AI-Driven Remarketing and Digital Infrastructure in Emerging Markets: Evidence from Tourism and Textile Enterprises in Uzbekistan

Abstract

This study comparatively evaluates the effectiveness of remarketing strategies under digital transformation in Uzbekistan’s service (tourism and hospitality) and manufacturing (textile) sectors, grounded in the Resource-Based View (RBV) and the Technology Acceptance Model (TAM). Using a sequential explanatory mixed-methods design, 280 enterprises (140 per sector) from four regions of Uzbekistan were surveyed, integrating quantitative analysis (OLS regression, t-test, χ²-test, PLS-SEM) and Monte Carlo simulation (20,000 iterations) with qualitative in-depth interviews (n = 32). The textile sector exhibited higher but more volatile returns (ROI = 82.1%; CV = 0.18), whereas the tourism sector achieved more stable yet lower returns (ROI = 48.3%; CV = 0.11) (t(278) = −22.84; p < 0.001; Cohen’s d = 2.73). AI-based personalization was positively associated with ROI (β = 0.28, p < 0.001) and with reduced revenue volatility through an indirect pathway (indirect effect = 5.04, 95% CI [4.10, 6.00]), with significantly stronger associations in the textile sector (Δ = 1.64, p < 0.05). This study contributes to digital marketing theory by demonstrating sector-specific heterogeneity in AI personalization mechanisms, providing empirical evidence of the infrastructure–ROI variability relationship in a transition economy, and demonstrating the value of integrating Monte Carlo–based uncertainty analysis with mixed-methods evidence as a robustness device. The findings carry direct implications for sustainable economic development in transition economies: by demonstrating how sector-specific digital marketing strategies are linked to and can enhance the long-term viability and resource efficiency of enterprises, this study contributes to advancing Sustainable Development Goal 8 (Decent Work and Economic Growth), SDG 9 (Industry, Innovation and Infrastructure), and SDG 12 (Responsible Consumption and Production).

1. Introduction

Over the past decade, digital transformation has fundamentally reshaped marketing strategies within the global economy. The widespread adoption of internet technologies, big data analytics, and artificial intelligence (AI) tools enables enterprises to monitor customer behaviors in real time and deliver tailored marketing communications [1,2]. In this context, remarketing—a digital marketing instrument designed to re-engage customers who have previously interacted with the enterprise but failed to complete a purchase—has assumed strategic significance [3].

However, the effectiveness of remarketing is not universal; it varies across sectors depending on the enterprise’s operational model, customer characteristics, and the level of digital infrastructure. According to the Resource-Based View (RBV) theory, firms convert unique and difficult-to-imitate resources into sources of strategic advantage [4]. From this perspective, the tourism and textile sectors are anticipated to exhibit systematic differences in assimilating digital resources (such as databases, algorithmic platforms, and AI tools): the tourism sector is distinguished by a B2C model grounded in seasonality and individual customer preferences, whereas the textile sector is characterized by a B2B model predicated on long-term contractual relationships. The Technology Acceptance Model (TAM) further posits that the adoption of AI and digital platforms hinges on the sector’s technical capabilities and users’ readiness [5,6].

1.1. Remarketing as a Digital Marketing Strategy

Remarketing—a strategy that targets potential customers based on previous website visits or product views—is recognized as a theoretically grounded and empirically validated tool in digital marketing [1]. From the perspective of Information Asymmetry Theory, remarketing provides firms with unique insights into customer behavior, allowing them to leverage this information as a source of competitive advantage [7]. Studies have shown that remarketing significantly increases conversion likelihood compared to traditional display advertising, as it targets users who have previously interacted with the brand [3].

However, the literature debates the universality of remarketing effectiveness. Lambrecht and Tucker [1] demonstrated in the U.S. market that remarketing effectiveness depends on data specificity—product-specific remarketing is more effective than general (“non-dynamic”) remarketing. Conversely, Chen and Stallaert [8] found that excessively repetitive remarketing ads may trigger psychological reactance in consumers, reducing conversions. The tension between optimal remarketing volume and precision remains unresolved and requires sector-specific investigation. From the RBV perspective, this tension can be reframed as a question of resource allocation: firms must determine how to deploy their digital remarketing resources (data assets, algorithmic platforms) most effectively, and the optimal configuration is likely to vary depending on sector-specific resource endowments [4].

1.2. Remarketing in the Tourism and Hospitality Sector

Remarketing is particularly important in the tourism and hospitality sector due to the long and multi-stage nature of consumer decision-making. Muñoz-Leiva et al. [9] used eye-tracking technology to study advertising effectiveness on Travel 2.0 websites, identifying the relationship between visual attention and conversion. Alghanayem [10] showed that in the tourism sector, remarketing campaigns positively affect post-purchase behavior, increasing the likelihood of rebooking.

A critical feature in tourism is high booking abandonment rates. The sector operates in a B2C context, where individual customer preferences, seasonality, and price sensitivity are highly influential. This necessitates flexible remarketing strategies, with distinct approaches during peak and off-peak seasons [9]. Existing studies primarily focus on developed markets (Europe, North America), leaving the characteristics of remarketing in emerging markets like Central Asia largely unexplored.

1.3. Remarketing in the Manufacturing (Textile) Sector

In the manufacturing sector, particularly textiles, remarketing is increasingly used in B2B contexts to enhance competitiveness in global export markets [11]. Unlike B2C remarketing, B2B decision-making involves multiple stakeholders and extended cycles, prolonging the remarketing process and increasing Customer Lifetime Value (CLV) [12].

Research on digital marketing in the textile sector remains limited. Syam and Sharma [11] highlighted the potential of AI and machine learning models to boost sales in manufacturing under Industry 4.0, but did not evaluate remarketing effectiveness separately. Stone et al. [13] examined AI’s impact on strategic marketing decisions and found that the manufacturing sector lags behind service sectors in digital marketing adoption, yet empirical evidence quantifying this gap is scarce. This gap is especially pronounced in emerging economies, indicating a strong research need.

The contrast between Section 1.2 and Section 1.3 underscores a fundamental asymmetry: the tourism sector faces demand-side challenges (seasonality, price sensitivity, high abandonment), whereas the textile sector confronts supply-side challenges (extended B2B cycles, multi-stakeholder decisions, export market volatility). This asymmetry suggests that remarketing strategies must be configured differently across sectors—a proposition that has not been empirically tested.

1.4. The Role of Artificial Intelligence in Remarketing

Artificial intelligence (AI) is widely recognized as a technological factor that can transform remarketing effectiveness. Huang and Rust [14] classified strategic applications of AI in marketing, showing how personalized interactions increase conversion probability. Puntoni et al. [15] examined consumer responses to AI-driven personalization, highlighting that outcome can be both positive and negative depending on user experience.

Hardcastle et al. [16] found that AI-personalized customer journeys are positively associated with ROI stability in the service sector. However, the literature has yet to examine whether AI personalization differentially impacts service and manufacturing sectors. The TAM [6] predicts such differences: TAM posits that technology adoption is mediated by perceived usefulness (PU) and perceived ease of use (PEOU). In sectors where AI tools are directly integrated into revenue-generating activities (e.g., B2B export buyer segmentation in textiles), perceived usefulness is expected to be higher, leading to stronger adoption effects. In contrast, in sectors where AI adoption faces technical and skill-related barriers (e.g., tourism in emerging markets), the PEOU constraint may attenuate AI’s impact on remarketing outcomes. This TAM-derived prediction has not been empirically tested in remarketing contexts and represents a key theoretical gap addressed by the present study.

1.5. Digital Marketing in Emerging Economies and the Uzbekistan Context

Digital marketing research in emerging economies is limited compared to developed countries [17]. Tashpulatova and Suyunova [18] analyzed sectoral digital development differences in Central Asia, showing systematic variation in digital maturity between tourism and manufacturing. Abdurakhmanova [19] identified key barriers to digital transformation in Central Asia, including weak infrastructure, limited digital skills, and regulatory constraints. In Uzbekistan, Ziyavitdinov [20] linked digital maturity to economic performance, showing sectoral disparities, while OECD [21] emphasized uneven digital infrastructure growth across sectors, affecting marketing capabilities.

The Uzbekistan context is particularly instructive for three reasons. First, the country’s rapid digital transformation under the “Digital Uzbekistan-2030” strategy (Presidential Decree No. UP-6079, 2020) creates a dynamic environment where the relationship between digital infrastructure and marketing effectiveness can be observed in real time, with internet user penetration reaching 85% and broadband networks expanding to over 23 million subscribers [22]. Second, the coexistence of a globally integrated B2B manufacturing sector (textiles, accounting for approximately 7% of GDP) and a domestically oriented B2C service sector (tourism) provides a natural comparative setting. Third, as a post-Soviet transition economy, Uzbekistan shares structural characteristics with other Central Asian and CIS countries, enhancing the potential generalizability of findings across similar contexts.

1.6. Monte Carlo Simulation as Uncertainty Analysis in Marketing Research

Monte Carlo simulation is widely used in finance and operations research to model investment decisions under uncertainty [23]. Its application in marketing remains emerging: Wedel and Kannan [7] highlighted the importance of probabilistic models in marketing analytics, and Chintalapati and Pandey [23] advocated for simulation approaches to evaluate AI-based marketing tools. In the present study, Monte Carlo simulation is employed strictly as an uncertainty and robustness analysis: it is not advanced as a methodological innovation, but rather as a complementary tool that characterizes the probability distribution of ROI under empirically calibrated parameters and thereby tests the stability of the findings obtained via OLS regression and PLS-SEM.

1.7. Research Gaps and Hypotheses

The above review identifies three specific gaps in the literature: (1) No prior empirical research directly compares remarketing effectiveness between service and manufacturing sectors; existing studies examine each sector separately, preventing systematic assessment of sectoral heterogeneity in digital marketing returns. (2) The differential impact of AI personalization in sectoral contexts—specifically, whether TAM-predicted differences in perceived usefulness moderate AI’s association with remarketing ROI—has not been empirically tested. (3) Remarketing effectiveness in emerging economies, particularly transition economies like Uzbekistan where digital infrastructure and sectoral digital maturity vary substantially, remains largely unexplored.

Drawing upon the aforementioned theoretical framework, this article tests four hypotheses:

H1: 

The degree of remarketing strategy implementation is associated with statistically significant differences between the service and manufacturing sectors.

H2: 

Consistent with RBV theory, the B2B-oriented textile sector is associated with higher yet more volatile return on investment (ROI) relative to the service sector.

H3: 

Grounded in TAM theory, the extent of AI personalization is positively associated with remarketing effectiveness in both sectors, operating through both direct associations with ROI and indirect associations mediated by revenue variability reduction.

H4: 

The adoption of remarketing strategies is positively associated with enterprises’ revenue stability, with the digital infrastructure index serving as the primary correlate.

The scholarly contribution of this research is threefold. First, it extends RBV theory by demonstrating that digital marketing resources function as sector-specific strategic assets whose returns vary systematically between B2B and B2C contexts in emerging economies. Second, it extends TAM by providing empirical evidence that sectoral differences in perceived usefulness moderate the association between AI-driven remarketing tools and performance outcomes. Third, it demonstrates the value of integrating KS-test-validated Monte Carlo uncertainty analysis with a sequential explanatory mixed-methods design—an integration intended as a robustness contribution rather than a methodological innovation.

From a sustainability perspective, this research addresses the intersection of digital transformation and sustainable economic development in emerging economies. The United Nations’ Sustainable Development Goals (SDGs) explicitly recognize the role of technology and innovation in achieving inclusive and sustainable industrialization (SDG 9) and promoting sustained, inclusive economic growth (SDG 8) [24]. Remarketing strategies, by optimizing resource allocation in marketing expenditures and reducing customer acquisition costs, contribute to more sustainable business practices aligned with SDG 12 (Responsible Consumption and Production). The present study extends this sustainability framework by empirically examining whether and how digital marketing tools enable enterprises in a transition economy to build sustainable competitive advantages—defined as advantages that can be maintained over time without depleting the firm’s resource base [4]. By linking remarketing effectiveness to long-term revenue stability rather than short-term profit maximization, this study reframes digital marketing as a mechanism for sustainable enterprise development.

The conceptual framework integrating these theoretical perspectives and hypotheses is depicted in Figure S1. The model posits that sector-specific digital resources (DII), AI-enabled personalization capabilities (AII), and remarketing adoption function as antecedents of marketing performance (ROI, revenue stability) through both direct and mediated pathways, with sector type (B2B manufacturing vs. B2C service) serving as a moderating variable.

2. Methodology

2.1. Research Design

This study employs a sequential explanatory mixed-methods design [25]. The design comprises two phases: the initial phase involves the collection and analysis of quantitative data; the subsequent phase entails the gathering of qualitative data to contextualize the quantitative findings [6].

This research adopts a cross-sectional design to identify associational relationships among variables rather than causal linkages. Accordingly, all conclusions in the results section are articulated using phrases such as “associated with,” “positively correlated with,” or “co-observed.” To establish causal relationships, future studies are recommended to apply difference-in-differences (DiD) models or fixed effects models based on panel data [26].

Justification for the multi-method quantitative strategy. The present study deploys several distinct quantitative techniques (independent samples t-test, Pearson χ²-test, OLS regression, PLS-SEM, and Monte Carlo simulation) rather than relying on a single workhorse model. The methods are not redundant; each addresses a specific hypothesis type, sampling structure, or robustness concern, and together they form an integrated analytical chain. Independent samples t-tests handle continuous between-sector comparisons (H1, H2) on variables with approximately normal within-sector distributions (Shapiro–Wilk W ≈ 0.98). Pearson χ²-tests handle binary adoption indicators (H1) for which a t-test is inappropriate. OLS regression with cluster-robust standard errors estimates conditional associations of multiple predictors with ROI simultaneously (H2 and H4), with clustering at the regional level addressing the multilevel sample structure. PLS-SEM is used to evaluate the measurement model and the moderated-mediation pathway AII → CV → ROI (H3), since the latent-variable mediation structure cannot be cleanly recovered by OLS alone, and the relatively small per-sector sample size (n = 140) favors PLS-SEM over CB-SEM. Monte Carlo simulation (20,000 iterations) is used solely as an uncertainty and robustness analysis: it characterizes the probability distribution of ROI under empirically calibrated parameters, evaluates the stability of OLS-based ROI estimates under stochastic variation, and provides probabilistic statements (e.g., p(ROI < 0)) that point estimates alone cannot supply. Accordingly, this study does not claim Monte Carlo as a methodological innovation but rather as a transparent robustness device complementing OLS and PLS-SEM.

2.2. Sampling and Data Collection

Enterprises were selected via multi-stage stratified random sampling from the Uzbekistan Agency of Statistics’ 2024 enterprise registry [27] (OKONX codes: 55–56 for tourism; 13–14 for textiles). Stratification was performed across four regions (Tashkent, Samarkand, Fergana, Andijan), two sectors, and three enterprise sizes (small, medium, large). The sample size of n = 280 was determined using Krejcie and Morgan’s [26] table, based on a finite population of approximately 1200 registered enterprises in these sectors and regions, assuming a 95% confidence interval and ±5% margin of error. This yields a minimum required sample of approximately 291; with the exclusion of 11 incomplete questionnaires, the final analytic sample of 280 meets this threshold.

A total of 412 enterprises were approached, yielding 291 complete responses (response rate: 70.6%). Eleven questionnaires were excluded during quality control. Non-response bias was assessed using Armstrong and Overton’s [28] method, revealing no significant differences between early and late responders across key variables (p > 0.05).

2.3. Data Validation and Triangulation

Marketing and financial indicators from 2022–2025 were validated through three sources: (1) enterprise surveys; (2) exported reports from digital platforms (Google Analytics, Yandex.Direct, Meta Pixel); (3) tax reports. While the risk of self-assessment bias in retrospective data was partially mitigated through triangulation, it was not fully eliminated—this limitation is discussed in detail in Section 5.

2.4. Treatment of Macroeconomic Factors

Macroeconomic factors during 2022–2025, such as fluctuations in the Uzbekistan som’s exchange rate against the US dollar (approximately 12% annual depreciation during the study period), adjustments in global textile trade policies (including EU GSP+ preferences), and residual impacts of the COVID-19 pandemic on the tourism sector (with domestic tourism recovering to 89% of pre-pandemic levels by 2024), were not incorporated as control variables in the regression model. This omission was driven by three methodological considerations: (1) the absence of firm-level data on exchange rate exposure; (2) the difficulty of disentangling macroeconomic effects from sector-level digital marketing outcomes in a cross-sectional design; and (3) the risk of introducing multicollinearity given the limited degrees of freedom.

The potential bias from these omitted variables was partially evaluated using Oster’s [29] method: the stability of selected variables remained robust (δ > 1.3), with the direction of primary β coefficients unchanged, suggesting a limited influence from unobserved factors. Nonetheless, it is acknowledged that macroeconomic conditions—particularly exchange rate volatility in the textile export sector and pandemic recovery dynamics in tourism—may have influenced the observed sectoral ROI differences.

2.5. Variable Operationalization

Independent variables:

• Sector type (service = 0, manufacturing = 1).
• Digital Infrastructure Index (DII): 5 components, 1–5 Likert scale, Cronbach’s α = 0.83, composite reliability (CR) = 0.86, average variance extracted (AVE) = 0.55.
• AI Personalization Index (AII): 4 components, 1–5 Likert scale, Cronbach’s α = 0.81, CR = 0.84, AVE = 0.52.
• Remarketing budget as a percentage of total marketing budget (%).

DII components (5 items, 1–5 Likert scale). (DII1) reliability of internet connectivity; (DII2) availability and use of digital marketing platforms (e.g., Google Ads, Meta Ads, Yandex.Direct, programmatic DSPs); (DII3) digital storage and structured management of customer data (CRM systems, customer-data platforms); (DII4) availability of analytics and tracking infrastructure (e.g., Google Analytics, Yandex.Metrica, Meta Pixel); (DII5) integration of payment and order-management systems with marketing platforms.

AII components (4 items, 1–5 Likert scale). (AII1) use of AI-based customer segmentation; (AII2) AI-driven targeting and audience-list optimization in remarketing campaigns; (AII3) algorithmic personalization of creative content (dynamic creative optimization, recommender systems); (AII4) AI-supported predictive analytics for customer behavior and churn.

Scoring procedure. For each construct (DII and AII), respondents’ 1–5 Likert ratings are first averaged across items to yield a raw construct score in [1, 5]. The raw score is then min–max normalized to the [0, 1] interval using the formula score = (raw − 1)/4, in line with standard practice in composite-index construction. The reported sample means (DII: 0.67 in tourism, 0.74 in textile; AII: 0.58 in tourism, 0.68 in textile) reflect the normalized measure.

Measurement model assessment. The measurement model was evaluated using PLS-SEM criteria in SmartPLS 4.0. All outer loadings exceeded 0.70. Discriminant validity was verified via the Fornell–Larcker criterion [30]: square roots of AVE for DII and AII (√0.55 = 0.74; √0.52 = 0.72) exceeded their inter-correlation (r = 0.48). The heterotrait-monotrait ratio of correlations (HTMT) between DII and AII was 0.56, below the conservative threshold of 0.85, further supporting discriminant validity. Variance inflation factor (VIF) values for all inner model constructs were below 3.3, indicating that common method bias is unlikely to be a serious concern in the PLS-SEM model.

AII measurement limitation: The AII relies on self-reported surveys, which introduces potential common method bias and social desirability effects. Harman’s single-factor test was conducted as a post hoc diagnostic: all items were entered into an unrotated principal component analysis, and the first factor accounted for 31.4% of total variance, well below the 50% threshold, suggesting that common method bias does not dominate the data. Nevertheless, no full confirmatory factor analysis (CFA) using CB-SEM was conducted due to the relatively small per-sector sample size (n = 140) and the use of PLS-SEM as the primary estimation approach. Future studies are strongly advised to employ objective measures, such as data from digital platform APIs (e.g., Google Ads DCO data, Meta Pixel engagement metrics), and to conduct full CFA with a larger sample to establish construct validity.

Dependent variables: ROI = (Revenue − Cost)/Cost × 100; revenue volatility coefficient CV = SD/M; conversion rate CVR (%); customer lifetime value CLV (USD); ROAS; CPA (USD).

2.6. Qualitative Data Collection

In-depth interviews (n = 32; 16 from tourism, 16 from textiles) were conducted via purposive sampling [31] among marketing managers and IT directors. Interviews followed a semi-structured format, averaging 52 min in duration.

Saturation analysis. Saturation was assessed using a systematic code-tracking protocol. After each interview was transcribed and coded, the number of new thematic codes generated was recorded. Saturation was operationally defined as the point at which two consecutive interviews produced fewer than two new substantive codes (i.e., codes representing new themes or sub-themes, excluding minor elaborations of existing themes). This threshold was reached after the 26th interview. Interviews 27 through 32 were conducted to confirm saturation and ensure adequate representation across both sectors and all four regions. A total of 47 unique codes were identified across 32 transcripts. The cumulative code emergence curve is available as Supplementary Materials (Figure S1).

Coding reliability. Two independent coders analyzed all 32 transcripts using NVivo 14 and thematic analysis procedures [32]. Overall, Cohen’s κ = 0.79, indicating substantial agreement [33]. Theme-specific κ values: Infrastructure κ = 0.81; Budget management κ = 0.77; Digital skills κ = 0.76; Customer characteristics κ = 0.80. Discrepancies were resolved through a third coder’s mediation. The coding tree and thematic matrix are available as Supplementary Materials (Table S1).

Confirmatory–disconfirmatory evidence. Interview segments contradicting the primary narrative were compiled in a dedicated table. For instance, two respondents in the textile sector deemed AI integration ineffective (TQ-3; TQ-17)—while not refuting the main narrative, this finding enriched the context and informed the limitations discussion.

2.7. Statistical Analysis Procedures

All analyses were performed using IBM SPSS Statistics 27.0 (descriptive statistics, t-tests, χ²-tests, OLS regression, Harman’s single-factor test) and R 4.3.1 (Monte Carlo simulation, sensitivity analysis, graphics). SmartPLS 4.0 was utilized for partial least squares structural equation modeling (PLS-SEM).

Multiple hypothesis correction. Given the testing of multiple hypotheses and sub-hypotheses, the Benjamini–Hochberg [34] procedure was applied to control the false discovery rate (FDR = 0.05). This correction is applied to all p-values reported in the Results section. Effect sizes are reported as Cohen’s d (for t-tests), Cramér’s V (for χ²-tests), and standardized β (for regression analyses).

Hypothesis testing procedures:

• H1: Pearson χ²-test and independent samples t-test.
• H2: Independent samples t-test; Levene’s test; OLS regression.
• H3: Independent samples t-test (AI adopters vs. non-adopters); PLS-SEM direct effect and mediation path (AI → CV → ROI) with bootstrapping (n = 5000).
• H4: Pearson correlation and OLS regression.

2.8. Endogeneity Considerations

AII and DII variables may exhibit reverse causality with ROI—namely, high-ROI enterprises may afford greater investments in AI and infrastructure. This endogeneity concern is a critical limitation of the present study’s cross-sectional design. Due to the absence of suitable time-varying data, instrumental variables (IVs)—such as regional broadband growth rates as an IV for DII—were not employed. This limitation necessitates caution in interpreting results: all identified β coefficients should be viewed as indicators of conditional associations rather than causal effects. IV analysis using panel data is recommended as a priority direction for future research [35].

Multilevel structure. To account for clustering of enterprises across four regions, standard errors were computed as cluster-robust. Multilevel linear modeling (HLM) to fully assess regional moderation effects is designated as a direction for future studies.

2.9. Monte Carlo Simulation Design and Parameter Calibration

The Monte Carlo simulation was conducted in R 4.3.1 (using mc2d and triangle packages) with 20,000 iterations. The simulation aimed to model the probability distribution of ROI under uncertainty for both sectors.

Parameter calibration procedure. Simulation parameters were calibrated from three sources, integrated as follows:

• Empirical statistics (primary source): Sample means and standard deviations of ROI served as the baseline parameters. For tourism: ROI ~ Normal (μ = 48.3, σ = 8.7); for textiles: ROI ~ Normal (μ = 82.1, σ = 15.2). Normal distributions were selected based on Shapiro–Wilk normality test results (tourism: W = 0.987, p = 0.241; textiles: W = 0.983, p = 0.178).
• Literature benchmarks (secondary source): Parameters were cross-validated against findings from Johnson et al. [3], who reported average B2B remarketing ROI ranges of 60–120%, and Lambrecht and Tucker [1], who documented B2C conversion rate improvements of 2–5%. Where sample estimates fell within literature-reported ranges, empirical parameters were retained; otherwise, bounded triangular distributions were employed.
• Delphi method (tertiary source, for uncertainty bounds): Eight industry experts (4 from tourism, 4 from textiles; all with >10 years of experience) participated in two rounds of Delphi consultation (consensus CR > 0.85). Experts provided estimates of plausible minimum and maximum values for ROI, CVR, and budget allocation parameters, which were used to define the bounds of triangular distributions for sensitivity analysis.

Sensitivity analysis. Gamma distributions, parameterized as Gamma (shape = (μ/σ)², rate = μ/σ²), and lognormal distributions, parameterized as LogNormal (μ_log = ln(μ²/√(σ² + μ²)), σ_log = √(ln(1 + σ²/μ²))), were additionally tested. Results were robust across distributional assumptions (probability of ROI < 0 varied within ±8%).

Parameter validation. Alignment between simulated and actual sample ROI distributions was tested via the Kolmogorov–Smirnov (KS) test. For tourism: KS = 0.041 (p = 0.312); for textiles: KS = 0.038 (p = 0.387)—indicating no statistically significant differences. QQ plots also confirmed good fit (Figure 1).

2.10. Mediation Analysis

To elucidate the mechanism linking Hypotheses H3 and H4, the following mediation model was examined:

AI Personalization (AII) → Revenue Variability (CV) → Return on Investment (ROI).

This model measures AI’s indirect association with ROI via reduced revenue variability, in addition to its direct association. Bootstrapped (n = 5000) 95% confidence intervals were computed. Industry type was included as a moderator, forming a moderated mediation model [36].

3. Results

This section presents the empirical findings for four hypotheses (H1–H4). The results of quantitative analyses (t-test, χ²-test, OLS regression, PLS-SEM) and Monte Carlo simulations are integrated with findings from qualitative thematic analysis. The Benjamini–Hochberg correction (BH, FDR = 0.05) was applied; all effect sizes were calculated as Cohen’s d (for t-tests), Cramér’s V (for χ²-tests), and standardized β (for regression analyses).

3.1. Descriptive Characteristics

The demographic and firmographic characteristics of the 280 firms included in this study are presented in

Table 1. Demographic and Firmographic Characteristics of Firms and Respondents.

Characteristic	Tourism and Hospitality (n = 140)	Textile (n = 140)	Total (n = 280)
Firm size: Small/Medium/Large	40%/25%/35%	20%/30%/50%	30%/27%/43%
Regional distribution: Tashkent/Samarkand/Fergana/Andijan	40%/25%/20%/15%	35%/30%/20%/15%	38%/28%/20%/14%
Respondent position: Marketing Manager/Executive/Other	60%/30%/10%	55%/35%/10%	58%/32%/10%
Average work experience (years, M ± SD)	5.8 ± 1.2	5.6 ± 1.1	5.7 ± 1.15
Response rate (%)	71.3	69.9	70.6

Note. M = mean; SD = standard deviation. Percentages may not sum to 100% due to rounding. Response rate = fully completed questionnaires/total questionnaires distributed × 100.

. The non-response bias analysis [29] indicated no statistically significant differences between early and late respondents across all key variables (p > 0.05), thereby ruling out concerns regarding sample representativeness bias.

3.2. Digital Maturity and Remarketing Adoption Level (H1)

The independent samples t-test results indicate that firms in the textile sector exhibit a statistically significantly higher Digital Infrastructure Index (DII) compared to tourism firms: M_Textile = 0.74 (SD = 0.10) and M_Tourism = 0.67 (SD = 0.12); t(278) = −6.25, p < 0.001, Cohen’s d = 0.63 (medium effect size). The distribution of the DII is visualized in Figure 2.

The χ²-test results for the adoption of remarketing tools are presented in

Table 2. Adoption Rates of Remarketing Tools by Sector.

Remarketing Tool	Tourism and Hospitality (%)	Textile (%)	χ2	p	Cramér’s V
Pixel tracking	62.1	78.3	10.24	0.001	0.19
Email retargeting	58.6	72.9	7.45	0.006	0.16
Programmatic advertising	31.4	45.0	6.11	0.013	0.15

Note. χ² = Pearson chi-square statistic; df = 1 for all tests. Cramér’s V = effect size (0.10–0.29 = small-to-moderate). All p-values remained statistically significant after BH correction.

. Pixel tracking adoption was significantly higher in the textile sector (78.3%) compared to tourism (62.1%) (χ² = 10.24, p = 0.001, Cramér’s V = 0.19). A consistent pattern was observed across all three tools. After applying the Benjamini–Hochberg correction, all differences remained statistically significant. These findings support Hypothesis H1.

3.3. Performance Indicators: Sectoral Comparative Analysis (H2)

The results for key performance indicators are presented in

Table 3. Key Performance Indicators by Sector: Means, Standard Deviations, and Between-Group Difference Tests.

Indicator	Tourism and Hospitality (M ± SD)	Textile (M ± SD)	t	p	Cohen’s d
ROI (%)	48.3 ± 8.7	82.1 ± 15.2	−22.84	<0.001	2.73
Customer Retention Rate (CRR, %)	41.2 ± 9.8	48.7 ± 8.5	−6.84	<0.001	0.82
Conversion Rate (CVR, %)	3.5 ± 0.9	4.2 ± 1.0	−6.16	<0.001	0.74
Return on Ad Spend (ROAS)	3.1 ± 0.7	3.8 ± 0.6	−8.98	<0.001	1.07
Customer Lifetime Value (CLV, USD)	1230 ± 320	1580 ± 410	−7.96	<0.001	0.95
Customer Acquisition Cost (CPA, USD)	45.6 ± 12.3	38.4 ± 10.5	5.27	<0.001	−0.63

Note. M = mean; SD = standard deviation. All t-tests are two-tailed independent samples tests. Cohen’s d = (M_Textile − M_Tourism)/SD_pooled. All p-values remained statistically significant after BH correction. The negative Cohen’s d for CPA indicates a lower (more favorable) value in the textile sector.

and Figure 3. Return on investment (ROI) was statistically significantly higher in the textile sector compared to the tourism sector: M_Textile = 82.1% (SD = 15.2) and M_Tourism = 48.3% (SD = 8.7); t(278) = −22.84, p < 0.001, Cohen’s d = 2.73.

It is important to note that the exceptionally large effect size (d = 2.73) reflects not solely the difference in remarketing effectiveness per se, but rather the combined influence of structural sectoral differences, including divergent business models (B2B vs. B2C), transaction volumes, export market dynamics, and exchange rate effects. The between-sector ROI comparison should therefore be interpreted as a composite indicator of sectoral divergence in digital marketing returns rather than a pure measure of remarketing treatment effect. Within-sector analyses (e.g., comparing AI adopters vs. non-adopters within each sector; see Section 3.5) provide a more controlled assessment of remarketing-specific effects.

The coefficient of variation (CV) of revenue also demonstrated substantial differences: CV = 0.18 in the textile sector and CV = 0.11 in the tourism sector. Monte Carlo simulation (N = 20,000 iterations) further confirmed and visualized this difference (see Figure 4). The probability of ROI < 0 was estimated at 3.2% (95% CI [2.8%, 3.7%]) for the textile sector and 0.8% (95% CI [0.6%, 1.1%]) for the tourism sector. These findings support Hypothesis H2, confirming that the textile sector is associated with higher, but more volatile, ROI compared to the tourism sector.

3.4. ROI Predictors: OLS Regression Analysis (H2, H4)

Table 4. OLS regression results: Predictors of ROI by sector (standardized β coefficients).

Predictor	β Tour.	p Tour.	β Text.	p Text.	β Overall	p Overall	VIF
Digital Infrastructure Index (DII)	0.34	<0.01	0.41	<0.001	0.38	<0.001	1.52
Remarketing Adoption Index	0.29	<0.05	0.36	<0.001	0.32	<0.01	1.68
Budget Allocation Share	0.18	0.087	0.22	<0.05	0.20	<0.05	1.24
AI Personalization Index (AII)	0.25	<0.01	0.30	<0.001	0.28	<0.001	1.71
R2 (Overall)					0.48	<0.001
R2 (Sector-specific)	0.43		0.53

Note. β = standardized regression coefficient. White’s heteroscedasticity-consistent (HC3) robust standard errors and cluster-robust standard errors (clustered by region) were applied. VIF = Variance Inflation Factor; all VIF values < 2.0, well below the conventional threshold of 5.0, indicating no multicollinearity concerns. F(4, 275) = 63.7, p < 0.001 for the overall model.

presents the OLS regression results for four predictors of ROI by sector. The overall model explained 48% of the variance in ROI (R² = 0.48, F(4, 275) = 63.7, p < 0.001). Sector-specific analysis yielded R² = 0.43 for tourism and R² = 0.53 for the textile sector. In both sectors, the Digital Infrastructure Index (DII) (β = 0.38, p < 0.001) and the AI Personalization Index (AII) (β = 0.28, p < 0.001) emerged as the strongest predictors. Budget allocation share was a statistically significant predictor only in the textile sector (β = 0.22, p < 0.05), while it did not reach statistical significance in the tourism sector (β = 0.18, p = 0.087). The moderating association of sector was statistically significant (p < 0.05), thereby supporting Hypothesis H4.

3.5. The Impact of AI Personalization on Remarketing Effectiveness (H3)

An independent samples t-test and PLS-SEM were conducted to evaluate differences between firms that adopted AI personalization tools (n = 168) and those that did not (n = 112). The t-test results indicated that firms implementing AI achieved statistically significantly higher ROI (p < 0.001). PLS-SEM analysis (SmartPLS 4.0, bootstrap n = 5000) revealed a direct and positive association between the AI Personalization Index and ROI (β = 0.28, p < 0.001). The PLS-SEM model was evaluated using standard criteria: all outer loadings > 0.70; SRMR = 0.037 (below the 0.08 threshold); the coefficient of determination (R²) for the endogenous construct was 0.48. The path diagram is presented in Figure 5.

In addition, firms adopting AI tools exhibited significantly lower revenue variability compared to non-adopters, with coefficients of variation of CV = 0.10 and CV = 0.19, respectively (p < 0.001). These findings support Hypothesis H3.

3.6. Qualitative Findings and Integration with Quantitative Data

In-depth interviews (n = 32; 16 tourisms, 16 textile) identified four key themes through thematic analysis [31] (inter-coder reliability: Cohen’s κ = 0.79). These themes are presented alongside quantitative findings in

Table 5. Sectoral Barriers and Enabling Factors: Joint Display of Qualitative and Quantitative Findings.

Theme	Tourism and Hospitality	Textile	Link to Quantitative Findings
Infrastructure	“Unreliable internet connectivity limits remarketing effectiveness” (TM-7)	“Export platforms provide access to broader markets” (TQ-4)	DII: 0.67 vs. 0.74; t(278) = −6.25, p < 0.001, d = 0.63
Budget management	“Seasonal fluctuations disrupt budget consistency” (TM-3)	“Quarterly budget planning has stabilized ROI” (TQ-12)	CV: 0.11 vs. 0.18; β = 0.18 (p = 0.087) vs. β = 0.22 (p < 0.05)
Digital skills	“Staff require additional training” (TM-11)	“Our IT department is small, slowing AI integration” (TQ-19)	Mean AII: 2.9/5.0 vs. 3.4/5.0; independent samples t-test, p < 0.01
Customer characteristics	“Seasonality complicates campaign timing” (TM-3)	“B2B transactions are longer but yield higher CLV” (TQ-8)	CVR: 3.5% vs. 4.2% (d = 0.74); CLV: $1230 vs. $1580 (d = 0.95)

Note. TM = tourism and hospitality respondent; TQ = textile respondent. Inter-coder reliability Cohen’s κ = 0.79 (substantial agreement; Landis & Koch, 1977 [33]).

using a joint display approach.

Respondents from the textile sector emphasized the advantages of infrastructure and structured budget management (TQ-4; TQ-12), whereas tourism sector participants highlighted technological constraints and seasonality challenges (TM-7; TM-3). A shortage of digital skills was reported in both sectors. Overall, the qualitative findings reinforce and contextualize the quantitative results.

3.7. Monte Carlo Simulation Validation

To assess the goodness-of-fit between the simulated ROI distribution and the empirical sample distribution, the two-sample Kolmogorov–Smirnov (KS) test was applied.

Tourism sector: KS = 0.041, p = 0.312—indicating no statistically significant difference. Textile sector: KS = 0.038, p = 0.387—indicating no statistically significant difference. For both sectors, p > 0.05, confirming that the simulated distributions closely approximate the empirical sample distributions. The QQ plots visually corroborate this alignment (see Figure 5), with percentile pairs closely following the ideal y = x reference line.

Sensitivity analysis additionally tested gamma and lognormal distributions. The probability of ROI < 0 varied within ±8% across all distributional assumptions, while the main conclusions remained unchanged.

3.8. Mediation Analysis: AII → CV → ROI (H3, H4)

To identify the mechanism linking Hypotheses H3 and H4, a moderated mediation model was tested [36]. AI personalization is hypothesized to be associated with ROI not solely through a direct pathway; rather, it may first be associated with reduced revenue variability (path a), which in turn is associated with higher ROI (path b). Sector type was incorporated into the model as a moderator. The indirect effects and their 95% bootstrap confidence intervals are presented in

Table 6. Mediation Analysis Results: AII → CV → ROI (Bootstrap n = 5000).

Path	Path a (β)	Path b (β)	Indirect Effect (a × b)	95% Bootstrap CI	p
Overall (n = 280)	−0.032	−45.2	5.04	[4.10, 6.00]	<0.001
Tourism (n = 140)	−0.028	−38.6	4.08	[2.87, 5.33]	<0.001
Textile (n = 140)	−0.039	−52.1	5.72	[4.38, 7.05]	<0.001
Sectoral difference (moderation)	—	—	Δ = 1.64	[0.42, 2.89]	<0.05

Note. Path a = AII → CV (standardized β); Path b = CV → ROI; Indirect effect = a × b; CI = bootstrap 95% confidence interval. Confidence intervals that do not include zero indicate statistically significant indirect effects. Δ = difference between indirect effects in the textile and tourism sectors.

and Figure 6.

The results indicate that, in both sectors, AI personalization is associated with an indirect pathway to ROI through reduced revenue variability (path a: β = −0.032 overall; p < 0.001), which in turn is associated with higher ROI (indirect effect = 5.04, 95% CI [4.10, 6.00]). The sectoral moderation is also statistically significant: the indirect association is significantly stronger in the textile sector than in the tourism sector (Δ = 1.64, 95% CI [0.42, 2.89], p < 0.05). It is important to note, however, that the cross-sectional design precludes confirmation of the temporal ordering assumed by the mediation model; the statistical mediation pattern is consistent with the hypothesized mechanism but does not constitute proof of causality.

3.9. Robustness Checks

To assess the stability of the main findings, several robustness checks were conducted.

Winsorized regression. To address potential sensitivity to extreme values, ROI was winsorized at the 5th and 95th percentiles, and the OLS regression was re-estimated. The direction and statistical significance of all four predictors remained unchanged: DII (β = 0.36, p < 0.001), Remarketing Adoption Index (β = 0.30, p < 0.01), Budget Allocation (β = 0.19, p < 0.05 for textile; p = 0.092 for tourism), and AII (β = 0.26, p < 0.001). The overall R² decreased marginally from 0.48 to 0.45, consistent with the removal of high-leverage observations. These results confirm that the main findings are not driven by outliers.

Alternative dependent variable specification. To ensure that results are not artifact of the specific ROI operationalization, the regression was re-estimated using ROAS as an alternative dependent variable. The pattern of results was substantively identical: DII (β = 0.35, p < 0.001), AII (β = 0.24, p < 0.01), with the textile sector again exhibiting stronger predictor effects. This suggests that the main findings are robust to the specific performance metric employed.

Subsample analysis by firm size. To assess whether firm size drives the observed sectoral differences, the OLS regression was re-estimated separately for small (n = 84), medium (n = 76), and large (n = 120) enterprises. The DII–ROI relationship was statistically significant across all three size categories (small: β = 0.31, p < 0.05; medium: β = 0.37, p < 0.01; large: β = 0.42, p < 0.001), although effect magnitudes were larger for large enterprises. The AII–ROI relationship was statistically significant for medium (β = 0.27, p < 0.05) and large (β = 0.33, p < 0.001) enterprises but did not reach significance for small enterprises (β = 0.19, p = 0.112), potentially reflecting the lower AI adoption rates in smaller firms.

Logistic regression for adoption variables. Given that remarketing tool adoption (pixel tracking, email retargeting, programmatic advertising) is measured as a binary variable, logistic regression was additionally employed to predict adoption as a function of DII, AII, firm size, and sector. Sector remained a statistically significant predictor of pixel tracking adoption (OR = 2.18, 95% CI [1.42, 3.35], p < 0.001) and email retargeting (OR = 1.89, 95% CI [1.21, 2.94], p = 0.005), consistent with the χ²-test results reported in Table 2.

Oster’s (2019) [29] coefficient stability test. As reported in Section 2.4, Oster’s [29] method was applied to assess the potential influence of omitted variable bias. For the DII–ROI relationship, δ = 1.47 (>1.0 threshold), and for the AII–ROI relationship, δ = 1.32 (>1.0 threshold), suggesting that the main results are unlikely to be fully explained by unobserved confounders. The identified set (β*) remained positive and statistically significant for both predictors under proportional selection assumptions.

Collectively, these robustness checks confirm that the primary findings—the positive associations of DII and AII with ROI, the sectoral moderation pattern, and the mediation pathway through revenue variability—are stable across alternative specifications, subsamples, and sensitivity analyses.

4. Discussion

4.1. Sectoral Adoption Differences and Digital Maturity (H1)

Hypothesis H1 was supported: the textile sector exhibited significantly higher adoption rates of remarketing tools across all indicators (pixel tracking: 78.3% vs. 62.1%; χ² = 10.24, p = 0.001). From the perspective of RBV theory [4], this disparity can be explained by the stronger pressure on textile firms operating in global export markets to transform digital capabilities into strategic competitive resources. For textile firms operating under a B2B model, digital tracking systems serve as mechanisms for maintaining engagement with potential buyers throughout extended decision-making cycles—consistent with the RBV principle of resource inimitability [4].

In contrast, the tourism sector lags behind in systematic digital adoption due to infrastructure constraints (DII = 0.67) and seasonal demand fluctuations. This finding aligns with Muñoz-Leiva et al. [9], who demonstrated that digital channel integration in the tourism sector remains insufficiently developed. However, while Alghanayem [10] identified remarketing as effective in B2C tourism contexts in developed markets, the present findings suggest that infrastructure barriers in Uzbekistan limit its effectiveness—reflecting the structural differences between developed and emerging markets emphasized by Kuldosheva [17]. The observed adoption gap also extends TAM: perceived usefulness of digital marketing tools appears to be contingent not only on individual user characteristics but also on sector-level infrastructure readiness—a contextual moderator not explicitly addressed in the original TAM framework [5].

4.2. ROI Differences and Revenue Variability (H2)

Hypothesis H2 was supported: the textile sector demonstrated significantly higher ROI (t(278) = −22.84, Cohen’s d = 2.73) but also greater variability (CV = 0.18), whereas tourism exhibited lower (ROI = 48.3%) but more stable performance (CV = 0.11).

The exceptionally large effect size (d = 2.73) warrants careful interpretation. This magnitude reflects not a pure remarketing treatment effect but rather the cumulative influence of multiple confounding structural factors, including: (a) the fundamental difference in business models (B2B textile transactions with higher per-unit values vs. B2C tourism transactions with lower per-unit values); (b) the approximately 12% annual depreciation of the Uzbekistan som during the study period, which may have inflated textile export ROI when calculated in local currency; (c) post-pandemic tourism recovery dynamics, with domestic tourism reaching only 89% of pre-pandemic levels by 2024; and (d) differential exposure to global trade policies (EU GSP+ preferences benefiting textiles). Within-sector comparisons (AI adopters vs. non-adopters) provide a more controlled assessment, yielding effect sizes in the moderate range (d = 0.63–1.07), which are more consistent with the typical impact of digital marketing interventions.

The textile sector’s higher but more volatile ROI can be explained by two key mechanisms. First, B2B sales cycles are longer and involve larger transaction values, meaning that individual successful conversions generate substantial revenue gains. Second, exposure to global export markets introduces greater volatility. This finding aligns with Johnson et al. [3], who reported large remarketing effects in B2B contexts. Tourism’s more stable ROI reflects the structured planning of seasonal remarketing campaigns and relatively standardized pricing models. However, the observed ROI of 48.3% is substantially lower than global averages, suggesting a negative association between emerging market constraints and remarketing effectiveness, extending the findings of Tashpulatova and Suyunova [18] into the domain of marketing performance.

4.3. The Impact of AI Personalization and the Mediation Mechanism (H3)

Hypothesis H3 received strong empirical support. The AI Personalization Index emerged as the second strongest predictor of ROI and was positively associated with performance (β = 0.28, p < 0.001). However, the most novel finding of this study lies in the mediation analysis: AI personalization is associated with ROI not only directly but also indirectly through reduced revenue variability (indirect effect = 5.04, 95% CI [4.10, 6.00]).

It is important to interpret these mediation results with appropriate caution. While the statistical mediation model suggests a pathway from AI personalization through revenue variability to ROI, the cross-sectional design means that the temporal ordering of these variables cannot be empirically confirmed. The possibility of reverse causality—whereby firms with higher ROI invest more in AI personalization—cannot be excluded. Nevertheless, the consistency of the mediation pattern across both sectors and the theoretical coherence with TAM predictions provide preliminary evidence for the proposed mechanism.

Sectoral moderation effects were also statistically significant: the indirect association was stronger in the textile sector than in tourism (Δ = 1.64, 95% CI [0.42, 2.89], p < 0.05). This finding is consistent with the TAM framework [6], which predicts that technology adoption effects will be stronger in contexts where perceived usefulness is higher. In the textile sector, where AI tools are directly integrated into export buyer segmentation and demand forecasting (as reported by respondents TQ-4 and TQ-8), perceived usefulness scores were higher (AII = 3.4/5.0 vs. 2.9/5.0), potentially explaining the stronger mediation pattern. This extends TAM by demonstrating that the model’s predictions hold not only at the individual adoption level but also at the sector–aggregate level in remarketing contexts.

At the same time, the caution raised by Puntoni et al. [15] is relevant: qualitative interviews revealed that excessive retargeting in tourism contexts may cause customer discomfort (TM-7), highlighting the importance of optimal AI-driven frequency management.

4.4. Revenue Stability and Infrastructure Effects (H4)

Hypothesis H4 was supported by regression results (R² = 0.48 overall; tourism R² = 0.43; textile R² = 0.53) and qualitative evidence. The DII emerged as the strongest positive predictor of revenue stability and ROI performance (β = 0.38, p < 0.001). Within the RBV framework, this finding reinforces the theoretical proposition that digital infrastructure functions as a critical strategic resource [4,7]. This extends RBV from its traditional focus on tangible and human resources to encompass digital infrastructure as a rent-generating asset in emerging market contexts.

In the textile sector, structured quarterly budget planning was positively associated with ROI stability (TQ-12; β = 0.22, p < 0.05). In contrast, budget allocation was not a statistically significant predictor in tourism (β = 0.18, p = 0.087), likely due to the destabilizing effects of seasonal demand variability (TM-3). The mediation analysis further enriches H4 by demonstrating that revenue stability (lower CV) serves as a link between AI personalization and ROI performance.

4.5. Theoretical Contributions

This study makes three distinct contributions to digital marketing theory.

First contribution: Sector-specific heterogeneity in AI personalization mechanisms. Existing literature [15,16] has examined the general effects of AI in marketing but has largely overlooked sectoral differentiation. This study demonstrates that AI personalization operates through distinct mediation patterns in B2B manufacturing versus seasonal B2C service sectors. Specifically, while Hardcastle et al. [16] found a positive AI–ROI relationship in service sectors, the present study shows that this relationship is mediated by revenue variability reduction—and that this mediation is significantly stronger in the B2B textile sector (indirect effect = 5.72 vs. 4.08).

Second contribution: Infrastructure as a correlate of ROI variability in transition economies. Prior research [17,18] has theoretically emphasized the relationship between infrastructure and performance but has not empirically examined ROI variability in remarketing contexts. This study addresses this gap by demonstrating that differences in digital maturity (DII = 0.67 vs. 0.74) correspond with differences in ROI variability (CV = 0.11 vs. 0.18) in Uzbekistan.

Third contribution: Robustness through the integration of Monte Carlo uncertainty analysis with mixed-methods evidence. Chintalapati and Pandey [23] highlighted the importance of simulation approaches in evaluating AI-based marketing performance but did not provide empirical implementation. This study advances analytical practice by integrating Monte Carlo simulation, validated through KS testing, into a mixed-methods research design as a robustness device. This integration is offered not as a methodological innovation per se, but as a transparent and replicable robustness pipeline that other researchers can adopt when working with cross-sectional data subject to substantial uncertainty.

4.6. Practical Implications

For tourism and hospitality enterprises: First, investment in digital infrastructure should be prioritized—the findings indicate that each unit increase in the DII is associated with a 0.34 standard deviation increase in ROI (β = 0.34, p < 0.01). Second, AI-driven remarketing strategies should incorporate seasonal demand forecasting. Third, enterprises should invest in digital skills training for marketing staff, as the lower AII scores in tourism (2.9/5.0 vs. 3.4/5.0) suggest that human capital constraints may be limiting the effectiveness of available AI tools.

For textile enterprises: First, enterprises should implement structured quarterly budgeting models to mitigate the higher revenue volatility (CV = 0.18). Second, AI personalization should be expanded to include predictive demand analytics and buyer behavior segmentation across international markets. Third, enterprises should diversify remarketing channel portfolios beyond pixel tracking and email retargeting to include programmatic advertising (currently adopted by only 45% of textile firms).

For policymakers within the “Digital Uzbekistan-2030” framework: First, sector-specific digital infrastructure investment programs should be designed. Second, targeted digital skills development programs should be differentiated by sector. Third, the creation of sector-specific digital marketing benchmarking databases would enable enterprises to evaluate their remarketing performance relative to industry standards.

4.7. Limitations

This study has several important limitations.

First and most critically, the cross-sectional design restricts causal inference. All observed relationships are correlational rather than causal. Reverse causality and endogeneity remain possible. All β coefficients and indirect effects should be interpreted as indicators of conditional associations rather than causal effects. Similarly, the mediation analysis identifies a statistical pattern consistent with the hypothesized mechanism but does not constitute proof of causal mediation, as the temporal ordering of variables cannot be confirmed from cross-sectional data.

Second, the exceptionally large between-sector effect size (Cohen’s d = 2.73 for ROI) reflects not only remarketing effectiveness differences but also confounding structural factors (business model divergence, transaction volumes, exchange rate effects, and pandemic recovery dynamics). This limits the extent to which the between-sector ROI difference can be attributed specifically to remarketing strategies.

Third, macroeconomic factors were not explicitly included in the regression model. Although Oster’s [29] sensitivity analysis (δ > 1.3) suggests limited influence of omitted variables, exchange rate depreciation may have inflated textile export ROI while simultaneously suppressing tourism revenue.

Fourth, the AII was based on self-reported measures. Although Harman’s single-factor test (31.4% variance explained by the first factor) and PLS-SEM inner model VIF values (<3.3) suggest that common method bias is not dominant, the absence of full CFA using CB-SEM leaves measurement validity partially unverified. Future research should employ multi-source data collection and conduct full CFA with larger samples.

Fifth, the sample was limited to four regions and two sectors, restricting generalizability to transition economies with similar characteristics.

4.8. Implications for Sustainable Development

The findings of this study carry significant implications for sustainable economic development in transition economies. The demonstrated positive association between digital infrastructure investment and marketing ROI stability (H4: β = 0.38, p < 0.001) suggests that investments in digital capabilities generate not only immediate performance gains but also contribute to long-term economic sustainability by reducing revenue volatility and enhancing firms’ resilience to external shocks.

From the perspective of SDG 8 (Decent Work and Economic Growth), the findings indicate that digitally mature enterprises achieve more sustainable growth trajectories. The textile sector’s higher ROI volatility (CV = 0.18 vs. 0.11) suggests that while B2B-oriented firms may achieve higher short-term returns, they require deliberate stabilization mechanisms—such as AI-driven demand forecasting—to achieve sustainable growth patterns. The mediation analysis (AII → CV → ROI) provides preliminary evidence that AI personalization may function as such a mechanism, consistent with the broader literature on technology’s role in sustainable economic development [37,38,39].

Regarding SDG 9 (Industry, Innovation and Infrastructure), the significant DII–ROI relationship across both sectors reinforces the importance of digital infrastructure as a foundation for sustainable industrial competitiveness [40,41,42,43]. The finding that infrastructure constraints disproportionately affect the tourism sector (DII = 0.67 vs. 0.74) highlights the need for sector-differentiated infrastructure investment strategies within the “Digital Uzbekistan-2030” framework, ensuring that the benefits of digital transformation are equitably distributed across economic sectors [44,45].

Finally, with respect to SDG 12 (Responsible Consumption and Production), remarketing inherently promotes resource efficiency in marketing expenditure by targeting consumers with demonstrated purchase intent rather than deploying undifferentiated mass advertising [46,47]. The lower CPA in the textile sector ($38.4 vs. $45.6) and higher ROAS in digitally advanced firms suggest that remarketing adoption reduces marketing waste, contributing to more sustainable resource utilization in enterprise operations.

5. Conclusions

This study empirically examined four hypotheses by comparatively evaluating the effectiveness of remarketing strategies in the tourism and hospitality and textile sectors of Uzbekistan under conditions of digital transformation, grounded in the RBV and the TAM.

H1 is supported: the textile sector is positively associated with a higher level of remarketing tool adoption (pixel tracking: 78.3% vs. 62.1%; χ² = 10.24, p = 0.001), and the Digital Infrastructure Index also exhibits a statistically significant difference (t(278) = −6.25, p < 0.001, d = 0.63). H2 is confirmed: the textile sector is associated with higher (d = 2.73) but more volatile ROI (82.1%; CV = 0.18), whereas the tourism sector demonstrates greater stability (CV = 0.11) but relatively lower returns (ROI = 48.3%). H3 receives strong empirical support: the AI Personalization Index is positively associated with ROI (β = 0.28, p < 0.001) and is also associated with an indirect pathway mediated through revenue variability (indirect effect = 5.04, 95% CI [4.10, 6.00]), with the indirect association significantly stronger in the textile sector (Δ = 1.64, p < 0.05). H4 is confirmed: the level of remarketing adoption is positively correlated with revenue stability (R² = 0.48, F(4, 275) = 63.7, p < 0.001), with DII emerging as the strongest correlate (β = 0.38, p < 0.001). All findings should be interpreted as conditional associations rather than causal effects due to the cross-sectional research design.

Future research should prioritize causal identification using difference-in-differences or panel data designs, address endogeneity through instrumental variables, employ multi-source data collection combining surveys with API-derived behavioral data, and replicate findings across Central Asian and CIS economies to strengthen external validity.

These findings contribute to the broader sustainability literature by demonstrating that digital marketing transformation—when strategically implemented with sector-specific considerations—can serve as a mechanism for achieving sustainable competitive advantages in transition economies, advancing the goals articulated in SDG 8, SDG 9, and SDG 12.