The Impact of Digital Content Marketing on Brand Defence: The Mediating Role of Behavioural Engagement and Brand Attachment

Abstract

Marketers are increasingly interested in digital content marketing (DCM) strategies that have the potential to enhance consumer behaviour. However, there is a dearth of academic research that delves into the impact of DCM on brand defence. This study aims to investigate the impact of DCM on brand defence through behavioural engagement and brand attachment (in a Jordanian fashion industry case study). The results demonstrate a notable benefit of the direct effect of DCM on behavioural engagement, brand attachment, and brand defence. DCM is the strongest determinant of brand defence, followed by behavioural engagement and brand attachment. Behavioural engagement exerts a substantial beneficial impact on brand attachment. The results of this research indicate that both behavioural engagement and brand attachment significantly influence brand defence. The mediation results indicate that brand attachment fails to mediate the positive impact of DCM on brand defence. Moreover, the study determines that behavioural engagement mediates the positive impact of DCM on brand defence. This study is of high importance for marketing strategies, emphasising the pivotal importance of DCM in developing effective marketing approaches.

1. Introduction

The advent of the digital era prompted numerous organisations and companies to transition their marketing strategies from traditional to digital formats. DCM has become a much sought-after, topical, and captivating subject, attracting a great deal of attention from consumers and marketers alike.

It holds immense significance as a conceptual framework and plays a pivotal role in driving the success and growth of companies. Furthermore, it represents a recent and notable advancement in the utilisation of online technology (Ezekiel, 2022). DCM has emerged as a pivotal component of a prosperous online marketing campaign and serves as the primary instrument inside the realm of digital marketing. In a corresponding manner, the revenue generated from worldwide DCM experienced an increase from USD 55.1 billion in 2021 to USD 72 billion in 2023. According to Statista (2023a), the anticipated value is expected to reach USD 107.5 billion by 2026.

Galdón-Salvador et al. (2024) indicated that DCM refers to the process of generating, disseminating, and exchanging pertinent, captivating, and timely content with the aim of engaging customers during their decision-making process and motivating them to achieve a favourable outcome for the organisation. In contrast to conventional methods, DCM in a digital context encompasses the production of many forms of content, including article blogs, social media postings, podcasts, videos, and other mediums, with the aim of effectively engaging a particular target demographic (Bui et al., 2023). Pektas and Hassan (2020) defined content marketing as a marketing strategy that strives to develop attractive, helpful information for the target consumers, disseminate it, and respond to it.

Despite the increasing significance of DCM, there has remained a lack of academic comprehension in this field up to now (Hollebeek & Macky, 2019). Due in large part to its relatively recent birth as a subject of study, there is still a major gap in the academic literature despite the acknowledged relevance of DCM (Muller & Christandl, 2019). Although numerous research works have examined its influence on consumer engagement and brand results, a thorough comprehension of its consequences, particularly in particular sectors, is still lacking (Alfraihat et al., 2024). Additionally, the growing lack of trust that consumers have in traditional marketing methods highlights the necessity for creative strategies like DCM in order to draw in and keep consumers (Galdón-Salvador et al., 2024). Therefore, further research is necessary to investigate the field of DCM.

Statista (2023b) reports that the fashion industry is still very profitable and continuously changing. In this sense, Jordanian firms have the opportunity to participate in international trade, which will strengthen the local economy, thanks to the worldwide fashion market, which is estimated to be worth USD 1.2 trillion (Ruth, 2021). According to Al-Nsour and Yusak (2023), one of the most attractive investment opportunities in the Jordanian economy is the fashion business. The Department of Statistics (2024) projects that Jordan’s population will rise to 11,587 in 2024, positioning it as an emerging market. Datareportal (2024) estimates that people under the age of 34 make up 66.3% of its population. Young people make up the greatest buyer group for fashion products according to Gadhavi and Sahni (2020). The fashion industry in Jordan is a long-term endeavour that mostly relies on effective marketing tactics for attracting new customers and retaining existing ones. This is achieved by emphasising customer preferences and keeping up with fashion trends (Alshurideh et al., 2024). Jordan’s fashion sector is dominated by about 10,700 small, independent fashion boutiques that sell clothes, shoes, and accessories; there are not many online merchants in the country. The industry employed over 57,000 workers from Syria and Jordan (Al-Nsour & Yusak, 2023).

In light of the above context, this study aims to investigate the impact of DCM on brand defence in the Jordanian fashion industry, with particular emphasis on the influence of behavioural engagement and brand attachment. The examination of these relationships will contribute to the current literature on DCM. In addition, it will offer significant new insights for companies seeking to improve their DCM strategies in order to have a favourable impact on brand defence and increase the likelihood of customers purchasing their promoted fashion products.

1.1. Literature Review for Conceptual Framework

A context-specific research methodology was essential for the implementation of this study. A structured model that illustrates the relationships between digital content marketing, behavioural engagement, brand attachment, and brand defence is shown in Figure 1. This conceptual framework was developed based on findings from the literature review and the theoretical and practical assumptions made (Bougie & Sekaran, 2019).

1.2. Digital Content Marketing (DCM)

DCM has garnered significant attention in recent years due to its crucial role within the realm of digital marketing (Galdón-Salvador et al., 2024). DCM is increasingly recognised as an essential approach for organisations aiming to engage meaningfully with their customers. Hollebeek and Macky’s (2019) findings indicate that DCM is essential for developing consumer relationships and enhancing consumer engagement and trust. DCM, often known as deep customer marketing, is as a strategy focused on building relationships. Its primary objective is to foster emotional connections and facilitate meaningful interactions between consumers and brands, rather than solely concentrating on direct brand promotion (Sawaftah et al., 2021). DCM, in contrast to traditional advertising methods, facilitates interactive communication on digital or virtual platforms, generating value for users through various activities such as social engagements and information retrieval, including blogs, e-newsletters, and online posts (Pektas & Hassan, 2020). DCM provides the advantage of accessing a broader audience while minimising marketing expenses, thereby reducing the necessity for advertising and direct sales initiatives (Alfraihat et al., 2024).

Consequently, DCM is focused on improving long-term sales performance through customer engagement and trust-building (Galdón-Salvador et al., 2024). DCM offers valuable content for an engaged target audience. Thus, understanding how content can be utilising in marketing for consumers’ engagement is important for generating effective marketing (Dewi et al., 2022). According to Taiminen and Ranaweera (2019), digital marketers anticipate that the usage of DCM will have a sustained impact on consumer behaviour, as conventional methods of product advertising and service promotion no longer effectively engage contemporary customers. In contrast, it is widely believed among digital marketers that content marketing presents a more cost-effective alternative to advertising across many media platforms, hence rendering it an enticing avenue for exploration within the realm of digital marketing.

1.2.1. Behaviour Engagement

The concept of customer engagement is conceptualised as a multifaceted construct including various forms of communication, including both purchase-related and non-purchase-related interactions, between individuals who have the potential to become consumers or who are already customers and a particular company (Carlson et al., 2019). In their study, Mohammad et al. (2021) delineated customer engagement as the complex interplay of behavioural manifestations, attitudinal orientations, and attachment levels exhibited by customers towards a commercial entity. Customer engagement encompasses three aspects that encompass cognitive, emotional, and behavioural responses (Chi et al., 2022). Cognitive engagement refers to a consumer’s concerns or interest in a brand and demonstrates their capacity to identify and learn more about the brand in question. Positive feelings towards the focal brand, like joy, affection, or pride, are indicated by emotional engagement as a result of the consumer–brand relationship (Alfraihat et al., 2024).

Behavioural engagement, according to Hollebeek et al. (2014), is the amount of time, effort, and focus customers give to their interactions with brands. Within online communities, engagement is reflected from a behavioural perspective, resulting from cognitive engagement and/or emotional engagement, where the emotional form activates the growth of the next behavioural engagement (Hollebeek & Macky, 2019). From a behavioural standpoint, customer engagement concentrates on the behavioural indicators concerning a brand (Sawaftah et al., 2021). Behavioural brand engagement might facilitate the value of the nexus to be more noticeable for the customers (Tarabieh et al., 2024).

1.2.2. Brand Attachment

Bilondatu and Tjokrosaputro (2023) indicated that brand attachment refers to the degree of emotional connection that an individual has with a specific brand. Vieira and Sousa (2020) suggest that the idea of brand attachment holds significant importance in capturing the affective aspects of the consumer–brand connection. According to Alves et al. (2022), the notion of brand attachment can be defined as the cognitive and emotional connection that links a specific brand to an individual’s sense of self. The resultant association has the potential to impact an individual’s cognitive and affective capacities. When a strong relationship is established, the brand will accrue advantages from its customers, including, but not limited to, recurrent purchases, heightened brand trust, and a propensity to pay a comparatively higher price (Davis & Dacin, 2022). Furthermore, the concept of brand attachment refers to a psychological inclination that has the potential to swiftly shape consumers’ perceptions of a specific brand (Shetty & Fitzsimmons, 2022). Vahdat et al. (2020) identified two distinct approaches in the measuring of brand attachment, each emphasising different aspects of attachment. The scale of emotional attachment developed by Thomson et al. (2005) and the scale of brand attachment developed by Park et al. (2010) are two notable measures in the field. Thomson et al. (2005) suggest that attachment exhibits variability primarily in terms of its intensity, with stronger attachments being associated with heightened emotional experiences, particularly in terms of connection, affection, and passion. However, the measurement framework developed by Park et al. (2010) primarily focuses on cognitive aspects related to the accessibility of a brand and its integration into a customer’s identity.

1.2.3. Brand Defence

In the world of marketing literature, the idea of brand defence is relatively new. Customer actions taken proactively to protect a brand from criticism and preserve its reputation are referred to as brand defence (Mostafa & Kasamani, 2020). The act of brand defence has been recognised as a discretionary action (Ali et al., 2021) which serves as the most potent manifestation of WOM and signifies the favourable perceptions within the consumer–brand association that prompt customers to protect a brand from negative evaluations (Sawaftah et al., 2021). In a more exact manner, consumer behaviour can be characterised as the act of advocating for, voicing support on behalf of, safeguarding, and proactively mitigating detrimental information concerning a brand, hence serving as a protective measure (Wilk et al., 2020). Javed et al. (2015) regarded the analysis of online brand advocacy as a significant component of their research. Ilhan et al. (2018) posited in their research that this behaviour extends beyond mere brand endorsement.

Brand defence strategies are crucial for influencing consumer perceptions and maintaining brand loyalty (Sawaftah et al., 2021). Ali et al. (2021) identified six distinct types of defence techniques: vouching, advocating, rationalising, trivialising, postponing, and doubting. The concept of advocacy in defence pertains to customer responses to critical comments perceived as unwarranted (Roy et al., 2023). Justifying defence parallels promoting defence; however, it is executed in a more equitable and nuanced manner (Dalman et al., 2019). Hassan and Casalo Arino (2016) characterise trivialising defence as the employment of humorous remarks which are devoid of a robust defending argument. Deferring in defence does not necessarily imply the validity of the complainant’s allegations. The articulation of negative thoughts or critiques regarding a specific brand is termed vouching defence (Ali et al., 2021). Questioning the defence ultimately raises concerns about the authenticity of the complaint (Au et al., 2021).

1.3. Hypothesis Development

1.3.1. The Impact of DCM on BE

DCM is essential for boosting consumer engagement, trust, and loyalty. Additionally, Dewi et al. (2022) look into how DCM affects consumer behaviour, emphasising its positive effects on brand loyalty and consumer engagement. The company must apply the concept of DCM in order to attain customer engagement (Galdón-Salvador et al., 2024). Every brand possesses various assets and, among them, having actively engaged customers is seen as a crucial asset, as they are recognised as brand advocates (Harrigan et al., 2021). Although social media serves as a platform for customer engagement behaviours, companies have the opportunity to utilise social media to promote their goods, motivate consumers, and establish a robust relationship between customers and the firm Sawaftah et al. (2021). To clarify, it may be argued that DCM serves both hedonic and utilitarian purposes, hence functioning as a source of inspirational content. Consequently, this can lead to increased levels of behavioural engagement (Hollebeek & Macky, 2019). Taiminen and Ranaweera (2019) state that cultivating long-lasting customer relationships and encouraging active customer interaction with enterprises are the main goals of using DCM. In light of these results, the following theory is put forth:

H1: 

DCM has a positive effect on BE.

1.3.2. The Impact of DCM on BD

According to Hollebeek and Macky (2019), DCM serves both hedonic and utilitarian ends; for that reason, it can offer motivational material, which helps with brand defence. Additionally, Sawaftah et al. (2021) demonstrate how various DCM strategies may impact brand defence, with varying degrees of impact based on the message sources that consumers choose. Customers’ behavioural intentions can improve when they assess the reliability of content sources, as found by AlSokkar et al. (2025). In marketing communications, the strength of persuasive influences is linked to the theory of source credibility. This has to do with how many people believe that a source is knowledgeable about the topic being discussed and, as a result, reliable when providing an unbiased analysis of the circumstances. Consequently, when individuals are emotionally exposed to a message source that is seen as credible, such as user-generated content, they are more likely to develop stronger attachments and engagement in comparison to sources that are perceived as less credible. Consequently, the consumers will endeavour to establish a sense of identification with fellow individuals and advocate for the aforementioned brand. The careful and thorough evaluation of the true merits of the provided information by an individual results in persuasion, such as the defence of a brand. The influence of DCM on brand defence is contingent upon the message source selected by the client (Sawaftah et al., 2021). Thus, the following hypothesis is proposed:

H2: 

DCM has a significant positive effect on BD.

1.3.3. The Impact of DCM on BA

According to the findings of Bilondatu and Tjokrosaputro (2023), DCM has the capacity to impact the emotional and cognitive states of both potential and current customers, which contributes to the establishment of brand attachment. According to Jun and Yi (2020), individuals who are regularly exposed to marketing content have heightened levels of brand attachment. Marketing strategy has a substantial role in captivating consumers. Marketing using social media is widely recognised as a highly efficacious approach in the contemporary era. Research has provided evidence to support the notion that DCM has the ability to influence consumer behaviour. Thus, the following hypothesis is proposed:

H3: 

DCM has a significant positive effect on BA.

1.3.4. The Effect of BE on BA

According to Wilk et al. (2018), brand attachment is enhanced by customer engagement. Therefore, the aforementioned data indicate that individuals who actively participate in online social media communities are inclined to form psychological connections with the business or community, and this increases brand attachment. According to Li et al. (2020), the act of customer engagement has a positive impact on the development of self–brand connection, which is a component of brand attachment. Additionally, it fosters emotional connections between customers and service providers. J. Kumar and Nayak (2019) imply that engaged customers are attached to a brand. Moreover, those who have developed an emotional connection to a particular brand demonstrate significant levels of loyalty towards that brand. According to Li et al. (2020), brand loyalty can be influenced by customer engagement through the mechanism of brand attachment. The objective of customer engagement is to cultivate emotional connections between service providers and customers, ultimately transforming customers into advocates for sellers (V. Kumar & Kaushik, 2020). According to Coelho et al. (2018), those who actively participate in the online brand community tend to establish robust emotional connections with the brand. Thus, the following hypothesis is proposed:

H4: 

BE has a significant positive effect on BA.

1.3.5. The Impact of BE on BD

According to Harrigan et al. (2021), customer brand engagement and brand advocacy and defence are positively correlated. Brand supporters showed higher levels of involvement with brands inside online forums than the ordinary customer, according to a study by Wilk et al. (2018). Li et al. (2020) found evidence to support the idea that engaging in tourism-related activities is positively correlated with good perceptions of a brand, which in turn leads to higher levels of consumer loyalty. In the discipline of psychology, Harrigan et al. (2021) claim that the degree of participation positively increases the motivation driving these attitudes. Mishra’s (2019) study found that customers who actively interact with a business are more inclined to suggest it. As a result, it may be assumed that brand defence will most likely result from engagement. Customers will actively engage with a brand and create a voluntary relationship that goes beyond simple transactions when they are inspired by aspects of that brand. Client engagement directly leads to brand defence. In contrast to individuals who are exposed to sources that are seen as less credible, customers who are emotionally exposed to a message source that they perceive as trustworthy are more likely to establish stronger attachments and display higher levels of engagement, according to attachment theory. As a result, consumers will make an effort to identify with their peers and promote the brand in question (Sawaftah et al., 2021). Consequently, the following hypothesis is proposed:

H5: 

BE has a significant positive effect on BD.

1.3.6. The Impact of BA on BD

Strong emotional ties and a robust bond between the customer and the brand constitute the foundation of brand defence. Customers exhibiting a strong emotional connection to a brand, coupled with a favourable attitude, are more inclined to engage in defensive behaviours to protect and maintain its reputation. According to Ali et al. (2021), true brand advocates are individuals who establish self-definitional relationships with a brand. According to Ilhan et al. (2018), individuals who express satisfaction with a particular brand demonstrate a propensity to share their favourable experiences with others to advocate for the brand. In the context of a dispute, criticism, attack, or threat, the provision of support may take on a defensive character (Wilk et al., 2018). Additionally, research indicates that consumers tend to adopt defensive behaviours to protect and maintain a brand when they establish a strong emotional connection with it (Sawaftah et al., 2021). Consequently, the subsequent hypothesis is proposed:

H6: 

BA has a significant positive effect on DC.

1.3.7. DCM, BE, and BD

Weerasinghe (2019) argues that delivering high-quality content that fosters brand engagement is essential for capturing consumer interest in a product. This is likely to result in a heightened propensity for future purchases. Moreover, a study conducted by Hollebeek and Macky (2019) supports the notion that digital content possessing both functional and hedonistic value can effectively enhance consumer engagement. Customers are subsequently motivated to voluntarily participate in behaviours such as brand defence due to this engagement, manifesting as behavioural engagement. The subsequent theory is therefore proposed:

H7: 

BE mediates the impact of DCM on BD.

1.3.8. DCM, BA, and BD

According to Chen and Qasim (2021), DCM contributes to enhancing brand attachment by demonstrating that repeated exposure to marketing information will enhance brand relationships. Ali et al. (2021) also reported that customers with emotional relationships to their brand will defend the brand. Sawaftah et al. (2021) also noted that when consumers have a strong emotional connection to a brand they are more likely to take an active role in protecting it. Consequently, the following hypothesis is proposed:

H8: 

BA mediates the impact of DCM on BD.

2. Materials and Methods

2.1. Instrument Development

The instruments for measuring each variable were developed by adapting questionnaires from prior studies. The survey questionnaire was designed following the positivist research paradigm and encompassed the demographic characteristics of the respondents along with scale items.

• The first section of the surveys collected data on respondents’ age, gender, and monthly income.
• In the second section of the survey questionnaire, the scale items were based on prior research studies.

The current study’s framework comprised four foundational constructs evaluated through scale items. The variables for DCM were measured using ten items that were modified from the scales of Pektas and Hassan (2020). Nine items, from the Chi et al. (2022) scale, were utilised to measure the BE variable, while four items from Li et al.’s (2020) research were used to assess BA. The questionnaire utilised for the purpose of measuring BD was adapted from the measurement scale developed by Sawaftah et al. (2021).

Table 1. Items used to measure constructs.

Constructs	Items	References
DCM		Pektas and Hassan (2020)
DCM1	DC expresses the facts.
DCM2	DC is important.
DCM3	I believe that DC is very informative.
DCM4	DC is a reliable source of information regarding product quality and performance.
DCM5	DC provides valuable information about products.
DCM6	DC provides the true image of the goods provided generally.
DCM7	I believe DC informs accurately.
DCM8	DC provides important fundamental information on products.
DCM9	I rely on the accuracy of DC.
DCM10	DC is a good approach to acquire information about products.
BE		Chi et al. (2022)
BE1	Using brand X makes me think about the brand.
BE2	Purchasing brand X stimulates my interest in learning more about the brand.
BE3	I feel really positive when I use brand X.
BE4	Using brand X makes me glad.
BE5	I feel great when I use brand X.
BE6	I’m pleased to utilise brand X.
BE7	Compared to other brands of the same product category, I spend a lot of time using brand X.
BE8	When I utilise items from this product category, I generally use brand X.
BE9	Brand X is one of the trademarks I usually use when using this product category.
BA		Li et al. (2020)
BA1	I am feeling personally linked to Brand X.
BA2	I feel emotionally linked to Brand X.
BA3	Brand X can represent me.
BA4	Brand X communicates to others about who I am.
BD		Sawaftah et al. (2021)
BD1	When others talk about the brand, I defend it.
BD2	When others talk about the brand negatively, I defend it
BD3	When others comment negatively about the brand, I talk about it positively.
BD4	When I hear others talking negatively about brand, I defend it.
BD5	I try convincing others to purchase the brand.
BD6	I share the positive aspects of this brand.

articulates the four constructs along with their associated references. Extensive research indicates that a five-point Likert scale is frequently preferred over a seven-point Likert scale in academic studies (Hair et al., 2016). The researchers employed a five-point Likert scale as the benchmark assessment in this study. Bougie and Sekaran (2019) asserted that a five-point rating scale is as effective as any alternative, and that enhancing the rating scale does not increase rating reliability. Each item on the five-point Likert scale was assessed from 1 (totally disagree) to 5 (totally agree), indicating the variables.

2.2. Population and Data Collection

The study sample included undergraduate students attending private universities who had a strong interest in fashion. To fulfil the objectives of this study, a convenient sample methodology was employed. Convenience sampling was a suitable approach for this research due to our limitations in time and budgetary resources, as well as the impracticality of accessing all the available sampling frames. Moreover, earlier studies have supported the use of convenience sample techniques (Kim & Hong, 2011; Cho & Workman, 2011). The researchers distributed 550 questionnaires in four Jordanian universities, namely the Al-Zaytoonah University of Jordan, University of Petra, Amman Arab University, and Princess Sumaya University for Technology. A six-month data collection phase resulted in the researchers being able to retrieve 440 distributed questionnaires. A total of 384 usable questionnaires were obtained from the sample set after collecting 440 distributed surveys, with 56 invalid responses removed. Among these, 26 participants left the questionnaire completely blank, while 30 provided only partial responses, leaving essential questions unanswered. Consequently, these 56 responses were excluded, and the final sample consisted of 384 fully completed and valid questionnaires used for data analysis.

2.3. Data Analysis

The statistical program SPSS 26 was employed to investigate the relationships among all the variables in this study and to conduct a descriptive analysis of the participants’ demographics. The investigation adhered to the guidance provided by Henseler et al. (2015) and employed SMART-PLS3 software to implement the two-stage structural equation modelling (SEM) approach. Confirmatory factor analysis (CFA) served as the initial phase in assessing the construct validity of the measures through the application of the measurement model. The second phase of the study focused on assessing the structural relationship among the latent elements to investigate assumptions related to causation and mediation effects.

3. Results and Discussion

3.1. CFA Findings

The two primary components of the CFA assessment are convergent and discriminant validity.

Table 2. Convergent validity and internal reliability.

Construct Variable	Item	Factor Loading	Average Variance Extracted (AVE)	Composite Reliability (CR)	Internal Reliability (Cronbach’s Alpha)
Digital Content Marketing (DCM)	DCM1	0.717	0.532	0.929	0.928
	DCM2	0.760
	DCM3	0.712
	DCM4	0.722
	DCM5	0.732
	DCM6	0.703
	DCM7	0.713
	DCM8	0.715
	DCM9	0.709
	DCM10	0.731
Behaviour Engagement (BE)	BE1	0.818	0.631	0.919	0.905
	BE2	0.847
	BE3	0.819
	BE4	0.825
	BE5	0.771
	BE6	0.758
	BE7	0.810
	BE8	0.763
	BE9	0.761
Brand Attachment (BA)	BA1	0.776	0.587	0.946	0.927
	BA2	0.713
	BA3	0.776
	BB4	0.765
Brand Defence (BD)	BD1	0.832	0.678	0.892	0.875
	BD2	0.842
	BD3	0.791
	BD4	0.729
	BD5	0.914

displays the findings from the convergent validity analysis, focusing on the extent to which various measures of the same concept align with one another (Hair et al., 2016).

Referring to Hair et al. (2016), all items exhibited standardised factor loadings exceeding the necessary threshold of 0.6. These findings are presented in Table 2. The loadings ranged from 0.703 to 0.914. Additionally, all average variance extracted (AVE) values surpassed the recommended threshold of 0.5, as proposed by Hair et al. (2016). AVE values indicate the extent to which the latent construct explains the variance in the indicators, with values ranging from 0.532 to 0.678. For every construct, the composite reliability (CR) values indicate how well the latent construct’s indicators match the construct beyond the acceptable cutoff point of 0.6, as proposed by Peterson and Kim (2013). The range of the CR values was 0.892 to 0.946. Cronbach’s Alpha scores ranged from 0.875 to 0.928, above Nunnally and Bernstein’s (1994) suggested cutoff point of 0.7. Cronbach’s Alpha values show how error-free a measure is.

The constructs’ levels, means, and scales are shown in

Table 3. Descriptive statistics and discriminant validity, using Fornell and Larcker’s approach and HTMT.

Variable	Mean	Level	CE	BA	BD	DCM
BE	4.16	High	0.800	0.529	0.831	0.781
BA	4.11	High	0.568	0.770	0.558	0.476
BD	4.11	High	0.609	0.569	0.829	0.823
DCM	4.16	High	0.705	0.538	0.633	0.731

The values in bold on the diagonal display the square roots of the average variance extracted; the values below the diagonal display the correlations of the Fornell and Larcker approach; the values above the diagonal display the HTMT results; all the constructs used a 5-point Likert scale: 1 = Strongly Disagree, 5 = Strongly Agree.

. Table 3 also demonstrates the idea of discriminant validity, which relates to how different a construct is from other constructs. There are two methods used to evaluate discriminant validity: Henseler et al. (2015) evaluate the Heterotrait–Monotrait ratio of correlations (HTMT), whereas Fornell and Larcker (1981) compare the standardised correlations with the square root of the average variance extracted (AVE).

According to the findings presented in Table 3, the square root of the average variance retrieved for each construct demonstrates larger values compared to the correlations seen between that particular construct and the other constructs (Hair et al., 2016). Furthermore, with values ranging from 0.538 to 0.705, the associations between the constructs were all below the predetermined threshold of 0.85. This implies that there are enough data to back up the constructs’ uniqueness (Kline, 2023). It was discovered that the latent constructs’ HTMT values, which ranged from 0.476 to 0.831, were below 0.90. As a result, it can be said that every evaluation of the latent constructs showed their total independence from one another (Henseler et al., 2015). The constructs’ descriptive statistics are provided in Table 3, which also contains the mean values of all the constructs that are higher than the midpoint of the five-point Likert scale. The customer engagement and DCM dimensions showed the highest mean scores of 4.16, while the brand attachment and brand defence dimensions showed the lowest mean scores of 4.11. Overall, all constructs’ mean levels had high values.

3.2. Hypotheses Finding

After the measuring model produced positive results, the structural model underwent additional evaluation. The claimed relationships were either confirmed or refuted using the coefficient values and the significance threshold. The t values of the model were calculated by the authors using a bootstrapping process with a sample size of 5000. According to Hair et al. (2006), a hypothesised correlation is statistically significant at a p value of less than 0.05 when the t values are equal to or greater than 1.645. The amount of variation explained, or R-squared, was used to evaluate how accurate the model was when making predictions. Customer engagement, brand attachment, and brand defence had R-squared values of 0.641, 0.339, and 0.806, respectively. Cohen (1988) asserts that all these values fall into the substantial category.

Table 4. Results of path analysis to examine causal effect hypotheses.

Path: IV→DV	β	SE	t	p
(H1) DCM→BE	0.336 ***	0.067	5.041	0.000
(H2) DCM→BD	0.448 ***	0.047	9.659	0.000
(H3) DCM→BA	0.191 *	0.080	2.345	0.010
(H4) BE→BA	0.187 ***	0.032	5.686	0.000
(H5) BE→BD	0.287 ***	0.037	7.832	0.000
(H6) BA→BD	0.096 **	0.029	3.251	0.001

β = standardized coefficient; SE = standard error; * p < 0.05. ** p < 0.01. *** p < 0.001 (two-tailed); DCM = Digital Content Marketing. BE = Behaviour Engagement; BA = Brand Attachment; BD = Brand Defence.

illustrates the findings of the examination of the causal effect hypotheses, numbered H1 to H6.

The findings outlined in Table 4 indicate that DCM significantly influences consumer engagement (β = 0.336, t = 5.041, p < 0.001, confirming H1), brand defence (β = 0.448, t = 9.659, p < 0.001, confirming H2), and brand attachment (β = 0.191, t = 2.345, p < 0.05, confirming H3).

The analysis revealed that customer involvement has a notable positive impact on brand defence (β = 0.287, t = 7.832, p < 0.01, which supports hypothesis 5) and brand attachment (β = 0.187, t = 5.686, p < 0.001, which supports hypothesis 4).

The study’s findings provide strong evidence for hypothesis H6, indicating a significant positive correlation between brand defence and brand attachment (β = 0.96, t = 3.251, p < 0.001). As a result, it was determined that all the proposed hypotheses, H1 to H6, concerning causal effects, received support.

3.3. Mediation Effects

Following the guidance of Hayes and Rockwood (2017), the researchers employed bootstrapping with a sample size of 5000 as a reliable approach for conducting mediation analysis. The process of calculating the sampling distribution involves a detailed and methodical approach that necessitates several iterations. The bootstrapping technique was initially employed to examine the route model, focusing solely on the overall effect while omitting the mediator from the analysis.

The assessment of the effects of the direct and indirect channels is conducted through the analysis of the sampling distribution (Awang, 2015). Hair et al. (2016) proposed the use of the variance accounted for (VAF) score as a means to assess the effectiveness of mediation. VAF computation is considered solely in instances where the indirect impact is assessed as statistically significant. In addition, Hair et al. (2016) reveal that mediation is deemed complete when the (VAF) value exceeds 80%. When the value of the VAF variable is situated between 20% and 80%, it is classified as a partial mediation. If the VAF is below 20%, it suggests that there is no mediation present. The findings regarding the mediation hypotheses, specifically H7 and H8, are detailed in

Table 5. Results of path analysis to examine mediation effect hypotheses.

Path: IV→M→DV	β	SE	t	p	VAF %
(H7) DCM→BE→BD	0.096 ***	0.024	3.748	0.000	1.94
(H8) DCM→BA→BD	0.009	0.006	1.179	0.118	1.62

β = standardised coefficient; SE = standard error. *** p < 0.001 (two-tailed); DCM = digital content marketing. BE = behaviour engagement; BA = brand attachment; BD = brand defence.

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According to the findings shown in Table 5, it can be observed that DCM exhibits a noteworthy positive indirect impact on BD by means of BE. The regression coefficient (β) for this relationship is 0.096, with a corresponding t value of 3.748, indicating statistical significance at a p value of less than 0.001. As a result, it may be concluded that hypothesis H7 has been substantiated. The value of the variance accounted for (VAF) was found to be 1.94, which was below the established threshold of 20%. The findings of the eight hypothesis tests show that DCM does not significantly affect BD through BA. The study’s non-significant beta coefficient of 0.009 (t = 1.179, p > 0.05) suggests that there is no significant indirect effect of DCM on BD through BA. Moreover, the VAF’s recorded value of 1.62 was less than the 20% threshold that was set. No discernible mediation effect in the phenomenon was observed. Consequently, hypothesis H8 was disproved. The results of the seventh hypothesis test show that BE has a significant indirect effect from DCM on BD. Figure 2 depicts the conceptual framework illustrating the model, along with the findings and the outcomes derived from the investigation of the research hypotheses.

In the light of the hypothesis H1 we can see clearly that DCM has a significant effect on BE. This result matches with previous research performed by Khairani and Fachira (2021), who discovered that DCM has a positive impact on consumers’ engagement through social media. The process of developing useful content ensures that companies can build positive attitudes and associations in customers, hence changing their attitude and behaviours. One more important finding in this study was that DCM has a significant and positive relationship with BD (supporting H2). This result agrees with the existing study by Sawaftah et al. (2021), who state that DCM is the most important approach in terms of encouraging BD. The study agrees with our findings obtained for DCM, showing that brands can enhance brand protection by paying more attention to behavioural engagement and outcome generation (Figure 2).

Building on this, research indicates that DCM creates brand defensive capabilities in a divergent manner based on industry specifications. Business-created content within the retail sector drives two essential outcomes: it grows brand worth and customer dedication while protecting company brands (Al-Abdallah et al., 2024). The use of user-generated content in telecommunications functions to build trust while protecting brands from negative perceptions, according to Eltokhy et al. (2024). Content marketing strategies in the lubricant sector create brand value jointly with customers, which establishes strong consumer devotion while safeguarding companies from market competition (Wickramasinghe, 2023). The process of behavioural engagement takes centre stage because interactive content generates consumer advocacy according to Sawaftah et al. (2021). The performance of DCM depends on generational traits, where UGC appeals to Gen Z but Gen Y prefers corporate content production (Sawaftah et al., 2021). These insights confirm that businesses should develop brand protection plans through specifically tailored DCM approaches that suit their industries.

The results indicated that DCM had a remarkable effect on BA in a way that supported hypothesis 3. This finding is consistent with the study performed by Bilondatu and Tjokrosaputro (2023), where it was established that whenever the customers engage socially with firms, that is, whenever they generate a positive form of communication, this is tagged as user-generated communication by consumers. Another way of strengthening BA is when consumers engage with the firm, interact with its material, and make an attachment to the brand. Other benefits that businesses enjoy include building brand equity, as well as building long-lasting relationships with customers through giving high-value, relevant, and engaging content.

Figure 2 shows that behaviour engagement has a significant and positive effect on BD, which supports H4. Ardiyansyah and Febrianti (2022) performed a study on the behavioural influence of BD and its impact on purchase decisions, confirming this observation. In their study, they established a significant relationship between consumer engagement and brand loyalty, which hence influences buying behaviour. This goes a long way to showing that attachment and emotional attachment with a company needs the customer to spend time and effort in being emotionally involved with the company. Since consumers can participate, they develop emotional bonding, commitment, and bonds, an important step towards creating a long-term consumer–firm relationship. In addition, the research results show that behavioural engagement has a significant and positive impact on BD (H5). This result is consistent with the results obtained by Vander Schee et al. (2020), which shows that customer engagement influences branding results. The understanding of these relations has a large significance concerning future consumers’ engagement. The degree of consumer engagement could be directly related to the overall degree of brand protection. The authors thus recommend the use of active participation as a way to enhance envisaged consumer perceptions, brand loyalty, and trust, all of which will support the protection of the brand.

Furthermore, this analysis suggested that brand attachment is a significant predictor of brand defence, thereby supporting hypothesis 6. This outcome is consistent with that indicated by Shimul et al. (2024) on the effects of brand attachment in consumer–brand relationships after brand misuse in the retail banking sector. The outcomes indicated that brand connections might increase customers’ attitudes of willingness to forgive the bank as a relevant stakeholder following wrongdoing, as well as possibly decreasing their intention to shift to another brand. This goes further to support the security role of brand affection in maintaining consumer–brand relationships. This can be expressed in the high level of customer identification with a brand, rational consumption, and willingness to turn a blind eye to a brand’s shortcomings.

4. Theoretical and Managerial Implications

4.1. Theoretical Implications

This empirical research enhances the theoretical comprehension of the effect of DCM on customer behaviour and brand defence, leading to a more profound understanding of marketing dynamics in the fashion sector. The theoretical framework, alongside developed metrics, demonstrates a universal application which extends past the fashion industry domain. This research brings results that can be transferred and applied across sectors, including tourism and cosmetics as well as consumer-oriented industries, to show that theoretical ideas can cross boundaries regardless of industry domain. This research enhances knowledge around behaviour engagement, brand attachment, and defence mechanisms by showing how the theoretical constructs function throughout various consumer markets. The research provides valuable foundational material for future investigations to strengthen the relationships among brand attachment, DCM, and brand defence.

Brand attachment proved to be statistically insignificant as a mediator between DCM and brand defence. Brand defence reactions appear to stem from factors different than brand attachment when DCM takes effect. The impact of digital content may function directly to develop consumer–brand identification, brand loyalty, and a sense of community, which independently strengthen brand defence while bypassing the role of brand attachment. Further theoretical investigation is needed to discover how DCM shapes brand defence, because its relationship with brand attachment fails to produce statistically meaningful effects. Theoretical development in this field can emerge from examining alternative mechanisms that transfer DCM’s effects into brand protection, such as brand trust, brand authenticity, and emotional brand engagement. The examination of potential consumer-related and industry-based variables could help researchers to determine the limitations within which digital content marketing influences brand defence.

This research clarifies significant elements impacting the fashion business, which provides critical guidance to fashion experts and academics who work in Jordanian fashion markets and comparable developing economies. The research enhances understandings of brand defence factors and emphasises how DCM subjects customers to fundamental brand-related perceptions while fostering brand consumer interactions. This research produces theoretical findings which marketers and planners could utilise in developing their strategies and campaigns.

4.2. Managerial Implications

Fashion sector managers should utilise the research findings to establish DCM strategies which produce better outcomes. Managers should direct their resources to DCM in order to develop behaviour engagement and strengthen brand attachment, as well as defence capabilities. Increased customer engagement together with enhanced brand loyalty drives the protection of fashion brands in their market competition. This research emphasises that fashion businesses need to view DCM as a fundamental strategy which managers should avoid under-valuing. Managers should establish digital content influence on consumer behaviour and brand defence initiatives by viewing it as a vital element of marketing practice. Businesses need to dedicate funds toward creating elite content and absorbing narratives along with interactive features to drive customer engagement and develop brand loyalty.

The case of Zara uses fast fashion operations as an example. The implementation of DCM at Zara helped to build consumer devotion while enhancing the brand’s relationship with its customers. Zara builds undeniable anticipation of new trends among target customers by posting attractive social media content featuring the most current styles. The brand uses influencers alongside original videos from users to maintain its active Instagram presence, which strengthens consumer emotions toward Zara. The brand protection against market competition along with its leading position in the quick fashion sector receives support from Zara’s digital content management approach.

DCM requires recognition as a beneficial approach for fashion businesses to use as their primary marketing method. Fashion industry managers can create one cohesive brand story across various digital channels through the proper implementation of DCM in their overall marketing strategy. Companies should implement these strategies to develop strong brand images, while enhanced brand recognition helps create unique market positions among competitors. Fashion industry managers receive an important practical benefit because brand attachment fails to act as a significant mediator between DCM and brand defence. The research implies that the increased emotional bond from DCM aids brand attachment, yet the positive brand defence effect does not necessarily depend on this bond. Managers should implement alternative digital content mechanisms to build brand trust and authenticity, as well as community structures, to develop brand defence effectively.

5. Conclusions

The present work has presented the effects of DCM on BE, BA, and BD within the context of the fashion industry in Jordan. This research study introduces a theoretical framework that integrates DCM as an additional direct factor influencing behavioural engagement, brand attachment, and brand defence by offering a more comprehensive explanation of the influence of DCM on BE and BA, ultimately bolstering BD within the fashion industry in Jordan. It should be noted that the influence of DCM on BD was more pronounced, and BE and BA exhibited a noteworthy and beneficial influence on BD. This means that BE has a greater influence on BD in the fashion industry compared to BA. In addition, the results indicate that the influence of DCM on BD is mediated through BE. There is no evidence to support the idea that BA plays a mediating role in the interaction between DCM and BD. It is possible that DCM does not have a major impact on BD through BA. This unexpected result requires the considered examination of the precise characteristics of DCM that influence BE, which will be considered in future research to understand the influence of BD in the context of DCM. The results obtained in this research provide an in-depth examination of how DCM techniques influence brand advocacy in the Jordanian fashion industry, thus contributing significantly to current marketing knowledge. This work addresses an important gap in the literature by elucidating these intricate relationships, which have often been neglected in discussions regarding the impact of DCM on consumer behaviour with regard to brand loyalty.

6. Limitations and Further Research

This study has made valuable contributions; however, acknowledging its limitations is crucial, as they may lead to new research opportunities. A key limitation is the constraint of collecting responses only once, facilitated by the use of a survey questionnaire. The research can be enhanced by integrating an experimental design and employing longitudinal data collection methods to broaden the scope of future studies. Furthermore, our research focused on a particular dimension of customer engagement. Future research could improve the theoretical framework by incorporating cognitive and emotional dimensions to evaluate their impact on brand defence. Researchers may investigate different types of DCM, including customer-generated and company-generated content. Subsequently, researchers may perform a comparative analysis to evaluate the effects of different types of DCM on behavioural engagement, brand attachment, and brand defence.

A potential area for future research includes the examination of alternative constructions associated with brand defence. While we have categorised brand defence as a form of positive voluntary conduct, subsequent research may investigate different frameworks to analyse the impact of DCM on broader brand advocacy or specific brand defence behaviours. This study focused on students attending private universities with a significant interest in fashion. It is essential to note that most participants identified as male and female within this specific demographic. The generalizability of our results to other populations may be limited. To address this limitation, future research may expand the study’s scope by including samples from various locations or countries, thereby enabling a more comprehensive analysis of the relationships explored in this study.