Digital Content Marketing, Consumer Trust, and Digital Engagement on Social Commerce Platforms: Effects on Brand Attitude in Iraq’s Hospitality Sector

Abstract

Digital Content Marketing (DCM) has become a key tool for brands to influence Brand Attitude on social commerce platforms, but its impact on consumer perception remains poorly understood in the emerging market context of the hospitality industry. Based on the theory of Uses and Gratifications (U&G), this study aims to explore the direct and indirect impact of DCM on Brand Attitude, with Consumer Trust and Digital Engagement playing mediating roles among social commerce consumers in Iraq. A quantitative cross-sectional design was employed, with 386 respondents obtained using the social media distribution of a self-administered online questionnaire. Partial Least Squares Structural Equation Modelling (PLS-SEM) was used in Smart PLS 4.0 to analyse the proposed model. All eight hypotheses were supported. Consumer Trust (β = 0.487) and Digital Engagement (β = 0.361) were both positively influenced by DCM, and both mediators had significant positive effects on Brand Attitude (β = 0.394 and β = 0.286, respectively). The total indirect effect of DCM on Brand Attitude (β = 0.351) was significantly larger than the direct effect (β = 0.187), supporting the strength of mediated effects. Serial mediation through Trust and then Engagement was also confirmed. These findings extend the application of U&G theory within an Iraqi social commerce context and offer evidence-based implications for hospitality brands designing content strategies on digital platforms. The findings are only applicable within the context of Iraq and the non-probability sample studied.

1. Introduction

In the last 10 years, the way brands communicate with customers has changed significantly. Social media platforms are no longer regarded as merely a secondary means of communication, but rather as a focal point for consumer–brand interaction and online purchasing. This change has helped to grow the social commerce field, which typically involves the application of social media technologies in the online purchasing process. Marketing has been transformed into numerous sectors with the expansion of digital platforms and consumers’ reliance on social media applications.

The digital marketing and social commerce activities are also becoming increasingly significant in the economy, as shown by the following market indicators: The digital marketing market is projected to grow from USD 653.65 billion in 2025 to USD 806.14 billion by 2035, expanding at a compound annual growth rate of 9.20% from 2025 to 2035 [1]. Likewise, according to a report by Business Insider, social commerce sales will reach USD 1.2 trillion across the globe by 2025, fueled by younger generations who are largely using social media apps like Instagram, TikTok, and Facebook for social commerce. These trends indicate that social media platforms are now playing an increasingly significant role in consumer information search, brand assessment, and shopping decisions.

A DCM strategy is a growing method that companies are using to build relationships with consumers in this rapidly changing digital landscape. DCM involves the creation and dissemination of relevant, informative, and brand-related content designed to capture consumer attention, improve engagement, and build trust-based relationships, rather than relying solely on traditional promotional messages [2,3]. Growing consumer preference for interactive, authentic online experiences has prompted organizations to shift to content-based communication that facilitates deeper engagement with the consumer and more enduring brand relationships. In this regard, content marketing is not merely about promotion; rather, it is becoming a means of generating value and enhancing consumer experience across digital channels.

Consumer Trust and Digital Engagement are widely considered two of the most critical factors influencing the effectiveness of DCM. Consumer Trust is the willingness to trust a brand due to positive expectations about its credibility, integrity and overall conduct [4]. In digital environments with high levels of information availability and rising consumer skepticism, trust has become one of the most important factors influencing online purchase behavior and future consumer relationships. A recent report by Edelman, for example, the Edelman 2025 Trust Barometer, showed that brand trust is now one of the most important drivers of consumer decision-making, in addition to price and product quality [5].

Likewise, Digital Engagement is a crucial aspect in enhancing the effectiveness of digital marketing initiatives. The concept of Digital Engagement usually refers to the mental, emotional and behavioral relationship that the consumer builds with brand-related content on digital platforms [6]. More involved engagement can lead to stronger brand attachments, greater interactive involvement and more positive brand perceptions. Previous research has also indicated that consumers who are engaged are more likely to have positive brand attitudes and show higher brand loyalty intentions [7].

Although the relationships between DCM, trust, engagement, and brand attitude appear conceptually important, the underlying mechanisms connecting these variables remain insufficiently examined, particularly within social commerce environments [8,9]. Previous studies have tended to focus on direct relationships between digital marketing activities and consumer responses rather than on the mediating processes by which digital content influences evaluations of the brand. This gap is even more apparent in new markets such as digital markets where social commerce is developing more rapidly and consumer social interaction can vary from what is seen in more mature markets.

One of the key concepts of consumer behavior studies is the brand attitude of the consumers, commonly defined as an evaluative perception and disposition toward a specific brand [10]. This construct plays an important role because of its relationship with several behavioral outcomes such as purchase intention, positive word-of-mouth communication, and loyalty customers in the long term [11]. In digital environments, especially on social commerce sites, consumers are constantly seeing brand-related information and have a growing opportunity to engage with, rate, repost, and publicly comment on brand communications. Thus, the process of brand attitude formation is more dynamic and complex than that in traditional advertising, which was based primarily on one-way communication channels [12].

Many researchers have used source credibility, perceived product quality and advertising effectiveness as the variables that relate to Brand Attitude [13]. However, relatively limited attention has been directed toward understanding how the various dimensions of DCM, including informativeness, entertainment, relevance, credibility, and interactivity, collectively contribute to shaping consumer attitudes toward brands [3,14]. The sequential mediating mechanisms linking DCM and Brand Attitude through Consumer Trust and Digital Engagement have not been fully explored in the context of social commerce. Understanding of these relationships can help to gain further insights into consumer processing of digital content and evaluative responses to brands in increasingly interactive online environments.

The present study is based on the U&G Theory, which was developed by Elihu Katz, Jay Blumler, and Michael Gurevitch [15]. In the area of digital media studies, online communication studies, and electronic commerce studies, U&G has been extensively used to account for how individuals actively select and use the media to satisfy their personal needs and motivations [16]. In contrast to traditional communication theories, which tend to focus on audiences as passive receivers of information, U&G assumes that consumers are active participants who actively consume media content to obtain gratifications in cognition, emotion, social interaction, and entertainment.

In the field of DCM, this theoretical approach implies that consumers do not simply browse through brand-based content in a random and passive way. Rather, they engage with digital content to obtain various forms of value and satisfaction. For instance, consumers can find functional gratification in accessing information about tourism and hospitality services that are useful, hedonic gratification in content that is visually appealing and entertaining, and social gratification in content they share with others that reflects their preferences or facilitates social interaction with others [3,17]. Consequently, when digital content succeeds in meeting these consumer needs, it is more likely to strengthen positive perceptions toward the brand, enhance consumer trust, and encourage sustained digital engagement.

In a theoretical sense, the U&G is a relevant framework to explain the sequential relationships suggested by the present study. In particular, the theory assumes that high-quality DCM can impact Brand Attitude indirectly through Consumer Trust and Digital Engagement [3,18]. Previous studies applied U&G to issues like social media usage and digital advertising effectiveness, but no research has investigated DCM as a multi-dimensional independent variable operating through sequential mediating variables such as trust and engagement as they impact Brand Attitude. Hence, the current research strives to further explore the applicability of U&G in social commerce contexts, with special focus on the context of emerging digital markets.

Another gap in the literature that is especially important is the geographic focus of previous research. However, the main focus of the majority of empirical studies addressing DCM, social commerce and Brand Attitude has concentrated on Western developed countries or technologically advanced Asian markets like China and Republic of Korea [8,9,19]. However, this focus means that the generalizability of the findings of this study is limited, because cultural norms, economic conditions, technological infrastructure and digital adoption differ greatly between countries and regions, which can influence consumer reactions to activities of digital marketing [20]. Therefore, studying these relationships in emerging markets can help gain a better understanding of consumer interaction with digital content across varying environments.

The context of Iraq is especially relevant and under-researched to examine these issues. Internet and social media usage in Iraq has expanded considerably in recent years, as shown in recent statistics. According to the data above, as of January 2025, Iraq had about 38.0 million internet users, corresponding to an internet penetration rate of 81.7%, and nearly 34.3 million active social media users, which is equivalent to 73.8% of the total population [21]. Moreover, the Iraqi digital commerce market is expected to grow to around USD 10.23 billion by 2024 and will grow at a compound annual rate of 9.34% through 2028 [22]. In this dynamic digital landscape, the travel and hospitality industry has increasingly turned to social commerce platforms as a key avenue for communicating with customers, promoting services, and engaging with brands [23]. Despite these developments, little empirical research has been conducted to examine the mediating effect of Consumer Trust and Digital Engagement between DCM and Brand Attitude among social commerce consumers in Iraq.

Although much of the existing literature highlights the positive role of DCM in improving consumer responses, some scholars have raised concerns regarding the potential negative consequences of excessive or intrusive digital content. According to previous research, there is a potential for consumer fatigue, a lower engagement rate, and lowered trust towards brands when the volume of promotional content is high [19,24]. Furthermore, there is still disagreement about the causal relationship between Consumer Trust and Digital Engagement. While some studies argue that trust functions as a precursor to engagement behaviors [9], others suggest that repeated engagement with digital content may itself contribute to the development of trust [25]. The present study addresses this debate by examining both relationships simultaneously through a sequential mediation framework. Furthermore, the applicability of U&G Theory within emerging-market contexts has also been questioned [20]. Thus, the testing of the assumptions of U&G in the context of the Iraqi social commerce environment can provide valuable theoretical and empirical insights to the existing literature.

2. Literature Review and Development of Hypotheses

2.1. Digital Content Marketing (DCM) and Consumer Behavior

DCM is typically defined as the process of creating content that is relevant, valuable, brand-related, and distributed through digital channels to improve the relationship with consumers, increase their engagement, and ultimately create trust through them, not just directly by using persuasive selling techniques [3]. Unlike traditional marketing methods that target short-term sales and benefits, the marketing of DCM aims to deliver value to the consumer by communicating information, entertainment and social engagement that builds positive associations with the brand and consumer-brand relationships over time [3,9].

In the context of social commerce, DCM can take many digital forms such as short videos, visual posts, live-streaming, interactive stories, and user-generated reviews. Such content formats vary by various significant properties including informativeness, entertainment value, credibility, relevance and interactivity [26,27]. Previous research has shown that such content attributes can have a significant impact on a range of consumer outcomes such as purchase intention, brand loyalty, consumer participation and positive word-of-mouth communication [9,11,26]. Digital content that is useful, authentic, engaging, and relevant to the consumer’s personal interests and online experiences is more likely to be well received.

Although various research studies have been conducted on DCM, the collective effects of these content characteristics on Brand Attitude have not yet been thoroughly examined, especially in the case of social commerce in emerging markets [3,8]. Specifically, few studies have explored the role of the dimensions of DCM in the development of Consumer Trust and Digital Engagement, and the role of these variables as intervening mediators in the development of consumers’ overall evaluation of the brands. This gap can be addressed to gain a more complete understanding of the psychological and behavioral mechanisms involved in consumer reactions to digital content in an interactive online context.

2.2. Uses and Gratifications Theory as a Theoretical Foundation

U&G Theory [15] provides the primary theoretical foundation for the present study. The theory was designed to understand why an individual chooses specific types of media and how they use media content to meet personally felt needs and motivations. Unlike conventional communication approaches that assume consumers are passive receivers of information, U&G holds that consumers are goal-directed and active participants who intentionally consume media to meet their cognitive, emotional, and social needs [15,17]. These needs may involve information, entertainment, emotional stimulation, and social interaction and expression via media usage.

In the context of DCM, U&G suggests that consumers interact with brand-related content on social commerce platforms in pursuit of multiple forms of gratification. Functional gratifications can include acquiring information about products, services, or traveling experiences, and hedonic gratifications can include enjoyment and entertainment, and emotionally stimulating content experiences. Social-interactive gratifications arise when consumers interact with content that is congruent with their identity, invites social involvement, or fosters relationships with other users within digital communities [3,16]. Thus, consumers are more likely to have positive reactions towards digital content that meets these various types of gratification.

Previous studies have tried to adapt U&G to digital marketing and social media contexts. For instance, Linda Hollebeek and Keith Macky [3] incorporated U&G into a DCM system, suggesting that the gratification-seeking behavior of consumers stimulates interaction with brand-related digital content and thus contributes to Consumer Trust and Digital Engagement. From this viewpoint, Trust and Engagement are manifested as psychological and behavioral responses immediately after a digital content interaction, whereas Brand Attitude is manifested as an evaluative response that is a result of these factors.

The present study adopts this theory as a basis to hypothesize that DCM can indirectly shape Brand Attitude through the satisfaction of consumer informational, emotional needs, and social needs, which will result in an increase in consumer trust towards the brand and increased digital engagement. Thus, the sequential relationship, in which DCM satisfies gratification needs, stimulates Consumer Trust, Digital Engagement, and finally improves Brand Attitude, has been used as the conceptual basis for the hypotheses developed in this study.

2.3. DCM and Consumer Trust

Consumer Trust, which is defined as a consumer’s willingness to rely on a brand based on positive expectations of its integrity, credibility, and behavior [4], is considered one of the most important links between marketing activities and consumer behavioral outcomes in digital environments [4,8,28]. Trust in social commerce platforms is a cognitive and emotional factor, influencing the perception and engagement of brands. In many online contexts, consumers are exposed to large volumes of digital content and often face uncertainty regarding the quality, reliability, and authenticity of online information. Thus, trust is an important determinant for deeper consumer content interaction and online engagement with the brand [8].

In the U&G perspective, consumers’ functional gratifications related to DCM, such as the acquisition of information, usefulness, and credibility, are more likely to foster positive perceptions of trust toward the brand. Consumers might feel that digital content meets their informational need and that it resolves their uncertainty about the brand, making them feel more reliable, transparent, and trustworthy [3,26]. This assumption has been supported by empirical studies that have shown that informativeness is one of the strongest predictors of Consumer Trust in digital content environments [27,28]. Furthermore, content credibility and relevance can increase customers’ trust in the competence and reliability of brands that are located on digital platforms [4,29].

Based on these theoretical and empirical arguments, the present study suggests that Digital Content Marketing, in social commerce environments, has a positive contribution to the development of Consumer Trust. Accordingly,

H1. 

Digital Content Marketing has a significant positive effect on Consumer Trust.

2.4. DCM and Digital Engagement

Digital Engagement refers to the cognitive, emotional, and behavioral interactions that consumers develop with brand-related content across digital platforms. These interactions may include consuming content, expressing reactions through likes and comments, and participating more actively by sharing, reposting, or creating content associated with the brand [6,30]. In social commerce environments, digital engagement reflects the extent to which consumers interact with brands beyond passive exposure, thereby indicating higher levels of involvement, interest, and online participation.

According to U&G Theory, consumers who seek hedonic and social-interactive gratifications are more likely to engage actively with digital content that they perceive as entertaining, interactive, and visually attractive. Such content can satisfy consumers’ needs for enjoyment, emotional stimulation, identity expression, and social connection with other users across digital communities [16,17]. As a result, consumers tend to interact more frequently with digital content that creates enjoyable and socially meaningful online experiences.

Previous empirical studies have consistently demonstrated that several dimensions of DCM, particularly entertainment value and interactivity, function as important antecedents of Digital Engagement within social media environments [9,11]. Interactive content formats often encourage consumers to participate in discussions, react to posts, and maintain continuous interaction with brand-related communications. This relationship appears particularly relevant within the travel and hospitality sector, where visually appealing destination content, travel experiences, and interactive promotional activities have been found to stimulate consumer engagement behaviors significantly [31].

Based on these theoretical and empirical considerations, the present study proposes that Digital Content Marketing positively influences Digital Engagement within social commerce platforms. Accordingly,

H2. 

Digital Content Marketing has a significant positive effect on Digital Engagement.

2.5. Consumer Trust and Brand Attitude

Brand Attitude, which reflects consumers’ overall evaluative perception and disposition toward a brand [10], has long been recognized as an important predictor of consumer behavioral outcomes such as purchase intention, customer loyalty, and positive brand advocacy [10,11]. In digital marketing environments, favorable brand attitudes are particularly important because they influence how consumers interpret brand communications, interact with digital content, and form long-term relationships with brands across social commerce platforms.

From the perspective of U&G Theory, the relationship between Consumer Trust and Brand Attitude can be explained through the satisfaction of trust-related gratifications. When consumers perceive a brand as reliable, credible, and capable of meeting their expectations, they are more likely to develop positive cognitive and emotional evaluations toward that brand [3]. Trust reduces uncertainty in digital interactions and strengthens consumers’ confidence in the authenticity and integrity of brand-related content, which may subsequently contribute to more favorable brand perceptions and attitudes.

Existing empirical evidence strongly supports this relationship. Previous studies have consistently shown that consumers who trust a brand’s digital content are more likely to evaluate the brand positively, perceive its products and services as more reliable, and demonstrate stronger behavioral intentions toward future interaction and purchasing activities [4,28]. Furthermore, trusted brands are often viewed as more capable of satisfying consumer needs, which enhances consumers’ willingness to maintain long-term engagement and loyalty relationships.

Based on these theoretical and empirical arguments, the present study proposes that Consumer Trust positively influences Brand Attitude within social commerce environments. Accordingly,

H3. 

Consumer Trust has a significant positive effect on Brand Attitude.

2.6. Digital Engagement and Brand Attitude

The relationship between Digital Engagement and Brand Attitude can be explained through both U&G Theory and the broader consumer brand engagement literature. According to U&G, active interaction with brand-related digital content represents a behavioral response that occurs when consumers’ informational, emotional, and social needs are successfully satisfied. As consumers continue to engage with brand content over time, these repeated interactions may gradually strengthen their cognitive and emotional connections with the brand, ultimately contributing to the development of more favorable brand attitudes [3,6].

In digital environments, engagement behaviors such as liking, commenting, sharing, and participating in online brand discussions often reflect higher levels of consumer involvement and psychological attachment to the brand. Consumers who actively engage with digital content are generally more likely to perceive the brand positively because continuous interaction enhances familiarity, emotional connection, and perceived relevance. Consequently, engagement may function not only as a behavioral outcome of successful digital content strategies but also as an important mechanism influencing how consumers evaluate brands.

Previous empirical studies provided strong support for this relationship. For example, Bruno Schivinski and colleagues [30] found that higher levels of social media engagement positively influence brand equity perceptions, with Brand Attitude representing one of the central dimensions of those perceptions. These findings suggest that consumer interaction with brand-related content across social platforms can significantly contribute to shaping favorable brand evaluations and stronger consumer–brand relationships.

Based on these theoretical and empirical foundations, the present study proposes that Digital Engagement positively influences Brand Attitude within social commerce settings. Accordingly,

H4. 

Digital Engagement has a significant positive effect on Brand Attitude.

2.7. Consumer Trust and Digital Engagement as Mediating Mechanisms

Building on the DCM framework proposed by Linda Hollebeek and Keith Macky [3], as well as the assumptions of U&G Theory, the present study proposes that Consumer Trust and Digital Engagement operate as dual mediating mechanisms in the relationship between DCM and Brand Attitude. In this context, DCM is the most fundamental marketing stimulus that meets the informational, emotional, and social gratification needs of consumers by providing valuable and interactive digital content.

According to U&G logic, satisfying these gratification needs may generate both cognitive and behavioral responses among consumers. Consumer trust can be considered a cognitive state that comes after consumers’ perceptions of the credibility and reliability of the content as well as its usefulness, and Digital Engagement is a behavioral state based on consumers’ interactions with brand content on social commerce platforms [3,17]. It is believed that these two mechanisms will work together to create more positive Brand Attitudes, with both increasing positive evaluations and the emotional ties consumers feel with the brand.

In addition to the parallel mediating roles of Consumer Trust and Digital Engagement, the present study also examines a sequential mediation pathway. Recent empirical findings indicate that increased Consumer Trust can motivate consumers to be more active in their engagement with digital content and brand-related interactions [8,32]; thus, this assumption is supported by recent empirical findings, suggesting that Consumer Trust is the primary mechanism through which DCM initiates positive Brand Attitude formation via higher Digital Engagement.

Based on these theoretical arguments and empirical findings, the following hypotheses are proposed:

H5. 

Digital Content Marketing has a significant direct positive effect on Brand Attitude.

H6. 

Consumer Trust has a significant positive effect on Digital Engagement.

H7. 

Consumer Trust mediates the relationship between Digital Content Marketing and Brand Attitude.

H8. 

Digital Engagement mediates the relationship between Digital Content Marketing and Brand Attitude.

3. Materials and Methods

3.1. Research Design

The present research adopts a quantitative, cross-sectional survey design to examine the proposed mediation model and test the hypothesized relationships between the study variables. The use of quantitative methods is common in digital marketing research and social commerce studies due to their ability to capture and quantify consumer perceptions and responses systematically over larger sample sizes [8,9,11]. This study employed a self-administered structured questionnaire to gather data and measure respondents’ perceptions of DCM, Consumer Trust, Digital Engagement, and Brand Attitude.

This questionnaire methodology is consistent with methodological approaches used in other studies of digital consumer behavior and online marketing environments, especially those examining relationships between latent constructs and mediation effects [8,9,11]. Furthermore, the study employed a cross-sectional approach, as it was intended to evaluate the perceptions and interactions of the respondents with the digital content at a specific time and place in the context of Iraqi social commerce.

Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) because it is appropriate for predictive and theory development research with complex relationships and multiple mediation pathways [33]. PLS-SEM is a suitable method for a study with a model containing multiple latent constructs and indirect relationships between variables since it allows for the assessment of the validity of the measuring instruments and also the analysis of the structural relationships between the variables at the same time. Furthermore, the technique has been extensively applied in marketing, consumer behavior, and social commerce research because of its flexibility in handling complex conceptual models and its effectiveness in predictive-oriented studies (e.g., [8,29,34]).

3.2. Sampling and Data Collection

The target population of the present study comprised individuals aged 18 years and older who have used any of the social commerce platforms (Instagram, Facebook, TikTok, or Snapchat) in the last 30 days before the data collection period. Furthermore, the respondents had to have been exposed to travel and hospitality brands’ content through these platforms before. The study’s emphasis on social commerce users was deemed relevant, since the emphasis is to explore how consumers react to DCM in the context of the usage of digital content, especially in the interactive online environment. A purposive sampling technique was employed to ensure that respondents met the study’s selection criteria and possessed direct, recent experience with social commerce platforms and hospitality brand content [35].

Purposive sampling was preferred over probability-based approaches because the target population, social commerce users with an active engagement in travel and hospitality brand content in Iraq, does not form a sampling frame that can be identified or enumerated [35]. To screen for relevance, the opening section of the questionnaire contained two filter items: (a) “Have you used at least one social commerce platform (Instagram, Facebook, TikTok, or Snapchat) in the past 30 days?” and (b) “Have you encountered digital marketing content from a travel or hospitality brand on any of these platforms in the past 30 days?” The respondents who answered positively to both these items went on to answer the main questionnaire. To increase the reach of the survey and facilitate participant recruitment, purposive sampling was supplemented by snowball sampling, whereby initial respondents were invited to share the survey link with other eligible individuals within their online networks. Data collection was conducted entirely online in 2025; the self-administered questionnaire was distributed via social media platforms (Instagram, Facebook, and TikTok) that are widely used across Iraq. This online approach was adopted because these platforms represent the natural environment in which target respondents encounter hospitality brand content [21,22]. Purposive and snowball sampling methods are recognized methods used to access specific online consumer populations when probability sampling is not feasible [35], but this reduces the statistical generalizability of findings as noted in Section 5.10.

The required sample size was determined based on the recommendations provided by Joseph Hair and colleagues [33], in addition to the statistical power criteria proposed by Jacob Cohen [36]. According to these guidelines, the sample size must be sufficiently large to obtain reliable estimation of the structural relationships in Partial Least Squares Structural Equation Modeling (PLS-SEM). Of the 412 questionnaires collected, 26 were excluded due to incompleteness, yielding a net sample of 386 valid responses that were retained for subsequent statistical analysis.

3.3. Measurement Instruments

All study constructs were measured using validated reflective multi-item scales adapted from established prior studies to ensure the reliability and validity of the measurement model. The use of previously validated scales is consistent with common practices in marketing and consumer behavior research, particularly in studies involving latent psychological and behavioral constructs. Minor modifications were introduced where necessary to align the measurement items with the context of DCM and social commerce platforms within the travel and hospitality sector.

The questionnaire items were translated for language accuracy and conceptual equivalence by the forward–backward translation method recommended in cross-cultural research [37]. The original English version was first translated into Arabic and then translated again from Arabic into English by an independent translator to ensure consistency and reduce possible translation errors.

A pilot test with 30 respondents was also conducted before the main data collection to test the clarity, readability, and comprehensibility of the questions in the questionnaire. Feedback obtained from the pilot participants contributed to minor wording adjustments that improved item clarity and reduced potential ambiguity. Each of the measurement items was evaluated on a 5-point Likert scale from 1 = Strongly Disagree to 5 = Strongly Agree. The five-point Likert scale was selected over a seven-point format for three converging reasons. First, empirical comparisons of scale length have consistently shown that five-point and seven-point formats yield statistically equivalent or highly comparable score characteristics—including reliability and discriminating power—once scores are rescaled, such that the shorter format does not compromise measurement quality [38,39,40]. Second, response-scale length is a recognised determinant of respondent cognitive burden: according to satisficing theory, increasing the number of response categories raises the cognitive effort required to discriminate between adjacent options, which increases the likelihood of shortcut, less-differentiated responding—particularly among respondents with lower task familiarity or motivation [41]. Given that the pilot sample included respondents with varying levels of familiarity with digital surveys, a five-point format was adopted to minimise this risk while preserving acceptable reliability. Third, the five-point format is consistent with the predominant convention in the consumer-trust and digital-content-marketing literature this study builds on (e.g., Hollebeek & Macky [3]), facilitating comparability of results with prior studies in the same research stream.

Table 1. Construct operationalizations and measurement items.

Construct	Sample Items (Adapted)	Source	Items
DCM	The brand’s content on this platform is informative and useful.	Hollebeek & Macky [3]; Lou & Yuan [26]	5
	The brand’s content is entertaining and enjoyable.
	The brand’s content is relevant to my travel interests.
	I find the brand’s content credible and trustworthy.
	The brand’s content allows me to interact and respond.
CT	I trust this brand based on its social media content.	McKnight et al. [4]; Chaudhuri & Holbrook [29]	4
	This brand’s content gives me confidence in its services.
	I feel secure engaging with this brand online.
	I believe this brand keeps its promises.
DE	I like or comment on this brand’s content regularly.	Schivinski et al. [30]; Brodie et al. [6]	4
	I share this brand’s content with others.
	I actively follow and interact with this brand online.
	I feel connected to this brand through its digital content.
BA	My overall impression of this brand is positive.	Mitchell & Olson [10]; Spears & Singh [42]	4
	I have a favorable attitude toward this brand.
	I evaluate this brand positively compared to alternatives.
	This brand matches my expectations as a travel brand.

Note: All items rated on a five-point Likert scale (1 = Strongly Disagree; 5 = Strongly Agree). Items adapted with minor wording modifications for cultural and contextual appropriateness.

presents the operational definitions of the study constructs and sample items from the questionnaire.

3.4. Data Analysis Approach

The data were analyzed with PLS-SEM software: SmartPLS, version 4.0 (SmartPLS GmbH, Bönningstedt, Germany) [43]. The analysis was conducted in two stages: the first stage was the evaluation of the measurement model (outer loadings ≥ 0.708 [33]; Cronbach’s α and CR ≥ 0.70 [33]; AVE ≥ 0.50 [44]; HTMT < 0.85 [45]) and the second stage involved the evaluation of the structural model (path coefficients, R², Q² via blindfolding, f² effect sizes). According to Hair et al. [33] and Preacher and Hayes [46], we tested mediation with bootstrapping with 10,000 subsamples and bias-corrected and accelerated (BCa) confidence intervals. A confidence interval not containing zero indicates a statistically significant indirect effect.

3.5. Ethical Considerations

Ethical approval was provided by the Ethics Approval Committee for Medical and Human Research at Northern Technical University in accordance with the principles of the Declaration of Helsinki. The study was approved on 10 October 2025 under the Ethical Approval Code (2026-3012).

3.6. Use of Generative Artificial Intelligence

This manuscript was prepared with the use of only Generative AI language enhancement, grammatical error correction, and stylistic improvement tools (ChatGPT, OpenAI, GPT-5). The tools did not contribute to the main academic content of the study, the theoretical framework, the research hypotheses, the research method, data collection or analysis, the interpretation of the results, or the design of tables and figures. All concepts, analysis, and intellectual contributions in this manuscript are solely the responsibility of the authors.

4. Results

4.1. Sample Profile

Of the 412 questionnaires distributed, 26 were excluded for incompleteness, yielding a usable sample of 386 responses, corresponding to an effective response rate of 93.7%.

Table 2. Demographic profile of respondents.

Variable	Male n (%)	Female n (%)	Freq. (%)
Gender	218 (56.5%)	168 (43.5%)	—
Age: 18–24	—	—	142 (36.8%)
Age: 25–34	—	—	158 (40.9%)
Age: 35–44	—	—	62 (16.1%)
Age: 45+	—	—	24 (6.2%)
Education: Bachelor’s or above	—	—	264 (68.4%)
Platform: Instagram	—	—	198 (51.3%)
Platform: TikTok	—	—	112 (29.0%)
Platform: Facebook	—	—	76 (19.7%)

Note: Percentages may not sum to 100% due to rounding. Platform categories are not mutually exclusive.

presents the demographic breakdown.

The demographic profile of the respondents indicates that the sample was predominantly male, representing 56.5% of the total participants, while female respondents accounted for the remaining proportion. In terms of age distribution, the largest segment of respondents belonged to the 25–34 age category, comprising 40.9% of the sample. This distribution appears consistent with recent reports describing the demographic characteristics of social media users in Iraq, where younger adults represent the most active segment of digital platform users [21].

Regarding educational background, the majority of respondents possessed relatively high educational qualifications, with 68.4% holding at least a bachelor’s degree. This characteristic may reflect the increasing use of digital technologies and social commerce platforms among educated consumers who are more actively engaged with online content and digital communication channels.

In relation to platform usage, Instagram emerged as the primary platform through which respondents encountered travel and hospitality brand content, accounting for 51.3% of responses. TikTok represented the second most frequently used platform at 29.0%, followed by Facebook at 19.7% [21,22]. These findings suggest that visually oriented and interactive social media platforms play an increasingly important role in shaping consumer exposure to digital marketing content within the Iraqi travel and hospitality sector.

4.2. Measurement Model Assessment

4.2.1. Indicator Reliability and Internal Consistency Reliability

The measurement model was evaluated using SmartPLS 4.0 [43] in accordance with the assessment procedures recommended by Joseph Hair and colleagues [33]. The evaluation focused on examining the reliability and validity of the reflective measurement constructs included in the study model.

Several statistical indicators were assessed to confirm the adequacy of the measurement model. Indicator reliability was examined through outer loading values, while internal consistency reliability was evaluated using Cronbach’s alpha (α) and Composite Reliability (CR). In addition, convergent validity was assessed through the Average Variance Extracted (AVE) for each construct. Descriptive statistics, including means and standard deviations, were also calculated to provide an overview of respondents’ evaluations of the study variables.

Table 3. Measurement model: outer loadings, reliability, and convergent validity.

Construct/Item	Loading	Mean	SD	α	CR	AVE
DCM				0.881	0.913	0.579
DCM1—Content is informative and useful	0.791	3.84	0.923
DCM2—Content is entertaining and enjoyable	0.762	3.71	0.941
DCM3—Content is relevant to travel interests	0.774	3.79	0.908
DCM4—Content is credible and trustworthy	0.748	3.65	0.979
DCM5—Content allows interaction and response	0.731	3.58	1.004
Consumer Trust (CT)				0.874	0.918	0.737
CT1—I trust this brand based on its content	0.867	3.72	0.911
CT2—Content gives me confidence in services	0.843	3.68	0.945
CT3—I feel secure engaging with this brand	0.858	3.76	0.887
CT4—I believe this brand keeps its promises	0.871	3.63	0.958
Digital Engagement (DE)				0.867	0.910	0.718
DE1—I like or comment on content regularly	0.834	3.61	0.998
DE2—I share this brand’s content with others	0.849	3.52	1.011
DE3—I actively follow and interact online	0.862	3.69	0.974
DE4—I feel connected to the brand via content	0.842	3.57	0.987
Brand Attitude (BA)				0.891	0.924	0.754
BA1—My overall impression is positive	0.878	3.89	0.862
BA2—I have a favorable attitude toward the brand	0.863	3.82	0.894
BA3—I evaluate this brand positively vs. alternatives	0.871	3.77	0.917
BA4—The brand matches my travel expectations	0.858	3.74	0.931

Note: α = Cronbach’s alpha; CR = composite reliability; AVE = average variance extracted. Thresholds: loading ≥ 0.708 [33]; α and CR ≥ 0.70 [33]; AVE ≥ 0.50 [44].

presents the results of the measurement model assessment, including outer loadings, mean values, standard deviations, Cronbach’s alpha coefficients, Composite Reliability values, and Average Variance Extracted (AVE) scores for all study constructs.

Measurement model assessment results indicated satisfactory indicator reliability, internal consistency, and convergent validity for all study constructs. In particular, every outer loading value surpassed the recommended cut-off of 0.708 [33], showing that the measurement items adequately captured their corresponding latent constructs. The outer loadings ranged from 0.731 for item DCM5 to 0.878 for item BA1, suggesting strong relationships between the indicators and their associated constructs. Regarding the individual DCM items, which each correspond to one content dimension (informativeness, entertainment, relevance, credibility, and interactivity), all five outer loadings were acceptable (range: 0.731–0.791), indicating that each dimension contributed meaningfully to the DCM construct. Because DCM was operationalised as a unidimensional reflective construct following Hollebeek and Macky [3], overall construct-level reliability is the primary indicator reported; sub-scale-level reliability statistics are not conventionally computed for individual items within a reflective measurement model [33]. Nonetheless, the loading pattern confirms that no single DCM dimension dominated the construct, suggesting balanced representation across the five content characteristics.

Internal consistency reliability was evaluated using both Cronbach’s alpha and Composite Reliability (CR). The findings revealed that Cronbach’s alpha values ranged between 0.867 and 0.891, while Composite Reliability values ranged from 0.910 to 0.924. All reliability coefficients exceeded the minimum recommended threshold of 0.70 [33], confirming that the study constructs demonstrated high levels of internal consistency and measurement reliability.

Convergent validity was assessed using the Average Variance Extracted (AVE). The AVE values ranged from 0.579 for the DCM construct to 0.754 for Brand Attitude (BA), with all values exceeding the recommended threshold of 0.50 [44]. These results indicate that the constructs explained a sufficient proportion of variance in their respective indicators, thereby confirming adequate convergent validity for the measurement model.

4.2.2. Discriminant Validity

Discriminant validity was assessed using the Fornell–Larcker criterion [44] and HTMT ratios [45].

Table 4. Fornell–Larcker criterion for discriminant validity.

Construct	DCM	CT	DE	BA
Digital Content Marketing (DCM)	0.761
Consumer Trust (CT)	0.624	0.858
Digital Engagement (DE)	0.591	0.637	0.847
Brand Attitude (BA)	0.573	0.662	0.619	0.868

Note: Diagonal values = square root of AVE. Off-diagonal = inter-construct correlations. Discriminant validity is established when diagonal values exceed off-diagonal values in the corresponding row and column [44].

and

Table 5. Heterotrait-Monotrait (HTMT) ratios for discriminant validity.

Construct Pair	HTMT	95% CI Lower	95% CI Upper	Assessment
DCM → CT	0.712	0.651	0.773	Satisfied
DCM → DE	0.681	0.617	0.746	Satisfied
DCM → BA	0.658	0.591	0.725	Satisfied
CT → DE	0.724	0.661	0.788	Satisfied
CT → BA	0.749	0.684	0.814	Satisfied
DE → BA	0.701	0.638	0.765	Satisfied

Note: CI based on 10,000 BCa bootstrap subsamples. All HTMT < 0.85 [45]; discriminant validity is confirmed when CI does not include 1.0.

present the results.

All HTMT ratios fell below 0.85 [45], ranging from 0.658 to 0.749, with no confidence interval reaching 1.0. The measurement model demonstrates satisfactory reliability, convergent validity, and discriminant validity throughout.

4.3. Common Method Bias Assessment

Table 6. Common method bias diagnostics.

Diagnostic	Value	Threshold	Assessment
Harman’s single factor (% variance explained)	24.7%	<50%	No dominant CMB
VIF—DCM (predictor collinearity)	2.11	<3.3	Acceptable
VIF—CT (predictor collinearity)	2.38	<3.3	Acceptable
VIF—DE (predictor collinearity)	2.19	<3.3	Acceptable

Note: VIF = Variance Inflation Factor. VIF < 3.3 indicates absence of pathological collinearity [47]. A Harman single-factor value above 50% would signal dominant CMB [48].

summarises the CMB diagnostics.

Harman’s test extracted a first factor accounting for 24.7% of total variance, well below the 50% threshold [48]. Collinearity VIF values ranged from 2.11 to 2.38, all below 3.3 [47]. Together, these diagnostics do not support the presence of dominant common method bias.

4.4. Structural Model Assessment

4.4.1. Path Coefficients and Hypothesis Testing

Table 7. Structural model: path coefficients and hypothesis testing results.

Hypothesis/Path	β	SE	t-Value	p-Value	f2	Result
H1: DCM → CT	0.487	0.042	11.598	<0.001	0.311	Supported
H2: DCM → DE	0.361	0.048	7.521	<0.001	0.172	Supported
H3: CT → BA	0.394	0.051	7.725	<0.001	0.201	Supported
H4: DE → BA	0.286	0.053	5.396	<0.001	0.114	Supported
H5: DCM → BA (direct)	0.187	0.059	3.169	0.002	0.048	Supported
H6: CT → DE	0.398	0.049	8.122	<0.001	0.209	Supported

Note: β = standardised path coefficient. Effect sizes (f²): ≥0.02 small; ≥0.15 medium; ≥0.35 large [36]. All H1–H6 supported at p < 0.001 except H5 (p = 0.002).

presents the standardised path coefficients (β), standard errors, t-statistics, p-values, and effect sizes (f²) obtained from SmartPLS 4.0 using bootstrap resampling based on 10,000 subsamples with bias-corrected and accelerated (BCa) intervals.

All six direct hypotheses were supported. The strongest path was DCM → CT (β = 0.487, f² = 0.311), indicating a large effect. DCM → DE yielded a medium effect (β = 0.361, f² = 0.172). Among the two mediators, Consumer Trust had a stronger impact on Brand Attitude (β = 0.394, f² = 0.201, medium effect) than Digital Engagement (β = 0.286, f² = 0.114, small-to-medium effect). The direct DCM → BA path, though small, was statistically significant (β = 0.187, p = 0.002), supporting H5. The sequential path CT → DE was significant with a medium effect (β = 0.398, f² = 0.209), confirming H6.

4.4.2. Coefficient of Determination and Predictive Relevance

Table 8. Coefficient of determination (R²) and predictive relevance (Q²).

Endogenous Construct	R2	R2 (Adj.)	Q2	Interpretation
Consumer Trust (CT)	0.379	0.376	0.268	Moderate predictive relevance
Digital Engagement (DE)	0.421	0.417	0.292	Moderate predictive relevance
Brand Attitude (BA)	0.538	0.533	0.391	Substantial predictive relevance

Note: R² ≥ 0.33 moderate; ≥0.67 substantial [33]. Q² > 0.25 moderate predictive relevance [33].

presents R², adjusted R², and Q² for all endogenous constructs.

The model accounted for 37.9% of variance in Consumer Trust, 42.1% in Digital Engagement, and 53.8% in Brand Attitude. Q² values of 0.268, 0.292, and 0.391, respectively were all well above zero, confirming adequate predictive relevance for all endogenous constructs [33].

4.4.3. Mediation Analysis

Table 9. Mediation analysis: specific indirect effects.

Indirect Path	Indirect β	SE	t-Value	95% LL	95% UL	Mediation Type
H7: DCM → CT → BA	0.192	0.034	5.647	0.127	0.261	Partial mediation
H8: DCM → DE → BA	0.103	0.027	3.815	0.053	0.158	Partial mediation
DCM → CT → DE → BA (serial)	0.056	0.018	3.111	0.024	0.094	Partial mediation
Total indirect effect: DCM → BA	0.351	0.041	8.561	0.272	0.431	Significant

Note: LL = lower limit; UL = upper limit of 95% BCa CI. Partial mediation is indicated when the direct effect (DCM → BA: β = 0.187, p = 0.002) remains significant alongside significant indirect effects [46]. No CI includes zero.

presents the specific indirect effects from the bootstrapping procedure.

Both mediation hypotheses were supported. The indirect effect through Consumer Trust (H7: β = 0.192, 95% CI [0.127, 0.261]) and through Digital Engagement (H8: β = 0.103, 95% CI [0.053, 0.158]) were each statistically significant. The serial pathway (DCM → CT → DE → BA: β = 0.056, 95% CI [0.024, 0.094]) was also confirmed. The total indirect effect (β = 0.351) substantially exceeded the direct effect (β = 0.187), establishing mediated mechanisms as the dominant pathway through which DCM shapes Brand Attitude. All eight hypotheses were supported.

5. Discussion

This study aimed to investigate the role of DCM in affecting consumer Brand Attitude on social commerce platforms in the travel and hospitality industry in Iraq and to track the two mediating roles of Consumer Trust and Digital Engagement in a U&G framework. All eight hypotheses found empirical support.

Table 10. Summary of hypotheses and results.

H	Path	β	p-Value	Result
H1	Digital Content Marketing → Consumer Trust	0.487	<0.001	Supported
H2	Digital Content Marketing → Digital Engagement	0.361	<0.001	Supported
H3	Consumer Trust → Brand Attitude	0.394	<0.001	Supported
H4	Digital Engagement → Brand Attitude	0.286	<0.001	Supported
H5	Digital Content Marketing → Brand Attitude (direct)	0.187	0.002	Supported
H6	Consumer Trust → Digital Engagement	0.398	<0.001	Supported
H7	DCM → Consumer Trust → Brand Attitude (indirect)	0.192	<0.001	Supported
H8	DCM → Digital Engagement → Brand Attitude (indirect)	0.103	<0.001	Supported

Note: β = standardised path coefficient from SmartPLS 4.0 (10,000 BCa bootstrap subsamples). All paths significant at p < 0.05.

summarizes the results.

5.1. DCM as a Predictor of Consumer Trust (H1)

The finding that DCM exerts the largest single direct effect on Consumer Trust (β = 0.487, f² = 0.311) sits comfortably within U&G theory: informative, credible, and relevant content satisfies functional gratifications and generates cognitive trust [3]. This is consistent with the results of Lou et al. [26] and Wang et al. [49], who found that content quality had a positive effect on trust in social commerce contexts in a meta-analysis. The size of the effect is noteworthy. In markets with less platform reputation, such as Iraq, where consumers can more easily trust content quality as a signal, DCM appears to play a more pronounced role in trust formation than in more established settings [8,22].

5.2. DCM as a Predictor of Digital Engagement (H2)

DCM’s significant positive effect on Digital Engagement (β = 0.361, f² = 0.172, medium effect) is consistent with U&G’s prediction that hedonic and social-interactive gratifications motivate active engagement behaviors [6,15] and aligns with the broader DCM and social media engagement literature [11]. Overall, the medium effect size is somewhat smaller than the DCM-to-Trust effect, which indicates that functional gratifications have a stronger influence than hedonic gratifications in this context. Iraqi consumers might prioritize informational usefulness over entertainment in evaluating travel brand content, which could be a cultural variation in how digital content is perceived and valued [50]. It is worth noting, however, that not all studies confirm a positive DCM–engagement relationship. Riedel et al. [51] found that excessive content frequency can trigger consumer disengagement, suggesting a potentially curvilinear relationship that the present study’s linear model does not capture. Future research should examine whether content volume moderates the DCM–engagement pathway in Iraqi social commerce settings. Additionally, consistent with U&G theory [15], the finding that engagement is driven by hedonic and social-interactive gratifications implies that brands whose content is predominantly informational rather than entertaining or interactive may elicit lower engagement levels even when that content successfully builds trust.

5.3. Consumer Trust as a Predictor of Brand Attitude (H3)

Consumer Trust was more strongly linked to Brand Attitude (β = 0.394, f² = 0.201), as per the theoretical hierarchy suggested by Hollebeek and Macky [3]. This confirms the findings of other studies, which have shown that trust is a significant attitudinal antecedent in social commerce [34,49] and that the cognitive pathway (DCM → Trust → Brand Attitude) exerts a stronger influence than the behavioral pathway (DCM → Engagement → Brand Attitude). Put differently, when DCM satisfies consumers’ need for reliable information, the resulting trust translates more powerfully into positive brand evaluation than does behavioral engagement alone.

5.4. Digital Engagement as a Predictor of Brand Attitude (H4)

The strong positive impact of Digital Engagement on Brand Attitude (β = 0.286, f² = 0.114) validates the notion that consumers who actively engage with the travel brand’s content over time develop more favorable evaluations of the brand [6,30]. The smaller effect size for Digital Engagement relative to Consumer Trust may reflect the temporal dynamics of the two pathways. Trust can be generated relatively rapidly through a single informative, credible interaction; attitude formation through engagement, by contrast, tends to be cumulative and requires sustained behavioral investment [3,6].

5.5. The Direct Effect of DCM on Brand Attitude (H5)

The small but statistically significant direct effect (β = 0.187, f² = 0.048, p = 0.002) indicates that DCM does influence brand evaluations partly through a residual direct route, which is broadly consistent with the Elaboration Likelihood Model’s prediction that high-involvement consumers may process content quality directly into attitude formation [11]. However, the direct effect is small compared to the total indirect effect (β = 0.351), confirming that mediated mechanisms are the dominant pathway through which DCM shapes Brand Attitude.

5.6. The Sequential Pathway: Consumer Trust → Digital Engagement (H6)

The strong path from CT to DE (β = 0.398, f² = 0.209) supports U&G sequential gratification logic [15]: consumers who believe in a brand due to content are more inclined to invest behavioral effort in interacting with the brand. This is in line with a 2025 JTAER systematic review [8] and with the prediction of social exchange theory that trust is a prerequisite for heightened behavioral investment in a relationship [52]. This sequential relationship has an important implication for practitioners: engagement initiatives launched before a credibility baseline is established are likely to underperform.

5.7. Mediation Analysis (H7 and H8)

The mediation effects of both hypothesized mediators were supported, with the indirect effect via Consumer Trust (H7: β = 0.192) being approximately 1.9 times larger than the indirect effect via Digital Engagement (H8: β = 0.103). The confirmed serial mediation path from DCM → CT → DE → BA: β = 0.056 provides empirical support for the complete U&G sequential process: DCM → CT → DE → BA. The partial mediation indicates that future studies should examine other intervening factors, such as perceived brand authenticity, brand identification, or social presence [3,8].

5.8. Broader Theoretical Implications

Three theoretical contributions stand out. First, the study provides an first empirical test of U&G theory as an integrated framework for DCM-driven brand attitude formation in an Arab emerging market, extending the geographic reach of existing U&G research [15,53]. Second, it empirically confirms the sequential model of gratification-engagement-attitude proposed by Hollebeek and Macky [3], thereby moving a conceptual framework into an empirically tested structural model. Third, the finding that trust mediates more powerfully than engagement contributes empirical evidence to the ongoing debate on the relative primacy of trust-based versus engagement-based cognitive versus behavioral pathways in brand attitude formation [8]. Collectively, these three contributions move the field forward in a way that extends beyond mere confirmation of prior findings: whereas existing studies have largely tested individual direct effects of DCM or examined trust and engagement as separate outcomes, the present study is the first to integrate these elements into a unified, empirically tested sequential mediation framework anchored in U&G theory and applied to an Arab emerging-market context. This advances the theoretical scope of U&G beyond its traditional application in Western digital media settings [15,53] and responds to repeated calls in the literature for theory-driven, context-sensitive DCM research in underrepresented markets [8,20].

5.9. Practical Implications

These findings carry several concrete implications for travel and hospitality brands operating on social commerce platforms in Iraq and comparable emerging markets.

First, because DCM’s effect on Consumer Trust was the strongest in the model (β = 0.487), brands should prioritize content quality dimensions that build credibility and informativeness. In practice, Iraqi hospitality brands on Instagram and TikTok (the dominant platforms in this sample) should include verified information about facilities, pricing, and service standards, as well as third-party endorsements and authentic guest testimonials. Clear pricing and descriptions of services are particularly important in Iraq because consumer skepticism of information on the internet is relatively high because of the immaturity of the digital commerce market [22].

Second, the sequential pathway (CT → DE: β = 0.398) indicates that trust must be established before engagement initiatives are likely to succeed. Hence, the brands need to adopt a two-phase approach: a trust-building phase with informative and credible content, and an engagement activation phase through interactive formats (live sessions, Q&As, participatory polls, UGC contests). Without a credibility baseline, launching engagement campaigns is likely to underperform because consumers need to believe the brand is credible before they allocate behavioral resources to engage with it.

Third, given that Instagram accounts for 51.3% of respondents’ primary platform and TikTok for 29.0%, content format recommendations differ by platform. High-quality visual storytelling on Instagram that includes destination photography and factual service information is highly recommended to address both informativeness and entertainment aspects. The short-form authentic video format can help foster this personal and trusted connection on TikTok, including behind-the-scenes footage, staff introductions, or real-time travel experiences. Finally, the model’s R² (0.538) shows that the DCM–trust–engagement framework explains over half of the variance in brand attitudes, offering a parsimonious but powerful foundation for evaluating content and designing KPIs. To translate these structural model findings into even more specific practitioner guidance: given the large effect size of DCM on Consumer Trust (f² = 0.311), hospitality marketing managers should allocate the majority of their content budget (recommended: 60–70%) to trust-building formats—factual service descriptions, verified reviews, transparency videos, and staff authenticity content—before activating engagement campaigns. The medium effect of DCM on Digital Engagement (f² = 0.172) suggests that interactive formats such as live Q&As, polls, and UGC contests should follow the trust-building phase rather than run in parallel. For platform-specific KPI design: on Instagram, measure content credibility scores (e.g., verified-claim click-through rates) alongside engagement metrics; on TikTok, track completion rate and save rate as trust proxies before optimising for comment and share engagement. The sequential CT → DE pathway (f² = 0.209) further implies that brands with lower trust baselines—such as new entrants to the Iraqi hospitality market—should invest in a trust-first phase of at least 4–6 weeks before launching interactive engagement activations.

5.10. Limitations and Future Research Directions

Several limitations should be acknowledged, each of which identifies productive directions for future inquiry. First, the cross-sectional design makes it impossible to make causal inferences. The results of PLS-SEM do not imply causality but are consistent with the proposed direction of the model. Longitudinal research that followed the development of trust and engagement over time would more closely test whether the proposed sequences of U&G theory occur as predicted.

Second, the use of purposive and snowball sampling restricts the statistical generalizability of findings. Because participants were not randomly selected from a defined population frame, the results cannot be assumed to represent all Iraqi social commerce users or the broader Arab consumer population. The sample’s overrepresentation of younger (77.7% aged 18–34) and more educated respondents (68.4% holding a bachelor’s degree or above) further constrains external validity, as older and less educated consumers—who constitute a growing segment of Iraqi social media users—may respond differently to DCM stimuli. Future studies should use stratified probability sampling for more representative coverage of the Iraqi population.

Third, reliance on a self-administered online questionnaire introduces common-method variance and social desirability bias, as respondents may over-report trust and engagement with brands they use. Although Harman’s single-factor test (24.7%) and collinearity VIF values (2.11–2.38) did not indicate dominant common-method bias, self-reported data can never fully substitute for behavioral observation or platform analytics. Future research may be able to validate the results of this survey by cross-referencing self-reported data with data collected from platform APIs regarding actual engagement metrics. Fourth, the geographic scope is limited to Iraq, and the single sector focus on travel and hospitality reduces the applicability of the concept to other sectors. Multi-country comparative designs replicating this model across Iraq, Egypt, Jordan, and Saudi Arabia would help establish the cultural stability of the U&G sequential mediation framework within the Arab region. Fifth, DCM was measured as a unidimensional construct, while future research should break it down to its five sub-dimensions to determine which one of them (informativeness, entertainment, relevance, credibility, and interactivity) has the greatest impact on trust and engagement alone. Last, however, the appearance of AI-generated content, virtual influencers, and the use of algorithms to personalize content as new DCM mechanisms requires further investigation in future studies of social commerce in the hospitality industry. Beyond the limitations noted above, three additional future research directions are recommended. First, cross-country comparative studies replicating the model in Saudi Arabia, Egypt, Jordan, and the UAE would allow multi-group analysis (MGA) to test whether the relative importance of Consumer Trust versus Digital Engagement as mediating pathways varies across cultural and institutional contexts within the Arab region, thereby establishing the boundary conditions of the U&G sequential mediation framework. Second, longitudinal panel designs tracking the same respondents over multiple waves (e.g., 6-month intervals) would allow examination of how Consumer Trust accumulates over sustained DCM exposure and whether trust-driven engagement intensifies Brand Attitude over time a dynamic process that cross-sectional data cannot capture. Third, multi-group analyses examining potential moderating roles of digital literacy level, platform type (Instagram vs. TikTok vs. Facebook), and brand familiarity would help identify conditions under which the DCM-trust-engagement-attitude pathway is stronger or weaker, thereby generating actionable insights for practitioners targeting diverse consumer segments.

6. Conclusions

This study examined the influence of Digital Content Marketing on Brand Attitude via the dual mediation of Consumer Trust and Digital Engagement in the context of social commerce platforms using the U&G theory in the travel and hospitality industry in Iraq. Based on PLS-SEM analysis of 386 valid responses, all eight hypotheses were supported. The cognitive pathway (DCM → Trust → Brand Attitude), the behavioral pathway (DCM → Engagement → Brand Attitude), the residual direct pathway (DCM → Brand Attitude), and the sequential pathway (DCM → Trust → Engagement → Brand Attitude) were all found to influence brand attitude.

There are three clear conclusions. First, Consumer Trust is the most important mediating variable (it has a larger impact on brand attitude than Digital Engagement), thereby reinforcing the functional and information-based gratifications that consumers experience when evaluating brands in this emerging market. Second, the sequential trust-to-engagement relationship highlights the role of trust as a psychological antecedent for continued digital engagement in social commerce, which implies that U&G has shifted from a parallel gratification to a sequential and multi-step process. Third, the study demonstrates the applicability of U&G theory in an Arab emerging market, enriching the theory’s contextual scope.

For practitioners, the message is direct. Travel and hospitality brands should focus first on creating content that is informative and credible to gain trust and then leverage that trust to create interactive and engaging content that turns it into active consumer engagement. As digital commerce continues its rapid expansion in Iraq and comparable emerging markets [21,22], understanding the mechanisms through which DCM shapes brand perceptions is not merely a theoretical exercise—it is a commercially vital imperative for brands competing in settings where consumer trust and engagement remain deeply contested resources.