Digital marketing tools and digital transformation capability as a factor in enhancing business performance in China and Kazakhstan

Abstract

In unstable economic environments, digital transformation has become a critical factor in business sustainability, particularly in countries with emerging digital infrastructure. However, the practical implementation of digital marketing tools still faces numerous challenges, necessitating a comprehensive analysis of the factors influencing the effectiveness of such initiatives. This study aims to identify the relationships between the adoption of digital marketing, investments in marketing technologies, consumer engagement, business capacity for digital transformation, and business performance. The empirical basis relies on a survey of 390 professionals from China and Kazakhstan, with data analyzed using structural equation modeling and discriminant validity testing. The results revealed that consumer engagement has the strongest influence on a company’s capacity for digital transformation (β = 0.418), followed by investments in digital technologies (β = 0.288). Accordingly, business capacity for digital transformation serves as a significant mediator affecting business performance (β = 0.809). The total explained variance was 60.2% and 66.2%. Thus, the proposed model refines the Technology Acceptance Model (TAM) framework and underscores the critical role of consumer engagement as a mediator between digital initiatives and the effectiveness of marketing programs. The practical contribution of this study lies in the development of a step-by-step digitalization strategy and a digital readiness checklist, applicable for assessing and planning corporate digital transformation prospects in emerging economies.

Supplementary Information

The online version contains supplementary material available at 10.1038/s41598-025-20967-x.

Introduction

The high dynamism of the business environment unfolding on the Internet is currently at the forefront of marketing discourse. Digital technologies have assumed the role of an innovative tool, which both enhances business efficiency and intensifies competition¹. As a result, businesses are confronted with a substantial number of new challenges and opportunities that can be addressed and leveraged through the adoption of advanced digital technologies². Digital marketing has become a recognized strategic resource that facilitates broad consumer outreach and contributes to business expansion^3,4.

However, despite the apparent advantages of digital marketing, not all enterprises, particularly small and medium-sized enterprises (SMEs) in developing countries, have access to these benefits. The primary barriers include the need for investment and a lack of skilled personnel⁵. It is evident that the rapid changes in the contemporary market environment have created challenges for some enterprises within developing economies, particularly in adopting new approaches to digital marketing⁶. Furthermore, the mere introduction of new marketing technologies is insufficient; achieving effectiveness through these tools necessitates their proper application⁷.

It is not surprising that a substantial body of research has focused on the financial effects of implementing digital marketing strategies⁸. Scholarly attention has been devoted to the role of digital marketing in shaping and advancing companies’ communication strategies,⁹ revealing its capacity to enable innovative forms of communication and interaction with consumers. Digital marketing has been shown to enhance market orientation, expand operational scale, and improve the accuracy of data regarding consumer preferences. These advantages allow companies to offer better value and achieve more effective sales¹⁰. Previous studies have demonstrated that digital technologies enhance business productivity and facilitate entrepreneurship¹¹.

However, critical research gaps persist. The existing literature primarily addresses large and multinational businesses, examining the impact of digital marketing strategies on their effectiveness^12,13. This limited focus impedes the expansion of knowledge regarding how digital marketing strategies influence the business environment and overall business performance¹⁴. Furthermore, the extent to which digital marketing transformation improves business performance remains contested in current literature¹⁵. Separate but related studies examine the challenges companies face when adopting new marketing technologies¹⁶. Given the above, this research aims to gather new insights into how companies in developing economies evaluate the potential of digital marketing for enhancing business performance. This study centers on the following research question: To what extent do the adoption of digital marketing tools, investments in technology, and consumer engagement enhance business capacity for digital transformation and, consequently, improve business performance in developing economies?

Literature review

Theoretical foundations of research concept modeling and development

The theoretical foundation of the approach developed in this research is grounded in the Technology Acceptance Model (TAM). The TAM was proposed by Fred Davis in 1986¹⁷ to explain the acceptance and use of technologies by users. This theory has found widespread application across various fields, including information and communication technology (ICT), marketing, and social sciences. The TAM comprises two components: perceived usefulness (PU), defined as the degree to which a user believes that a technology enhances their performance and facilitates their activities; and perceived ease of use (PEOU), referring to the extent to which a user believes that the technology is easy to use and straightforward to operate. The theory also accounts for external factors that may also influence how users accept and utilize technologies, including social norms, organizational characteristics, and individual factors that describe the users themselves¹⁸.

At the current stage of economic and business environment development, it is virtually impossible for companies to establish a competitive market position without the active implementation of digital technologies. Consequently, companies must engage in investment activities, including in the field of digital marketing tools and technologies,¹⁹ such as artificial intelligence (AI), big data analytics, cloud computing, and the Internet of Things (IoT). Another critical aspect is a company’s capacity for digital transformation and the adoption of digital marketing tools, which can be assessed through the perceived usefulness and ease of use of marketing technologies²⁰. A company’s ability to finance and integrate new marketing technologies into its operations contributes to a key advantage—digital transformation. This process enhances operational efficiency, as digital systems enable more effective resource and supply chain management, driving productivity growth, cost reduction, and, consequently, improvements in financial sustainability and competitiveness. Moreover, digital transformation facilitates more effective managerial decision-making⁷.

In this study, the TAM was applied to understand how companies in developing economies perceive digital marketing technologies and how their perceptions influence the adoption of these technologies in business operations. It was hypothesized that if digital marketing tools were perceived by businesses as sufficiently easy to use and beneficial for their operations, there would be a higher likelihood of these technologies being practically implemented and integrated into company activities, thereby positively impacting performance⁷. In the process of digital transformation, businesses gradually incorporate digital technologies into various aspects of their operations, altering approaches to work and value creation for consumers²¹.

Previous studies suggest that SMEs typically adopt successful digital marketing strategies only after these strategies have proven effective for larger business entities⁶. However, the need to allocate financial and human resources poses a major barrier to implementation, while the effective adaptation of digital marketing practices to business-specific contexts remains challenging. These limitations create significant obstacles for businesses striving to achieve the maximum possible return from new marketing technologies²².

Moreover, in the contemporary dynamic environment, technologies, including digital marketing, evolve so rapidly that SMEs often struggle to keep pace with innovations and to implement them promptly upon availability²³. Additionally, organizations may exhibit resistance to change, as the introduction of new technologies and digital transformation strategies entails alterations in established working methods²⁴. Ultimately, it is anticipated that the final decision regarding the adoption of new digital technologies is influenced by several key predictors. These include business readiness (organizational preparedness based on established business infrastructure, financial capabilities, and management vision), perceived benefits of innovation (such as cost reductions, increased productivity, and sustainable competitiveness), market pressure from consumers and competitors, and alignment with the overall business strategy²⁵.

Theoretical gap: hypotheses and research model

As one of the most common types of digital marketing, social media marketing can build a substantial user base with relatively low investment while also improving relationships with that audience²⁶. Social media platforms are convenient for marketing activities because real or potential consumers spend a significant amount of time there, making them effective online venues for fostering deeper connections, enhancing retention, and increasing loyalty²³. Online advertisements can be targeted based on consumers’ demographic characteristics, thereby ensuring higher conversion rates and better return on investment²⁷. Search engine optimization enhances the visibility of a company and its products within search engines, leading to increased web traffic. Consequently, more frequent visits to the company’s website or social media pages drive higher sales²⁸. Content marketing expands the brand’s presence in online environments. Specifically, this strategy attracts and retains the target audience, strengthening market positioning through valuable and engaging content, including articles, videos, infographics, and other materials²⁹.

Thus, the following hypotheses can be proposed: (H1) The implementation of digital marketing tools positively influences a company’s capacity for digital transformation; and (H2) The implementation of digital marketing tools positively influences business performance.

Undoubtedly, virtually no modern business can survive in today’s business environment without utilizing digital technologies to at least some limited extent. This requirement necessitates investment in digital marketing tools and technologies (the IoT, cloud computing, artificial intelligence, and big data analytics) that increase business efficiency³⁰. However, SMEs often lack in-house specialists, particularly those skilled in developing client applications, which raises concerns regarding the return on investment for such expenditures²³. Overall, the impact of investments in technology depends on the digital adaptability of specific operational areas within a company³¹. Innovative approaches, such as artificial intelligence and client applications, enable businesses to minimize costs and maximize profits. To achieve the most significant effect, it is recommended to monitor in real time how implemented digital marketing technologies influence company revenue¹⁴.

Thus, the following hypotheses can be established: (H3) Investments in digital marketing technologies have a positive impact on business capacity for digital transformation; and (H4) Investments in digital marketing technologies have a positive impact on business performance.

An essential condition for the effective functioning of any business is the ability to attract consumers and retain customers. The application of digital marketing technologies facilitates the achievement of this objective through social media, official company pages, online advertisement platforms, mobile applications, and other channels. As a result, interaction with consumers becomes more effective, increasing the likelihood of repeat purchases³². It is unsurprising that engaged consumers are more likely to transition into loyal customers; therefore, companies strive to foster loyalty through mobile app messaging, high-quality content on social media platforms, and personalization³³. For instance, the most effective strategies include the implementation of customizable email marketing programs with personalized advertisements, active communication with consumers on social media, and promotional campaigns with prompt feedback mechanisms³⁴.

Based on the preceding discussion, the following hypotheses can be formulated: (H5) Increased consumer engagement positively impacts business capacity for digital transformation; and (H6) Increased consumer engagement positively influences business performance.

Business performance is a critical factor in growth and development, prompting companies to maximize profits, as operational efficiency is essential to sustaining productivity improvements³⁵. The adoption of digital marketing innovations enhances a company’s market orientation, thereby increasing its ability to rapidly adapt to market changes, maintain competitiveness, boost productivity, and create new opportunities by establishing cost-effective communication channels with a broad consumer base. Digitalized companies have easier access to data on consumer preferences and greater capacity to analyze consumer reactions, enabling them to swiftly implement changes in products and services to better meet market demands³⁶.

A distinct focus of this research involves assessing ethical risks associated with web analytics practices in online retail. These risks include the excessive collection of IP addresses, behavioral tracking without explicit consent, and insufficient transparency in personal data storage. Such practices may erode user trust and generate legal liabilities for businesses³⁷.

Digital marketing strategies positively influence business performance by optimizing workflows and minimizing cost factors¹⁴. Consequently, the digital transformation of businesses increases consumer engagement and contributes to more effective information gathering and analysis, providing the necessary infrastructure for digital marketing strategies²³. The integration of technologies such as big data analytics, AI, and the IoT into marketing models enhances business efficiency³⁴. Companies that adopt and utilize digital tools can create product and service offerings that not only meet but also anticipate consumer desires³⁸. This advantage results in higher returns on investment, improved productivity, and strengthened competitiveness¹⁴.

This discussion leads to the formulation of hypothesis H7: Business capacity for digital transformation positively impacts business performance.

Thus, the research model, grounded in the Technology Acceptance Model (TAM), posits that factors such as digital marketing tools, investments in digital marketing technologies, and increased consumer engagement enhance business performance through the growth of business capacity for digital transformation (Fig. 1).

Fig. 1.

Research model. Compiled by the authors using available data^{14,23,24,26,30,31,33,34,36,37}.

Given the above, this study aims to measure how these factors influence business capacity for digital transformation and ultimately business performance. The research objectives include (1) examining the theoretical foundations of digital marketing and its role in enhancing business performance, (2) exploring the features of digital marketing applications and their potential for attracting consumers, and (3) identifying the challenges associated with the acceptance and use of digital marketing technologies from the perspective of the Technology Acceptance Model (TAM).

In line with the proposed hypotheses, the study introduces several conceptual innovations that extend beyond the traditional application of the Technology Acceptance Model (TAM). Unlike classical approaches, the focus shifts from purely subjective perceptions of technology to tangible organizational actions—such as investments in digital marketing tools and enhancing consumer engagement. This research perspective broadens the practical applicability of the TAM by incorporating resource-based and operational determinants of digital transformation alongside behavioral predictors. Furthermore, the study proposes a mediator framework in which business capacity for digital transformation serves as the link between digital initiatives and ultimate business performance. This structure captures latent causal relationships that are difficult to identify through correlation analysis alone. The use of structural equation modeling (SEM) and discriminant validity testing strengthens the reliability of the findings. These analyses are particularly important in the context of developing economies, where superficial variable relationships may distort the true dynamics. A key theoretical contribution is the inclusion of consumer engagement as an independent construct within the model. Despite its evident role in the digital economy, this factor has rarely been examined as a mediator or predictor of digital transformation. Thus, this study expands the applicability of the TAM and adapts it to the unique conditions of rapidly evolving markets.

Methods and materials

Rationale for country selection

The study’s empirical foundation is based on data from the People’s Republic of China and the Republic of Kazakhstan. These countries exhibit contrasting yet comparable trajectories in digital transformation, allowing for a robustness check of the analytical model under varying levels of institutional maturity. In Kazakhstan, for instance, the digital sector grew by 40% in 2023, reflecting the rapid expansion in technological infrastructure and increasing business interest in digital solutions³⁹. In contrast, China demonstrates a more stabilized growth pattern: the business digital transformation index increased from 37 to 54 points between 2018 and 2021, with over half of companies (53%) planning to double their investments in this domain⁴⁰. This combination of an emerging and a relatively mature digital landscape provides a productive framework for comparative analysis.

Theoretical framework and questionnaire design

The methodological foundation of this study builds upon the Technology Acceptance Model (TAM) originally proposed by F. Davis¹⁷ and subsequently expanded in later empirical studies^18–20. The research focuses on analyzing how digital marketing tools are perceived and utilized within companies and how this adoption impacts business performance.

The questionnaire was structured around five key dimensions (Appendix):

1. Digital marketing adoption (4 items)—assessing routine practices in utilizing digital consumer engagement channels^23,26;

2. Digital technology investments (5 items)—examining financial commitments to AI tools, big data solutions, CRM applications, and related technologies^14,23,30,31;

3. Consumer engagement (4 items)—evaluating respondents’ perceptions of the depth and breadth of consumer interactions^33,34;

4. Readiness for digital transformation (5 items)—measuring perceived organizational capacity to adapt to new technological environments^23,24,26;

5. Business performance (4 items)—encompassing indicators such as process optimization, productivity gains, and cost reduction^14,36,38.

Responses were recorded using a five-point Likert scale. Additionally, the questionnaire included demographic sections capturing gender, age, education level, country of residence, and marketing work experience.

Methodological design

This study employs a quantitative approach utilizing a cross-sectional design, which enables the examination of current relationships between key variables. This methodological choice is particularly appropriate for capturing dynamics within rapidly evolving digital environments. To test hypotheses and assess latent constructs, the study applied structural equation modeling (SEM). This analytical approach accounts for complex interdependencies among factors while enhancing the validity of findings. The research design underwent preliminary validation during a pilot phase that included expert consultations and instrument testing.

The research process consisted of six sequential phases:

1. Formulation of the research objective and hypotheses based on the TAM framework and the unique characteristics of developing digital markets;

2. Questionnaire development and evaluation: selection and adaptation of items from validated scales, expert review of the wording, and pilot testing involving 16 marketing professionals;

3. Data collection: a survey conducted through Google Forms with targeted distribution across professional networks, including LinkedIn communities, Telegram channels, and specialized forums;

4. Data cleaning and preparation: filtering responses based on completeness criteria and response consistency;

5. Data analysis: descriptive statistics, factor analysis, regression analysis, t-tests, analysis of variance (ANOVA), along with discriminant validity assessment using Fornell–Larcker and the Heterotrait-Monotrait (HTMT) ratio;

6. Interpretation of results: comparison with the theoretical model, visualization of relationships, and derivation of intermediate and final conclusions.

Data collection

The primary data collection phase was conducted from December 1, 2023, to July 20, 2024. During the preliminary stage, the questionnaire underwent pilot testing with 16 marketing practitioners. Their feedback, particularly regarding item wording and scale structure, was incorporated into the final revision. The main data collection was conducted online using Google Forms as the primary platform. The survey was distributed through professional networks and thematic communities where the target audience was present.

Sample characteristics

The questionnaire was distributed to 467 respondents selected based on the criterion of having at least five years of marketing experience. After filtering valid responses, 390 completed questionnaires were included in the analysis, representing a 94% response rate. The sample consisted of 60.3% Chinese respondents (n = 235) and 39.7% Kazakhstani respondents (n = 155). Males accounted for 56.7% of the sample, while females represented 43.3%. The predominant age group was 35–44 years (43.8%). The majority of participants had 5 to 15 years of professional marketing experience (76.9%).

Data analysis methods

The preliminary analysis employed descriptive statistics to examine the sample’s general characteristics. To investigate structural relationships between variables, the study utilized factor analysis and structural equation modeling (SEM). To assess group differences, three analytical approaches were implemented: regression analysis to evaluate the dependence of effectiveness on perception and investment factors; t-tests and ANOVA to examine cross-group variations by country, age, and professional experience; and discriminant validity assessment using the Fornell-Larcker criterion and the HTMT ratio^14,41. Data processing and analysis were conducted using SPSS Statistics with specialized SEM modules.

Reliability and validity assessment

Reliability and validity metrics demonstrated strong instrument consistency. Factor loadings ranged from 0.739 to 0.879, indicating good construct validity. Cronbach’s α coefficients for individual scales varied between 0.858 and 0.931. Composite reliability values fell within the 0.731–0.871 range, while average variance extracted (AVE) scores were between 0.541 and 0.679. All HTMT values remained below the 0.85 threshold, confirming discriminant validity and demonstrating the distinctiveness of latent constructs.

Methodological limitations

The primary constraint stems from using the Technology Acceptance Model (TAM), which does not incorporate several factors crucial to digital marketing, including social influence, trust in technology, and risk perception. Nevertheless, the TAM remains a widely recognized and extensively validated framework in similar research contexts^18,20. The study’s geographical scope presents another limitation. Focusing on only two countries prevents direct generalization of findings across all developing economies, creating opportunities for future comparative research. The sample composition introduces further constraints, with Chinese companies representing 60.3% of respondents and all participants having over five years of marketing experience. This selectivity may limit the applicability of the results to smaller enterprises and organizations at earlier stages of digital maturity in other developing regions. The predominance of large, established companies in our sample poses additional challenges. Such organizations typically possess more advanced digital resources and management practices, which may not only enhance their actual performance but also lead to potential overestimation of achievements. This factor creates a risk of systematic bias in aggregated data interpretation, particularly when attempting to extrapolate findings to more vulnerable business segments. Methodologically, the reliance on self-reported survey data introduces concerns about social desirability bias. When evaluating sensitive metrics, such as digital transformation success or business performance, respondents may have tended toward overly positive assessments or alignment with corporate strategies. Furthermore, the cross-sectional design inherently limits the ability to identify causal relationships. While structural equation modeling (SEM) provides directional insights, it cannot replace experimental or longitudinal approaches that offer stronger causal evidence. To address these limitations, future research should incorporate longitudinal designs to track the dynamics of digital transformation and consumer engagement. Experimental or quasi-experimental methods would help clarify the causal mechanisms underlying the observed relationships. Additionally, semi-structured interviews with companies at varying digital maturity levels—from low to advanced—could provide valuable qualitative insights. This mixed-methods approach would enable a verification of quantitative findings while more effectively revealing real-world barriers and drivers of digital transformation. Additional investigations could particularly illuminate how consumer engagement manifests in practice and its true impact on performance metrics. The interview methodology would complement quantitative results through methodological triangulation, expanding the interpretative framework for the findings.

Results

Descriptive statistics and model validity

The structural model developed for this study included methods for establishing relationships between latent variables to assess the degree of their correlation as path coefficients. Structural modeling was employed to decompose the complex object into a series of simpler subsystems. This, in turn, made it easier to examine the properties of the object’s individual elements and identify the relationships between them even at the initial stage of research. A critical feature in defining a system is its interaction with the environment—encompassing both external objects that influence the system and those influenced by the system—independent of the behavior of its individual elements.

The first phase of the study involved assessing the appropriateness of the research design and the suitability of the model. To confirm the validity of conclusions in accordance with the study’s design and methodology, the reliability of the employed instrument was assessed using an appropriate statistical measure. The Fornell-Larcker criterion was used to conduct a discriminant validity test for the measurement model. The presence of discriminant validity ensures that the constructs measured by latent variables (which cannot be directly measured but can be inferred through the construction of mathematical models using observable predictors) are distinct and do not exhibit strong correlations. The main procedures at this stage included calculating the mean values for each group of predictors in the model and finding the correlations between the model’s constructs. Table 1 presents the results of the calculations for the test based on the Fornell-Larcker criterion.

Table 1.

Discriminant validity matrix according to the Fornell–Larcker criterion (diagonal values represent √AVE; off-diagonal values indicate inter-construct correlations).

	Increased consumer engagement	Implementation of digital marketing tools	Business capacity for digital transformation	Investment in digital marketing technologies	Business performance
Increased consumer engagement	0.889
Implementation of digital marketing tools	0.774	0.818
For digital transformation	0.715	0.789	0.736
Investment in digital marketing technologies	0.711	0.679	0.696	0.690
Business performance	0.631	0.494	0.809	0.631	0.684

Developed by the author.

The results presented in Table 1 indicate that the values along the diagonals of the matrix—and, consequently, the correlations relative to other constructs—support the hypothesis of discriminant validity in the proposed model. Specifically, the diagonal values are 0.889 for “increased consumer engagement,” 0.818 for “implementation of digital marketing tools,” 0.736 for “business capacity for digital transformation,” 0.688 for “investment in digital marketing technologies,” and 0.684 for “business performance.” Accordingly, all diagonal constructs in the discriminant validity assessment matrix, as determined by the Fornell-Larcker criterion, elucidate the dispersion of a random variable around its expected value.

The Fornell-Larcker criterion is an effective and widely recognized method for assessing discriminant validity. However, the method is not without limitations. These limitations can be addressed through the incorporation of additional methods, such as the Heterotrait-Monotrait (HTMT) method. This approach facilitates a comprehensive validation of the research instruments and identifies potential issues at an early stage, thereby enhancing the reliability of the research tool.

Therefore, in the subsequent calculations, the HTMT method was employed as a criterion for assessing discriminant validity and evaluating the correlation between two latent variables. The results of the assessment are presented in the form of a matrix of multi-trait multi-method correlations. To determine discriminant validity, the observed correlations were compared against a predetermined threshold; if the value exceeds this threshold, it indicates a lack of discriminant validity. The table below presents the results of the HTMT test for model validity (Table 2).

Table 2.

The HTMT ratio matrix assessing discriminant validity (threshold: HTMT < 0.85).

	Increased consumer engagement	Implementation of digital marketing tools	Business capacity for digital transformation	Investment in digital marketing technologies	Business efficiency
Increased consumer engagement
Implementation of digital marketing tools	0.697
Business capacity for digital transformation	0.738	0.759
Investment in digital marketing technologies	0.614	0.668	0.725
Business performance	0.773	0.601	0.655	0.791

Developed by the author.

The HTMT test results presented in Table 3 confirm the discriminant validity of the latent variables in the study, indicating that the model is applicable for measuring the included constructs. To elaborate on the results, for the predictor group “implementation of digital marketing tools,” the correlation with the latent variable “increased consumer engagement” was 0.697. For the predictor “business capacity for digital transformation,” the correlations were 0.738 (factors in the category “increased consumer engagement”) and 0.759 (“implementation of digital marketing tools”). For “investment in digital marketing technologies,” the correlations were 0.614 (“increased consumer engagement”), 0.668 (“implementation of digital marketing tools”), and 0.725 (“business capacity for digital transformation”). With regard to the variable “business performance,” the analysis revealed the following correlations: 0.773 for the category “increased consumer engagement,” 0.601 for “implementation of digital marketing tools,” 0.655 for “business capacity for digital transformation,” and 0.791 for “investment in digital marketing technologies.” All observed values fall below the established minimum threshold of 0.85, supporting the presence of discriminant validity within the model.

Table 3.

Coefficients of determination (R²) and adjusted R² for the model’s dependent variables.

Predictor	R 2	Adjusted R2
Business capacity for digital transformation	0.602	0.591
Business performance	0.662	0.657

Developed by the author.

Subsequently, the study tested the formulated hypotheses by calculating the extent to which the variance of the explanatory variable “business capacity for digital transformation” and the outcome variable “business performance” could be accounted for by the independent variables included in the model. The coefficient of determination (R²) was computed for the predictors “business capacity for digital transformation” and “business efficiency” to measure the proportion of variance in the dependent variable that can be explained by the model’s independent variables. This indicator tends to increase as more factors are included in the model. Therefore, adjusted R² values were also calculated to account for the number of predictors, as they decrease when additional model variables do not significantly contribute to the model’s validity. The table below presents the results of these calculations (Table 3).

Thus, for the predictors “business capacity for digital transformation” and “business performance,” R² = 0.602 and the adjusted R² = 0.591; for “business performance,” R² = 0.662 and the adjusted R² = 0.657. Before drawing conclusions about the acceptability of the model, it is important to note that an acceptable R² value depends on several factors, including the research context, the nature of the data collected, and the specific field of study. In particular, for social sciences, an R² value between 0.50 and 0.99 is considered acceptable, especially if most explanatory variables are statistically significant and no multicollinearity is detected. Since a comprehensive validation of the research instruments has already been conducted, the values of R² and adjusted R² can be considered acceptable.

Hypothesis testing

The findings from testing the hypotheses formulated in the study are presented below (Table 4; Fig. 2).

Table 4.

Hypothesis testing results: standardized coefficients (β), errors, t-values, and significance levels (p-values).

Correlations	β-coefficient (standardized)	Standard error (SE)	t-statistic	p-value	Result
Implementation of digital marketing tools → Business capacity for digital transformation	0.146	0.076	2.004	0.047	Acceptable
Implementation of digital marketing tools → Business performance	0.119	0.060	2.010	0.047	Acceptable
Investment in digital marketing technologies → Business capacity for digital transformation	0.288	0.099	3.113	0.004	Acceptable
Investment in digital marketing technologies → Business performance	0.247	0.079	3.018	0.003	Acceptable
Increased consumer engagement → Business capacity for digital transformation	0.418	0.068	5.798	0.000	Acceptable
Increased consumer engagement → Business performance	0.338	0.065	5.400	0.000	Acceptable
Business capacity for digital transformation → Business performance	0.809	0.038	2.594	0.000	Acceptable

Developed by the author.

Fig. 2.

Model for testing the proposed research hypotheses. Developed by the author.

The results suggest that the implementation of digital marketing tools enhances a company’s capacity for digital transformation (β = 0.146, t = 2.004, p = 0.047) and business performance (β = 0.119, t = 2.010, p = 0.047). Similarly, investment in digital marketing technologies stimulates a company’s capacity for digital transformation (β = 0.288, t = 3.113, p = 0.002) and impacts business performance (β = 0.247, t = 3.018, p = 0.003). Increased consumer engagement also affects a company’s digital transformation capacity (β = 0.418, t = 5.798, p = 0.000) and performance (β = 0.338, t = 5.400, p = 0.000). Additionally, the moderating variable “business capacity for digital transformation” has a substantial impact on business performance (β = 0.809, t = 2.594, p = 0.000). These findings substantiate all formulated hypotheses.

Interpretation of results

Figure 2 illustrates the results of testing the research hypotheses regarding the influence of digital marketing tools, investment in digital marketing technologies, and increased consumer engagement—mediated by business capacity for digital transformation—on business performance. Specifically, Fig. 2 provides a visual summary of the confirmed model, displaying the standardized path coefficients obtained through structural equation modeling (SEM) analysis.

Thus, the main results of the analysis can be summarized as follows. While correlations existed among all variables, they were not always significant. The highest correlation was observed between the variable “business capacity for digital transformation” and “business performance” (β = 0.809), confirming the hypothesis of a relationship between a business adopting new digital marketing technologies, its ability to utilize these technologies, and its performance in the market. The identified dependence of business performance on digital transformation suggests that successfully implemented digital initiatives lead to increased productivity and higher consumer satisfaction. Ultimately, companies employing this strategy positively benefit from higher profits and competitive market positioning compared to companies that do not integrate digital marketing technologies.

With regard to correlations between individual predictors and digital transformation capacity, the strongest association was detected for the variables “increased consumer engagement” and “business capacity for digital transformation” (β = 0.418). This may be explained by the fact that businesses digitize business processes by actively implementing innovative technologies for user data processing, online advertising, active communications, social media management, and consumer-facing applications. Digitalization results in increased consumer engagement and the resulting revenue increase, further stimulating transformational processes. The lowest correlation was observed between the “implementation of digital marketing tools” and “business capacity for digital transformation” (0.146). Although this pair exhibited the weakest effect compared to other elements of the model, its statistical significance was nonetheless confirmed. One possible explanation is that while potential revenue growth is critical for businesses, the use of innovative digital marketing tools alone may be insufficient to actively drive a reassessment of business operation strategies.

Regarding the hypotheses formulated in the study, several aspects require consideration. The identified impact of implementing digital marketing tools on business capacity for digital transformation (β = 0.146, t = 2.004, p = 0.047) and business performance (β = 0.119, t = 2.010, p = 0.047) confirms hypotheses H1 (The implementation of digital marketing tools positively influences a company’s capacity for digital transformation) and H2 (The implementation of digital marketing tools positively influences business performance). The observed effect of investment in digital marketing technologies on business capacity for digital transformation (β = 0.288, t = 3.113, p = 0.002) and business performance (β = 0.247, t = 3.018, p = 0.003) provides a confirmation for hypotheses H3 (Investments in digital marketing technologies have a positive impact on business capacity for digital transformation) and H4 (Investments in digital marketing technologies have a positive impact on business performance). The impact of increased consumer engagement on business capacity for digital transformation (β = 0.418, t = 5.798, p = 0.000) and business performance (β = 0.338, t = 5.400, p = 0.000) validates hypotheses H5 (Increased consumer engagement positively impacts business capacity for digital transformation) and H6 (Increased consumer engagement positively influences business performance). Finally, the significant effect of business capacity for digital transformation on its performance (β = 0.809, t = 2.594, p = 0.000) supports the validity of hypothesis H7 (Business capacity for digital transformation positively impacts business performance).

Discussion

This study examines how digital marketing strategies influence the performance of companies in emerging economies, considering aspects such as online advertising, the use of marketing tools in social media, search optimization opportunities, digital channels for attracting consumers, and investments in digital technologies. The application of the Technology Acceptance Model (TAM) as a theoretical foundation enabled the development of a research approach to examine marketers’ perceptions of the impact exerted by digital transformation on companies’ marketing strategies and business performance. The current study expands upon earlier research that employed the TAM framework to demonstrate the relationship between satisfaction with digital marketing and the intention to adopt and utilize digital marketing technologies⁴². The analysis conducted in this study reveals several key findings. Specifically, the implementation of digital marketing tools has a positive impact on business capacity for digital transformation (H1). These findings support earlier studies positioning digital transformation as a key area of innovation in modern business. Previous research also emphasizes that digital transformation—particularly in the field of marketing—can fundamentally reshape the traditional value creation process⁴³. However, for some companies, it remains challenging to comprehend and effectively leverage the potential of innovative marketing instruments⁷.

This study confirmed the hypothesis regarding the positive effect of digital marketing tools on business performance (H2). Therefore, to enhance operational effectiveness, businesses in emerging economies are recommended to invest in digital technologies and integrate big data analytics, artificial intelligence, or cloud computing. Other regional studies, particularly those examining China’s financial sector, demonstrate a strong positive relationship between digital transformation and enterprise resilience⁴⁴. As previously stated, the increase in business profitability resulting from digital transformation can be attributed to enhanced market analysis capabilities, more effective pricing strategies, improved management of distribution channels, and closer relationships with consumers⁴⁵. This finding is also consistent with earlier research highlighting that effective digital marketing strategies allow companies to reach a wider audience and foster consumer engagement⁴⁶.

The current results support the hypothesis that investments in digital marketing technologies positively influence a company’s capacity for digital transformation (H3) and business performance (H4). These findings corroborate previous studies that have shown how financing and adopting digital technologies improve overall business efficiency and contribute to cost reduction⁴⁷.

The present study demonstrated that increased consumer engagement positively influences a company’s capacity for digital transformation, confirming H5. The results of other scholars’ investigations similarly suggest that greater interaction between a company and its customers—including through personalized messaging and active communication via social media⁴⁸—leads to more trusting and closer relationships. Enhanced relationships with consumers, in turn, positively impact a company’s organizational effectiveness by retaining old and attracting new customers⁴⁹. This is particularly significant in the context of developing economies, as shown by regional studies conducted in the least developed countries. For instance, case studies on African businesses emphasize that perceived economic benefits are the primary factor influencing the adoption of digital marketing tools⁴⁹. Research findings reveal that in transitional economies, organizational culture profoundly influences technology adoption patterns. Collectivist orientations appear to facilitate the implementation of modern practices, while high power distance creates barriers to managerial change. Long-term orientation emerges as a driver of green innovation initiatives, whereas positive attitudes toward new technologies promote digital transformation⁵⁰.

The study established that increased consumer engagement positively affects business performance (H6). This conclusion corroborates earlier findings suggesting that stronger consumer interaction drives businesses toward digital transformation, ultimately improving their market performance^51,52. The present study offers novel insights that extend the applicability of the classic Technology Acceptance Model (TAM) to the contexts of developing economies. Most notably, the study identifies consumer engagement as a critical mediating factor between digital initiatives and their practical adoption. These empirical findings align with recent research on consumer behavior models in Bangladesh, where research has demonstrated that factors such as perceived usefulness, trust, and compatibility exert both direct and mediated effects on e-commerce adoption patterns (Perceived Usefulness → Adoption) within an expanded TAM framework⁵³.

However, the aforementioned study did not consider incorporating engagement as an independent model component, despite its growing relevance in both academic discourse and practical applications. The proposed solution—positioning engagement as a mediator—substantially enhances the TAM’s transformational potential, particularly within developing markets. This approach receives further validation from consumer behavior research in India, which demonstrated that satisfaction fully mediates the effect of perceived usefulness on both emotional and behavioral engagement. That is, perceived usefulness contributes to engagement only through satisfaction^54,55. These findings underscore the importance of accounting for intermediary variables, especially in contexts where digital practices are still emerging and TAM predictors operate through complex behavioral mechanisms rather than directly. Equally compelling evidence comes from phygital product research (hybrid physical-digital solutions) in Pakistan. The study indicates that consumer engagement considerably influences repurchase intention, with personalization and sustainable consumption serving as mediators of this effect^56,57. While this research did not apply the classical TAM directly, its focus on audiences navigating rapidly evolving digital landscapes aligns with the current study’s emphasis. Accordingly, engagement functions as the crucial link between consumer motivation and actual purchasing behavior.

In the proposed framework, engagement mediates the relationship between digital marketing and business performance (Digital Marketing → Capability → Business Performance). This model not only refines and extends the TAM within the specific sample but also prompts a broader reconsideration of its applicability in developing contexts. Engagement transitions from being a peripheral consideration to becoming a central, nonlinear mechanism explaining digital transformation success. Unlike mature economies where TAM predictors typically directly determine user behavior, in environments with infrastructure limitations and social barriers, consumer engagement emerges as the critical factor ensuring both model robustness and contextual sensitivity.

These conclusions build upon traditional TAM theories, which identify perceived ease of use and usefulness as key predictors influencing technology adoption⁵². The significant impact of a company’s capacity for digital transformation on its performance (H7) further underscores the importance of such transformation for businesses seeking to improve their organizational efficiency^53,54.

Practical recommendations and digital readiness assessment

The findings enable the formulation of several practice-oriented recommendations for companies in developing economies, tailored to business scale, digital maturity, and resource availability. The analysis reveals that investments in consumer engagement tools yield the highest returns. The most effective solutions include an active social media presence, personalized email campaigns, and the use of messaging platforms, mobile applications, and online tools for user behavior analytics. These solutions not only strengthen customer relationships but also enhance the flexibility of business processes. These conclusions support research on the effectiveness of social media analytics in online retail, particularly regarding performance improvements across all customer journey stages—from acquisition to retention. Social media analytics help companies optimize costs and increase return on investment through precise targeting and content personalization^58,59. This effect is especially valuable for SMEs, where low-cost digital tools, such as social platforms, can serve as effective entry points for digital transformation. For SMEs facing budget and staffing constraints, a phased approach to digital transformation is advisable, beginning with simple, adaptable solutions. Recommended initial steps include open-source CRM systems, automated email marketing, targeted social media advertising, website chatbots and feedback forms, and basic web analytics (e.g., Google Analytics). Larger and more technologically mature companies may benefit from scalable solutions, such as AI-powered predictive analytics and recommendation algorithms; Big Data platforms integrated with ERP systems; mobile applications and customer web portals; and real-time analytics with personalized interfaces. To evaluate a company’s current level of digital readiness—whether for internal business purposes or for designing government support programs—the following diagnostic checklist can be employed, with its structure detailed in Table 5.

Table 5.

Checklist for digital readiness assessment.

Evaluation criteria	Sample question/indicator
1. Digital infrastructure	Does the company have a website, CRM, CMS, payment systems, or an email subscriber database?
2. Customer online engagement	What percentage of sales or customer inquiries occur through digital channels?
3. Digital marketing budget	Is a portion of the marketing budget allocated to digital tools?
4. Leadership support	Has a strategic vision for digital transformation (Digital Vision) been formally articulated?
5. Team digital competencies	Does the workforce include digital marketers, data analysts, or UX/UI specialists?
6. AI/big data/IoT initiatives	Has at least one pilot project leveraging these technologies been implemented?

Developed by the author.

This proposed tool serves dual purposes. Companies can use it for self-assessment and digital transformation planning. Government agencies may apply it when developing SME digitalization support programs and monitoring sector-wide technological maturity.

Conclusions

This study introduces a universal tool designed to evaluate the impact of specific variables—such as the implementation of digital marketing instruments, investments in digital marketing technologies, and increased consumer engagement—on a company’s capacity for digital transformation, and ultimately, its overall performance. The proposed model assesses real-world experiences with digital marketing technologies and utilizes the Technology Acceptance Model (TAM) to clarify how companies in developing economies perceive and apply digital marketing technologies.

The findings indicate that digital transformation plays a crucial role in ensuring that the implemented digital marketing tools positively influence a company’s performance. The study highlights the advantages of companies that implement successful digital marketing initiatives, invest in digital marketing technologies, and integrate consumer engagement tools. Companies that adopt digital marketing technologies more effectively enhance productivity, improve consumer satisfaction, attract new clients, and boost overall business performance and competitiveness compared to those that do not.

The study’s limitations include a relatively small sample consisting solely of representatives from two countries: the People’s Republic of China and the Republic of Kazakhstan. Consequently, the model’s behavior may differ in developed economies, limiting the extent to which the findings can be confidently generalized to other contexts with the same level of precision. Nonetheless, the model serves as a universal tool that can be refined through comparative modelling in other countries and regions.

This study contributes to the further development of marketing theory. It offers deeper insights into the challenges associated with the implementation of digital marketing tools, investment in digital transformation, and the establishment of consumer relationships. Specifically, the study addresses the impact of digital marketing strategies on business productivity and efficiency in developing economies. The application of the Technology Acceptance Model (TAM) enhances the scientific understanding of how companies in developing economies adopt and utilize digital marketing technologies. The proposed approach may serve as a useful framework for scholars in entrepreneurship and marketing to explore the relationships between digital marketing strategies, specific tools, and business performance.

To enhance the generalizability and contextual robustness of the findings, follow-up studies should be conducted in other emerging economies, particularly across Africa and Latin America. This approach would enable comparative analysis of determining factors in different regional contexts while clarifying the universal applicability of the identified relationships between digital transformation, consumer engagement, and business performance. Special attention should be given to including representatives of micro and small enterprises in the sample. It is also necessary to involve respondents possessing varying levels of digital competency. Such methodological considerations would yield a more balanced representation of diverse digital development practices and barriers.

The practical implications of the study include the provision of a clear analytical tool for assessing the impact of digital marketing tools, investments in digital marketing technologies, and increased consumer engagement on business performance through the lens of digital transformation capacity. Marketers and managers are advised to integrate tools such as big data analytics into their marketing practices to obtain more accurate data on consumer behavior, preferences, and market trends. Furthermore, the study holds practical relevance for business executives by providing a foundation for informed decision-making regarding investments in digital marketing. The adoption of innovative technologies enables companies to respond more promptly to stakeholder demands, thereby enhancing overall business efficiency. For policymakers in developing economies, it is recommended to design and implement financial and advisory support programs aimed at fostering digital innovation in national enterprises. Such initiatives can create an environment conducive to the development of more efficient companies.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

TAM

Technology acceptance model

ICT

Information and communication technology

PU

Perceived usefulness

PEOU

Perceived ease of use

SME

Small and medium-sized enterprises

H

Hypotheses

HTMT

Hetero-Mono Trait criterion

SEM

Structural equation modeling

Author contributions

Conceptualization, S.G.; methodology, S.G.; software, S.G.; formal analysis, S.G.; investigation, S.G.; resources, S.G.; data curation, S.G.; writing—original draft preparation, S.G.; writing—review and editing, S.G.; visualization, S.G.; supervision, S.G.; project administration, S.G.; funding acquisition, S.G.

Funding

This research received no external funding.

Data availability

All data generated or analyzed during this study are included in this published article.

Declarations

Competing interests

The authors declare no competing interests.

Institutional review board statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of AL-FARABI KAZAKH NATIONAL UNIVERSITY (protocol code 63108 and 20 November 2023).

Informed consent

Informed consent was obtained from all subjects involved in the study.