From Big Data to Cultural Intelligence: An AI-Powered Framework and Machine Learning Validation for Global Marketing

Abstract

This research addresses the ‘cultural blind spot’ in Big Data and AI, where algorithms treat global user-generated content monolithically, fostering biased marketing models. It proposes a dynamic ‘contextual value amplification’ framework, integrating Impression Management and Construal Level Theories. The study argues that service context—luxury versus budget—systematically reconfigures how cultural values are expressed in online customer reviews. A dual-method approach was applied to 284,746 negative hotel reviews. First, a high-dimensional fixed-effects model provided evidence for ‘cultural complaint signatures’ and revealed a novel mechanism: the luxury context amplifies individualists’ focus on relational Service but dampens their focus on transactional Value. Second, an XGBoost model offered computational validation. Including these theoretically derived features improved the model’s ability to classify a reviewer’s cultural orientation by over 220%. The study proposes a dynamic, context-contingent theory of cross-cultural expression, offers a methodological template fusing econometrics and machine learning to mitigate bias, and advances a conceptual framework for ‘Cultural Intelligence’.

1. Introduction

The integration of Big Data and Artificial Intelligence (AI) has instigated a paradigm shift in marketing, fundamentally altering how firms manage customer relationships [1] and extract insights from the digital “voice of the customer.” This revolution, fueled by vast quantities of user-generated content (UGC) from online customer reviews (OCRs), has reshaped the business landscape [2]. OCRs now serve as a primary information source for consumers, significantly influencing their decision-making and purchase intentions [3,4]. Recognizing this, firms invest heavily in analyzing this data [5], assuming it holds the key to optimizing global marketing. Electronic word-of-mouth (eWOM), encompassing OCRs, is recognized for its speed, reach, and influence, often exceeding traditional WOM [2,6,7].

However, as firms scale these data-driven strategies globally, they encounter a critical and largely unexamined flaw: the ‘cultural blind spot’ [8]. The dominant practice in marketing analytics often treats global Big Data as a culturally monolithic resource [9], ignoring the deep-seated cultural schemas that systematically shape how consumers perceive, evaluate, and articulate their experiences [10,11]. This oversight presents significant challenges, as algorithms trained on culturally skewed data risk learning a biased model of service quality, leading to flawed global strategies [12,13].

Addressing this requires moving beyond purely data-driven approaches to re-engage with foundational theories of cultural influence. A nascent but growing stream of research confirms that culture shapes UGC [14], influencing everything from review valence and emotionality to the specific product aspects discussed [15,16]. For instance, studies have found that Asian guests are more likely to complain about service failures, whereas non-Asian guests focus more on room issues [17]. Similarly, Chinese reviewers tend to focus on product functionality, while American reviewers prioritize usability [18]. Culture also moderates the relationship between eWOM and market share [19] affects how consumers complain after service failures [20,21].

Despite these insights, this body of work has key limitations. First, it often focuses on dyadic country comparisons, which limits generalizability [18]. Second, and more critically, it has predominantly documented the static main effects of culture [14,17], failing to provide a unifying framework that explains how and why context alters cultural expression [22]. A significant portion of this research relies on Hofstede’s [23] cultural dimensions, a framework that has faced justifiable criticism for its potential obsolescence and questionable generalizability [24,25]. This has prompted urgent calls for alternative theoretical frameworks that can better capture the nuances of digital consumer behavior [8,26]

This study addresses these gaps by proposing and validating a framework of contextual value amplification. I integrate three theoretical lenses—foundational cultural theory [27], Impression Management (IM) Theory [28,29], and Construal Level Theory (CLT [30])—to argue that the service context (e.g., budget vs. luxury) systematically reconfigures which culturally prescribed values become most salient. The act of writing a review is a public performance aimed at managing impressions, and CLT provides the mechanism explaining how psychological distance associated with the context alters the level of abstraction, thereby amplifying or dampening the expression of these values. This leads to my primary research questions:

• RQ1: How does a user’s national culture, through its influence on their impression management motivations, systematically predict the substantive topics they focus on in negative online reviews?
• RQ2: How is this relationship moderated by the service-market context (i.e., budget vs. luxury), potentially via context-induced shifts in construal level?

To answer these questions, this research employs a multi-stage, AI-powered design on a dataset of 284,746 negative hotel reviews. This approach first leverages advanced, semi-supervised AI (Seeded LDA) to quantify cultural expressions from raw text, then fuses high-dimensional fixed-effects (HDFE) econometric modeling for robust identification [31] with an XGBoost machine learning model for predictive validation [32]. This study offers three primary contributions. First, I advance a dynamic, context-contingent theory of cross-cultural digital expression, moving beyond the static explanations that have dominated the literature [33]. Second, I provide a methodological template for mitigating algorithmic bias [13] by integrating theory-driven modeling with predictive machine learning. Finally, my work presents a computationally supported framework that offers a pathway for firms to advance from culturally blind Big Data [34] to actionable ‘Cultural Intelligence’ [35], offering a sustainable competitive advantage [36].

2. Theoretical Framework and Hypotheses Development

An effective predictive model is not built on data alone; its explanatory power and validity are maximized when grounded in a robust theoretical framework [35]. The successful fusion of established management theory with data science is critical for moving from correlation to causation and from prediction to actionable insight [37]. Accordingly, this section develops the theoretical underpinnings of my predictive framework by integrating three powerful and complementary lenses: foundational cultural theory, IM Theory, and CLT (See Figure 1). First, I establish the link between cultural schemas and the impression management goals that motivate online expression. I then introduce CLT as the mechanism explaining how the service context systematically moderates this expression.

2.1. Cultural Schemas, Impression Management, and Online Consumer Expression

My framework begins with the premise that the content of UGC is a form of culturally scripted impression management. Culture, the “collective programming of the mind” [23], functions as a cognitive schema that automatically guides an individual’s attention, perception, and behavior [10,11]. These cultural values have a profound impact on consumer behavior, from expectations and satisfaction to complaining intentions [3,21,38]. In the digital sphere, these schemas do not disappear; they are materialized in the text of online reviews [14,25], shaping how consumers from different backgrounds express their satisfaction or dissatisfaction [17].

The act of writing a review is not merely a passive report of an experience; it is a public performance enacted for a vast, often anonymous audience [35,39]. Consumers act as social actors engaged in IM—the goal-directed process of controlling information to influence how others perceive them [28,29]. This strategic self-presentation is deeply intertwined with a desire to project a particular social identity [40,41]. The motivations for engaging in eWOM are complex, ranging from concern for other consumers and self-enhancement to seeking social benefits and venting negative feelings [42].

A robust body of research categorizes self-presentation tactics as either acquisitive (assertive), aimed at gaining social approval, or protective (defensive), aimed at avoiding social disapproval [43,44]. The crucial link, and the foundation of my framework, is that these IM strategies are powerfully scripted by culture. Cross-cultural psychology provides compelling evidence that cultural values systematically predict a preference for one style over the other. Individualistic cultures, which value uniqueness and self-expression, tend to foster acquisitive self-presentation styles aimed at showcasing individual competence and asserting one’s rights [45,46]. In contrast, cultures that prioritize group harmony and “face,” such as many collectivistic societies, encourage protective self-presentation to avoid disrupting social relations or appearing incompetent [47,48,49]. This is consistent with findings from the OCR literature that consumers from collectivist cultures are less likely to deviate from average ratings and are more hesitant to express extreme emotions [50,51,52].

While a burgeoning stream of research using UGC provides fragmented evidence of these culturally scripted IM strategies [17,18,53], it has often lacked a unifying theoretical framework, proceeding with ad hoc hypotheses [2]. I address this gap by proposing that the acquisitive-protective IM dichotomy serves as the core organizing mechanism linking abstract cultural values to concrete online expressions. This IM-based framework allows me to move beyond simply describing differences and toward explaining them as rational, goal-directed behaviors [35].

To provide a test of this framework, I adopt a focused theoretical approach, deliberately selecting the cultural dimensions and service attributes most relevant to this acquisitive-protective dichotomy. Specifically, I focus on Individualism (IND) as the primary driver of acquisitive, self-enhancing IM [54], and on Masculinity (MAS) and Long-Term Orientation (LTO) as key drivers of protective, pragmatic IM [47]. This choice is not arbitrary; these dimensions map cleanly onto the core pillars of a service evaluation. Complaining about relational Service and transactional Value serves the acquisitive goal of asserting one’s rights and discernment, consistent with the emphasis on the independent self-concept in individualistic cultures [10,21]. A service failure can be perceived as a deprivation of power, prompting a desire to complain to regain a sense of control [20,55]. This aligns with research showing that customers in individualistic cultures have higher expectations and complain more directly [56,57].

In contrast, complaining about functional Room Features and pragmatic Value serves the protective goal of demonstrating pragmatism and prudence. This aligns with the achievement-oriented logic of MAS, where functional failures impede success [58,59], and the investment-oriented logic of LTO, where value failures represent a poor use of resources [60]. This is consistent with findings that consumers from these cultures focus more on functionality and utility in their evaluations [61].

I deliberately exclude other dimensions like Power Distance (PD) and Uncertainty Avoidance (UA) from my main effects hypotheses on content, as prior work suggests they primarily influence the style or propensity of complaining rather than its substantive focus [21,62]. While these dimensions are controlled for in my model, my focus remains on the dimensions most directly linked to the content of impression management. This focused approach allows for a clean, robust test of my central theoretical proposition: the existence of distinct cultural complaint signatures driven by divergent impression management goals.

2.2. A Construal Level Theory Perspective

The influence of culture and its associated impression management goals is not monolithic; it is powerfully shaped by situational cues that can activate or reconfigure the expression of underlying values [21,63]. While international marketing has long acknowledged the importance of context [64], the specific psychological mechanisms through which marketing contexts moderate cultural expression in digital environments remain undertheorized [35]. I introduce CLT as the central mechanism of my framework to explain this dynamic process of contextual value amplification.

CLT posits that individuals can represent events at varying levels of abstraction, from concrete and detail-oriented (a low-level construal) to abstract and essential (a high-level construal) [30]. The specific level of construal is systematically driven by psychological distance. Events perceived as psychologically distant—in time, space, social circles, or likelihood—trigger high-level construals. This abstract mindset causes people to focus on the core, desirable features of an event and ask “why” it is important, evaluating its ultimate goals and values [65]. Conversely, psychologically proximal events trigger low-level construals, causing a focus on concrete, feasibility-related details and the mechanics of “how” an event will unfold [66].

I argue that budget and luxury service contexts systematically evoke different construal levels. A budget hotel stay is often a utilitarian transaction, making it psychologically proximal [67]. The consumer’s focus is typically on concrete, feasibility-related questions: ‘Is the bed clean? How do I get to the city center?’ This low-level construal prioritizes the functional and instrumental attributes of the service, such as cleanliness and basic room features, which are common sources of complaints for lower-class hotels [17,68].

In contrast, a luxury hotel stay is inherently psychologically distant. This distance is multifaceted. It is socially distant, associated with a high-status, aspirational reference group [69], and often temporally distant as a non-routine, special occasion [18]. More critically, it is hypothetically distant, deeply intertwined with a consumer’s ideal self-concept and personal values rather than their everyday, actual self [35,70]. The evaluation shifts from the “how” of execution to the “why” of the experience—Why am I having this experience, and what does it say about who I am?—reliably triggering a high-level construal [65]. This aligns with the concept of the “customer journey,” which encompasses a customer’s cognitive, emotional, and social responses throughout their interactions with a firm [35,71].

This shift to a high-level construal is not trivial; it fundamentally reorients the basis of judgment. As CLT research robustly demonstrates, an abstract mindset acts as a cognitive spotlight, illuminating an individual’s core values and making them the primary lens through which the experience is interpreted and judged [72,73]. Abstract principles that define the self (e.g., ‘I deserve respect,’ ‘I am a discerning person’) become paramount. Service failures in this context are no longer mere inconveniences but are perceived as significant threats to one’s self-concept and social standing [20,74]. Therefore, I predict that the psychologically distant luxury context will cause a consumer’s underlying cultural schemas and their associated impression management goals to “speak louder.” This cognitive shift will reconfigure how consumers articulate a service failure, leading them to prioritize complaints that align with their most central, abstract, and culturally prescribed values. This provides a mechanism to move beyond the static, main-effects findings of prior cross-cultural UGC studies [14,25] toward a more dynamic and interactionist perspective.

2.3. Hypotheses Development

My theoretical framework posits that cultural values shape the content of online complaints through the mediating mechanism of impression management goals, and that this relationship is systematically moderated by the service context, which induces different levels of psychological construal. I develop my specific hypotheses below.

• The Main Effects of Culture-Driven Impression Management

The relational and transactional schemas central to high-IND cultures foster an acquisitive impression management style aimed at asserting one’s status as a discerning and respected consumer [54]. The relational facet, rooted in the independent self-concept, demands dignity and respect, making a failure in Service a direct interpersonal affront that warrants a vocal complaint to assert one’s rights and regain a sense of personal power [10,20,55]. The transactional facet demands a fair exchange, making a failure in Value a violation of equity that must be called out to signal one’s astuteness and avoid being exploited [17,75]. This dual focus is strongly supported by a growing body of UGC research showing that consumers from individualistic cultures prioritize these aspects in their online reviews and complaining behaviors [17,18,21,38]. Therefore, I hypothesize:

Hypothesis 1a:

A reviewer’s Individualism is positively associated with a greater focus on Service and Value, but not on Room Features, in negative online reviews.

In sharp contrast, the instrumental schemas of high-LTO and high-MAS cultures foster a protective impression management style aimed at demonstrating practicality and prudence [47]. From this perspective, a service is a means to a functional end [35]. A failure in tangible Room Features is thus a critical impediment to performance, violating the achievement-oriented logic of MAS where the focus is on success and tangible rewards [58,59]. Similarly, a failure in Value is viewed as a “bad investment,” a misallocation of resources that runs counter to the pragmatic, future-oriented mindset of LTO [60]. Complaining about these functional and economic failures serves the protective IM goal of signaling competence and fiscal responsibility [76]. This linkage is consistent with findings that consumers from these cultures focus more on functionality and utility in their evaluations [17,61]. Thus, I propose:

Hypothesis 1b:

A reviewer’s Long-Term Orientation and Masculinity are positively associated with a greater focus on Value and Room Features, but not on Service, in negative online reviews.

• The Moderating Effect of Context-Induced Construal Level

My framework suggests that the luxury context does not merely amplify all cultural expressions uniformly. Instead, by inducing a high-level construal, it triggers a cognitive re-prioritization of evaluative criteria [35], an effect I argue is most pronounced for the abstract, self-centric schema of Individualism.

As established by CLT, the psychological distance of the luxury context makes core personal values more salient guides for judgment [73]. For a high-IND consumer, this transforms the meaning of a service failure. The abstract value of personal respect becomes the central grievance. A failure in Service is no longer a simple inconvenience but is interpreted as a significant, abstract threat to their self-concept and social standing [74,77]. The acquisitive IM goal thus shifts from demonstrating transactional savvy to affirming one’s high status, which in turn amplifies the complaint focus on the relational aspect of Service. This aligns with research indicating that high-class hotel guests complain more about service-related issues [17,78].

Hypothesis 2a:

The positive relationship between a reviewer’s Individualism and their focus on Service will be significantly stronger (amplified) in the luxury context than in the budget context.

Conversely, in this high-level construal state, the concrete, feasibility-related concern of Value becomes cognitively secondary. As CLT predicts, when consumers focus on abstract desirability rather than concrete feasibility [30], their attention shifts away from pragmatic considerations like price. Furthermore, complaining about price in a luxury context is antithetical to the newly salient IM goal of signaling high status [69]. This cognitive shift away from concrete details and the recalibration of IM goals should therefore dampen the focus on Value. This is consistent with findings that while guests of high-class hotels complain more about service, they are less focused on functional attributes like room quality or cleanliness compared to guests of low-class hotels [17].

Hypothesis 2b:

The positive relationship between a reviewer’s Individualism and their focus on Value will be significantly weaker (dampened) in the luxury context than in the budget context.

In contrast, I propose that the evaluative frameworks for LTO and MAS, while also containing abstract goals like ‘prudence’ or ‘achievement,’ are less susceptible to moderation by the luxury context due to their primarily instrumental nature. For these cultures, a service experience—even a luxury one—is often viewed as a means to an external end (e.g., a successful business trip, a prudent family investment). The evaluative focus thus remains on the functional and pragmatic aspects that facilitate that external goal, such as Room Features and Value [17,50]. This differs fundamentally from the IND schema, where values of self-concept and social standing are intrinsically tied to the experience itself [35]. For a high-IND consumer, the luxury context is not merely a service but a stage for identity affirmation. Therefore, the shift to a high-level construal in this context makes the IND schema, which is centered on the self, uniquely sensitive to abstract, identity-relevant threats like poor service. The instrumental schemas of LTO and MAS, however, are less likely to be re-prioritized by this specific identity-focused construal. Therefore, I do not predict a significant moderation effect for these cultural dimensions.

3. Research Methodology

To test my hypotheses and validate the framework, I adopt a two-stage analytical strategy that combines econometric modeling for robust identification with machine learning for predictive validation. This dual approach allows me to not only explain the underlying drivers of cultural expression but also to test the real-world predictive power of my theoretical framework, a form of methodological triangulation that enhances the robustness and practical relevance of the conclusions [79].

3.1. Data and Sample

My research required a large-scale, global dataset of authentic consumer opinions from a context characterized by high cultural variance and a reliance on eWOM. The international hotel industry, with TripAdvisor.com as its largest eWOM platform, provides an ideal setting for this inquiry [17]. The resulting dataset exhibits the core “5V” characteristics of Big Data: high Volume, Variety, and Velocity, from which my analysis is designed to extract Value while ensuring Veracity [80].

The data collection and sample construction involved several systematic steps. I began by crawling an initial dataset of over 10 million unique reviews from TripAdvisor. To isolate cultural effects from linguistic ones, I first filtered for reviews written in English, a standard approach as English functions as the de facto lingua franca of global platforms [3,17]. I explicitly acknowledge that this filter may introduce a selection bias towards cultures with higher English proficiency; I demonstrate this bias directly with a formal check in Appendix B. After removing observations with missing data for key variables (e.g., reviewer’s home country), I obtained a clean dataset of 3,965,825 reviews.

From this large dataset, I implemented a final theoretical sampling step. As my framework posits that cultural values are most saliently expressed during potent events like service failures, which can lead to significant dissatisfaction and complaining behaviors [20,21], I focused my analysis on the content of articulated dissatisfaction. I therefore filtered for negative experiences, defined as reviews with a star rating of one or two (out of five) [17]. This yielded my final analytical sample of 284,746 negative reviews for 21,644 unique hotels, providing substantial statistical power and enhancing the generalizability of my findings.

3.2. Measurement of Variables

Dependent Variables: Complaint Focus. To transform the unstructured review text into quantitative variables, I employed Seeded Latent Dirichlet Allocation (Seeded LDA), an advanced, AI-driven semi-supervised topic modeling approach [81]. Unlike purely unsupervised LDA, which can produce topics that are difficult to interpret or theoretically irrelevant [82], this method allows my analysis to be guided by pre-defined seed words derived directly from my theoretical framework [76]. This ensures the resulting topics align with my constructs of Service, Value, and Room Features, thereby increasing construct validity. This approach builds on prior research that has successfully used LDA and other text mining techniques to extract meaningful themes from online reviews [17,18]. The model outputs a per-document topic distribution, with my dependent variable being the proportion of each topic in a given review. A detailed description of this process and the seed word list is provided in Appendix A.

Independent and Moderating Variables. My primary independent variables are the three dimensions of national culture from Hofstede’s [58] framework that align with my theoretical model: IND, MAS, and LTO. The systematic protocol for sourcing and assigning these national cultural scores to the dataset is detailed in Appendix C. While I acknowledge the risk of the ecological fallacy in applying national-level scores to individual behaviors [83], this framework remains the most robust and widely used tool for large-scale, cross-country analysis where individual-level measures are not feasible [84]. Its continued relevance in eWOM and service research is well-documented [21]. My key moderating variable, Hotel Market Tier, was operationalized using the hotel’s star rating, a salient and widely used heuristic for service context and quality. I recognize this is a proxy and discuss its limitations in Section 5.3.

Control Variables: To isolate the effect of culture, I include a comprehensive set of control variables at both the review and country levels. At the review level, I control for structural text characteristics such as Word Count (WC) and Words Per Sentence (WPS). WC accounts for the fact that longer reviews may naturally cover more topics, ensuring my dependent variable captures topical focus rather than mere verbosity, while WPS accounts for variations in linguistic complexity [26]. I also include the review date to control for time-varying factors. Furthermore, to account for linguistic style, I incorporate features from the Linguistic Inquiry and Word Count (LIWC) dictionary, specifically controlling for Analytic thinking to disentangle cognitive style from complaint content, and for emotional Tone to isolate the effect of cultural values from the intensity of the language used [85]. Finally, at the country level, I include GDP per capita and all other Hofstede cultural dimensions to ensure my variables of interest are not capturing the confounding effects of economic development or other cultural traits [25].

3.3. Analytical Strategy: A Two-Stage Approach

3.3.1. Stage 1: Econometric Modeling for Robust Identification

A primary challenge in cross-cultural research using observational data is omitted variable bias. To overcome this, my primary specification is a high-dimensional fixed-effects (HDFE) model. The model is specified as:

Topic_d,c = β1Culture_c + X’_d,cβ2 + α_chain + α_traveler + ε_d,c

Here, Topic_d,c is the proportion of a given topic in review d from a reviewer from country c. Culture_c is the vector of cultural dimension scores for country c, and X’_d,c is a vector of all control variables.

The key terms are α_chain and α_traveler, which represent full sets of hotel brand chain fixed effects and traveler type fixed effects, respectively. By including dummy variables for each hotel chain (e.g., ‘Marriott’, ‘Hilton’) and traveler type (e.g., ‘Family’, ‘Business’) associated with the review, this specification absorbs all time-invariant characteristics common to those groups. α_chain absorbs chain-specific factors, whether observed (e.g., brand standards, global marketing, quality positioning) or unobserved (e.g., underlying service philosophy), while α_traveler absorbs factors common to that travel purpose (e.g., shared expectations, price sensitivity).

This powerful identification strategy means that the coefficient of interest, β1, is estimated by comparing reviews from different cultures within the same hotel chain and from the same traveler type, effectively differencing out a vast array of confounding factors at these levels [31].

3.3.2. Stage 2: Machine Learning for Predictive Validation

To validate my framework’s practical utility, I built an XGBoost machine learning model, a state-of-the-art gradient boosting algorithm known for its high performance and efficiency with large, complex datasets [32]. The primary goal of this stage was not to build a perfect classifier, but to use a classification task as a robust method for computational validation, specifically testing whether the ‘cultural complaint signatures’ identified in my econometric analysis possess real-world predictive power [17,76].

The dependent variable for this binary classification task was a reviewer’s cultural orientation, specifically high-Individualism versus low-Individualism. I focused on IND as it is a central construct in my theory. To create this categorical target variable, I converted the continuous IND score using the sample median (89.00) as a threshold.

To isolate the predictive value of my theoretical constructs, I designed a comparison of three models based on their features. The Baseline Model used only stylistic and structural variables (e.g., Word Count, LIWC features), establishing a performance benchmark based on how a review is written. The Full Model then added my theoretically derived topic proportions (Service, Value, Room Features) to the baseline features. The central test of my framework lies in the performance lift from the Baseline to the Full Model; a significant improvement would confirm that the substantive content (what is written), as conceptualized by my theory, adds meaningful predictive power. Finally, the Optimized Model used the same features as the Full Model but applied a class weight to address the data imbalance. Given this imbalance, I evaluate the models not only on Accuracy but also on more robust metrics like Cohen’s Kappa and Specificity (the true negative rate), which are better suited for assessing classification skill on imbalanced datasets [86].

4. Results

This section presents the findings from my two-stage empirical analysis. I first report the results from the econometric models testing my hypotheses about the main and moderated effects of culture on complaint focus. I then present the results of the machine learning validation, which assesses the real-world predictive power of my theoretical framework.

4.1. Econometric Modeling Results

Table 1. Descriptive Statistics.

Variable	N	Mean	Std. Dev.	Min	Median	Max
Cultural Variables
Power Distance (PDI)	284,746	46.27	17.75	11.00	39.00	100.00
Individualism (IND)	284,746	72.64	24.45	11.00	89.00	91.00
Masculinity (MAS)	284,746	57.81	13.67	5.00	62.00	100.00
Uncertainty Avoidance (UAI)	284,746	47.81	18.56	8.00	46.00	100.00
Long-Term Orientation (LTO)	284,746	44.58	18.15	4.00	51.00	100.00
Indulgence (IVR)	284,746	59.73	15.72	4.00	68.00	100.00
Control Variables
Word Count (WC)	284,746	179.92	142.02	5.00	138.00	1005.00
Words Per Sentence (WPS)	284,746	19.68	16.92	1.80	16.50	822.00
Hotel Star Rating	284,746	3.51	1.30	0.00	4.00	5.00
Country GDP p.c.	284,746	33,652.27	18,589.40	1095.04	36,085.84	94,277.96
Dependent Variables
Value	284,746	0.30	0.23	0.001	0.27	0.96
Service	284,746	0.06	0.08	0.001	0.03	0.87
Room Features	284,746	0.06	0.09	0.001	0.03	0.88

presents the descriptive statistics for the key variables. The data shows substantial variance in both cultural values and review characteristics, rendering it suitable for investigating my research questions. As noted and detailed in Appendix B, the sample-weighted mean for Individualism is 72.64, significantly higher than the global population average. This confirms a notable selection bias, often observed in studies using English-language global platforms, toward more individualistic cultures, underscoring the importance of my analytical approach that controls for baseline differences. Regarding my dependent variables, complaints pertaining to Value (mean = 0.30) represent the most frequent component of articulated dissatisfaction. Complaints focusing on relational Service (mean = 0.06) and functional Room Features (mean = 0.06) are less common but still constitute a significant portion of consumer grievances, aligning with prior findings.

Table 2. Main Effects of National Culture on Complaint Focus (HDFE Model).

	(1) Value	(2) Service	(3) Room_Features
Cultural Variables
IND	0.00017 *	0.00006 **	0.00002
	(0.00008)	(0.00002)	(0.00003)
MAS	−0.00006	−0.00006 ***	0.00009 ***
	(0.00007)	(0.00002)	(0.00003)
LTO	0.00018 ***	−0.00008 ***	0.00014 ***
	(0.00004)	(0.00001)	(0.00002)
Controls	YES	YES	YES
Fixed Effects	YES	YES	YES
Observations	284,746	284,746	284,746
R2 (within)	0.0370	0.0206	0.0155

Notes: Standard errors in parentheses. Significance levels: *** p < 0.001, ** p < 0.01, * p < 0.05.

summarizes the results from the HDFE model testing my main effects hypotheses (H1a and H1b). The findings offer strong support for my theory of culturally scripted impression management. In full support of H1a, Individualism is positively and significantly associated with a focus on both Service (β = 0.00006, p < 0.01) and Value (β = 0.00017, p < 0.05). This aligns with my argument that individualistic consumers employ an acquisitive IM strategy, emphasizing relational respect (Service) and transactional fairness (Value) to assert their rights.

Hypothesis 1b, which predicted that Long-Term Orientation and Masculinity would be positively associated with a focus on Value and Room Features driven by protective IM goals, received partial support. As predicted, both MAS (β = 0.00009, p < 0.001) and LTO (β = 0.00014, p < 0.001) were positively and significantly associated with a focus on Room Features, consistent with the emphasis on functionality in these cultural orientations. LTO also showed the expected positive association with Value (β = 0.00018, p < 0.001), reflecting a pragmatic concern for resource utilization. However, contrary to my hypothesis, MAS was not significantly associated with Value complaints. More notably, unexpected and significant negative relationships emerged for both MAS (β = −0.00006, p < 0.001) and LTO (β = −0.00008, p < 0.001) in relation to Service complaints. This suggests that consumers from cultures high in these dimensions not only prioritize functional aspects but may also be significantly less inclined to articulate complaints about relational service failures.

While the estimated coefficients are small in absolute magnitude, they represent statistically significant shifts in expressive focus across a massive dataset, estimated with high precision.

Table 3. Moderating Effect of Hotel Market Tier on Complaint Focus (HDFE Model).

	(1) Value	(2) Service	(3) Room_Features
Cultural Main Effects
IND	0.00019 **	0.00006 **	0.00002
	(0.00007)	(0.00002)	(0.00003)
MAS	−0.00007	−0.00006 ***	0.00009 ***
	(0.00007)	(0.00002)	(0.00003)
LTO	0.00017 ***	−0.00008 ***	0.00014 ***
	(0.00004)	(0.00001)	(0.00002)
Interaction Terms
IND × hotel_star_rating	−0.00006 *	0.00003 **	0.00002
	(0.00003)	(0.00001)	(0.00001)
MAS × hotel_star_rating	−0.00005	0.00000	−0.00002
	(0.00004)	(0.00001)	(0.00002)
LTO × hotel_star_rating	0.00003	−0.00001	0.00002
	(0.00003)	(0.00001)	(0.00001)
Controls	YES	YES	YES
Fixed Effects	YES	YES	YES
Observations	284,746	284,746	284,746
R2 (within)	0.0371	0.0208	0.0155

Notes: Standard errors in parentheses. Significance levels: *** p < 0.001, ** p < 0.01, * p < 0.05.

presents the results of the interaction model testing my moderation hypotheses (H2a and H2b). I find strong support for my proposed contextual re-prioritization mechanism. In support of H2a, the interaction term between IND and hotel star rating is positive and significant in predicting a focus on Service (β = 0.00003, p < 0.01). This indicates that the effect of Individualism on relational complaints is significantly amplified in the luxury context, consistent with CLT’s prediction that psychological distance heightens the salience of abstract, core values like personal respect.

In support of H2b, the interaction effect between IND and hotel star rating is negative and significant for Value complaints (β = −0.00006, p < 0.05). This demonstrates that the individualistic consumer’s focus on transactional value is significantly dampened in the luxury context. This aligns with CLT’s prediction that abstract construals shift focus away from concrete feasibility concerns (like price) towards desirability, and is also consistent with the IM goal of signaling high status in luxury settings.

Consistent with my theory, the effects of LTO and MAS on complaint focus were not significantly moderated by the hotel star rating. These findings collectively support the contextual value amplification framework, suggesting that the service environment dynamically shapes how cultural values are expressed, particularly for abstract, self-relevant cultural schemas like Individualism.

4.2. Machine Learning Validation Results

Having established the systematic relationship between culture, context, and complaint content, I next sought to validate the practical predictive utility of my framework using machine learning. I tested whether the textual features derived from my theory (i.e., the topic proportions for Service, Value, and Room Features) could significantly improve the prediction of a reviewer’s cultural orientation. The purpose of this analysis was not to build a production-level classifier, but rather to perform a computational validation of the theory itself—testing if the identified ‘cultural signatures’ contain a meaningful predictive signal beyond mere stylistic variation.

Table 4. Predictive Model Performance Comparison.

Model	Accuracy	Kappa	Specificity
1. Baseline (Style Only)	0.7547	0.0600	0.0425
2. Full Model (+Topics)	0.7566	0.1014	0.0858
3. Optimized (Weighted)	0.6399	0.1925	0.5578

and Figure 2 present the performance comparison of the three XGBoost models.

The Baseline Model, utilizing only stylistic and structural features (e.g., Word Count, LIWC Tone), achieved a high accuracy of 0.7547. However, its very low Cohen’s Kappa (0.0600) and Specificity (0.0425) reveal this performance was largely illusory. The model predominantly predicted the majority class, failing to identify the minority class—a common pitfall in models trained on imbalanced data [17].

The Full Model, which incorporated my theoretically derived topic proportions alongside the baseline stylistic features, demonstrated a marked improvement in true classification skill. While accuracy remained similar (0.7566), Kappa increased substantially to 0.1014 (a 69% improvement), and Specificity more than doubled to 0.0858. This indicates that the substantive content of the review, as conceptualized by my framework, contains significant predictive power regarding the reviewer’s cultural background, beyond simple stylistic cues.

Finally, the Optimized Model, which applied a class weight to the Full Model’s features to counteract the data imbalance, reveals the framework’s true classification potential. As expected, accuracy decreased to 0.6399; this reflects a necessary trade-off, moving away from simply guessing the majority class towards genuinely distinguishing between the classes. In exchange, the model’s ability to correctly identify the target minority class (high-IND reviewers) improved dramatically. Specificity surged to 0.5578, and Kappa, the measure of classification skill beyond chance agreement, increased significantly to 0.1925—more than tripling (a 220% increase) the Kappa score of the Baseline Model.

This final result provides strong computational validation for the practical relevance of my theoretical framework. While the absolute predictive performance of the Optimized Model (Kappa = 0.1925) indicates only ‘slight agreement’ and remains modest for reliable individual-level classification [86], the key insight lies in the significant relative improvement achieved by incorporating the theory-derived topic features. The tripling of the Kappa score demonstrates that the ‘cultural complaint signatures’ identified in my econometric analysis possess tangible predictive value. This confirms that my framework captures a robust cultural signal within the review text, sufficient to enhance classification skill well beyond chance and stylistic markers, thereby validating the utility of integrating cultural theory into predictive analytics.

5. Discussion

This research confronted the critical ‘cultural blind spot’ in modern marketing analytics, a domain where the scale of Big Data has often obscured the nuance of human culture [8,35]. My two-stage analysis provided a robust empirical answer to how culture manifests in the digital sphere and how this expression is dynamically shaped by context. My econometric models did not simply confirm that culture matters; they identified specific “cultural complaint signatures,” such as the link between Individualism and relational Service failures [17,18], and uncovered a novel mechanism of contextual re-prioritization, demonstrating that the influence of culture is not static but elastic, systematically reconfigured by the service environment [22]. The subsequent machine learning models provided strong computational validation, confirming that these theoretical signatures possess significant and actionable predictive power [17]. This chapter now turns to a deeper discussion of the theoretical contributions, strategic implications, and future directions stemming from these findings.

5.1. Theoretical Contributions

This study makes three primary and interlocking theoretical contributions that collectively advance the fields of international marketing, cross-cultural consumer behavior, and information systems.

First, I advance a dynamic, integrated framework for cross-cultural digital expression, moving beyond the static, main-effects explanations that have characterized prior research. The existing literature, while valuable, has largely documented the main effects of culture on UGC, often presenting culture as a stable, monolithic force [14,17,25]. My research provides a critical evolution by proposing and validating a micro-level psychological mechanism that explains the situational contingency of cultural expression. By synthesizing cultural schemas [10], Impression Management Theory [40], and Construal Level Theory [30], I answer the call for a more unifying, motivation-based framework [2]. It reveals an “elasticity” in cultural expression, whereby the luxury context systematically amplifies abstract, identity-relevant grievances while dampening concrete, functional ones for individualistic consumers. This finding contributes a more sophisticated, interactionist perspective to the literature [20] and directly responds to calls for alternative theoretical lenses to explain complex digital behaviors [8,26].

Second, I offer a normative framework for mitigating algorithmic bias by fusing theory-driven econometric modeling with predictive machine learning. The uncritical application of AI to global UGC risks creating a new generation of culturally biased technologies [12,13]. My two-stage methodology provides a direct corrective. By first using a high-dimensional fixed-effects model to isolate the systematic effect of culture from a vast array of confounders [31], I generate a cleaner, theory-consistent signal. This signal then serves as a high-quality input for the XGBoost model, enabling more accurate and less biased predictions. This approach demonstrates a pathway to escape the “garbage-in, garbage-out” problem that plagues atheoretical machine learning and responds to urgent calls for more responsible and transparent AI in marketing [3]. It serves as a real-world example of the powerful synergy between management theory and data science that scholars have advocated for [37], moving beyond using machine learning simply for prediction to using it for theory validation.

Finally, my work contributes to the discussion of ‘Cultural Intelligence’ as a data-driven organizational capability. Drawing on the resource-based view (RBV) of the firm, I propose that the ability to systematically decode cultural nuance from unstructured data can be framed as a strategic asset that is valuable, rare, and difficult to imitate [87]. This capability, which involves integrating complex theories with advanced analytics, possesses qualities of causal ambiguity and social complexity, making it a potential source of sustainable competitive advantage [35]. My research thus complements the common focus on Big Data as a resource, emphasizing instead that advantage is derived from the firm’s distinctive capability to transform that data into actionable intelligence [36]. A particularly noteworthy finding is the unexpected negative relationship between MAS and a focus on Service complaints. This suggests that consumers from high-MAS cultures are not merely indifferent to relational aspects but may be systematically less likely to articulate them. From an IM perspective, articulating complaints about interpersonal service might conflict with the goal of projecting a competent, task-oriented self-image [28], a stark contrast to the response in cultures where relational harmony or individual respect is paramount [21].

5.2. Managerial Implications

The strategic imperative for global firms is to transition from reactive damage control to proactive, culturally resonant customer experience design [71]. My findings provide a clear roadmap for this transition.

First, firms must build a ‘Cultural Analytics Dashboard’ as a core Decision Support System. Managers should move beyond simplistic sentiment analysis to track the prevalence of specific cultural complaint signatures across markets and service tiers [76]. For instance, a hotel chain could monitor whether complaints about Room Features from high-MAS markets are increasing, signaling a potential gap in functional service delivery that machine learning models can predict and flag [17]. This data-driven approach allows for the precise allocation of resources to the service attributes that matter most to specific cultural segments, turning analytics into a direct input for strategic decision-making [25].

Second, managers must design culturally intelligent and context-specific service recovery protocols. My moderation findings provide a direct playbook. A high-IND guest complaining about poor Service at a luxury property is expressing a threat to their self-concept; the optimal recovery is an abstract, status-affirming response, such as a sincere personal apology from a senior manager that restores their sense of power and respect [20]. Conversely, a high-LTO guest complaining about Value at a budget property is expressing a pragmatic concern about a ‘failed investment’; the optimal recovery is a concrete, transactional solution, such as a swift partial refund [21]. A one-size-fits-all recovery strategy is a missed opportunity to mitigate the powerful impact of negative eWOM.

Third, firms should architect a ‘Cultural Personalization Engine’ to power next-generation AI-driven CRM. The insights from my model can be embedded into automated systems to create culturally aware experiences [35]. This engine could power ‘culturally aware chatbots’ that adjust their communication style based on inferred cultural orientation [88], inform the content of pre-arrival emails to highlight culturally relevant benefits, or even dynamically alter website imagery to align with cultural preferences [89]. The successful deployment of such technologies requires a data-driven, culturally empathetic corporate culture, supported by continuous employee training and clear strategic commitment from leadership. This can transform OCRs from a reactive management challenge into a proactive tool for enhancing customer purchase intention and loyalty [90].

5.3. Limitations and Future Research

While this study offers a novel framework, its findings must be considered in light of several limitations, which collectively provide a clear agenda for future research.

A primary conceptual limitation is the study’s reliance on a macro-level cultural framework. The application of national-level Hofstede scores to individual-level UGC introduces the risk of the ecological fallacy [83], assuming a level of intra-country cultural homogeneity that may not exist [25]. Furthermore, the operationalization of service context via hotel star ratings is a useful but imperfect proxy for the theoretical mechanism of psychological distance, and the core mediating construct of Impression Management was inferred rather than directly measured.

Methodologically, the study’s scope is bounded by its data sample. The exclusive focus on English-language reviews, while necessary to isolate cultural from linguistic effects, results in a significant selection bias towards high-individualism cultures [24]. This constrains the generalizability of the findings. The analysis is also confined to negative reviews within the hotel industry. Cultural scripts may differ for praise, and the salience of different service attributes likely varies across industries (e.g., search, experience, and credence goods) [22].

In interpreting the results, two caveats are warranted. First, while the HDFE model provides a powerful strategy for robust identification, claims of causality from observational data must be made with caution. Second, the predictive performance of the machine learning model, while demonstrating the theoretical validity of my framework, is modest in its current form. Its primary contribution is as a computational validation of the theory’s relevance rather than as a market-ready tool for reliable individual-level classification [86].

These limitations inform a forward-looking research agenda. First, future research must move toward greater conceptual and causal granularity by incorporating individual-level measures of cultural values (e.g., CVSCALE) to mitigate the ecological fallacy. Experimental designs that directly manipulate psychological distance and IM goals are also needed to provide a more definitive causal test of the proposed mechanisms. Second, the data ecosystem should be expanded. Applying multilingual Natural Language Processing (NLP) to analyze UGC in native languages would capture a more authentic global consumer voice [22]. Future work should also examine different complaint behaviors across hotel classes and product categories. Finally, the application of this technology opens an important ethical frontier. As firms gain the ability to personalize based on culture, critical questions arise regarding the potential for reinforcing stereotypes and the governance structures needed to prevent algorithmic discrimination [12,13]. Navigating this ethical landscape will be as important as refining the technology itself.