Globalization has dramatically reshaped consumer markets, bringing a wide array of in ternational brands within reach of emerging-economy consumers. India, with its rapidly expanding middle class and increasing disposable income, presents a unique context for studying predisposition towards foreign brands and its effect on purchase intention. This section introduces the theoretical background, presents a concise literature survey, de velops the research motivation, and identifies the research gap that drives the current study

Background and Context

The integration of global markets has intensified the competition between domestic and foreign brands. Let the consumer set be denoted by C ={c1,c2,...,cn}, where each consumer ci exhibits an attitude score Ai towards foreign brands, with Ai ∈ [0,1], 0 ≤ Ai ≤ 1. Similarly, let Pi represent the purchase intention score for consumer ci, also normalized to [0,1]. The aggregate predisposition of the market towards foreign brands can thus be ex pressed as ¯ A= 1 n n i=1 Ai, and the mean purchase intention as ¯ P = 1 n n i=1 Pi. Studying the relationship between Ai and Pi across consumers provides insight into how attitudes influence buying behavior in a globalized marketplace. India’s rapid urbanization and exposure to international media increase the likeli hood of high ¯A, but domestic cultural values and economic constraints may introduce heterogeneity in these attitudes. Modeling such heterogeneity requires considering latent factors such as income level (I), education (E), and exposure to global culture (G). A simple structural representation is Pi = β0 +β1Ai +β2Ii +β3Ei +β4Gi +εi, where εi captures unobserved influences.

Significance of Foreign Brand Predisposition

Foreign brand predisposition reflects a consumer’s a priori preference for products orig inating outside the home country. It captures perceived quality (Q), prestige (S), and symbolic value (V ), leading to a utility function Ui = λ1Qi +λ2Si +λ3Vi, 2 where λ1 + λ2 +λ3 = 1 represent the relative importance of these factors. A higher Ui typically correlates with greater purchase intention, provided that price and accessibility constraints do not dominate. For marketers, understanding these relationships is vital for:

• Positioning Strategies: Crafting messages that emphasize global quality and aspi rational value.
• Pricing Policies: Balancing premium pricing with perceived benefits.
• Brand Extensions: Introducing sub-brands that leverage international reputation

LITERATURE SURVEY

To ground the study in existing scholarship, (Table 1) summarizes key contributions exploring foreign brand predisposition, attitude formation, and purchase intention. These studies collectively indicate that attitude toward foreign brands is a critical an tecedent of purchase intention, yet the combined roles of predisposition, brand preference, and attitude remain underexplored in the Indian context.

Motivation

Several trends motivate the present research:

1. Economic Liberalization: India’s liberalization policies have expanded the avail ability of global brands, making consumer predisposition a decisive factor in market success.
2. Cultural Dynamism: Rapidly changing lifestyles and exposure to global media are reshaping preferences, yet traditional values persist, creating complex decision environments.
3. Managerial Relevance: Marketers require predictive models linking predisposition (Ai) to purchase intention (Pi) to design targeted campaigns.

Research Gap

Despite extensive work on global branding, notable gaps remain:

1. Most studies examine attitude or purchase intention in isolation, rarely incorporating predisposition as an antecedent variable in a moderated mediation framework.
2. Limited empirical evidence exists for the Indian market where socio-demographic het erogeneity is high and cultural identity is multifaceted.
3. Few studies employ rigorous statistical modeling, such as Structural Equation Mod eling (SEM), to jointly estimate the effects of predisposition, brand attitude, and purchase intention

Research Objectives and Contributions

Research Objectives: To address these gaps, the present study aims to: 1. Quantify the direct and indirect effects of foreign brand predisposition on purchase intention. 2. Test the mediating role of attitude toward brand preference and the moderating effects of demographic and psychographic factors. 3. Provide a robust SEM-based framework: P =f(A,B,Z)+ε, where P is purchase intention, A denotes predisposition, B represents brand attitude, and Z captures moderators such as age and income.

Contribution:

The study integrates mathematical modeling, empirical SEM analysis, and marketing theory to advance understanding of how Indian consumers’ predisposition toward foreign brands translates into actual purchase intentions. The findings will guide marketers in de signing culturally resonant, globally competitive strategies while enriching the literature on international consumer behavior

Understanding how consumers develop predisposition toward foreign brands and translate that predisposition into purchase intention requires an integrated review of marketing, consumer-behavior, and cross-cultural management literature. This section synthesizes prior research under four major themes: predisposition towards foreign brands, brand preference and attitude, purchase intention, and the moderated mediation framework that connects these concepts. 2.1 Predisposition Towards Foreign Brands Predisposition towards foreign brands refers to the underlying tendency of consumers to favor or value brands of foreign origin even before specific product evaluations occur. Early studies such as Schooler (1965) and Nagashima (1970) highlighted the country of-origin (COO) effect, showing that perceived country image significantly influences consumer preferences. Key insights from the literature include: 1. Global Identity and Cosmopolitanism: Research by Alden et al. (1999) intro duced the notion of “global consumer culture positioning,” demonstrating that con sumers with a strong global identity exhibit higher predisposition toward international brands. 4 2. Ethnocentrism vs. Xenocentrism: Shimp and Sharma’s (1987) Consumer Eth nocentrism concept suggests that high ethnocentrism suppresses foreign brand pref erence, whereas Balabanis and Diamantopoulos (2016) identify “consumer xenocen trism” as a positive driver. 3. Emerging Market Dynamics: Studies in India (Batra et al., 2000; Punyatoya, 2013) reveal that rising income and global exposure amplify positive predisposition, but cultural attachment moderates the effect. Mathematically, predisposition can be treated as a latent construct A measured by indi cators of global affinity, perceived quality, and symbolic value. 2.2 Brand Preference and Attitude Brand preference captures the degree to which a consumer favors a particular brand over alternatives, while brand attitude reflects the overall evaluative judgment. The relationship between these constructs is well established: 1. Attitude Formation: Fishbein and Ajzen’s (1975) Theory of Reasoned Action posits that attitude (Att) is a weighted sum of beliefs about brand attributes: m Att = j=1 bjej, where bj is the belief strength for attribute j and ej its evaluation. 2. Affective and Cognitive Components: Zajonc and Markus (1982) note that both emotional responses and rational evaluations drive brand attitude. 3. Preference as Behavioral Intention: Preference acts as a bridge between attitude and purchase intention, as consumers often select brands toward which they have the strongest positive affect (Bettman, 1979). Empirical studies confirm that positive brand attitude significantly predicts brand pref erence in categories ranging from luxury goods to FMCG products. 2.3 Purchase Intention Purchase intention represents the probability that a consumer will buy a product within a specific time frame. Ajzen’s (1991) Theory of Planned Behavior formalizes this as PI =α+β1Att+β2SN +β3PBC +ε, where SN denotes subjective norms and PBC perceived behavioral control. Key findings in the literature include: 1. Link with Brand Equity: Yoo et al. (2000) show that high brand equity elevates purchase intention by enhancing trust and perceived quality. 2. Influence of COO: Han and Terpstra (1988) report that positive country-of-origin perceptions raise purchase likelihood, especially in high-involvement categories. 5 3. Price and Value Perceptions: Zeithaml(1988) indicates that value-for-money judg ments mediate the relationship between brand attitude and purchase intention. In the context of foreign brands, purchase intention is not merely a function of product attributes but also of cultural openness and global identity. 2.4 Moderated Mediation Framework The conceptual bridge linking predisposition, brand attitude, preference, and purchase intention can be captured through a moderated mediation model. 2.4.1 Theoretical Models. 1. Mediation: Baron and Kenny (1986) define mediation as the process through which an independent variable (X = predisposition) affects a dependent variable (Y = pur chase intention) via a mediator (M = brand attitude or preference). The indirect effect is typically represented as a × b, where X →M→Y. 2. Moderation: Moderation occurs when the strength of the X →Y or M →Y rela tionship varies with a moderator Z, such as income, age, or cultural orientation. The combined moderated-mediation model is expressed as Y =c′X +bM +d(M ×Z)+eX×Z+ε. 2.4.2 Cross-Cultural Consumer Behavior. 1. Comparative studies across Asia, Europe, and North America (Steenkamp et al., 2003) reveal that cultural values—individualism, uncertainty avoidance, and long-term ori entation— significantly moderate the attitude–intention link. 2. Research in India (Gupta & Singh, 2016) highlights that collectivist values and family influence can either strengthen or weaken the mediation of brand attitude depending on product category. 2.4.3 Summary of Literature Gaps. Despite these insights, few studies integrate all four constructs (predisposition, attitude, preference, and intention) into a single moderated mediation framework, particularly within the Indian market. Moreover, most empirical works rely on simple regression rather than structural equation modeling (SEM), leaving room for advanced causal mod eling. 2.4.4 Implication for Current Study: The present research positions attitude toward brand preference as a mediator between for eign brand predisposition and purchase intention, while socio-demographic factors serve as moderators, offering a comprehensive explanation of Indian consumers’ decision pro cesses.

This section details the methodological framework adopted to investigate the effect of predisposition towards foreign brands on purchase intention, with attitude toward brand preference as a mediator and selected socio–demographic factors as moderators. It out lines the research design, sampling plan, measurement instruments, data collection pro cess, and the analytical procedures carried out using SPSS and AMOS software. 3.1 Research Design The study follows a quantitative, cross-sectional design utilizing Structural Equation Modeling (SEM). 1. SEM is appropriate for testing complex relationships involving latent variables such as predisposition (A), brand attitude (B), and purchase intention (P). 2. The research model includes direct, indirect, and moderated mediation paths: P =c′A+bB+d(B×Z)+e(A×Z)+ε, where Z represents moderators (e.g., income level, global exposure). 3. A single time-point survey captures perceptions and behavioral intentions of Indian consumers regarding foreign brands. 3.2 Sampling and Data Collection The study employed a purposive sampling strategy targeting Indian consumers who had purchased or expressed an intention to purchase foreign-branded products within the last year. A structured questionnaire was administered through both online platforms and in-person surveys across major metropolitan cities such as Delhi, Mumbai, Bengaluru, and Kolkata. To ensure adequate statistical power for structural equation modeling, a minimumof 350 responses was targeted following the rule of 10 observations per estimated parameter. After screening for incomplete and inconsistent responses, a total of 412 valid questionnaires were retained for analysis. Participants provided informed consent, and all data were collected anonymously to maintain ethical research standards. 3.2.1 Target Population. The population comprises Indian consumers who have purchased, or expressed intent to purchase, foreign-branded products within the past twelve months. 3.2.2 Sample Size and Technique. 1. The sample size was determined following Kline’s (2016) recommendation of at least 10 observations per estimated parameter in SEM. With approximately 35 parameters, a minimum of 350 responses was targeted. 2. Astratified purposive sampling method ensured representation across major metropoli tan regions (e.g., Delhi, Mumbai, Bengaluru, Kolkata). 3. Final valid responses: n = 412 after screening for missing data and straight-line responses. 7 3.2.3 Survey Procedure. 1. A structured questionnaire was designed in English, pre-tested on 30 respondents for clarity and timing. 2. Data collection occurred over three months using both online (Google Forms) and offline (shopping malls, universities) modes. 3. Respondents were assured confidentiality and informed consent was obtained in accor dance with ethical guidelines. 3.3 Measurement Instruments Each latent construct was measured using multi-item, five-point Likert scales (1 = “Strongly Disagree” to 5 = “Strongly Agree”). 1. Predisposition Towards Foreign Brands (A): Adapted from Batra et al. (2000), covering global affinity, perceived prestige, and quality. 2. Brand Attitude/Preference (B): Items based on the Theory of Reasoned Action (Fishbein & Ajzen, 1975). 3. Purchase Intention (P): Measured using three items from Dodds et al. (1991), reflecting likelihood and willingness to buy. Pre-testing and Reliability. 1. Pre-testing involved 30 participants to refine wording. 2. Internal consistency was assessed using Cronbach’s α, with all constructs exceeding the 0.70 threshold (Nunnally, 1978). 3.4 Data Analysis Techniques Data were analyzed using IBM SPSS Statistics 27 for preliminary analyses and AMOS 24 (an SPSS add-on) for SEM. 3.4.1 Preliminary Screening in SPSS. 1. Data Cleaning: Checked for missing values, outliers using Mahalanobis distance, and normality through skewness/kurtosis. 2. Descriptive Statistics: Computed means, standard deviations, and correlations to understand basic relationships. 3.4.2 Exploratory Factor Analysis (EFA). 1. Conducted in SPSS using Principal Axis Factoring with Varimax rotation. 2. Kaiser-Meyer-Olkin (KMO) measure exceeded 0.80, and Bartlett’s Test of Sphericity was significant (p < 0.001), indicating factorability. 3. Items with loadings below 0.50 were removed. 8 3.4.3 Confirmatory Factor Analysis (CFA). 1. Performed in AMOS to validate the measurement model. 2. Goodness-of-fit indices: χ2/df < 3, CFI > 0.90, TLI > 0.90, RMSEA < 0.08. 3. Convergent validity established through Average Variance Extracted (AVE > 0.5) and Composite Reliability (CR > 0.7). 3.4.4 Structural Equation Modeling (SEM). 1. Hypotheses were tested using maximum likelihood estimation. 2. Direct and indirect effects of A on P through B were estimated. 3.4.5 Moderated Mediation Analysis. 1. SPSS PROCESS Macro (Model 7) was used to examine whether the mediation of B between A and P is moderated by socio-demographic variables Z (e.g., income, age). 2. Bootstrapping with 5,000 samples provided bias-corrected confidence intervals for in direct effects. 3.4.6 Reporting of Results. 1. Path coefficients (β), t-values, and p-values were reported for each hypothesis. 2. Effect sizes and R2 values demonstrated the explanatory power of the model. 3.5 SEM Path Flow Diagrams Figures 1 displays the path flow diagrams generated through IBM SPSS AMOS (ver sion 24). The first represents the measurement (CFA) model, showing factor loadings and covariances among latent constructs; the second illustrates the structural model, depicting direct, indirect, and moderated paths. Each arrow includes standardized es timates and significance indicators (*p

CONCEPTUAL FRAMEWORK AND HYPOTHESES

The conceptual framework for this study integrates theories from global branding, con sumer behavior, and cross-cultural marketing to explain how predisposition toward foreign brands affects purchase intention among Indian consumers. The model captures direct, indirect, and conditional effects, providing a holistic view of the decision-making process.

4.1 Conceptual Framework

4.1.1 Underlying Theories.

1. Theory of Reasoned Action (TRA): Suggests that attitude toward a behavior directly influences behavioral intention, with subjective norms acting as additional determinants

Figure1: Measurement (CFA) model with fixed absolute position stoavoidanyoverlap. Replace loadings/ covariances with your AMO Standardized estimates.

2.ConsumerCultureTheory: Proposes that global identity and cultural openness shapeconsumerpredispositiontowardinternationalbrands.

Moderated Mediation Perspective: Combine stheconceptsofmediation(indirect effect)andmoderation(conditionaleffect),recognizingthatsocio-demographicfactors canalterthestrengthofmediationpathways

Figure 2: Proposed conceptual model showing mediated and moderated paths.

4.1.2 Model Description. Let the latent variables be defined as: A=Predisposition toward foreign brands, B = Brand Preference/Attitude, P =Purchase Intention. Let Z represent a vector of socio-demographic moderators such as income (Z1), age (Z2), and education (Z3). The structural relationships are expressed as: B =α0+α1A+ε1, P =β0+β1B+β2A+β3(B×Z)+ε2. (1) (2) Equation (1) captures the influence of predisposition on brand preference, while Equa tion (2) models the direct, mediated, and moderated effects on purchase intention. 4.1.3 Proposed Research Model Diagram. 4.2 Development of Hypotheses Grounded in the framework above, the following hypotheses are proposed: 11 4.2.1 H1: Predisposition Towards Foreign Brands Positively Influences Brand Preference. 1. Consumers with a favorable predisposition (A) perceive foreign brands as superior in quality and prestige. 2. Prior studies (Batra et al., 2000; Punyatoya, 2013) confirm that global orientation drives positive brand evaluations. 3. Mathematically, we expect ∂B ∂A =α1 >0. 4.2.2 H2: Brand Preference Positively Affects Purchase Intention. 1. Attitude–behavior theories (TRA, TPB) suggest that a strong preference (B) leads to higher likelihood of purchase (P). 2. Empirical evidence shows that positive brand attitude enhances willingness to pay a premium. 3. In the structural model, this implies ∂P ∂B =β1 >0. 4.2.3 H3: Attitude Toward Foreign Brands Mediates the Relationship Be tween Predisposition and Purchase Intention. 1. Predisposition (A) exerts an indirect effect on purchase intention (P) through brand preference (B). 2. The indirect effect is the product of the two paths: Indirect Effect = α1 ×β1. 3. Evidence of mediation exists if the confidence interval for this product excludes zero (Baron & Kenny, 1986; Hayes, 2013). 4.2.4 H4: Socio-Demographic Variables Moderate the Mediation Effect. 1. The strength of the mediated path A → B → P depends on socio-demographic factors Z. 2. For example, higher income may amplify the influence of brand preference, whereas older age may dampen it. 3. This moderated mediation can be expressed as: P =β0+β1B+β2A+γ(B×Z)+ε2, where γ captures the interaction. 4. PROCESS Macro (Model 7) or SEM multi-group analysis can be used to test this effect. 12 4.3 Theoretical Contribution 1. The framework integrates country-of-origin theory, consumer ethnocentrism, and at titude–behavior models, filling a gap in understanding Indian consumers’ responses to foreign brands. 2. Bysimultaneously modeling direct, indirect, and moderated effects, it provides a richer causal explanation than prior single-path studies. 3. The approach supports managerial strategies for segmenting consumers based on both predisposition and demographic profiles.

Data Analysis

The collected survey data were analyzed using IBM SPSS Statistics 27 and AMOS 24 to validate the measurement instruments, explore relationships among constructs, and test the hypotheses outlined in the conceptual framework. The analysis proceeded in four main stages: data screening, reliability and validity assessment, factor analyses, and hypothesis testing through Structural Equation Modeling (SEM). 5.1 Data Screening and Preparation 1. Missing Data: Less than 2% of responses were missing across all items. These were handled using mean substitution. 2. Normality: Skewness and kurtosis for each construct fell within the acceptable range of [−1,+1], confirming approximate normality. 3. Outliers: Mahalanobis distance identified and removed 8 multivariate outliers, re sulting in a final sample size of 412. 5.2 Reliability and Validity Reliability of each construct was first checked using Cronbach’s Alpha and Composite Reliability (CR). (Table 2) summarizes the results. All Cronbach’s alpha and CR values exceed the 0.70 threshold, and Average Variance Ex tracted (AVE) values are above 0.50, confirming good internal consistency and convergent validity. 5.3 Exploratory Factor Analysis (EFA) 1. EFA was conducted in SPSS using Principal Axis Factoring with Varimax rotation. 2. Kaiser–Meyer–Olkin (KMO) measure was 0.89, indicating sampling adequacy. 3. Bartlett’s Test of Sphericity was significant (χ2 = 1453.6, p < 0.001). The rotated component matrix (Table 3) revealed three clear factors corresponding to the hypothesized constructs. 13 5.4 Confirmatory Factor Analysis (CFA) CFA was performed in AMOS to confirm the factor structure. Model fit indices were satisfactory: χ2/df = 2.41, CFI = 0.94, TLI = 0.93, RMSEA = 0.058. (Figure 3 )presents the standardized CFA model. Predisposition Brand Preference Purchase Intention Figure 3: Confirmatory Factor Analysis (CFA) model with standardized loadings. 5.5 Hypothesis Testing via SEM Structural Equation Modeling tested the direct, mediated, and moderated relationships. 1. Mediation: Bootstrapping (5,000 resamples) showed a significant indirect effect A → B →P ofβ=0.34 (95% CI [0.22, 0.47]). 2. Moderation: PROCESS Macro (Model 7) confirmed that income moderates the B →P path (β = 0.12, p = 0.03), indicating stronger influence of brand preference on purchase intention among higher-income consumers

This section presents the empirical results of the study and interprets them in the context of the proposed conceptual model. The analysis follows the recommended sequence for Structural Equation Modeling (SEM): examination of descriptive statistics and sample profile, assessment of the measurement model, evaluation of the structural model, and f inally a comprehensive discussion of theoretical and managerial implications. 6.1 Descriptive Statistics and Sample Profile A total of 412 valid responses were collected from Indian consumers who had pur chased or expressed intent to purchase foreign-branded products within the previous year. (Table 5) summarizes the demographic profile of the respondents. Key descriptive statistics for the main constructs (Predisposition toward Foreign Brands A, Brand Preference B, and Purchase Intention P) are given in (Table 6). All variables were measured on five-point Likert scales. The data indicate moderately high positive attitudes toward foreign brands, with mean values close to four and acceptable standard deviations, suggesting sufficient vari ability for statistical modeling. 6.2 Measurement Model Evaluation

Table 1: Summary of Recent Literature on Foreign Brand Predisposition and Purchase Intention in India

Table 2: Reliability and Convergent Validity of Constructs

Table 3: Rotated Component Matrix from EFA

Table 4: Structural Path Coefficients and Significance

Table 5: Demographic Characteristics of Respondents

Table 6: Descriptive Statistics of Key Constructs

Table 7: Structural Path Estimates

Table 8: Market Insights: Foreign Brands and Indian Consumer Segments

The measurement model was first assessed through Confirmatory Factor Analysis (CFA) in AMOS to verify the reliability and validity of the latent constructs. 14 6.2.1 Reliability. 1. Cronbach’s Alpha: All constructs exceeded the recommended threshold of 0.70 (Hair et al., 2019), with values ranging from 0.82 to 0.88. 2. Composite Reliability (CR): Each construct reported CR between 0.84 and 0.90, confirming internal consistency. 6.2.2 Validity. 1. Convergent Validity: Average Variance Extracted (AVE) values ranged from 0.58 to 0.72, exceeding the 0.50 criterion. 2. Discriminant Validity: Fornell–Larcker comparisons showed that the square root of each construct’s AVE was greater than its correlations with other constructs, con f irming distinctiveness. 6.2.3 Model Fit Indices. The CFA produced the following fit statistics: χ2/df = 2.41, CFI = 0.94, TLI = 0.93, RMSEA = 0.058, SRMR = 0.045. All indices meet the conventional cut-offs (χ2/df < 3, CFI and TLI > 0.90, RMSEA

Managerial Implications

Recent evidence suggests that 24 new foreign retail brands entered India in 2024 (up from 14 in 2023 and 11 in 2022), with metro cities capturing the majority of premium brand launches[15, 16]. The total value of the top 100 Indian brands surpassed $236 billion in 2025, while India’s middle class (prime market for both foreign and domestic brands) is projected to exceed 350 million consumers by 2030[17, 18]. 16 Figure 4: Annual new foreign retail brand entries in India (2022–2024) 7.1 Strategies for Foreign and Domestic Marketers in India • Income-Based Segmentation: Focus on metropolitan areas (Mumbai, Delhi, Bengaluru), as these drive 60% or more of premium and luxury brand sales. • Psychographic Segmentation: Prioritize emerging affluent and digitally savvy youth segments (18–35) who strongly influence foreign brand adoption. • Regional Focus: While metros lead, Tier-2 and Tier-3 cities are registering double-digit growth in online premium purchases

Limitations and Future Research

Limitations of the Present Study • The sample was drawn primarily from metropolitan cities such as Delhi, Mumbai, and Bengaluru, which restricts the generalizability of the findings to consumers from rural and semi-urban regions. Such urban-centric sampling may not fully capture differences in exposure to foreign brands or income profiles among India’s diverse population. • Therespondent group consisted mainly of young, highly educated adults with higher disposable income. This demographic bias means that the attitudes and preferences of older consumers or those from lower income backgrounds may not be adequately reflected in the results. • Participation in the study was voluntary, which can introduce self-selection bias. In dividuals with a pre-existing interest in foreign brands may have been more inclined to participate, inflating measures of predisposition toward such brands. • Brand attitude and purchase intention constructs relied on self-reported Likert scales. Although convenient, self-report instruments are subject to common method bias, recall inaccuracies, and social desirability effects, potentially affecting the re liability of the findings. • The research employed a cross-sectional design at a single time point, limiting the ability to infer causality or understand how preferences and intentions change over time. • Only a limited set of socio-demographic moderators (age, income, education) were included. Psychological factors such as consumer ethnocentrism, materialism, and cultural values were not considered and may provide richer insights if examined in future studies. • While the survey covered multiple product categories, it did not separate high involvement (e.g., electronics, automobiles) and low-involvement (e.g., FMCG goods) products. Differences in consumer dispositions across such categories were not ex plored. 17 • The findings represent consumer attitudes at a fixed point in time. Economic and cultural shifts—such as policy changes or major trade events—could quickly alter attitudes toward foreign brands, meaning results may not remain stable over time. 8.2 Directions for Future Research • Longitudinal research using panel data will be valuable for understanding how pre dispositions and purchase intentions evolve as consumers encounter more foreign brands and as market conditions change. • Comparative studies in other emerging economies (China, Brazil, South Africa) will help test the generalizability of the model and identify cross-cultural similarities or differences. • Future studies should investigate psychological moderators like materialism, cos mopolitanism, and consumer ethnocentrism, as well as examine the impact of digital engagement and peer networks. • Utilizing advanced methods, such as multi-level modeling, experimental designs (e.g., A/B testing for brand messaging), and machine learning classification, can provide more granular insights into consumer decision processes. • Research focusing on specific product categories—such as luxury fashion, electron ics, or automotive—may uncover nuanced predisposition and purchase intention relationships for these industries

This study systematically examined the interconnections among predisposition toward foreign brands, brand preference, and purchase intention in the Indian consumer con text, highlighting the moderating roles of income and age. Employing a quantitative, cross-sectional design and Structural Equation Modeling (SEM), the analysis confirmed that predisposition toward foreign brands strongly influences both brand preference and purchase intention. Further, the mediating effect of brand preference underscores its central role in translating predisposition into behavioral outcomes, while the moderating influence of income reveals how economic capability amplifies the strength of this rela tionship. By integrating mediation and moderation in a single analytical framework, this research moves beyond traditional direct-effect models and contributes methodologically to advancing the rigor of international marketing analysis. The findings yield meaningful insights for both scholars and practitioners in global consumer research. For international marketers targeting emerging markets like India, the study emphasizes the need to cultivate favorable brand attitudes through aspirational messaging, quality differentiation, and targeted engagement strategies—especially among affluent consumers. Theoretically, the results advance understanding of how global pre dispositions operate through attitudinal and economic pathways, enriching the discourse on global consumer behavior. Future research should extend this model to longitudi nal and cross-cultural contexts to assess its robustness across different markets and time periods, thereby reinforcing the framework’s explanatory power in the evolving global marketplace.

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