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The impact of Chinese automotive companies’ corporate reputation on Russian consumers’ purchase intentions: a dual-dimensional attitudinal approach

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13.05.2025
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Библиографическое описание
Лесина, Г. С. The impact of Chinese automotive companies’ corporate reputation on Russian consumers’ purchase intentions: a dual-dimensional attitudinal approach / Г. С. Лесина. — Текст : непосредственный // Молодой ученый. — 2025. — № 20 (571). — С. 347-359. — URL: https://moluch.ru/archive/571/125158/.


In recent years, global trade dynamics have undergone significant transformations, particularly within the automotive sector. The geopolitical context has led to notable shifts in trade patterns, prompting countries to seek new opportunities for economic cooperation. Amid these changes, China has emerged as a prominent exporter of automotive products to Russia, contributing to the diversification of Russia's import sources. This study examines the multifaceted dimensions of corporate reputation and their influence on Russian consumers' purchase intentions towards Chinese automotive brands. By employing a dual-dimensional model grounded in attitude theory, this research utilizes quantitative methods to identify key factors shaping corporate reputation and their direct and indirect effects on consumer behavior. The findings offer valuable insights for Chinese automotive companies aiming to enhance their market strategies and build a robust corporate reputation in Russia, thereby expanding their market position. This study underscores the importance of understanding consumer perceptions in building robust corporate reputation and driving purchase intentions.

Keywords: corporate reputation, consumers’ purchase intentions, reputation influence factors, attitude theory.

1 Research Background and Objectives

In the globalized economy, the strategic utilization of corporate reputation has emerged as a pivotal factor in shaping consumer behavior and market dynamics. The automotive industry, in particular, has witnessed significant transformations driven by geopolitical shifts and technological advancements. The automotive industry, in particular, has witnessed significant transformations driven by geopolitical shifts and technological advancements. Amid these changes, China has emerged as a prominent exporter of automotive products to Russia, contributing to the diversification of Russia's import sources. Despite the increasing market share of Chinese automotive brands in Russia, the consumers continue to exhibit skepticism and distrust towards these brands and Chinese cars in general. This phenomenon underscores the critical importance of understanding the factors influencing consumer perceptions in building a robust corporate reputation.

Corporate reputation is a multifaceted construct that has been extensively studied across various disciplines. Traditionally, reputation has been viewed as a collective perception of an organization held by its stakeholders, encompassing both cognitive and affective dimensions. This dual-dimensional perspective aligns with the broader understanding of attitudes in social psychology. Attitudes are defined as evaluative predispositions towards an object, person, or situation, characterized by cognitive, affective, and behavioral components [1, 315–334]. In the context of corporate reputation, these components translate into consumers' beliefs about a company's capabilities (cognitive dimension), their emotional reactions to the company (affective dimension), and their behavioral intentions towards the company's products or services (behavioral dimension).

Schwaiger further elaborates on this perspective by proposing that corporate reputation should be conceptualized as an attitude [2, 46–71]. According to Schwaiger, corporate reputation is not merely a static construct but a dynamic and evaluative perception held by stakeholders. This perception is influenced by various factors, including direct experiences with the company, indirect information from third parties, and the company's overall behavior and practices. By framing corporate reputation as an attitude, Schwaiger emphasizes the importance of understanding the cognitive and affective underpinnings of stakeholder perceptions. This approach provides a more comprehensive understanding of how corporate reputation influences consumer behavior and, ultimately, a company's market success.

In contemporary markets, the role of corporate reputation in shaping consumer behavior cannot be overstated. A strong corporate reputation can significantly enhance consumer trust, reduce perceived risk, and ultimately drive purchase intentions. However, the complexities of international markets, particularly those influenced by geopolitical events, necessitate a deeper exploration of the factors that influence corporate reputation and their subsequent impact on consumer behavior. This is especially relevant in the context of the Russian market, where consumer perceptions of Chinese automotive brands are still evolving.

This study aims to fill this gap by evaluating the corporate reputation of Chinese automotive companies in the eyes of Russian consumers and analyzing the underlying factors that shape their purchase intentions. By employing a dual-dimensional model of corporate reputation, this research seeks to uncover the mechanisms through which cognitive and affective dimensions of reputation influence consumer behavior. The findings of this study will contribute to the existing literature by providing a comprehensive understanding of the interplay between corporate reputation and consumer purchase intentions in a cross-cultural context. Moreover, the insights derived from this research will offer practical guidance for Chinese automotive companies seeking to enhance their market strategies and build a robust corporate reputation in Russia.

The primary objective of this study is to investigate the impact of Chinese automotive companies’ corporate reputation on Russian consumers’ purchase intentions. Additionally, this study aims to explore the underlying factors that shape the cognitive and affective dimensions of corporate reputation and their subsequent influence on consumer behavior. Specifically, the following are the research questions of the study:

  1. How do Russian consumers perceive the corporate reputation of Chinese automotive companies?
  2. What are the key factors influencing the cognitive and affective dimensions of corporate reputation for Chinese automotive companies in Russia?
  3. How do the cognitive and affective dimensions of corporate reputation influence Russian consumers’ purchase intentions towards Chinese automotive brands?
  4. What role do cognitive and affective dimensions of corporate reputation play as mediators between key factors and purchase intentions?

2 Literature review

2.1 Corporate Reputation

The multiplicity of definitions surrounding corporate reputation arises from its interdisciplinary nature, with scholars across management, marketing, finance, and organizational theory operationalizing the construct differently based on their epistemological orientations. Furthermore, the heterogeneity of stakeholder perceptions—where investors prioritize financial credibility, employees value ethical governance, and consumers assess service quality—necessitates context-specific conceptualizations. This definitional plurality reflects the construct’s inherent complexity rather than conceptual ambiguity, as reputation functions as a meta-construct whose manifestations depend on theoretical lenses and empirical contexts.

The conceptualization of corporate reputation has evolved significantly over time, transitioning from a singular perspective to a more comprehensive, multi-dimensional understanding. Fombrun [3, 390–395] defines it as «A perceptual representation of a company’s past actions and future prospects that describes the firm’s overall appeal to all of its key constituents when compared with other leading rivals». This definition became foundational, and it encapsulates three critical attributes: the multidimensional nature of reputation, it reflects collective perceptions of different groups of stakeholders, and it is judged relative to competitors in the industry. This seminal work has been widely cited and has laid the groundwork for subsequent research.

Building on this foundation, Walsh and Beatty introduced a customer-centric approach by defining customer-based reputation (CBR), which is: «A customer’s overall evaluation of a firm based on his or her reactions to the firm’s goods, services, communication activities, interactions with the firm, and/or known corporate activities». [4, 127–143]. This definition not only consolidates previous conceptualizations but also emphasizes the pivotal role of the customer segment, integrating direct and indirect interaction mechanisms into the formation of reputation. This study adopts the CBR definition, viewing corporate reputation as a collection of attitudes formed by customers through their interactions with the company. This perspective aligns with the growing recognition that customer perceptions and experiences are central to the construct of corporate reputation, thereby providing a robust framework for understanding its dynamics in the marketplace.

2.2 Corporate Reputation measurement systems

Measuring corporate reputation presents unique challenges due to its intangible and multifaceted nature. Various methods have been developed to address these challenges, each with its strengths and limitations.

Traditional approaches include the Fortune magazine's AMAC and GMAC indices, which assess companies based on eight dimensions such as management quality, product quality, innovation, and financial stability. These indices, derived from large-scale surveys, offer a comprehensive evaluation of corporate performance from a broad stakeholder perspective. However, they often prioritize financial and managerial aspects, potentially overlooking the nuanced perceptions of consumers.

The Reputation Quotient (RQ), introduced by Fombrun [3, 390–395] represents another influential measurement tool. Comprising 32 items that cover diverse stakeholder perceptions, the RQ has been widely recognized for its applicability across cultures and industries. Despite its widespread use, the RQ has been criticized for conflating reputation with its drivers and focusing excessively on financial performance, thus limiting its ability to capture the full spectrum of consumer attitudes.

RepTrak, an extension of the RQ, addresses some of these limitations by incorporating emotional dimensions such as trust, admiration, respect, and identification. This holistic approach provides a more balanced view of corporate reputation, integrating both cognitive and affective components. However, similar to the RQ, its multi-stakeholder orientation may dilute the distinct consumer perspective.

Recognizing these gaps, Walsh and Beatty's CBR scale offers a focused, consumer-oriented alternative [4, 127–143]. Developed through a rigorous mixed-methods approach, the CBR scale includes 28 items across five dimensions: customer orientation, good employer image, financial robustness, product & service quality, and social & environmental responsibility. This scale not only captures the key dimensions shaping corporate reputation but also integrates outcome variables such as customer satisfaction, trust, loyalty, and word-of-mouth, thereby providing a comprehensive and actionable measure of consumer-based reputation. Given its alignment with the study's focus on consumer attitudes, this research employs the CBR scale to assess corporate reputation, leveraging its robustness and relevance in capturing the consumer-centric aspects of this construct.

2.3 Corporate Reputation as an Attitude

The conceptualization of corporate reputation as a form of attitude represents a significant shift in the academic discourse, emphasizing the subjective and evaluative nature of this construct. Drawing on Allport's definition of attitude as a «mental and neural state of readiness», researchers have begun to view corporate reputation through the lens of cognitive and emotional responses [5, 1–13].

Schwaiger proposed that corporate reputation should be understood as an attitude integrating cognitive and emotional components [2, 46–71]. This perspective highlights the importance of stakeholders' experiences and perceptions in shaping their evaluations of a company. Burke et al. further elaborated on this concept, arguing that corporate reputation is essentially a collective attitude formed by stakeholders' cognitive assessments and emotional reactions toward an organization [6, 225–227]. This collective attitude not only reflects the company's attributes but also encapsulates the evaluative judgments of its stakeholders.

Veh et al. reinforced this view through a systematic review, identifying «belief» and «evaluation» as core attributes recurring in reputation definitions. They emphasized that conceptualizing corporate reputation as an attitude underscores the evaluator's perspective, distinguishing between the company's attributes and the stakeholders' attitudes. This distinction enhances the conceptual clarity and discriminant validity of corporate reputation, providing a more nuanced understanding of its antecedents and consequences [7, 315–353].

Building on this theoretical foundation, this study integrates the emotional and cognitive dimensions of corporate reputation into the CBR scale. By doing so, it aims to enhance the scale's internal consistency and validity, offering a more comprehensive and theoretically grounded measure of consumer-based corporate reputation.

2.4 Impact of Corporate Reputation on Purchase Intentions

Corporate reputation has a significant impact on consumer behavior, particularly in shaping purchase intentions. Smaizien research has shown that a strong corporate reputation can mitigate consumers' perceived risks, thereby facilitating purchase decisions [8, 718–723]. This risk-buffering effect is crucial in environments where consumers face uncertainty and information asymmetry. Additionally, a positive corporate reputation enhances consumers' evaluations of products, reducing the costs associated with information search. Consumers often rely on corporate reputation as a heuristic cue for product quality, which in turn increases their purchase satisfaction [9, 655–664]. In turn, negative corporate reputation can significantly diminish consumers' intentions to purchase, highlighting the fragility of consumer trust [10, 1–20]

Corporate reputation also acts as a mediator between factors, influencing consumers’ attitudes towards the brand, and their behaviors. Agmeka examined how corporate reputation mediates the relationship between discount strategies and consumer purchase intentions. The findings indicated that brand reputation and brand image play a pivotal role in influencing how consumers perceive and respond to promotional offers, highlighting the mediating function of corporate reputation in marketing strategies [11, 851–858]. Moreover, Meilatinova explored the factors influencing customer repurchase intentions and word-of-mouth, finding that trust and satisfaction are positively influenced by corporate reputation and information quality, and corporate reputation served as a partial mediator [12, 9].

In addition to these direct and mediating effects, corporate social responsibility (CSR) has emerged as a critical factor influencing consumer attitudes and behaviors. Bianchi demonstrated that consumers' perceptions of a company's CSR initiatives significantly impact their purchase intentions and overall reputation evaluations [13, 206–221]. This study aims to further elucidate these complex dynamics by examining how corporate reputation, as a consumer attitude, influences purchase intentions within the broader framework of consumer decision-making.

3 Research Model and Hypothesis

3.1 Research Model and Variables Definition

The conceptual model of this study, presented in Figure.1, builds upon Walsh and Beatty's [4, 127–143] Customer-Based Reputation (CBR) framework, which emphasizes the multidimensional nature of corporate reputation from a consumer perspective. This approach represents a significant advancement over traditional reputation studies that relied primarily on financial data or media reports, placing consumer perceptions at the core of corporate reputation evaluation.

The research model incorporates five key dimensions of corporate reputation, defined by Walsh & Beatty, that capture both external behaviors and internal values of concern to consumers:

  1. Customer Orientation (CO) — the extent to which consumers perceive that a company and its employees are dedicated to meeting customer needs and interests;
  2. Good Employer (GE) — consumers’ evaluations of a company as an employer, including perceptions of fair employee treatment, good working conditions, and effective leadership;
  3. Reliable and Financially Stable Company (RF) — consumers’ perceptions of a company's financial health, business competence, and ability to remain competitive in the long term.
  4. Product and Service Quality (PS) — consumers’ overall evaluations of a company's offerings in terms of reliability, innovation, and value relative to competitors.
  5. Social and Environmental Responsibility (SE) — consumers’ perceptions of a company's commitment to ethical business practices, community welfare, and environmental sustainability.

Building on attitude theory and Schwaiger’s definition of corporate reputation, which should be understood as an attitude integrating cognitive and emotional components, the model further divides corporate reputation into two distinct components:

Affective Reputation (AR): Reflects consumers' emotional responses and sympathy toward a company.

Cognitive Reputation (CR): Represents consumers' knowledge-based evaluations of a company's competence.

Purchase Intention (PI) serves as the outcome variable, representing the behavioral tendency component of attitude theory. Purchase intention is described as the likelihood or probability that a consumer intends to purchase a product [14, 1–8].

Fig. 1. Research Conceptual Model

3.2 Hypothesis Development

Corporate reputation significantly influences consumer purchase decisions by shaping perceptions of reliability and value. A strong reputation reduces perceived risk in consumer decision-making while simultaneously increasing confidence in product quality. Fombrun's seminal work established that reputation functions as a heuristic for consumers facing complex purchase decisions, particularly in high-involvement product categories like automotive purchases [3, 362].

The Customer Orientation dimension of reputation directly impacts purchase intentions by creating perceptions of service excellence. Brown et al. demonstrated that customer-centric behaviors enhance trust and reduce perceived uncertainty in buyer-seller relationships [15, 110–119]. In automotive retailing, where after-sales service is crucial, strong customer orientation significantly increases repurchase intentions [16, 84–96].

Perceptions of a company as a Good Employer similarly influence purchase behavior through inferred product quality signals. Walsh and Beatty established that consumers use employer reputation as a proxy for product reliability, particularly in service-intensive industries [4, 127–143]. Recent studies confirm that consumers tend to believe that companies with high employee satisfaction are better able to provide high-quality products and services. [17, 113]

Reliable and Financially Stable Company perceptions play a critical moderating role in unstable economic environments. Akatieva's [18, 46] research in emerging markets revealed that consumers are more inclined to choose financially stable enterprises because they can guarantee long-term after-sales support and parts supply.

Product and Service Quality perceptions remain the most direct driver of purchase intentions. Automotive consumers are most concerned about reliability, safety, and technological innovation [19, 88–91]. A good reputation for product quality can also significantly reduce consumers' perceived risk [8, 718–723]. In the Russian market, Chinese car brands have long faced the stereotype of «low quality», so positive evaluations of actual performance by consumers are particularly important. If consumers believe that a certain brand of car has high cost-effectiveness and durability, their willingness to purchase will significantly increase.

Social and Environmental Responsibility perceptions increasingly influence purchase behavior, especially among younger consumers. Corporate social responsibility (CSR) behavior can enhance consumers' brand identity [20, 2692–2700]. Companies with good environmental performance are more likely to gain consumer support [21, 1789]. In Russia, due to increasing environmental awareness, consumers are more inclined to choose car brands that meet green standards.

On the basis of these empirical findings, the following hypothesis is proposed:

H1: Corporate reputation dimensions have significant positive effects on Purchase Intentions.

Corporate reputation dimensions influence consumer evaluations through parallel emotional and rational pathways, each contributing distinct yet complementary mechanisms to brand perception. The five key dimensions simultaneously shape both affective reputation (AR) and cognitive reputation (CR) through interconnected processes.

Customer orientation operates through dual mechanisms: it fosters AR by creating emotional bonds via personalized service experiences [22, 220] while simultaneously building CR through demonstrated reliability and competence in customer support [23, 299–314]. This dual effect is particularly pronounced in Russia's automotive market, where consistent after-sales service addresses both emotional security needs and rational quality concerns.

Similarly, Good Employer perception generates AR through ethical alignment and shared values while establishing CR as consumers rationally associate employee treatment with manufacturing quality [20, 1–10]. Perception of a company as Reliable and Financially Stable provides emotional security while simultaneously offering concrete evidence of business stability for cognitive evaluation [25, 336–345].

Product and Service quality creates pride of ownership [26, 171] while providing objective performance data, especially crucial for Chinese brands overcoming quality stereotypes Social and Environmental responsibility builds moral admiration while demonstrating measurable ethical commitments that align with Russian consumers' values [21, 1789]. Based on the evidence, this study proposes the following hypothesis:

H2: Corporate reputation dimensions each positively influence Affective Reputation.

H3: Corporate reputation dimensions each positively influence Cognitive Reputation.

A large number of empirical studies have confirmed that emotional reputation (AR) and cognitive reputation (CR) have a significant mediating effect between the antecedent variables of corporate reputation and consumer behavior outcomes. According to the attitude theory framework, these two dimensions of reputation are transformed into external stimulus behavioral intentions through different psychological mechanisms: emotional reputation operates through emotional contagion and brand attachment mechanisms, while cognitive reputation relies on rational evaluation and information processing pathways.

The longitudinal study by Gatti et al. [27, 65–76] showed that emotional reputation as a mediating variable enhances the relationship between CSR activities and purchase intention, and emotional resonance enhances consumers' moral identification with the company. Negative events indirectly lead to a significant decrease in purchase intention by damaging emotional reputation, and demonstrates the core role of emotional repair in crisis recovery [28, 2162]. Emotional reputation, as a mediating variable, strengthens the relationship between customer experience orientation and customer loyalty, indicating that positive emotional responses can amplify experiential value [29, 1560–1584]. Based on these findings, this article proposes a mediating hypothesis for emotional reputation:

H4: Affective Reputation mediates the relationship between corporate reputation dimensions and Purchase Intentions.

Existing research also provides sufficient evidence on the mediating effect of cognitive reputation. Cognitive reputation, as a mediating variable, strengthens the relationship between financial indicators and customer deposit intentions, highlighting the importance of rational evaluation in financial services [30, 15]. Cognitive reputation variables strengthen supplier compliance and procurement willingness, while rational evaluation reduces information asymmetry [23, 311]. Based on this, a mediating hypothesis for cognitive reputation is proposed:

H5: Cognitive Reputation mediates the relationship between corporate reputation dimensions and Purchase Intentions.

4 Methodology

4.1 Research Design

This study adopts a non-experimental quantitative research design to explore relationships between key variables and validate hypothesized causal pathways using comparative analysis. Non-experimental designs are particularly suitable for examining complex social phenomena, such as consumer responses to corporate reputation and purchase intentions under specific economic circumstances. Unlike experimental designs, which manipulate independent variables, this approach observes natural interactions between variables—essential when studying real-world consumer behavior.

Data were collected via an online survey distributed to Russian consumers aged 18 and older, ensuring respondents had legal purchasing autonomy. The questionnaire was administered through Yandex Forms, a platform widely used in Russia for its accessibility and data security. To maximize reach, the survey was disseminated via VK and Mail.Ru, two of Russia’s most popular social media platforms, over a four-week period (October 30–November 28, 2024). The final sample comprised 408 responses, and after detailed check of the response data for outliers, there are 384 valid responses that can be used for research analysis. Respondents represented diverse demographic profiles, including variations in age, income, education, and prior experience with Chinese automotive brands. This diversity ensures the findings capture a broad spectrum of consumer perspectives, enhancing the generalizability of results.

4.2 Measurement

The measurement instruments for this study were carefully adapted from established scales in prior reputation and consumer behavior research. To ensure cultural relevance and conceptual clarity in the Russian context, the questionnaire underwent a rigorous translation from English to Russian and validation process.

The study employed a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree) to measure all latent variables. This balanced scale provides sufficient response granularity while avoiding the potential confusion associated with more complex scales in cross-cultural research. Table 1 presents the complete set of measurement indicators adapted from previous studies.

Table 1

Measurement Indicators

Construct

Measurement Indicators

Adapted Source

Customer Orientation

CO_1 Chinese automobile companies have employees who treat customers courteously

CO_2 Chinese automobile companies have employees who are concerned about customer needs.

CO_3 Chinese automobile companies are concerned about its customers.

CO_4 Chinese automobile companies treat customers fairly

CO_5 Chinese automobile companies attach great importance to customer needs and complaint feedback

Walsh&Beatty,2007

Burlea-Schiopoiu,2017

Good Employer

GE_1 Chinese automobile companies look like good companies to work for.

GE_2 Chinese automobile companies seem to treat its people well.

GE_3 Chinese automobile companies seem to have excellent leadership.

GE_4 Chinese automobile companies maintain high standards in the way they treat people

Walsh&Beatty,2007

Reliable and Financially Stable company

RF_1 Chinese automobile companies tend to outperform competitors.

RF_2 Chinese automobile companies seem to recognize and take advantage of market opportunities.

RF_3 Chinese automobile companies look like they have strong prospects for future growth.

RF_4 Chinese automobile companies look like they would be a good investment.

RF_5 Chinese automobile companies have sustained profitability

Walsh&Beatty,2007

Fombrun,2000

Product and Service Quality

PS_1 Chinese automobile companies are strong, reliable companies.

PS_2 Chinese automobile companies develop innovative services.

PS_3 Chinese automobile companies offer high quality products and services.

PS_4 Chinese automobile companies stand behind the services that they offer

Walsh&Beatty,2007

Social and Environmental Responsibility

SE_1 Chinese automobile companies seem to make an effort to create new jobs.

SE_2 Chinese automobile companies seem to be environmentally responsible.

SE_3 Chinese automobile companies would reduce their profits to ensure a clean environment.

SE_4 Chinese automobile companies appear to support good causes.

SE_5 Chinese automobile companies have a positive impact on society

Walsh&Beatty,2007

Fombrun,2000

Affective Reputation

AR_1 I identify with Chinese automobile companies more than competitors.

AR_2 I would miss Chinese automobile companies more than other companies if they disappeared.

AR_3 I think Chinese automobile companies are likable enterprises.

AR_4 I have positive emotions towards Chinese automobile companies

Schwaiger,2004

Cognitive Reputation

CR_1 Chinese automobile companies are top competitors in the industry.

CR_2 Chinese automobile companies are recognized globally.

CR_3 Chinese automobile companies perform at a premium level.

CR_4 Chinese automobile companies are industry leaders

Schwaiger,2004

Purchase Intention

PI_1 I am willing to purchase products or services from Chinese automotive companies.

PI_2 I am highly likely to choose products or services from Chinese automotive companies.

PI_3 I have a strong desire to purchase products from Chinese automotive companies

Kukar-Kinney,2007

Bigne and Curras,2005

Kukar-Kinney,2007

5 Statistical Analysis

5.1 Respondent Characteristics

As shown in Table 2, the sample was nearly evenly split by gender, with 50.7 % female and 49.3 % male respondents. The majority of participants were aged 24–55, with 39.7 % in the 35–55 age group and 36.8 % in the 24–35 age group. In terms of education, 70.3 % held higher education degrees. Regarding occupation, «experts» constituted the largest group (32.8 %), followed by workers (16.9 %) and students (12.7 %). For income, 19.4 % reported earnings between 100,000 and 200,000 rubles, while 20.3 % chose not to disclose their income. Additionally, 83.1 % of respondents owned a car, but only 25.2 % had experience purchasing Chinese automobiles.

Table 2

Summary of Respondent Characteristics

Descriptive Variable

Frequency (N=384)

Percentage

Gender

Female

195

50.7 %

Male

189

49.3 %

Age

18–24

77

18.9 %

24–35

150

36.8 %

35–55

162

39.7 %

55 and older

19

4.7 %

Level of Education

Higher Education

287

70.3 %

Incomplete Higher Education

52

12.7 %

Incomplete Secondary Special Education

6

1.5 %

Secondary Special Education

21

5.1 %

Secondary Education

42

10.3 %

Occupation

Unemployed

11

2.7 %

Housewife

12

2.9 %

Other

13

3.2 %

Retired

7

1.7 %

Entrepreneur

19

4.7 %

Worker

69

16.9 %

Middle Manager

47

11.5 %

Clerk

44

10.8 %

Expert

134

32.8 %

Student

52

12.7 %

Salary (Rubles/Month)

100,000–200,000

79

19.4 %

20,000–40,000

25

6.1 %

40,000–60,000

53

13.0 %

60,000–80,000

48

11.8 %

80,000–100,000

59

14.5 %

Below 20,000

35

8.6 %

Prefer not to answer

83

20.3 %

Over 200,000

26

6.4 %

Car Ownership

Yes

339

83.1 %

No

69

16.9 %

Experience of purchasing Chinese Cars

Yes

103

25.2 %

No

305

74.8 %

5.2 Data distribution analysis

The study further assessed the distribution characteristics of the data. Results showed significant deviations from normality across all variables, with skewness ranging from -0.994 to 0.042 and kurtosis from -1.091 to 1.142, both exceeding conventional thresholds (absolute value > 0.5). The Kolmogorov-Smirnov and Shapiro-Wilk tests further confirmed non-normality (all p < 0.001), therefore the null hypothesis of normality was rejected, consistent with the inherent characteristics of Likert-scale, which typically fail to meet the distribution requirements of continuous variables. Graphical diagnostics (histograms and Q-Q plots) also revealed clear deviations from normality. Given these findings, robust statistical methods (e.g., Bootstrap) were employed in subsequent analyses to ensure reliability.

5.3 Validity and Reliability Analysis

To assess the validity and reliability of the measurement tools in this study, a comprehensive analysis was conducted in SPSS 26. As a quality assurance component of quantitative research, the core of reliability and validity analysis lies in verifying whether observed variables can accurately reflect underlying concepts and whether measurement results are stable and consistent.

5.3.1 Exploratory Factor Analysis

Exploratory-factor analysis is one of the important methods for testing the construct validity of the scale. The sample size (N=384) exceeded the minimum requirement of 300 for EFA. This paper analyzes the validity of the KMO test and Bartlett’s test of sphericity on the data of three-dimensional items of the independent variables, the measurement results are obtained in Table 3.

Table 3

KMO and Bartlett's Test

Test indicators

Result

KMO(Kaiser-Meyer-Olkin) test

.917

Bartlett’s test of sphericity

Approximate chi-square(χ²)

6418.552

Degree of freedom(df)

253

Significance(p)

.000

The tests show that the KMO value in the test results records 0.917 > 0.6, and the sig value is 0, which is less than 0.001, implying that the research data has passed the validity test.

For further validity-tests, factor analysis has been conducted on the items of all measured constructs. A Principal Component Analysis (PCA) with Promax oblique rotation (κ = 4) was employed to account for potential correlations between factors. The analysis retained factors with eigenvalues > 1.0 and items with factor loadings ≥ 0.50 and cross-loadings < 0.32. Initial EFA revealed cross-loadings for items CO_4, CO_5, GE_3, RF_1, RF_4, PS_2, and SE_1, which were subsequently removed. Second EFA showed improved loading patterns, but AR_3, AR_4, and CR_2 were excluded due to weak discriminant validity. Final EFA yielded an 8-factor solution (Table 4) with clear factor structures and no cross-loadings.

Table 4

Pattern Matrix

Component

1

2

3

4

5

6

7

8

PI_2

.962

PI_3

.936

PI_1

.934

RF_2

.913

RF_3

.898

RF_5

.746

CO_2

.942

CO_1

.872

CO_3

.744

SE_2

.908

SE_3

.883

SE_4

.711

GE_2

.982

GE_4

.843

GE_1

.527

CR_3

.817

CR_4

.754

CR_1

.537

PS_3

.806

PS_4

.767

PS_1

.687

AR_2

.865

AR_1

.737

Note: Extraction method: principal-component analysis. Rotation method: Promax with Kaiser normalization. Rotation converged in 7 iterations.

5.3.2 Convergent-Validity and Reliability Tests

The Average Variance Extracted (AVE) serves as the core indicator of convergent validity, reflecting the explanatory power of latent concepts for all observed variable variations. Values for all variables significantly exceed threshold of 0.5, which is considered acceptable.

In terms of reliability analysis, this study simultaneously reported two indicators: Cronbach's alpha coefficient and composite reliability (CR). The alpha coefficient, as the most commonly used internal consistency indicator, with a value greater than 0.7, is considered an acceptable threshold, while in this study, the alpha coefficients for all dimensions exceeded 0.78, indicating an excellent level of reliability. Composite reliability (CR), as a derivative indicator based on factor loading, can avoid the limitation of the alpha coefficient being affected by the number of items. Its calculated results are highly consistent with the alpha coefficient (average difference<0.03), further verifying the stability of the measurement tool.

The results of reliability and validity analysis are presented in Table 5.

Table 5

Model Reliability and Validity Analysis

Construct

AVE

CR

Cronbach’s α

CO

0.703

0.875

0.857

GE

0.651

0.841

0.831

RF

0.734

0.891

0.843

PS

0.570

0.798

0.787

SE

0.732

0.891

0.821

AR

0.646

0.784

0.859

CR

0.508

0.751

0.850

PI

0.891

0.961

0.968

5.4 Correlation Analysis

Correlation analysis is used for the realization of the closeness among variables, which is carried out based on the Pearson research in this paper by introducing the SPSS 26.0. The data results in Table 6 show that all variables have a significant moderate-positive-correlation (ps < 0.01).

Table 6

Component Correlation Matrix

Component

PI

SE

RF

CO

GE

CR

PS

AR

PI

1.000 **

SE

.413 **

1.000 **

RF

.333 **

.392 **

1.000 **

CO

.325 **

.401 **

.355 **

1.000 **

GE

.285 **

.487 **

.519 **

.510 **

1.000 **

CR

.504 **

.356 **

.402 **

.163 **

.332 **

1.000 **

PS

.304 **

.558 **

.518 **

.451 **

.573 **

.345 **

1.000 **

AR

.520 **

.490 **

.300 **

.256 **

.329 **

.410 **

.398 **

1.000 **

Note: *At the level of 0.01 (two-tail), the correlation is significant.

5.5 Regression Analysis and Hypothesis Testing

This section aims to uncover the relationships between the five factors (CO, GE, RF, PS, SE) and affective reputation (AR), cognitive reputation (CR), and purchase intention (PI), thereby validating the study's hypotheses. The primary hypothesis posits that AR and CR mediate the relationship between the five factors and PI. To test the prerequisites for mediation effects, this study employs the analytical framework proposed by Baron and Kenny [31, 1173–1182]. This method, widely used in behavioral and marketing research, involves verifying the indirect effects through two pathways: first, the independent variables (CO, GE, RF, PS, SE) must significantly predict the dependent variable (PI); second, these variables must also significantly predict the mediator variables (AR, CR).

5.5.1 Direct Effects Regression Analysis

To meet the first condition of the Baron and Kenny framework, the study conducted separate univariate linear regression analyses for each independent variable (CO, GE, RF, PS, SE) against the dependent variable (PI) using SPSS 26. The results, presented in Table 7, show that all reputation dimensions significantly predict PI (p < 0.05), satisfying the first prerequisite for mediation analysis.

Table 7

Regression between IV and DV

PI

Variable

Regression Coefficient (β)

Standard Error

T value

P value

F value

CO

.325

.048

6.710

.000

.105

45.020

GE

.285

.049

5.814

.000

.081

33.802

RF

.333

.048

6.910

.000

.111

47.746

PS

.304

.049

6.244

.000

.093

38.982

SE

.413

.047

8.876

.000

.171

78.782

All reputation dimensions exhibit significant positive relationships with PI (p < 0.001), fully supporting hypotheses H1. SE demonstrates the strongest predictive power (β = 0.413), explaining 17.1 % of the variance in PI. RF (β = 0.333) and CO (β = 0.325) show comparable influence, while PS (β = 0.304) and GE (β = 0.285) have slightly weaker but still significant effects. These results lay a solid foundation for subsequent mediation effect analysis.

Next, the study examines the impact of the independent variables on the mediator variables (AR and CR) to fulfill the second condition of the Baron and Kenny framework. Separate univariate linear regression models were built for each reputation dimension against AR and CR. The results are detailed in Tables 8 and 9.

Table 8

Regression between IV and Mediator Variable AR

AR

Variable

Regression Coefficient (β)

Standard Error

T value

P value

F value

CO

.256

.049

5.182

.000

.066

26.855

GE

.329

.048

6.800

.000

.108

46.237

RF

.300

.049

6.142

.000

.090

37.721

PS

.398

.047

8.481

.000

.158

71.924

SE

.490

.045

10.979

.000

.240

120.536

Table 9

Regression between IV and Mediator Variable CR

CR

Variable

Regression Coefficient (β)

Standard Error

T value

P value

F value

CO

.163

.050

3.224

.001

.026

10.396

GE

.332

.048

6.875

.000

.110

47.266

RF

.402

.047

8.576

.000

.161

73.554

PS

.345

.048

7.175

.000

.119

51.474

SE

.356

.048

7.438

.000

.127

55.330

The regression results indicate that all reputation dimensions significantly influence both AR and CR (p < 0.01). SE has the strongest impact on AR (β = 0.490), explaining 24.0 % of its variance, while RF shows the highest predictive power for CR (β = 0.402), accounting for 16.1 % of its variance. These findings validate hypotheses H2 and H3, confirming that the independent variables significantly affect the mediator variables.

5.5.2 Mediation Effects Analysis

To assess the mediating role of AR and CR between the independent variables and PI, the study employs both the traditional Baron and Kenny method and the Bootstrap method using PROCESS 4.2. The Bootstrap method, recommended for its robustness in estimating indirect effects, involves 5,000 bootstrap samples to compute 95 % confidence intervals.

CR

Path

Total Effect (β)

Direct Effect (β)

Indirect Effect (β)

Mediation Proportion (PM)

Bootstrap 95 % CI

F value

CO → AR

0.325*

0.205*

0.120*

0.369

[0.063, 0.188]

0.105

85.56

CO → CR

0.325*

0.249*

0.076*

0.233

[0.022, 0.137]

0.105

87.59

GE → AR

0.285*

0.128*

0.157*

0.551

[0.094, 0.232]

0.081

76.06

GE → CR

0.285*

0.132*

0.153*

0.536

[0.098, 0.213]

0.081

70.49

RF → AR

0.333*

0.195*

0.139*

0.417

[0.087, 0.203]

0.111

37.721

RF → CR

0.333*

0.156*

0.178*

0.535

[0.117, 0.248]

0.111

73.554

PS → AR

0.304*

0.116*

0.189*

0.622

[0.122, 0.265]

0.093

71.925

PS → CR

0.304*

0.148*

0.156*

0.513

[0.098, 0.225]

0.093

51.474

SE → AR

0.414*

0.209*

0.205*

0.495

[0.138, 0.281]

0.171

120.536

SE → CR

0.414*

0.268*

0.146*

0.353

[0.091, 0.210]

0.171

55.330

The mediation effect analysis reveals that AR and CR significantly mediate the relationship between each independent variable and PI. For instance, AR mediates 62.2 % of the effect of PS on PI, while CR mediates 51.3 %. Similarly, AR mediates 49.5 % of the effect of SE on PI, and CR mediates 35.3 %. These findings support hypotheses H4-H5, indicating that both AR and CR play significant mediating roles in the relationship between the reputation dimensions and PI.

6 Conclusions, management implications and research limitations

This study advances the understanding of how corporate reputation influences Russian consumers' purchase intentions toward Chinese automotive brands, particularly through the dual-dimensional lens of affective and cognitive reputation. The findings reveal that corporate reputation is not a monolithic construct but operates through distinct psychological pathways, with social responsibility and financial stability emerging as particularly influential dimensions. This contributes to the underexplored area of cross-cultural reputation management in emerging markets, where consumer trust in foreign brands remains a critical challenge.

The most significant insight is that affective reputation—driven by emotional connections—plays a more substantial role than cognitive reputation in shaping purchase intentions. Specifically, consumers' perceptions of Chinese automakers' social and environmental responsibility (SE) exert the strongest influence on their emotional attachment to brands, while perceptions of financial stability (RF) dominate cognitive evaluations. This underscores the need for companies to balance ethical positioning with demonstrations of economic resilience. Public and private stakeholders should collaborate to amplify the visibility of corporate social responsibility (CSR) initiatives, such as sustainability programs or community engagement, to strengthen emotional brand ties. Simultaneously, transparent communication about financial performance and long-term market commitment can bolster rational consumer confidence.

Interestingly, while product quality (PS) and employer image (GE) significantly impact both affective and cognitive reputation, their effects are more evenly distributed. This suggests that functional attributes (e.g., vehicle reliability) and symbolic values (e.g., fair labor practices) are equally vital in shaping consumer attitudes. However, customer orientation (CO) shows weaker direct effects, likely due to limited consumer interactions with Chinese automakers in Russia. This highlights a critical gap: enhancing after-sales service experiences could bridge emotional and cognitive gaps in reputation building.

From a practical perspective, these findings offer actionable strategies for businesses and policymakers. For Chinese automotive firms, prioritizing CSR campaigns tailored to Russian environmental and social concerns—such as local job creation or green technology partnerships—can foster emotional loyalty. Concurrently, showcasing financial metrics (e.g., growth in regional investments) through localized marketing can address cognitive barriers. Governments, meanwhile, could incentivize cross-border sustainability collaborations or consumer education programs to align market expectations with corporate reputation efforts.

However, this study has limitations that warrant future research. The sample, though robust, primarily reflects urban, digitally engaged consumers, potentially overlooking rural or offline populations. Expanding data collection to include in-depth interviews or regional surveys could yield richer insights. Additionally, the study focuses on static reputation perceptions; longitudinal research could track how geopolitical or economic shifts (e.g., trade policies) dynamically alter consumer attitudes. Finally, comparative studies with European or Japanese automakers in Russia would help contextualize the unique challenges faced by Chinese brands.

In conclusion, this research demonstrates that corporate reputation in international markets is a tapestry of emotional and rational threads. For Chinese automakers, weaving these threads into a cohesive strategy—one that harmonizes ethical storytelling with tangible proof of stability—will be key to unlocking Russian consumer demand. Future studies should explore how digital platforms (e.g., social media) amplify reputation effects and whether cultural adaptation (e.g., localized branding) moderates these relationships. By addressing these gaps, scholars and practitioners can further decode the complexities of global reputation management.

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corporate reputation
consumers’ purchase intentions
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attitude theory
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