which involve internal consistency, reliability and discriminant reliability (Sarstedt et al., 2022). The internal consistency and reliability of a measurement model can be assessed using several statistics, including Cronbach’s alpha, composite reliability (CR) (rho_c) and average variance extracted (AVE). Based on the results, Cronbach’s alpha coefficients for all five constructs are above the commonly recommended threshold of 0.7, indicating good internal consistency (Hair et al., 2017). The CR coefficients for all constructs are also above the recommended cutoff of 0.7, indicating good construct reliability (Hair et al., 2019). Convergent validity is evaluated through AVE; values above 0.5 are generally considered acceptable (Fornell & Larcker, 1981). In the current data, all constructs meet this threshold. Overall, the measurement model appears to have good internal consistency and reliability. The factor loading scores show that all items have high loadings on their respective constructs. As suggested by Hair et al. (2019), factor loadings above 0.7 are an indicator of reliability. In this study, all items have factor loadings above this threshold, ranging from 0.715 to 0.885, indicating that the items are measuring their intended constructs well. Table 3 and Figure 2 summarize these results.

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Table 3.

Construct Validity and Reliability.

Constructs	Items	Items	Loading	Mean	SD
Customer orientation (Cronbach’s α = 0.728; CR = 0.846; AVE = 0.647)	All people in an organization treat key customers with great care	CO1	0.838	3.725	1.038
	An organization makes an effort to find out what our key customer needs	CO2	0.833	3.961	1.028
	When an organization finds that customers would like to modify a product/service, the departments involved make coordinated efforts to do so.	CO3	0.723	3.944	0.982
Technology-based CRM (Cronbach’s α = 0.771; CR = 0.866; AVE = 0.683)	The organization maintains a comprehensive database of our customers	TEC1	0.852	3.596	1.034
	The organization has the right technical personnel to provide technical support for the utilization of computer technology in building customer relationships	TEC2	0.808	3.434	1.157
	Web-based customer	TEC3	0.818	3.733	1.12
Knowledge management (Cronbach’s α = 0.752; CR = 0.858; AVE = 0.668)	The organization fully understands the needs of our key customers via knowledge-leaning	KM1	0.856	3.708	1.036
	Customers can expect prompt service from employees of my organization	KM2	0.834	3.745	1.04
	KM5 My organization provides channels to enable ongoing, two-way communication with our key customers and us	KM3	0.759	3.426	1.096
SCR (Cronbach’s α = 0.725; CR = 0.801; AVE = 0.572)	Restore operations, processes and functions to normal	SR1	0.773	3.966	1.045
	Quickly respond to and salvage the situation	SR2	0.781	3.512	1.12
	Recover from operational/process/functional losses	SR3	0.713	3.539	1.056
Customer satisfaction (Cronbach’s α = 0.848; CR = 0.908; AVE = 0.767)	You are satisfied with the manners and methods that SMEs have organized and managed to provide services	CS1	0.867	3.725	1.104
	Overall, you are satisfied when using services provided by SMEs	CS2	0.876	3.762	1.041
	You are satisfied with the services provided by and the working attitudes of the staff members	CS3	0.885	3.838	1.082

OPEN IN VIEWER Figure 2.

Full Path Research Model.

Structural Model Assessment

A structural model assessment was conducted to examine the relationship between customer satisfaction and SCR. The assessment involved analysing several statistical indices, including the standardized root mean square residual (SRMR), the effect size index (f²) and the coefficient of determination (R²) (Hair et al., 2019).

The SRMR value of 0.070 suggests that the model provides a good fit to the data (Sarstedt et al., 2022). The effect size index (f²) value of 0.403 indicates that the relationship between CRM, customer satisfaction and SCR is moderate to strong (Hair et al., 2017).

The R² value of 0.494 for customer satisfaction indicates that 49.4% of the variance in customer satisfaction can be explained by CRM. The R² value of 0.375 for SCR suggests that 37.5% of the variance in SCR can be explained by CRM dimensions.

Hypotheses Testing

Hypothesis testing is an essential step in SEM that involves evaluating the statistical significance of the relationships between variables in a proposed model. Partial least squares (PLS) is a widely used SEM approach that can accommodate complex models and handle small sample sizes (Hair et al., 2019). In PLS, the bootstrapping method can be used to test the significance of the estimated path coefficients and their standard errors (Henseler et al., 2016). Additionally, the β coefficient can be used to determine the strength and direction of the relationships between variables in the model (Sarstedt et al., 2022). Table 4 and Figure 2 represent the results of the hypotheses tests.

Table 4 represents the findings of hypotheses (H₁–H₇). The results of our study accepted H₁ and H₂, stating that customer orientation and knowledge management positively and directly influence customer satisfaction with highly significant β values of 0.304 and 0.301, respectively. Hence, both hypotheses are supported through our study. On the other hand, H₃ is not confirmed by our study which stated that technology-based CRM has a direct impact on customer satisfaction since its p value is greater than .05. Therefore, H₃ is rejected. Furthermore, H₄ stated that SCR positively influences customer satisfaction and the findings of our study confirm this hypothesis (β = 0.201, p < .05). Thus H₄ is supported. H₅ is not supported by our study which stated the mediation role of SCR between customer orientation and customer satisfaction since its p value is greater than .05. Thus, our result rejects the H₅. Whereas, H₆ and H₇ predicted that SCR mediates the relationship between knowledge management and customer satisfaction and technology-based CRM and customer satisfaction. The findings of our study confirmed that SCR plays a mediating role between knowledge management and customer satisfaction and technology-based CRM and customer satisfaction with β values of 0.084 and 0.030, respectively (p < .05). Thus, both H₆ and H₇ are supported in our study.

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Table 4.

Direct and Indirect Path Analysis.

H	Path	(β)	Standard Deviation	t-Statistics	P values	Decision
1	Customer Orientation → Customer Satisfaction	0.304	0.057	5.297	.000	Supported
2	Knowledge Management →Customer Satisfaction	0.301	0.062	4.830	.000	Supported
3	Technology-based CRM → Customer Satisfaction	0.036	0.057	0.628	.530	Rejected
4	SCR → Customer Satisfaction	0.201	0.053	3.766	.000	Supported
5	Customer Orientation → SCR → Customer Satisfaction	0.025	0.014	1.775	.076	Rejected
6	Knowledge Management → SCR → Customer Satisfaction	0.084	0.025	3.400	.001	Supported
7	Technology-based CRM → SCR → Customer Satisfaction	0.030	0.015	1.977	.048	Supported

Theoretical Implication

Our study provided some theoretical implications for future research. It gives a comprehensive understanding of the association between the different dimensions of CRM and customer satisfaction by employing survey data on 529 customers of SMEs in the Middle East region. First, this study has demonstrated a direct and indirect relationship between dimensions of CRM and customer satisfaction, but this study is a pioneer attempt to evaluate the mediating effect of SCR on CRM and customer satisfaction. Therefore, studying the mediating effect is imperative for researchers as it provides better insights into the influence of the third variable on the association between two variables in a model. The findings revealed that a resilient SC can be used to build CRM and will increase customer satisfaction (Bahrami & Shokouhyar, 2022). Second, this study will provide an understanding of the customer-oriented approach by reducing the disruptive effect on the performance of enterprises which directly influences customer satisfaction (Xu et al., 2014) and long-term relationships with customers can be maintained through knowledge management about the preferences and interests of customers (Hassan et al., 2015).

Managerial Implications

This study has managerial implications for the top management of SMEs to establish a more resilient SC by emphasizing customer orientation, knowledge management and technology-based CRM (Wicaksono & Illes, 2022). Since, understanding and prioritizing the needs of their customers, SMEs can develop more effective SC strategies that can withstand disruptions and changes in the market, which in turn will contribute to building a more resilient SC. In addition, this study will provide help to top managers of SMEs in evaluating SCR and CRM in determining customer satisfaction and this will foster their capabilities to implement more resilience in their current SC management system (Das & Hassan, 2022). Additionally, this study highlights that SMEs can promote partnerships between their suppliers and partners through a customer-focused approach. Therefore, it is recommended that top management of SMEs invest in technology-based CRM to improve their adaptability and resilience across the SC which will assist in achieving the satisfaction of customers by boosting the functions of SCs and reducing disruptions (Santouridis & Veraki, 2017). Furthermore, this study has also raised another important managerial implication. SMEs are more inclined towards labour as compared to large-scale businesses because of the expensive machinery and equipment. Therefore, this study showed that customer satisfaction is adversely affected by the knowledge and skills of employees. However, top management of SMEs should invest in the knowledge and skills of their employees by providing training to them.

This study has a few limitations as we focused on the population of SMEs in Middle Eastern countries. In the future, scholars can explore CRM practices and SCR in different sectors, for instance, large-scale enterprises (textile, steel, automobile and manufacturing industries). Furthermore, this study used a quantitative approach to examine the effect of CRM and SCR on customer satisfaction and the mediating role of SCR between CRM and customer satisfaction. Future research can adopt a mixed method approach for the analysis and can also study the factors affecting CRM’s different dimensions, SCR and customer satisfaction. This study provided the demand-side perspective; future research can focus on the supply-side perspective of CRM and SCR.

There is still much domain for research on SCR in the small and medium industries, like circumstances of exposure because of the effect of the bullwhip in SCM, and commencement of information technology and its implementation may also be investigated in future studies. Furthermore, in the future, researchers may employ longitudinal data to capture the resilience of the SC before disruption, at the time of disruption and after the disruption stage linked with outcomes.