The Effects of a Digital Marketing Orientation on Business Performance

Abstract

Customer relationship management (CRM) has become increasingly important as a result of the pressure organizations are under to remain competitive. CRM has been and is widely promoted as essential to a company’s ability to survive. According to this study, CRM is more than just a computer program or software package. We believe that for organizations to use CRM effectively, it must be viewed from a strategic point of view. Therefore, this study focuses on the consequences of digital marketing on business performance, specifically, the consequences of customer relationship orientation and the use of CRM technologies as a support for analysis, data integration, and access on business performance. This study addresses a contemporary and relevant research problem that has national and international relevance. The research is based on quantitative methods to test hypotheses. Data were gathered from 73 organizations. The findings show the relationships between CRM and customer satisfaction, market effectiveness, and profitability. Customer relationship orientation positively affects customer satisfaction, market effectiveness, and market profitability. CRM technologies significantly improve business performance. This research contributes to the existing knowledge by shedding light on the complex relationships among CRM, customer relationship orientation, market effectiveness, market profitability, and customer satisfaction. Based on the research results, we provide practical recommendations for managers and decision makers.

1. Introduction

Intense attention from researchers and practitioners has led to relationship marketing (RM) and customer relationship management (CRM) gaining increasing recognition since the early 1990s. Indeed, much of the extensive literature on RM considers this marketing development as a 1990s phenomenon. In recent years, with the digitization of the economy and society, digital transformation has meant, for most organizations, costly investments in infrastructure, hardware, IT solutions, and related specialists. Today, with the help of system integrators and cloud-based applications, any company or organization can easily access the latest and most effective business solutions.

Recent technological advances have had a big impact on marketing. Increasingly, complex consumer experience management across all digital platforms extends to pure brand communication. Thus, IT solutions are taking center stage as primary tools for marketers. Customers in today’s market demand personalized services. To reach customers with the right offer at the right time, this technique requires the advertiser to be aware of their buying habits, interests, and needs.

The ways in which companies and customers have embraced new technologies, as well as the ways in which technology has enabled new market behaviors, new ways of interacting, and new consumer experiences, all testify to the digital transformation of marketing [1]. With 95% of marketers reporting a collaborative relationship with IT, marketing technology plays a critical role in cross-functional success. By strengthening these connections, marketers are able to implement and optimize sophisticated marketing technologies that facilitate advanced data sharing across multiple channels for more personalized communication with customers.

Relationship marketing and the concept of CRM are closely related because both focus on building and maintaining long-term customer relationships. Relationship marketing emphasizes the importance of continuous and personalized interactions with customers in order to increase their loyalty and satisfaction. On the other hand, CRM provides the tools and technologies needed to effectively manage these relationships by collecting and analyzing customer data to provide personalized experiences and anticipate their needs. According to Morgan and Hunt [2], relationship marketing involves creating emotional and trusting bonds between the company and customers, leading to mutual benefits and long-term value for both parties.

Over time, the term “digital marketing” has changed from referring only to the promotion of goods and services through digital channels to referring to the use of digital technologies to increase sales by attracting new customers and satisfying their preferences. As far as the American Marketing Association is concerned, digital marketing encompasses many actions, institutions, and processes that are enabled by digital technology and are aimed at creating, communicating, and delivering value to customers and other stakeholders. Therefore, taking a broader view, we characterize digital marketing as an adaptive, technical process that companies use in partnership with customers and partners to create, communicate, deliver, and maintain value for all stakeholders. Digital marketing research identifies critical moments in marketing strategies and tactics where digital technologies have or could have a major influence.

This paper addresses the topic of sustainability by highlighting several key and contextual aspects that reveal the importance of using CRM technologies in today’s business landscape. Also, this study addresses a contemporary and topical issue, as the integration of CRM into business strategies contributes to the sustainability of organizations, helping them to quickly adapt to market changes and maintain serious relationships with customers.

Sustainability in business refers to the adoption of practices that support long-term development, ensuring a balance between economic growth, environmental protection, and social responsibility. The effective use of CRM technologies can play an essential role in achieving sustainability goals, improving organizational processes, and contributing to the creation of value for all actors involved.

This research is devoted to exploring the impact of digital marketing on business performance, specifically the consequences of customer relationship orientation and the use of CRM technologies as a support for analysis, data integration, and access on business performance. This study contributes to understanding the complex relationships among CRM technologies, customer relationship orientation, market effectiveness, market profitability, and customer satisfaction.

2. Literature Review

2.1. Concept and Key Elements of Digital Marketing

Since 2020, digital marketing has continued to evolve and the terms and associated definitions reflect this dynamic. Organizational environments are also evolving rapidly due to digital technologies, and asymmetries between buyers and sellers are being significantly reduced by digital technologies. Examining how consumer behaviors are changing due to access to a range of technologies and devices in both online and mobile contexts is the first step in analyzing the connections between digital technologies and environmental components.

Digital marketing has revolutionized the way businesses interact with customers and has provided unprecedented opportunities to reach target audiences, build strong relationships, and drive business growth. Digital marketing is a set of strategies, tactics, and techniques to promote products or services using online channels and digital platforms and focuses on interacting with target audiences in the digital environment with the aim of generating awareness, engagement, and conversions. Digital marketing is a form of marketing that uses technology to deliver online content through digital channels in order to connect with consumers. It emerged in the late 1990s, and by 2014 it had become the main way of marketing for businesses [3].

According to Chaffey and Ellis-Chadwick [4], digital marketing is the set of activities that use digital technologies to promote products and services. Unlike traditional marketing, digital marketing offers a wider range of tools and channels to reach consumers in a more personalized and interactive way. Digital marketing can accelerate and streamline the marketing process. As a result, marketing materials can be uploaded to servers instantly, eliminating the need for long print times and making them constantly available to potential customers. When it comes to company customer support, problems are solved considerably faster by sending a simple email rather than writing letters or making calls [5].

Digital marketing is a dynamic and ever-evolving field, and to be successful online, businesses need to take a strategic approach and use a variety of tools and channels. By understanding the key elements of digital marketing and adapting to new trends, companies can build strong relationships with customers and achieve significantly improved performance [6].

Personalization of communication with customers has become a necessity in the digital age, and modern technologies allow companies to provide highly accurate and relevant information about products and services, including promotions, discounts, and real-time availability. Thus, the customer experience is significantly improved, increasing their loyalty and satisfaction [7]. In this regard, many studies have examined how technology positively impacts business performance [8].

2.2. Customer Relationship Orientation

Customer relationship orientation (CRO) is a strategic approach that emphasizes long-term customer development and retention. In the context of digital marketing, this orientation becomes even more important due to the extended possibilities of interaction and personalization offered by digital technologies. According to Narver and Slater [9], customer orientation refers to a firm’s ability to generate greater value based on a thorough understanding of its customers. A confidence scale for assessing market orientation was developed by Narver and Slater [9], who used it to examine how CRO affects business performance. This market orientation model is based on two choice criteria, namely, long-term orientation and profitability, and operationalized through three behavioral dimensions: customer orientation, competitor orientation, and cross-functional coordination.

The concept of market orientation is well established in the literature and indicates that organizations with a strong market orientation perform better. Specifically, previous studies have shown that adopting market orientation improves the performance indicators of the new product development process and the performance of those products in the marketplace [10].

Collection and dissemination of target market information within the organization are included in customer and competitor orientation. Cross-functional coordination demonstrates how marketing works with other departments and how their efforts are linked to achieving company goals. A balance between the two orientations is required and recommended because competitor orientation is not a substitute for customer orientation [11].

The foundation of any effective business strategy is customer relationship orientation, and companies can build lasting relationships and promote business expansion by putting customers’ interests first, providing outstanding service, making constant adjustments to meet their demands, and continuously adapting to their changing expectations [12]. A customer-oriented company emphasizes transparent communication, efficient problem solving, and exceptional service. Therefore, customer relationship orientation is a long-term investment that brings multiple benefits to companies. By building strong trusting relationships with customers, companies can increase sales, improve their reputation, and gain a significant competitive advantage [13]. The link between customer relationship orientation and digital marketing is critical to business success. By adopting a customer-centric approach and effective use of digital technologies, organizations can improve customer satisfaction, business effectiveness, and profitability [14].

2.3. The Concept of Customer Relationship Management (CRM)

CRM stands for customer relationship management and is a collection of techniques, policies, and methods used to improve interactions with customers. CRM refers to both a customer relationship management philosophy and the technological solutions or methodologies required to implement it, as each tool and each level of CRM implementation is a true reflection of the CRM philosophy or strategy itself [15]. CRM is defined as a dual value creation management strategy that focuses on specific customers, customer groups, and the integration of business networks and organizational processes that work together to create customer value. It also involves the wise application of data and technology, gathering and sharing customer insights with stakeholders, and establishing appropriate (long-term) customer relationships [16].

Thanks to technological advances, customer relationship management (CRM) enables companies to provide personalized and relevant experiences for each customer. By collecting and analyzing data, organizations can identify individual customer needs and preferences, resulting in more meaningful interactions and increased customer satisfaction. In essence, CRM represents a commitment to customers that translates into loyalty and business growth [17].

To help implement an integrated CRM strategy, a CRM system manages an organization’s external operations, which include managing customers, prospects, and business partners, as well as its internal activities, which include managing employees and departments. In addition, CRM optimizes the value of customer and prospect data by bringing together customer and prospect interactions, which helps to attract new customers and showcase opportunities to current ones.

The field of customer relationship management (CRM) is dynamic, and modern digital technologies have completely changed the way customers and companies communicate with each other. CRM systems give companies the chance to manage the customer lifecycle through each stage based on customer data to create more profitable connections. As a result, the new dimension of CRM is focused on customer interaction and is based on relationship marketing. According to Zablah et al. [18], customer relationship management is a strategy, procedure, skill, and/or technology for attracting and retaining attractive customers who generate revenue for the company.

CRM is necessary for the future of the company. CRM systems enable companies to better analyze their customers’ behaviors, anticipate future actions, provide individualized customer service, and build lasting customer relationships. However, it would be a serious mistake for a business to assume that CRM is restricted to technology. Companies cannot deliver exceptional customer experiences, value, and service by relying solely on investments in CRM technologies. To deliver exceptional customer service and experience, a company’s operating procedures and culture must be strategically integrated with the CRM philosophy. Therefore, businesses cannot reap the benefits of CRM unless they have a viable CRM strategy.

CRM technologies have evolved significantly over the past few decades, moving from simple customer interaction tracking systems to complex platforms that integrate data from various sources to provide a holistic view of the customer. A major factor contributing to this evolution is the involvement of artificial intelligence (AI). By integrating AI into CRM, companies can optimize customer interactions, personalize offers and user experiences, and anticipate user needs, improving customer satisfaction and loyalty. Automating repetitive processes is one of the most obvious uses of AI in CRM, as data entry, maintaining customer profiles, and providing automated responses to queries are just some of the tasks AI can handle. AI-based CRM systems can generate complete customer profiles by examining past customer behaviors, preferences, and interactions [19].

In addition to operational benefits, AI can significantly contribute to strategic decision making because, by integrating AI into CRM, managers can obtain detailed reports and analyses that can help them to better understand market trends and customer behaviors. This information can be used to develop more effective marketing strategies, optimize advertising campaigns, and identify new business opportunities.

2.4. Use of CRM Technologies

Researchers and practitioners have paid great attention to customer relationship management (CRM) as a means of improving corporate performance. Despite the massive investments companies have made in CRM technologies, empirical research on the subject shows conflicting results on the benefits of CRM technologies for organizational effectiveness. Previous research has modeled the use of technologies in CRM, with marketers realizing that these technologies are potential enablers for CRM and are essential for relationship marketing. CRM is an important area of research that has observed the impact of customer interest [20].

CRM plays a crucial role in the brand development of a service or product, taking into account the interactions between different customers. It also gives businesses a competitive advantage in markets where product offerings are fairly uniform. The focus has shifted from transactional data on paper to relationships via software in the dynamic business landscape, thanks to the rise of relationship marketing as a modern philosophy and its development as a marketing function within CRM. According to Greenberg [21], social CRM is a business philosophy and strategy that is supported by technology and a system that aims to engage the customer in cooperative engagement that creates value for both parties in a transparent and trusting corporate environment.

Today’s competitive business landscape places increasing emphasis on a firm’s ability to manage CRM, as this facilitates the creation and execution of customer strategies that are more efficient and effective. Based on this notion, many companies have made significant investments in CRM technologies in an effort to effectively upgrade their CRM. Despite the large sums of money that companies have invested in CRM technologies, empirical research on the topic shows mixed results regarding its ability to improve organizational performance [22]. Researchers and practitioners used to see CRM as an investment in software technology. In fact, CRM and its technologies are frequently used interchangeably [23].

The use of CRM technologies has evolved significantly over the last few decades, becoming an essential tool for modern companies that want to improve their relationship with customers and optimize their marketing and sales strategies. Using CRM technologies as a support for analytics offers numerous advantages, including forecasting customer preferences, measuring customer loyalty, calculating customer lifetime value, and evaluating product profitability. One of the most valuable functionalities of CRM technologies as a support for analysis is the ability to forecast customer preferences. By collecting and analyzing data about buying behaviors, this forecast enables the personalization of offers. A recent study demonstrated that the use of machine learning algorithms in CRM can significantly increase the accuracy of customer preference forecasts [24].

Measuring customer loyalty is crucial to retention and loyalty strategies, and CRM technologies provide the tools to monitor and evaluate customer loyalty. These tools allow companies to identify and encourage long-term loyalty. Calculating customer lifetime value is another function of using CRM technologies and represents the total value a customer brings to a company over the lifetime of their relationship with it. This information is important for effectively allocating marketing resources and developing retention strategies that maximize the long-term value of each customer. Companies that use customer value calculations as the primary metric for customer segmentation experience a significant increase in profitability [25]. Evaluating product profitability is facilitated by the use of CRM technologies by analyzing sales data and associated costs. By monitoring the performance of each product against customer segments and sales channels, companies can identify the products that generate the most profit and those that require adjustments or withdrawal from the market. This assessment helps to optimize the product portfolio and make informed decisions regarding the development of new products or the modification of existing ones.

The use of CRM technologies for data integration and access support plays a crucial role in optimizing management, and these technologies not only allow the combination of customer transaction data with external data sources but also integrate customer information obtained from different touch points, thus facilitating employee access to centralized customer data. In this context, analyzing the importance and benefits of these CRM technological functionalities highlights how companies can improve their performance and create a superior experience [26]. Combining customer transaction data with external data sources means that information about customer purchasing behaviors, transaction histories, and preferences can be supplemented with external data, such as demographics, market trends, and social media data. This integration enables a deeper understanding of customers and their preferences and, implicitly, the development of more effective marketing and sales strategies. A well-integrated CRM system will facilitate employee access to centralized customer data so all relevant departments, from sales to marketing to support and service, have access to the same up-to-date customer information. This unified access enables better coordination between teams, reduces redundancies, and improves the quality of service provided to customers.

Overall, the use of CRM technologies for data integration and as an access support significantly contributes to improving the company’s performance. By centralizing and easily accessing data, companies can make informed and strategic decisions based on concrete and up-to-date data [27]. Investments in CRM technologies and related analytics capabilities are critical to long-term success in an increasingly competitive and customer-centric business environment.

The involvement of AI in the use of CRM technologies is a major step toward streamlining and personalizing interactions with customers. Automating repetitive tasks, personalizing the customer experience, predictive analytics, and improving customer support are just a few of the benefits AI brings to this field. However, to fully benefit from these advantages, companies must address the associated challenges and implement AI in an ethical and responsible manner. Therefore, AI is not only transforming the way companies manage their customer relationships but also redefining the standards of excellence.

Since CRM technologies are referred to as IT tools that support data analysis and integration, we focus specifically on two functions of CRM technologies: the use of CRM technologies to support analysis and the use of CRM technologies to support data integration and access.

2.5. Impact of AI and Machine Learning on CRM

In an age dominated by data and technology, CRM has evolved from a simple information organization tool to a strategic platform critical to business success. The crucial role of CRM in creating and maintaining customer relationships has been significantly enhanced by the advancement of artificial intelligence (AI) and machine learning (ML) technologies. These emerging technologies are transforming the way companies interact with their customers, providing unprecedented opportunities for personalization and anticipation of their needs. Therefore, the ability to provide new personalized experiences can significantly differentiate a company from its competitors. AI and ML enable rapid and accurate analysis of large amounts of customer data, including purchase histories, consumer behaviors on the site, preferences, and past interactions [28].

By using machine learning algorithms, CRM systems can identify patterns and trends in customer behaviors and provide personalized recommendations, and these personalized interactions not only improve the customer experience but also increase the likelihood of repeat purchases and brand loyalty. Anticipating customer needs is another area where AI and ML are proving their worth in that instead of reacting to customer requests and behaviors, companies can use AI to predict customer needs before they become apparent. This is achieved by analyzing historical and behavioral customer data, allowing CRM systems to identify early signs of customer intent and preferences [29].

In addition to personalization and anticipation, AI and ML contribute significantly to the automation of CRM processes, increasing operational efficiency. Tasks such as classifying and prioritizing customer requests can be automated using ML algorithms. This allows support teams to focus on more complex interactions and solving problems that require human intervention, as AI-powered chatbots and virtual assistants can provide 24/7 support and answer frequently asked customer questions. These solutions not only reduce waiting time for customers but also save resources for the company.

However, despite the many advantages, integrating AI and ML into CRM systems is not without its challenges, and one of the main concerns is data protection and customer privacy. Excessive use of personal data for personalization and anticipation must comply with strict privacy regulations, such as GDPR (general data protection regulation). Companies must ensure that customer data are collected, stored, and used ethically and transparently. Also, effective implementation of AI and ML requires significant investment in technology and expertise, and companies must be prepared to allocate resources to developing and maintaining these advanced systems. In addition, it is important to educate and train employees to work effectively with new technologies and to correctly interpret the results generated by AI [30].

In conclusion, AI and ML technologies have the potential to fundamentally transform CRM systems and provide great opportunities for personalization and anticipation of customer needs. These technologies enable companies to improve customer experience, optimize resources, and remain competitive in a dynamic and ever-changing business landscape. However, the success of integrating AI and ML into CRM depends on a responsible and ethical approach to data use and investments in technology and training. As these technologies continue to evolve, the CRM reader promises to be more proactive and efficient.

2.6. Business Performance

Organizational performance has been one of the most discussed topics in the specialized literature in recent years. Recent business concerns have drawn attention to this topic, and although these concerns are varied, almost all of them focus on improving business performance. In the literature [23,31], subjective and objective indicators are used to measure the success of an organization; therefore, while objective variables refer to economic indicators of businesses that are available in various financial databases, subjective indicators consider management’s assessment of firm performance.

As a consequence of both individual and group performance in the effectiveness of meeting objectives, a company’s performance is the outcome of its activities and is a crucial indicator in determining whether or not a company has gained a competitive advantage [32,33]. Organizational, social, and economic performance are the three main categories of business performance that have been recognized in the literature [34]. It is essential to understand and track performance in a dynamic environment, and most businesses use a variety of strategies to do so. According to Taamneh et al. [35], a well-performing firm provides more financial resources and investment prospects in a number of sectors.

Although Hadiyati [36] finds little evidence of a significant direct impact of digital marketing on business performance, the link between the two variables is complex and is shown through the mediation of competitiveness. The performance of a business can be measured by a variety of indicators, but three of the most essential are customer satisfaction, market effectiveness, and market profitability. These indicators are not only evaluation metrics but also interdependent components of an organization’s success. Customer satisfaction is the extent to which a company’s products and services meet or exceed customer expectations. According to Razak and Shamsudin [37], customer satisfaction measures how well the buyer’s expectations of value are met by the product usage experience. Because it serves as a bridge between the different phases of the purchasing process, customer satisfaction is viewed as a critical outcome of marketing initiatives [38]. Customer satisfaction, achieved by providing genuine value and delivering products and services that meet or exceed their requirements, is critical to retaining valuable customers and ensuring the long-term success of the firm.

Market effectiveness refers to a business’s ability to use resources effectively to achieve its marketing and sales goals. This includes optimization of marketing processes, correct market segmentation, and strategic allocation of resources [39]. While effectiveness, the second dimension of business performance, shows the proportion of organizational resources used to achieve a particular goal, efficiency refers to the extent to which the organization’s objectives are met. Market effectiveness is manifested by increased market share relative to competitors, increased sales revenues, attracting new customers, and increased sales to existing customers.

Market profitability is the extent to which a business manages to generate profit from its business activities. This is a critical indicator because it reflects the financial health of the business and its ability to create shareholder value. A company’s bottom line should demonstrate its profitability as well as methods to improve it. This topic has generated much discussion in the literature and is still relevant in the field of marketing. Profitability is an important measure for evaluating the performance of a business because it shows how well it can use its assets to make a profit and is reflected in its future value [40].

Risks and assessments of the link between survival difficulties, financial performance, and business options are evaluated in terms of investment, profitability, and cost of capital [41]. In addition, performance is influenced by company operations and strategy [42]. Market profitability, reflected by business profitability and return on investment, is critical to meeting long-term financial goals.

Customer satisfaction, market effectiveness, and market profitability are three fundamental indicators that determine the performance of a business. A holistic approach that integrates these elements enables businesses to create long-term value, keep customers loyal and satisfied, and use resources efficiently and productively. By continuously monitoring and optimizing these indicators, businesses can ensure not only survival but also prosperity in today’s competitive environment.

Considering the views that were found in the literature, to measure performance in the current study, we used the constructs of customer satisfaction, market effectiveness, and profitability, the measurement scales of which were derived from previous research [31].

2.7. Previous Research, Antecedents, and Consequences

The purpose of this study is to determine the extent to which customer relationship orientation is used by different firms with different customer profiles and the potential importance of this tool in relation to digital marketing, and the impact of using CRM technologies to support analytics, data integration, and access on business performance. Marketing research and the literature [43,44] indicate that the organizational culture affects the processes that are established within the organization. Therefore, the culture of an organization that prioritizes customer requirements will have better customer relationship management skills.

As a key element of organizational culture, market orientation puts the customer at the center of strategic business actions and serves as the foundation for customer relationship orientation. Although in other research regarding the effect of market orientation on firm performance [45,46,47] the results of studies found a positive relationship between market orientation and firm performance, in the more recent study by Ismail, Narsa, and Basuki [48], the results did not show a significant correlation between market orientation and firm performance.

Previous studies have shown that CRM technologies are successfully incorporated into CRM processes, the use of CRM technologies has a positive impact on business performance, and the use of CRM technologies helps two important processes, customer engagement initiatives and relationship information processes [18,20,23,49,50]. A recent study by Gil-Gomez et al. [51] demonstrated that CRM technologies aim at digital transformation and sustainable business model innovation, making it a type of green IT, and showed the advantages and effects of using CRM technologies from the perspective of innovation as well as sustainable business models.

3. Research Hypotheses, Conceptual Model, and Methodology

Theoretically, the degree of creativity in a research study is influenced by various factors, including research design, development conditions, population and sample characteristics, data collection methodologies, and covariate control. These factors add to the originality of the hypotheses, according to Coolican [52]. In a well-known essay on the subject, McGuire [53] argues that, despite its significance, the process of hypothesis formulation is not given sufficient attention in research skills training.

To determine the extent of CRM technology use based on the study hypotheses, the authors surveyed the CRM literature and included key components relevant to CRM practice in a questionnaire-based survey that was offered to employees of many firms. According to the literature, the old industry is not adopting CRM technologies and processes as quickly as other emerging industries.

In the present research, a review of the marketing literature enabled the development of research hypotheses designed to establish the associations and causal relationships among the constructs included in the conceptual model of the antecedents and consequences of customer relationship orientation and CRM technology use.

These theories were developed to investigate how CRM technology use and customer relationship orientation impact business performance. In order to assess the relationships between the variables customer satisfaction, effectiveness, and profitability and the variables customer relationship orientation and use of CRM technologies as a support, nine hypotheses were developed following the models designed and developed by Jayachandran et al. [43] and Vorhies and Morgan [31], as follows:

H1. 

Customer relationship orientation has a positive effect on customer satisfaction.

H2. 

Customer relationship orientation has a positive effect on market effectiveness.

H3. 

Customer relationship orientation has a positive effect on market profitability.

H4. 

Use of CRM technologies to support analytics has a positive effect on customer satisfaction.

H5. 

Use of CRM technologies to support analytics has a positive effect on market effectiveness.

H6. 

Use of CRM technologies to support analytics has a positive effect on market profitability.

H7. 

Use of CRM technologies for data integration and as an access support has a positive effect on customer satisfaction.

H8. 

Use of CRM technologies for data integration and to support access has a positive effect on market effectiveness.

H9. 

Use of CRM technologies for data integration and to support access has a positive effect on market profitability.

The conceptual model within this study serves as a theoretical and logical structure to guide the research process and helped to formulate hypotheses and interpret the results obtained. In the conceptual model, represented in Figure 1, the research hypotheses to be tested are presented.

The theoretical and empirical studies in the literature reviewed were used to clarify and define the concepts used in the research and the research methodology.

We conducted a quantitative, descriptive-explanatory study to test the proposed hypotheses. We used a survey based on a questionnaire administered to a convenience sample.

The data were collected from a sample of 73 organizations, but given the restrictions related to the time period allocated for data collection, in the present research, we did not intend to select a representative sample for the statistical population under investigation (all companies in the territory of Romania). The sample was made by using non-probabilistic sampling methods, namely, convenience sampling and the snowball method. The questionnaires were sent to the sample members for completion by mail, with the link to the platform from which they could be accessed. Respondents were assured of the confidentiality of individual responses (see Appendix A).

The profile characteristics of the sample members to be descriptively analyzed are firmographic and refer to the following aspects: the origin of the company’s capital, the number of employees, the company’s turnover, the duration of the company’s experience, and the economic sector in which it operates. Furthermore, due to the small sample size and the impossibility of using probability sampling techniques in this empirical research, we are only able to present the structure of the final sample descriptively.

For data processing, we used SPSS 23 and AMOS (version 20) software. The data analysis process went through six stages: (1) descriptive univariate analysis; (2) testing the reliability of the scales used using Cronbach’s alpha coefficient; (3) testing the appropriateness of using factor analysis by calculating the KMO (Kaiser–Meyer–Olkin) coefficient and performing partial correlations using the Bartlett sphericity test; (4) Performing confirmatory factor analysis (CFA) in order to eliminate items with factor loadings lower than the minimum allowed value of 0.5 and in order to aggregate the investigated constructs; (5) testing the discriminant validity using the Pearson correlation coefficient; and (6) testing the relationships that exist among the constructs included in the conceptual model by structural equation modeling (SEM) using the AMOS 20 program. The profile characteristics of the sample members that were descriptively analyzed are firmographic and concern the following aspects: the origin of the firm’s capital, number of employees, firm turnover, length of the firm’s experience, and the economic sector in which it operates.

After testing the reliability of the scales used, the adequacy of factor analysis was tested in the next stage of statistical data analysis. The correctness of the factor analysis was tested using the KMO coefficient and Bartlett’s test. This analysis was performed for each construct, which led to the determination of the appropriateness of performing factor analysis for each construct (the value of the KMO coefficient was greater than 0.5 for each construct, and the significance level (p-value) for each Bartlett’s test was less than 0.05). Considering that performing factor analysis proved to be appropriate for each construct, one of the forms of factor analysis was performed, namely confirmatory factor analysis (CFA). With the help of CFA, the aggregation of directly observable variables in dimensions and dimensions in constructs was achieved. This step was necessary to test the research hypotheses regarding the links among the constructs included in the conceptual model. In the next stage of the data analysis process, we tested the validity of the constructs included in the proposed conceptual model using the Pearson linear correlation coefficient, and in the last stage of the statistical data analysis process, the hypotheses of the proposed conceptual model were tested by modeling the structural equations (SEM), using the AMOS 20 program.

The research process is a systematic set of steps through which organizations collect, analyze, and interpret relevant information to make informed decisions and develop effective marketing strategies. This research process is represented in Figure 2 and comprises the following main stages:

4. Results of the Descriptive Analysis

In this context, the firmographic characteristics of the study sample were described using univariate descriptive analysis. This information was collected through a survey using questionnaires that were distributed to 73 organizations from various industries.

Due to the small sample size and the impossibility of using probability sampling techniques in this empirical research, we are only be able to present the structure of the final sample descriptively.

The profile characteristics of the sample members to be descriptively analyzed are firmographic and refer to the following aspects: the origin of the company’s capital, the number of employees, the company’s turnover, the duration of the company’s experience, and the economic sector in which it operates.

Depending on the origin of the firm’s capital, the structure of the sample is presented in Figure 3.

Within the sample, 73.97% of the firms reported that their capital was of Romanian origin, 17.86% were home-owned, and 8.17% of the sampled firms reported that their capital was of mixed origin.

These data indicate that most of the firms in the sample have Romanian capital, which may reflect a business environment dominated by local investments.

Depending on the number of employees, the structure of the sample is presented in Figure 4.

Regarding the number of employees, within the sample, it can be observed that 25% of the firms are micro-enterprises (with less than 10 employees), followed by medium-sized firms with between 50 and 250 employees (70.5%), and small firms with between 10 and 49 employees comprise 4.5% of the sampled firms. These data suggest that most of the firms in the sample are medium-sized enterprises, which indicates a trend toward medium-sized firms in the industries studied.

Depending on the property structure, the sample distribution is shown in Figure 5.

Also, within the sample, it is observed that 73.33% of the firms have a majority private ownership structure and 26.67% have a majority public ownership structure. The prevalence of privately owned firms may indicate a trend toward privatization and a dynamic business environment. Depending on the activity sector, the structure of the sample is presented in Figure 6.

When asked about the field of activity, within the sample, it is observed that the largest share is held by firms operating in the IT&C field (32.50%), the services field (27.50%), followed by those belonging to the production of consumer goods (25.33%), and other fields (14.67%). These results show us that the IT&C field has the largest share and reflects the importance of technology in local industries.

Depending on the nature of the organization, the structure of the sample is shown in Figure 7.

Within the sample, it can be observed that, in terms of the nature of the organization, 57.50% are traditional firms with new operations in the digital environment, followed by those who initially had a start-up in the digital environment and are now developing operations in the offline environment (24.33%), and firms with operations only in the digital environment (18.17%). These data show a significant trend toward digitization, either by integrating digital technologies into traditional firms or by starting and operating exclusively in the digital environment.

Next, we summarize the relevant findings of the data. The prevalence of Romanian capital suggests a predominance of local investments, which can influence local economic policies and strategies. The dominance of medium-sized enterprises indicates a business structure that favors medium-sized firms that can benefit from operational flexibility and efficiency. The prevalence of majority private ownership reflects a competitive and dynamic business environment in which private firms have a significant share. The significant share of IT&C and services firms underlines the importance of these sectors in the Romanian economy, probably due to the high demand for technology and related services. The trend toward digitization also highlights the adaptation and innovation of companies to remain competitive in the digital age, either through the digital transformation of traditional firms or through digital start-ups that expand offline operations.

4.1. Analysis of the Reliability of the Scales Used and the Convergent and Discriminant Validity of the Conceptual Model Constructs

In order to test the conceptual model of the research, in the first step, we analyzed the reliability of the measurement scales and the convergent and discriminant validity of the studied constructs. In this sub-chapter, the results obtained from testing the reliability of the measurement scales based on Cronbach’s alpha coefficient, the convergent validity of the constructs based on factor analysis, and the discriminant validity based on Pearson correlation coefficient analysis are presented.

The customer relationship orientation construct is unidimensional; the use of CRM technologies as a support comprises 2 dimensions—the use of CRM technologies as a support for analytics and data integration and as a support for access; and performance comprises 3 dimensions—customer satisfaction, market effectiveness, and market profitability.

The measurement scales used to measure each construct included in the conceptual model are shown in

Table 1. Measurement scales.

Construct	Scale Items	Variable Codes
Customer relationship orientation
	(1) Our employees are encouraged to focus on cus-tomer relationships	OCR1
	(2) In our organization, customer retention is considered the top priority	OCR2
	(3) In our organization, customer relationships are considered to be a valuable asset	OCR3
	(4) Our management emphasizes the importance of customer relationships	OCR4
	(5) The company focuses on customizing offers for individual customers	OCR5
Use of CRM technologies
Use of CRM technologies as a support for analysis	(1) Enables forecasting of customer preferences	CRMSA1
	(2) Enables measurement of customer loyalty	CRMSA2
	(3) Allows customer lifetime value calculation	CRMSA3
	(4) Enables assessment of product profitability	CRMSA4
	(5) Enables assessment of the profitability of products	CRMSA5
Use of CRM technologies for data integration and as a support for access	(1) Combines customer transaction data with external data sources	CRMDIA1
	(2) Integrates customer information obtained from various customer contact points	CRMDIA2
	(3) Enables employees to access centralized customer data	CRMDIA3
Performance/Profitability
Customer satisfaction	(1) Customer satisfaction	PerfSatisf1
	(2) Delivering customer value	PerfSatisf2
	(3) Delivering products/services according to customer requirements	PerfSatisf3
	(4) Retaining customers who bring value to the firm	PerfSatisf4
Market Effectiveness	(1) Increased market share relative to competitors	PerfME1
	(2) Increased sales revenue	PerfME2
	(3) Attracting new customers	PerfME3
	(4) Increased sales to existing customers	PerfME4
Market Profitability	(1) Profitability of the business	PerfMP1
	(2) Return on investment	PerfMP2
	(3) Meeting financial targets	PerfMP3

Source: Jayachandran et al., 2005 [43]; Vorhies & Morgan, 2005 [31].

, including the sources from which the scales were taken, scale items, and variable codes.

4.2. Analysis of the Constructs Included in the Conceptual Model

In the following, we will highlight the results obtained as a result of testing the reliability of the scales, performing confirmatory factor analysis, and as a result of testing the research hypotheses.

4.2.1. Construct Analysis Customer Relationship Orientation

The 5-item scale used to measure the construct customer relationship orientation recorded a Cronbach’s α coefficient level of 0.852, indicating that the scale was reliable. By eliminating any of the 4 variables, the value of Cronbach’s α coefficient would decrease. Therefore, all 5 variables were retained for further statistical processing.

Factor analysis resulted in a single factor extracted that explained 86.92% of the total variance of the 4 retained variables. The values of the factor loadings for the 5 variables were sufficiently high so that it was not necessary to eliminate any variable on the basis of this criterion. Therefore, factor scores were calculated taking into account all 5 variables. These scores were retained in the SPSS software as a new variable called customer relationship orientation, which was used in the next step of the statistical data analysis process.

4.2.2. Construct Analysis Use of CRM Technologies as a Support for Analysis

The 5-item scale used to measure the construct use of CRM technologies as analysis support recorded a Cronbach’s α coefficient level of 0.752, indicating that the scale was reliable. By eliminating any of the 5 variables, the value of Cronbach’s α coefficient would decrease.

Therefore, all 5 variables were retained for further statistical processing.

Factor analysis resulted in a single factor extracted that explained 76.41% of the total variance of the 5 retained variables. The values of the factor loadings for the 5 variables were sufficiently high so that it was not necessary to eliminate any variable on the basis of this criterion. Therefore, factor scores were calculated taking into account all 5 variables. These scores were retained in the SPSS software as a new variable called the use of CRM technologies as analysis support, which was used in the next step of the statistical data analysis process.

4.2.3. Construct Analysis Use of CRM Technologies for Data Integration and as Support for Access

The 3-item scale used to measure the construct use of CRM technologies for data integration and as a support for access recorded a Cronbach’s α coefficient of 0.798, indicating that the scale was reliable. By eliminating any of the 3 variables, the Cronbach’s α coefficient value would decrease.

Therefore, all 3 variables were retained for further statistical processing.

Factor analysis resulted in a single factor extracted that explained 81.30% of the total variance of the 3 retained variables. The values of the factor loadings for the 3 variables were sufficiently high so that it was not necessary to eliminate any variable based on this criterion. Therefore, factor scores were calculated taking into account all 3 variables. These scores were retained in the SPSS software as a new variable called market profitability, which was used in the next step of the statistical data analysis process.

4.2.4. Construct Analysis Customer Satisfaction

The 4-item scale used to measure the customer satisfaction construct had a Cronbach’s α coefficient of 0.779, indicating that the scale was reliable. By eliminating any of the 4 variables, the value of Cronbach’s α coefficient would decrease.

Therefore, all 4 variables were retained for further statistical processing.

Factor analysis resulted in a single factor extracted that explained 61.07% of the total variance of the 4 retained variables. The values of the factor loadings for the 4 variables were sufficiently high so that it was not necessary to eliminate any variable on the basis of this criterion. Therefore, factor scores were calculated taking into account all 4 variables. These scores were retained in the SPSS software as a new variable called customer satisfaction, which was used in the next step of the statistical data analysis process.

4.2.5. Construct Analysis Market Effectiveness

The 4-item scale used to measure the construct market effectiveness recorded a Cronbach’s α coefficient of 0.809, indicating that the scale was reliable. By eliminating any of the 4 variables, the value of Cronbach’s α coefficient would decrease.

Therefore, all 4 variables were retained for further statistical processing.

Factor analysis resulted in a single factor extracted that explained 65.66% of the total variance of the 4 retained variables. The values of the factor loadings for the 4 variables were sufficiently high so that it was not necessary to eliminate any variable on the basis of this criterion. Therefore, factor scores were calculated taking into account all 4 variables. These scores were retained in the SPSS software as a new variable called market effectiveness, which was used in the next step of the statistical data analysis process.

4.2.6. Construct Analysis Market Profitability

The 3-item scale used to measure the market profitability construct had a Cronbach’s α coefficient of 0.859, indicating that the scale was reliable. By eliminating any of the 3 variables, the value of Cronbach’s α coefficient would decrease.

Therefore, all 3 variables were retained for further statistical processing.

Factor analysis resulted in a single factor extracted that explained 79.30% of the total variance of the 3 retained variables. The values of the factor loadings for the 3 variables were sufficiently high so that it was not necessary to eliminate any variable on the basis of this criterion. Therefore, factor scores were calculated taking into account all 3 variables. These scores were retained in the SPSS software as a new variable called market profitability, which was used in the next step of the statistical data analysis process.

The measurement items for this study are shown in Appendix B. Most of the items were inspired by the previous research [31,43], but new measures were developed after an extensive review of existing literature. All the measurement items were measured using a seven-point Likert scale with ‘strongly agree’ and ‘strongly disagree’ as the anchors.

4.3. Discriminant Validity of the Constructs Included in the Proposed Conceptual Model

Discriminant validity is the degree to which directly observable variables measure and converge toward the same construct [54]. By using Pearson correlation coefficients to test the discriminant validity of the constructs, the directly observable variables of each scale should have the strongest association with its corresponding construct at a significance level less than the 0.05 cutoff. Two requirements must be met for each directly observable variable in the construct scale composition to be eligible for this type of validity testing: the variable must have a factor loading of at least 0.50 and statistical significance [55]. The items on each scale have the highest correlation with the construct that it measures, according to the findings of the discriminant validity test, and the associated significance level is always smaller than the maximum permitted value of 0.05. We confirmed the discriminant validity of each construct in the conceptual research model based on these results.

4.4. The Results of Testing the Conceptual Model of the Research

We performed an assessment based on descriptive indicators of the level of fit (goodness-of-fit) and an inferential statistical analysis based on the χ² statistical test to determine how well the conceptual model of the current research suited the data produced by the sample. Therefore, we looked at two indicators—the χ² statistic value and the corresponding p-value (significance level)—in the inferential statistical evaluation. We followed the recommendation to also use descriptive indicators to decide whether to accept or reject the model [56] for the relatively small sample size obtained in the present research. This is because the χ² value is sensitive to sample size, making this statistical test more relevant in determining model fit in large sample sizes [57]. Thus, RMSEA (root mean square error of approximation), NFI (normed fit index), CFI (comparative fit index), PNFI (parsimony normed fit index), AIC (Akaike information criterion), and ECVI (expected cross-validation index) are the descriptive indicators of the level of fit that were analyzed. The results of the assessment of the fit of the model to the sample-generated data presented in

Table 2. Assessment of the research model.

Indicator	Optimal Value of Indicator	Value for the Research Model
χ2 (df)	0 ≤ χ2 (df) < 2 × df	0.368 (2)
p	0.05 < p ≤ 1	0.821
χ2/df	0 < χ2/df < 2	0.145
RMSEA	0 ≤ RMSEA ≤ 0.5	0.008
NFI	0.95 ≤ NFI ≤ 1	0.969
CFI	0.97 ≤ CFI ≤ 1	0.999
PNFI	0 < PNFI ≤ 1	0.200
AIC	AIC value less than the AIC value for the comparison model	36.251
ECVI	ECVI value lower than the ECVI value for the comparison model	0.441

Source: Schermelleh-Engel et al. (2003: 52) [56].

suggested that the research model was fit. Thus, the χ² value was 0.368 (for two degrees of freedom), falling within the recommended range [0, 4), and the associated significance level was 0.821, exceeding the minimum allowable limit of 0.05 and less than 1.

Furthermore, the descriptive indicators for model evaluation in the sample fell within the recommended ranges for the model to be considered adequate (RMSEA = 0.008; CFI = 0.999; PNFI = 0.200; NFI = 0.969). Based on these results, we could conclude that the research model was appropriate to the empirically observed data for the sample.

Using the indicators mentioned above and based on the results of the assessment of the adequacy of the research model, it was found that the research model was adequate for the data generated by the sample, and the results are summarized in Table 2.

4.5. Results of Testing the Research Hypotheses

The values of the estimated standardized β coefficients of the regression equations provided useful data for analyzing and comparing the relationships among the variables included in the conceptual model of the research. These values are summarized in Appendix C.

H1. 

Customer relationship orientation has a positive effect on customer satisfaction.

This research hypothesis guides the analysis of the direct effect that customer relationship orientation has on customer satisfaction.

On the basis of these results, we can consider that hypothesis H1 is confirmed, as the link between customer relationship orientation and customer satisfaction is positive and statistically significant (p = 0.000) for this sample. The strength of this effect is relatively strong, with the value of the standardized coefficient of the regression function (β) = 0.338.

H2. 

Customer relationship orientation has a positive effect on market efficiency.

This research hypothesis guides the analysis of the direct effect that customer relationship orientation has on market effectiveness.

On the basis of these results, we can consider that hypothesis H2 is confirmed, as the link between customer relationship orientation and market effectiveness is positive and statistically significant (p = 0.000) for this sample. The strength of this effect is relatively strong, with the value of the standardized coefficient of the regression function (β) = 0.665.

H3. 

Customer relationship orientation has a positive effect on market profitability.

This research hypothesis guides the analysis of the direct effect that customer relationship orientation has on market profitability.

On the basis of these results, we can consider that hypothesis H3 is confirmed, as the link between customer relationship orientation and market profitability is positive and statistically significant (p = 0.000) for this sample. The strength of this effect is relatively strong, with the value of the standardized coefficient of the regression function (β) = 0.609.

H4. 

Use of CRM technologies as a support for analysis has a positive effect on customer satisfaction.

The research hypothesis guides the analysis of the direct effect that the use of CRM technologies to support analytics has on customer satisfaction.

On the basis of these results, we can consider that hypothesis H4 is confirmed, as the link between the use of CRM technologies as a support for analysis and customer satisfaction is positive and statistically significant (p = 0.000) for this sample. The strength of this effect is relatively strong, with the value of the standardized coefficient of the regression function (β) = 0.551.

H5. 

Use of CRM technologies as a support for analysis has a positive effect on market efficiency.

That research hypothesis guides the analysis of the direct effect that using CRM technologies as a support for analysis has on market effectiveness.

Based on these results we can consider that hypothesis H5 is confirmed, as the link between the use of CRM technologies as analysis support and market effectiveness is positive and statistically significant (p = 0.000) for this sample. The strength of this effect is relatively strong, with the value of the standardized coefficient of the regression function (β) = 0.432.

H6. 

Use of CRM technologies as a support for analysis has a positive effect on market profitability.

This research hypothesis guides the analysis of the direct effect of using CRM technologies as a support for analysis on market profitability.

On the basis of these results, we can consider that hypothesis H6 is confirmed, as the link between the use of CRM technologies to support analysis and market profitability is positive and statistically significant (p = 0.000) for this sample. The strength of this effect is relatively strong, with the value of the standardized coefficient of the regression function (β) = 0.568.

H7. 

Use of CRM technologies for data integration and as a support for access has a positive effect on customer satisfaction.

The research hypothesis guides the analysis of the direct effect of using CRM technologies for data integration and access support on customer satisfaction.

Based on these results, we can consider that hypothesis H7 is confirmed, as the link between the use of CRM technologies for data integration and access support and customer satisfaction is positive and statistically significant for this sample (p = 0.000). The strength of this effect is relatively strong, with the standardized regression function coefficient value (β) = 0.498.

H8. 

Use of CRM technologies for data integration and as a support for access has a positive effect on market efficiency.

This research hypothesis guides the analysis of the direct effect of using CRM technologies for data integration and access support on market effectiveness.

Based on these results, we can consider that hypothesis H8 is confirmed, as the link between the use of CRM technologies for data integration and access support and market effectiveness is positive and statistically significant for this sample (p = 0.000). The strength of this effect is relatively strong, with the standardized regression function coefficient value (β) = 0.615.

H9. 

Use of CRM technologies for data integration and as a support for access has a positive effect on market profitability.

The research hypothesis guides the analysis of the direct effect of using CRM technologies for data integration and access support on market profitability.

Based on these results, we can consider that hypothesis H9 is confirmed, as the link between the use of CRM technologies for data integration and access support and market profitability is positive and statistically significant for this sample (p = 0.000). The strength of this effect is relatively strong, with the value of the standardized coefficient of the regression function (β) = 0.588.

The results of the testing of all research hypotheses are integrated in

Table 3. Synthesis presentation of the results of the research hypothesis testing.

The Research Hypotheses	The Results
H1. There is a direct, positive, and statistically significant relationship between customer relationship orientation and customer satisfaction.	Confirmed: a statistically significant effect was identified (p = 0.000) between the two analyzed variables.
H2. There is a direct, positive, and statistically significant relationship between customer relationship orientation and market effectiveness.	Confirmed: a statistically significant effect was identified (p = 0.000) between the two analyzed variables.
H3. There is a direct, positive, and statistically significant relationship between customer relationship orientation and market profitability.	Confirmed: a statistically significant effect was identified (p = 0.000) between the two analyzed variables.
H4. There is a direct, positive, and statistically significant relationship between the use of CRM technologies as a support for analysis and customer satisfaction.	Confirmed: a statistically significant effect was identified (p = 0.000) between the two analyzed variables.
H5. There is a direct, positive, and statistically significant relationship between the use of CRM technologies as a support for analysis and market effectiveness.	Confirmed: a statistically significant effect was identified (p = 0.000) between the two analyzed variables.
H6. There is a direct, positive, and statistically significant relationship between the use of CRM technologies as a support for analysis and market profitability.	Confirmed: a statistically significant effect was identified (p = 0.000) between the two analyzed variables.
H7. There is a direct, positive, and statistically significant relationship between the use of CRM technologies for data integration and as a support for access and customer satisfaction	Confirmed: a statistically significant effect was identified (p = 0.000) between the two analyzed variables.
H8. There is a direct, positive, and statistically significant relationship between the use of CRM technologies for data integration and as a support for access and market effectiveness.	Confirmed: a statistically significant effect was identified (p = 0.000) between the two analyzed variables.
H9. There is a direct, positive, and statistically significant relationship between the use of CRM technologies for data integration and as a support for access and market profitability.	Confirmed: a statistically significant effect was identified (p = 0.000) between the two analyzed variables.

Source: Own interpretation.

.

The constructs included in the proposed conceptual model were operationalized using measurement scales established in the marketing literature. In this study, we have shown that the measurement scales used to operationalize the constructs of customer relationship orientation, use of CRM technologies to support analytics, use of CRM technologies for data integration and as a support for access, customer satisfaction, market effectiveness, and market profitability are valid in the context of an emerging economy.

Based on these results, we can consider that all hypotheses are confirmed, the relationships among all variables proposed and analyzed (customer relationship orientation, use of CRM technologies to support analytics, use of CRM technologies for data integration and to support access, customer satisfaction, market effectiveness, and market profitability) are positive and statistically significant (p = 0.000) for this sample.

5. Discussion and Conclusions

5.1. Discussion

The univariate descriptive analysis of the firmographic data reveals key trends and characteristics of the firms in the sample, such as the predominance of Romanian capital, majority private ownership structure, and a strong orientation toward digitalization. These findings are essential to understand the current dynamics of the business environment and to formulate appropriate development and investment strategies.

The research problem focused on investigating the consequences of digital marketing on business performance, with a particular focus on customer relationship orientation and the use of CRM (customer relationship management) technologies for analysis, data integration, and support on business performance. This is a contemporary and relevant issue with significant impact both nationally and internationally.

The purpose of the research was to synthesize the main conclusions resulting from the analysis of the consequences of digital marketing on business performance. The research seeks to highlight how customer relationship orientation and the use of CRM technologies contribute to business performance by facilitating data analysis and integration.

The research objectives addressed the effects of CRM technology use and customer relationship orientation on business performance through the current study. The study shows that various emerging and existing sectors are integrating CRM procedures and technologies faster than the traditional marketing methodology of previous organizations.

Following the comparative analysis, acceptable levels of internal consistency, reliability, and discriminant validity were established for the construct of customer relationship orientation.

The effect of customer relationship orientation on customer satisfaction is quite strong (β = 0.338), and from a statistical point of view, the effect is significant (p = 0.000).

The effect of customer relationship orientation on market effectiveness is strong (β = 0.665), and from a statistical point of view, the effect is significant (p = 0.000).

The effect of customer relationship orientation on market profitability is strong (β = 0.609), and from a statistical point of view, the effect is significant (p = 0.000).

Following the comparative analysis, acceptable levels of internal consistency, reliability, and discriminant validity were established for the construct of the use of CRM technologies as a support for analysis.

The effect of the use CRM technologies as a support for analysis on customer satisfaction is strong (β = 0.551), and from a statistical point of view, the effect is significant (p = 0.000).

The effect of the use CRM technologies as a support for analysis on market effectiveness is relatively strong (β = 0.432), and from a statistical point of view, the effect is significant (p = 0.000).

The effect of the use CRM technologies as a support for analysis on market profitability is relatively strong (β = 0.568), and from a statistical point of view, the effect is significant (p = 0.000).

Following the comparative analysis, acceptable levels of internal consistency, reliability, and discriminant validity were established for the construct of the use of CRM technologies for data integration and as a support for access.

Through our study we empirically confirmed the direct, positive, and statistically significant effect of using CRM technologies for data integration and as a support for access on customer satisfaction.

The effect of the use CRM technologies for data integration and as a support for access on customer satisfaction is relatively strong (β = 0.498), and from a statistical point of view, the effect is significant (p = 0.000).

The effect of the use CRM technologies for data integration and as a support for access on market efficiency is strong (β = 0.615), and from a statistical point of view, the effect is significant (p = 0.000).

The effect of the use CRM technologies for data integration and as a support for access on market profitability is strong (β = 0.588), and from a statistical point of view, the effect is significant (p = 0.000).

The research’s overall conclusions are thought to support the hypothesis that the industry requires these kinds of systems, that they can strengthen bonds between businesses and their clients, and that these businesses risk falling behind other emerging sectors in adopting CRM procedures and technologies if they do not make use of digital marketing or new technologies.

The three performance dimensions (customer satisfaction, market effectiveness, and market profitability) all benefit from customer relationship orientation, according to our findings. Thus, in order to have happy customers, to be effective, and to record profits, managers should concentrate on customer relationships and client retention through personalizing offers.

5.2. Contributions, Recommendations, and Implications

Our study contributes to the literature and practice by highlighting the importance of CRM systems in improving organizational performance. By analyzing the relationships among customer orientation, the use of CRM technologies as a support for analysis, the use of CRM technologies for data integration and as a support for access, customer satisfaction, market effectiveness, and market profitability, the study demonstrates the positive impact of these factors on business performance.

Regarding the theoretical and practical relevance of our research, based on the results obtained, we have derived a series of recommendations and managerial implications.

The first recommendation would be to improve customer orientation by organizations adopting a customer-centric approach, investing in training and educating employees to better understand and respond to customer needs. It is also recommended to invest in advanced CRM analytics technologies to gain valuable insights into customer behaviors and preferences, which can lead to improved customer satisfaction and loyalty. Last but not least, companies should ensure that all relevant data are effectively integrated and accessible, enabling a holistic view of customers.

In terms of theoretical implications, the study extends existing knowledge about the impact of CRM technology use on business performance by providing empirical evidence of positive and significant relationships between customer orientation and the use of CRM technologies as a support for analysis and the use of CRM technologies for data integration and as a support for access to organizational results. This research contributes to the understanding of how different components of CRM can influence customer satisfaction, market effectiveness, and market profitability.

In addition to theoretical implications, our study also has practical implications for managers and practitioners. Managers can use this information to develop and implement more effective CRM strategies by focusing on employee training, technology investments, and data integration. Organizations can also gain competitive advantages by adopting well-structured CRM practices that improve customer relationships and optimize internal operations.

5.3. Conclusions

This study shows that the range of communication tools and access platforms that comprise internet channels is reflected in digital marketing resources [58]. These online platforms are a helpful resource for marketers looking to build relationships with their clients. Any business looking to boost productivity must embrace and make use of the marketing tools and resources offered by different digital platforms. CRM is one of the most widely used technologies and managerial strategies of the past 20 years.

It takes continual learning and training, as well as changing performance metrics to recognize and reward customer focus, to establish a company-wide commitment to CRM. While business processes inside an organization are expected to facilitate the exchange of customer knowledge and information, there does not seem to be much customer-oriented training available, and performance evaluations are assumed to be primarily focused on hitting sales goals.

Although database technologies are not thought to be at the core of customer relationship management (CRM), they can support and improve it. Divergent views exist regarding the technologies employed by businesses, indicating that current technologies might be improved to increase CRM in businesses.

CRM technologies for analytics involves using advanced tools to collect, process, and interpret customer data. This allows companies to better understand customer behaviors and preferences, making it easier to make informed decisions and optimize marketing and sales strategies. By using CRM technologies for analytics, companies can identify and eliminate inefficient processes, reducing resource consumption, and minimize waste. Advanced analysis of customer data enables companies to develop products and services that better meet market needs, thereby reducing overproduction, which promotes responsible consumption and reduces environmental impact. The importance of using CRM technologies as a support for analysis, data integration, and access is due to its ability to provide organizations with the necessary tools to understand and meet customer needs in a strategic and sustainable way, thus ensuring competitiveness and success in the short term.

The main essential and contextual aspects that reveal the sustainability and importance of CRM in the current business landscape are: the competitive context, the definition of CRM, the role of technology in CRM, the impact of digital marketing, as well as the relevance of sustainability.

The competitive context refers to competitive pressure because companies are under constant pressure to remain competitive in the market, and this is amplified by globalization and rising customer expectations. Survival in the market is also important, and in this context, CRM becomes essential for the survival and prosperity of companies, giving them a competitive advantage by improving customer relations.

CRM is not just a computer program but an integrated system that includes strategies, processes, and technologies designed to manage and analyze customer interactions. To be effective, CRM must be viewed from a strategic perspective, integrated with the organization’s business goals and plans.

Regarding the role of technology in CRM, it enables the collection, storage, and analysis of customer data, which provides a deeper understanding of customer behaviors and preferences. Therefore, CRM facilitates quick access to essential information and enables informed decision making, thus improving overall business performance.

Digital marketing has changed the way businesses interact with customers, placing a greater emphasis on personalization and long-lasting relationships. In this research paper, we also highlighted the fact that using CRM in the context of digital marketing allows for personalization of interactions and marketing campaigns, which leads to customer satisfaction and loyalty.

Integrating CRM into business strategies helps firms to remain sustainable by enabling them to quickly adapt to market changes and maintain meaningful relationships with their consumers; therefore, this study addresses a topical issue.

Data obtained through CRM technologies can be used to identify trends and opportunities for innovation, enabling the development of sustainable solutions to meet future customer demands. CRM data integration involves centralizing and unifying information from different sources to create a holistic view of customers. Access support ensures that these data are accessible and usable by all relevant departments in the organization. The use of CRM technologies for data integration and as an access support promotes transparency and collaboration between departments, facilitating a coordinated and effective approach to sustainability issues through responsible resource management and implementation of environmental practices.

The results of this research emphasize the importance of effective implementation and use of CRM systems in organizations. CRM orientation and the use of CRM technologies for analysis and data integration are critical factors that contribute significantly to customer satisfaction, market effectiveness, and market profitability. These results suggest that investments in CRM technologies and practices not only improve customer relationships but also have positive impacts on the operational and financial performance of organizations.

The findings show that digital marketing and the use of CRM technologies have a significant impact on business performance. Most of the firms in the sample were open to digitalization and integrated modern technologies to improve customer relations. This indicates a strong trend toward using data and technology to support business decisions and optimize performance.

The study highlights the importance of adopting digital marketing strategies and CRM systems in the contemporary business environment, revealing the need for continued investment in these areas to remain competitive in the global market. The use of CRM technologies for analysis, data integration, and as an access support plays a crucial role in promoting business sustainability. These technologies allow companies to optimize resources, personalize offers, and innovate and collaborate more effectively, all of which contribute to a positive impact on the environment and society. In a world where sustainability is becoming increasingly important to consumers and stakeholders, the effective implementation and use of CRM technologies can provide a significant competitive advantage and support the sustainable development of organizations.

According to our findings, the three dimensions of performance—customer satisfaction, market effectiveness, and market profitability—all benefited from customer relationship orientation. Thus, in order to have satisfied customers, to be efficient and to record profits, managers should focus on customer relations and customer loyalty by personalizing offers.

We also found that the use of CRM technologies to support data analysis, integration, and access has a positive effect on the three dimensions of performance (customer satisfaction, effectiveness, and market profitability).

The general conclusions of the research are considered to support the hypothesis that the industry needs these types of systems, that they can strengthen the links between businesses and their customers, and that these businesses are at risk of falling behind other emerging sectors in adopting CRM procedures and technologies if they do not use digital marketing or new technologies.

5.4. Limitations and Future Research Directions

The present research has certain limitations that form the basis for the formulation of future research directions. The main limitation of the present research is the small sample size in data collection, resulting in 73 usable questionnaires. Future research could therefore propose to replicate this research using a larger sample size to increase the generalizability of the results. Another limitation of this research is that we used two non-probability sampling techniques, namely, convenience sampling and snowball sampling. Thus, a possible research direction could be to repeat this research using probability sampling techniques to increase the generalizability of the results. Another identified limitation relates to the sample structure, which consists exclusively of firms operating in Romania. Therefore, future research could aim to replicate this research on a sample of firms active in several emerging economies and conducting comparative analyses of the results obtained.