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Navigating the Digital Landscape: The Impact of Social Media Agility on Customer-Based Brand Equity, Customer Engagement and Customer Motivation

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17 October 2025

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20 October 2025

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Abstract
Despite the increasing number of social media users and the advantages linked to agility in other areas, the implementation of agility within a social media framework remains unexamined. A quantitative method was utilized. Data was collected from 420 Social Networking Site users in Turkey, utilizing the convenience sampling method. SmartPLS was used for data analysis. The results show that perceived social media agility has a positive impact on customer-based brand equity, customer engagement, and customer motivation. Customer engagement and customer motivation were found to impact customer-based brand equity significantly. Furthermore, customer motivation has no significant impact on customer engagement. Change-seeking has a positive influence on customer engagement and customer motivation. Customer engagement and customer motivation were found to significantly mediate the link between perceived social media agility and customer-based brand equity. The study provides an exhaustive framework that incorporates social media agility, customer motivation, and engagement, thereby enhancing theoretical models of brand equity and customer behaviour. Managers must prioritize the development of flexible social media strategies that can swiftly adapt to changes and trends, cultivating a dynamic and participatory online presence. This study advances digital marketing literature by integrating the investigated constructs into a unified conceptual framework.
Keywords: 
brand
;  
engagement
;  
marketing
;  
motivation
;  
social media agility
Subject: 
Business, Economics and Management  -   Marketing

1. Introduction

Traditionally stemming from operations management, agility has gained prominence as an essential characteristic for companies functioning in highly dynamic and complicated environments (1). Dynamic customer expectations have forced businesses to learn how to quickly change their strategies, tactics, and operations in order to survive in today's highly competitive business world (2) (3). Social media has profoundly influenced customer expectations. Diverse phenomena and disciplines have employed the concept of agility (4). Change-seeking is a proactive approach to exploring opportunities for growth and innovation in social media (5). Change seekers proactively pursue new opportunities instead of simply reacting to external environmental changes (6). Businesses may remain competitive and adapt to the evolving social media ecosystem by embracing change (7).
The rise of social media as a medium for enhancing customer involvement has been acknowledged in the literature (8) (9) (10). Projections indicate that the number of global social media users will reach 4.89 billion by 2023, up from 2.77 billion in 2019 and 2.46 billion in 2017 (11). Social media usage in Turkey has been consistently rising, with over 76 million users annually (12). Businesses capitalize on this trend by improving customer engagement, employing tailored advertising, and collecting real-time feedback (13). Prominent platforms, such as Instagram (approximately 58.3 million users), Facebook (1.9 million users), and Twitter (21.5 million followers), facilitate businesses in enhancing brand visibility, disseminating content efficiently, and developing tailored marketing strategies. Despite the increasing number of social media users and the advantages linked to agility in other areas, the implementation of agility within a social media framework remains unexamined (14) (15). Moreover, there is a limited understanding of how the implementation of agility within a social media framework influences favourable customer-related characteristics, such as customer engagement and customer-based brand equity (CBBE) (5). Social media agility denotes the capacity to rapidly adjust and react to alterations, trends, and audience engagements across digital channels. It entails immediate response, adaptable content, and strategic interaction to sustain relevance in a fluctuating online environment.
This study aims to address the paucity of research on the topic. According to the authors, there is a scarcity of research on perceived social media agility, with notable exceptions being studies by (17) (18) (19) (20). This research distinguishes itself by addressing crucial gaps in understanding how customer motivation and engagement mediate the link between perceived social media agility and customer-based brand engagement within a comprehensive framework. Unlike previous studies that examined these elements separately, this study integrates them to provide a complete view of how social media agility influences brand engagement through mediating factors. It is the first to explore the impact of change-seeking behaviour on customer motivation and engagement, a subject not yet covered in existing literature. Brands can leverage these findings to enhance customer interactions, strengthen brand loyalty, and optimize their social media strategies. By understanding the mediating role of customer motivation, brands can tailor content to match customers' intrinsic and extrinsic motivators, thereby increasing the relevance and personalization of interactions. Recognizing the significance of perceived social media agility allows brands to create adaptive, responsive, and trend-focused campaigns that keep audiences engaged. Consequently, this work offers multiple theoretical contributions.
First, we contribute to social media literature by extending the previous literature on the constructs of perceived social media agility, customer motivation, customer engagement, change seeking, and customer-based brand equity, and we tested these proposed constructs within a unified conceptual framework. This innovative approach can assist brands in effectively formulating strategies to meet social media customers' expectations (21). Secondly, we posited that perceived social media agility, facilitated by customer engagement and motivation, can enhance customer-based brand equity, recognized as a crucial source of competitive advantage for brands. Comprehending the determinants of customer-based brand equity is essential for fulfilling customer requirements via social media platforms. By examining these antecedents, precise and effective methods that promote meaningful relationships, enhance loyalty, and ultimately propel success can be formulated. Third, we extend the research of (17) (18) (19) (20), addressing the call for additional investigation into the antecedents of CBBE. Although (17) have investigated the mediating role of customer engagement in the relationship between perceived social media agility and customer-based brand equity (CBBE), it is essential to consider additional mediators. Therefore, we respond to this call by examining the mediating function of customer motivation alongside customer engagement within a unified conceptual framework. Customer motivation denotes the fundamental factors that affect individuals' decision-making processes and purchase behaviour (22). It includes the requirements, aspirations, and objectives that drive customers to search for, assess, and select particular items or services (23). Furthermore, to augment prior studies and offer a more thorough understanding, we investigate the influence of change-seeking on improving customer motivation and engagement.
Fourth, the study was carried out in Turkey, a distinct geographic region with more than 76 million users of social networking sites, and this figure is predicted to rise. The integration of social media into daily life in Turkey has become a vital aspect, particularly among younger demographics. This study is notably innovative within the Turkish context due to its unique cultural and geographical setting. Turkey presents a diverse and dynamic digital landscape that offers valuable insights into consumer behaviours. Examining perceived social media agility and its impact on customer-based brand equity in this region may provide significant insights for companies seeking to engage with a technologically proficient and socially active audience.
Service-Dominant (S-D) Logic represents a theoretical framework that transitions the focus from traditional goods-dominant logic to a service-oriented perspective on value creation (24). Conceived by Stephen Vargo and Robert Lusch, S-D Logic posits that value is co-created through interactions among multiple stakeholders, rather than being inherent to the products or services themselves (25). The Service-Dominant (S-D) Logic theory effectively elucidates the relationship among perceived social media agility, customer-based brand equity (CBBE), customer engagement, and customer motivation. S-D Logic emphasizes the collaborative generation of value through interactions and relationships between companies and customers. In this context, perceived social media agility enhances customer engagement by facilitating more responsive and participatory communication (26). This increased engagement, in turn, augments CBBE by fostering stronger customer-brand connections and loyalty. Furthermore, customer motivation is crucial in promoting engagement and shaping the perceived value of the brand. S-D Logic provides a robust framework for understanding the interconnections among these factors by emphasizing dynamic interactions and value co-creation.

1.1. Perceived Social Media Agility, Customer-based Brand Equity, Customer Engagement, and Customer Motivation

Perceived social media agility (PSMA) is an organization's ability to adapt, respond, and engage with its audience in real-time on social media platforms (5). According to (27) (28), customer-based brand equity (CBBE), which includes brand awareness, associations, loyalty, and perceived quality, is impacted by PSMA. PSMA enables brands to personalize communication, address customer concerns quickly, and maintain relevance, making it a strategic need in a competitive digital environment (29). Customer engagement (CE) is the emotional and behavioural connection customers have with a brand (30). (31) (17) suggest that PSMA promotes CE by creating timely, relevant, and engaging experiences. This flexibility attracts modern customers who value authenticity and responsiveness. Customer motivation (CM) is influenced by psychological factors like information, entertainment, or social connection (32). PSMA can impact CM by delivering real-time, relevant, and emotionally stimulating content that aligns with customer trends and preferences, creating an engaging environment for brand-related activities (33,34).
H1: 
Perceived SMA has a significant positive influence on CBBE.
H2: 
Perceived SMA has a significant positive influence on CE.
H3: 
Perceived SMA has a significant positive influence on CM.

1.2. Customer Engagement and Customer-Based Brand Equity

The association between CE and CBBE is established in the literature. CE promotes meaningful interactions that improve customers’ emotional and cognitive connections to a brand (30). These connections support brand associations that contribute to enhanced brand awareness, perceived quality, and loyalty (35). Moreover, engaged customers often share positive WOM, advocate for the brand, and co-create value, which further strengthens the brand's equity in the market (36). Findings by (37) on a sample of 316 respondents who used digital payment apps and those of (5), who recruited a total of 200 adult subjects from Amazon MTurk, revealed a significant association between CE and CBBE. Other studies with similar findings include (38) (39) (40) (41) (42) (43).
H4: 
CE significantly influences CBBE.

1.3. Customer Motivation, Customer-Based Brand Equity, and Customer Engagement

Customer motivation (CM) is an internal state that drives customers to identify and buy products or services that fulfil conscious and unconscious needs or desires. It arises from the need to satisfy personal, social, or psychological goals (44). CM impacts customers’ brand-related attitudes and behaviours. It also directly influences customers' perception of a brand's value offer (45). Intrinsic and extrinsic motivation are key factors in customer engagement, influencing brand quality and loyalty (32). Intrinsic motivation stems from genuine interest, curiosity, or passion, while extrinsic motivation, driven by incentives, can boost engagement but may lead to temporary interactions (46) (47). Highly motivated customers exhibit higher levels of behavioural and emotional involvement with brands, thereby increasing brand visibility and credibility (48) (49). Research shows a strong association between intrinsic and extrinsic motivation and CE with a brand (50) (44) (51).
H5: 
CM has a significant positive influence on CBBE.
H6: 
CM has a significant positive influence on CE.

1.4. Change Seeking, Customer Engagement, Customer Motivation

Change-seeking behaviour refers to a customer's inclination to seek out novel experiences, products, or services, so impacting their engagement behaviours (17). Ambitious change-seekers exhibit curiosity and a spirit of adventure, interacting with pioneering brands (52,53). (54) (55) (56) indicate that individuals exhibiting change-seeking behaviour may allocate fewer resources to relationships, hence influencing their participation in online brand communities. Hence, they are less likely to be engaged with the firms.
CM has been empirically found to be impacted by change-seeking tendencies (57). Intrinsic motivation often leads to behaviours that include trying new products, adopting innovative technologies, or exploring unconventional solutions (58). (59) highlighted that customers with higher change-seeking tendencies show stronger motivational forces (59). Thus, change-seeking behaviour results in a heightened level of CM, particularly in cases where brands offer opportunities for experimentation, growth, and self-expression (60).
H7: 
CS significantly influences CE.
H8: 
CS significantly influences CM.

1.5. The Mediation Role of Customer Engagement and Customer

Motivation Research highlights that PSMA strategies enhance the brand's ability to capture customer attention and effectively address their specific needs. According to Self-Determination Theory (61), CM is essential in translating brand efforts into positive outcomes. Intrinsic and extrinsic motivations drive customer perceptions of brand value (62). Brands that show PSMA inspire CM through timely and relevant interactions. This motivation leads to stronger CBBE, as motivated customers tend to perceive the brand as valuable, credible, and consistent with their expectations.
CE is widely recognized as an important outcome of effective social media strategies (63) (64). PSMA enhances CE by promoting dynamic, personalized, and interactive experiences that captivate and sustain customer attention. Perspectives from engagement theory (65) reveal that highly engaged customers form strong emotional connections with the brand, which increases CBBE. Studies have shown that customers who are actively engaged through social media content tend to perceive the brand as valuable (66) (67) (68). Hence, it is proposed that CE will mediate the association between a brand's agility in social media and its ability to build CBBE.
H9: 
CM significantly mediates the link between PSMA and CBBE.
H10: 
CE significantly mediates the link between PSMA and CBBE.

2. Materials and Methods

The study employed a quantitative research method. It gathers statistical data and offers insight about an event or organization (69) (70). The research starts with a deductive theory, which the results then test or confirm, creating a framework for the research questions and hypotheses (71) (72). The proposed model encompasses several key constructs, including perceived social media agility, change seeking, customer engagement, customer-based brand equity, and customer motivation, all of which have been empirically discussed by scholars. See Figure 1.

2.1. Sampling and Data Collection

Social media users in Turkey who are actively engaged with social networking sites (SNSs) comprise the research population. Turkey's distinctive customer dynamics, expanding digital market, and socio-cultural variety make it a representative instance for examining customer engagement and brand interaction. Turkey, characterized by rapid digital transformation and strong brand loyalty, offers valuable insights for emerging countries where digital engagement strategies are crucial for effective branding. This study provides insights relevant to emerging markets. The study used a convenience sampling technique to collect data from the respondents. (73) confirmed that sample size has a positive effect on reliability and claimed that measurement theory cannot usually tolerate large doses of sampling error, and then recommended 300 as an adequate sample size for analysis. Hence, we confirm that the study’s sample size (420) is sufficient for further analysis. All participants were required to complete the survey. The participants received the questionnaires along with a cover letter. We assured the participants that their responses would remain confidential. The questions in the survey were adopted from the literature. The research instrument consists of five variables: perceived social media agility (6 items), change seeking (7 items), customer engagement (4 items), customer-based brand equity (4 items), adopted from (5), and customer motivation (4 items), adapted from (74). The authors constructed the questionnaire on a 7-point Likert scale, which ranges from 1 (strongly disagree) to 7 (strongly agree). See Table A1 in the Appendix section.

2.2. Data Collection and Procedure

Primary data was collected from the respondents with experience in social networking sites through a self-administered questionnaire. The survey approach enabled the collection of data from 420 users. A pilot study using thirty participants was carried out, and the pilot data were used to assess reliability and validity measures. The outcome demonstrated strong validity and reliability metrics (i.e., Cronbach's alpha > 0.70; AVE > 0.50).

2.3. Data Analysis

The partial least squares (PLS) approach was employed due to its efficacy in forecasting the primary statistical objective of the study, accommodating small sample sizes, analysing extensive residuals, and assessing intricate models (75). The postulated hypotheses were evaluated using PLS-SEM with SmartPLS software.

3. Results

3.1. Measurement Model Assessment

3.1.1. Convergent Validity

The examination of convergent validity involves computing AVE, reliability, and Composite Reliability (CR) to assess internal consistency (75). Indicator reliability clarifies the variation in items due to a variable. A score of 0.70, 0.80, or above for reliability and CR score signifies that the associated measure possesses substantial reliability. (refer to Table 1).

3.1.2. Discriminant Validity

The degree to which a factor shows significant variation from others is termed discriminant validity. To set out discriminant validity, it is essential for the Fornell-Larcker criterion diagonal values to be larger than the inter-construct correlations (76). Discriminant validity is established when an indicator's loadings are higher than the associated items of a different construct. The Fornell-Larcker result is presented in Table 2.
HTMT is the ratio of the average correlations. This ratio helps in assessing if the constructs are distinguishable from each other. An HTMT value below 0.85 generally indicates good discriminant validity (76). In more conservative scenarios, a threshold of 0.90 may be applied. Hence, the values of THMT indicate good discriminant validity. See Table 3.

3.2. Structural Model Assessment

The structural model was examined, using the path coefficients to evaluate the importance and relevance of the links within the model (77) (78). Furthermore, a concise overview of the path coefficients and the relationship between the latent ideas, together with their related t-test values, was provided. Following the process of bootstrapping, Figure 2 presents the connection between the study variables PSMA, CBBE, CE, CM, and CS. The process of bootstrapping is utilized to iteratively estimate the route model by employing significantly modified data configurations, as reported by (79).

3.2.1. R-Square, Q²Predict, and f-Square

The coefficient of determination (R2) values indicates how well the model explains the variability of the outcome data. The closer the R² value is to 1, the better the model explains the variability (80) (81) (82) (83). The endogenous variables guarantee the forecast significance of the structural model CBBE, CE, and CM, which possess corresponding R2 values of 0.606, 0.424, and 0.519, respectively. Q-square measures the extrapolative relevance, where a value greater than zero indicates a good level of analytical relevance (84) (85) (86). This value denotes whether a model can precisely predict outcomes. The values of Q2 for CBBE, CE, and CM were 0.592, 0.416, and 0.513, respectively. The f2 establishes a considerable effect of one variable on another (84) (87) (88). (89) defined the thresholds for interpreting the F-square value (f² ≥ 0.02 = small effect size, f² ≥ 0.15 = medium effect size, f² ≥ 0.35 = large effect size). See Table 4.

3.2.2. Hypotheses Test

Hypothesis 1 analyses the impact of PSMA on CBBE (β = 0.253, t= 5.156, p < 0.05). Consequently, H1 is supported. H2 evaluates if PSMA has a substantial impact on CE (β = 0.229, t = 4.330, p < 0.05). Therefore, H2 is supported. H3 was evaluated to ascertain the impact of PSMA on CM (β = 0.304, t = 6.639, p < 0.05). Consequently, H3 is supported. H4 evaluates if customer experience (CE) has a substantial impact on CBBE (β = 0.291, t = 7.052, p < 0.05). Consequently, H4 is supported. The impact of CM on CBBE was evaluated by testing H5 (β = 0.395, t = 9.208, p < 0.05). Consequently, H5 is supported. H6 assesses the impact of CM on CE (β = 0.018, t = 0.286, p > 0.05). Consequently, H6 is not supported. H7 evaluates the significant impact of CS on CE (β = 0.484, t = 8.092, p < 0.05). Therefore, H7 is supported. H8 establishes that CS has a significant impact on CM (β = 0.509, t = 12.414, p<0.05). Consequently, H8 is supported. A model is deemed to have an acceptable fit when the Standardised Root Mean Square Residual (SRMR) is less than 0.08, according to Shi et al. (2020). The SRMR number is beneath the threshold, signifying an adequate match. Refer to Table 5.
Hypothesis 9 assesses the mediation effect of CM between PSMA and CBBE. The result shows that CM significantly and partially mediates (β = 0.120, t = 5.767, p < 0.05) the link between PSMA and CBBE. Therefore, H9 is supported. Hypothesis 10 examines the mediating effect of CE on the relationship between PSMA and CBBE. The result shows that CE significantly and partially mediates (β = 0.067, t = 3.536, p < 0.05) the link between PSMA and CBBE. Hence, H10 is supported. See Table 6.
The direct, indirect, and total effect of the exogenous construct on the endogenous construct is presented in Table 7.

4. Discussion

4.1. Main Findings

The study investigated the influence of perceived social media agility on customer-based brand equity. The study's findings are substantially useful for researchers, lawmakers, and private organisations aiming to improve customer experience.
Perceived social media agility denotes a brand's ability to promptly and efficiently react to alterations and trends on social media platforms. The present study found that PSMA significantly affects CBBE (direct effect) (β = 0.253, t = 5.156, p < 0.05). The result is consistent with the findings of (90) and (5), who contend that agile brands on social media may communicate with customers in real time, rapidly respond to problems, and stay abreast of current trends. This timeliness and relevance augment customers' view of the brand's value and dependability, resulting in enhanced brand equity (91).
The present study found that PSMA significantly affects CE (β = 0.229, t = 4.330, p < 0.05). The results are similar to those of (5), who found that PSMA has a significant influence on CE and stated that by producing timely and pertinent content that resonates with their audience, PSMA increases contact and participation. Agility has been shown to improve a brand's ability to interact with its customers. It has been linked to increased customer satisfaction and brand loyalty, which have been found to positively impact customer engagement.
The study found that PSMA has a significant effect on CM (β = 0.304, t = 6.639, p < 0.05). This finding is consistent with (92); they found that proactive and adaptable characteristics of businesses encourage customer loyalty and investment, as individuals perceive a greater sense of connection and value. Social media agility is essential for establishing and sustaining robust customer relationships (91).
The study found that CE significantly affects CBBE (β = 0.291, t = 7.052, p < 0.05). (93) and (94) suggest that when customers are highly engaged, they are more likely to form a strong connection with the brand, resulting in higher levels of customer-based brand engagement.
Our study found that CM significantly affects CBBE (β = 0.397, t = 9.208, p < 0.05). Highly motivated customers engaged deeply with the brand, leading to higher levels of CBBE (95).
Our study found that CM has no significant effect on CE (β = 0.018, t = 0.286, p > 0.05). This finding contradicts those of (96), who found that motivated customers interact with brand content, participate in loyalty programs, and spread positive word-of-mouth. This active engagement helps build stronger relationships between the brand and its customers, ultimately enhancing brand loyalty and overall customer satisfaction.
The study found that CS significantly affects CE (β = 0.484, t = 8.092, p < 0.05). (5) and (97) found that change-seeking behaviour significantly influences customer engagement by driving customers to actively seek out new experiences and interactions with brands. This behaviour fosters a deeper connection between the customer and the brand, as it aligns with their desire for novelty and improvement.
The study found that CS significantly affects CM (β = 0.509, t = 12.414, p < 0.05). Customers inclined towards change are more likely to be motivated by the appeal of novelty and innovation, leading them to engage more deeply with companies that offer novel and constantly changing products or services (98). This behaviour corresponds with their inherent aspiration for novel experiences and self-improvement, thereby augmenting their incentive to engage with and maintain loyalty to the brand.
The results of the mediation revealed that CM significantly mediates the connection between PSMA and CBBE (β = 0.120, t = 5.767, p < 0.05). The study of (99) and (5) supports our finding; they found that when a brand is seen as agile on social media, it can enhance customer motivation, which in turn positively impacts the brand's equity.
The mediation results show that CE significantly mediates the link between PSMA and CBBE (β = 0.067, t = 3.536, p < 0.05). The study of (5) and (99) found that when a brand is seen as agile on social media, it enhances customer engagement, which in turn positively impacts the brand's equity.
The findings of this study align with Service-Dominant (S-D) Logic, which emphasizes the collaborative creation of value, resource integration, and active engagement with clients during service exchanges. The notable impact of brands' ability to swiftly adapt to social media indicates that those who respond promptly to online interactions cultivate stronger relationships with customers, thereby enhancing the collaborative value creation inherent in service-dominant logic. Furthermore, Customer engagement and motivation significantly influence brand equity and loyalty, suggesting that organizations should exhibit agility in social media marketing strategies, fostering customer-driven interactions to enhance brand value.

5. Conclusions

The authors investigated the influence of perceived social media agility on customer-based brand equity. The current research puts forward hypotheses based on the Service-Dominant (S-D) Logic. The utilisation of the theory has highlighted the significance of perceived social media agility, customer engagement, customer motivation, and change-seeking on customer-based brand equity. This study distinguishes itself from prior research by incorporating essential themes, including perceived social media agility, engagement with customers, consumer motivation, and change-seeking, into a cohesive framework. This methodology seeks to analyse the cumulative effect of these principles on customer-based brand equity. This research provides a comprehensive perspective aligned with Service-Dominant (S-D) Logic, emphasising the collaborative creation of value and the dynamic interactions among brands, in contrast to previous studies that examined these elements in isolation. This study investigates the relationship between customer engagement and customer management in linking perceived social media agility to customer-based brand equity, providing empirical evidence of the impact of social media agility on brand equity through consumer behaviours. Unlike earlier studies, it recognises change-seeking as a critical element affecting consumer motivation and engagement, highlighting a hitherto neglected facet of customer behaviour.
This study builds upon existing work about Service-Dominant (S-D) Logic, revealing that perceived social media agility has a significant impact on customer engagement, customer motivation, and customer-based brand equity. Brands demonstrating adaptation on social media are better positioned to attract and retain customer engagement. This agility yields timely and relevant information, rapid responses to client enquiries, and proactive participation in online dialogues. Engaged and motivated clients are more likely to develop a deep emotional and psychological bond with the company, leading to enhanced brand loyalty and advocacy. Moreover, change-seeking was found to influence customer engagement and motivation strongly. Individuals who seek new experiences and innovations are more likely to engage with agile brands. This behaviour substantially affects customer engagement and motivation, hence enhancing brand equity.

5.1. Theoretical Implications

This study's findings significantly enhance the existing research on perceived social media agility and its impact on customer-based brand equity, particularly within the context of Service-Dominant (S-D) Logic. This study demonstrates that PSMA has a direct and substantial impact on consumer-based brand equity, customer engagement, and customer motivation. These findings underscore that PSMA serves as a crucial tool for enterprises to influence customer choices and enhance engagement. The study offers a comprehensive framework that integrates social media agility, customer motivation, and engagement, thereby improving theoretical models of brand equity and consumer behaviour. This framework can serve as a foundation for subsequent research examining the complexities of digital marketing strategies and their effects on customer relationships and brand outcomes.

5.2. Managerial Implications

The findings underscore the significance of perceived social media agility in enhancing customer-based brand equity, customer engagement, and customer motivation. Managers must prioritise the formulation of adaptable social media strategies that can rapidly respond to changes and trends while fostering a dynamic and engaging online presence. Therefore, companies may significantly improve engagement and motivation, which are critical components of brand equity. Given that customer engagement and motivation moderate the relationship between social media agility and brand equity, managers need to focus on strategies that attract and retain customer interest and participation. Engaging, significant, and customised interactions, along with timely responses, can strengthen the relationship between the brand and its customers. Moreover, the advantageous effect of change-seeking behaviour on engagement and motivation suggests that organisations should consistently innovate and offer distinctive experiences to their clients. Incorporating customer insights into a brand's social media tactics may maintain a dynamic and appealing business image. Social media managers can improve customer engagement, brand perception, and strategic flexibility by utilising perceived social media agility. They can formulate real-time marketing plans, customise campaigns, and explore creative content formats. This enhances customer-centric brand equity and fosters enduring loyalty, ensuring brands stay competitive and customer-focused in a swiftly changing digital environment.

5.3. Limitations and Future Research

This study has several limitations that should be acknowledged. First, the small sample size, primarily consisting of social media users, may constrain the generalizability of the results. In the future, researchers may use a more diverse sample from a broader range of geographical areas to learn more about the things being studied. The cross-sectional study design makes it difficult to see how customer behaviour changes over time. The study only illustrates the relationship between PSMA, CBBE, and other significant factors at a specific moment in time. A longitudinal strategy would have facilitated the observation of the examined relationships as they evolve. Potential cultural bias resulting from a single-region sample limits this study. Due to cultural conventions, geographical trends, and economic realities, the results might not be fully applicable to different geographic places. By conducting cross-regional comparisons or expanding the sample to include a wide range of participants, future research may overcome this limitation.

Author Contributions

Conceptualization, Figen YESILADA and Iman AGHAEI; methodology, Chinedu Felix Ikoko; software, Iman AGHAEI; validation, Figen YESILADA, Iman AGHAEI; formal analysis, Iman AGHAEI; investigation, Chinedu Felix Ikoko; resources, Chinedu Felix Ikoko; data curation, Iman AGHAEI; writing—original draft preparation, Chinedu Felix Ikoko; writing—review and editing, Figen YESILADA; visualization, Iman AGHAEI; supervision, Figen YESILADA. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data will be available on request due to privacy.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
PSMA Perceived social media agility
CS Change seeking
CE Customer engagement
CBBE Customer-based brand equity
LSMU Level of Social Media Use
CM Customer Motivation

Appendix A

Appendix A.1

Table A1. Survey Questionnaire.
Table A1. Survey Questionnaire.
Construct Item
Perceived social media agility
PSMA1 This brand can quickly detect changes in the social media environment
PSMA2 This brand can promptly identify changes in customer needs in the social media environment
PSMA3 This brand can quickly respond to changes in the social media environment
PSMA4 This brand can quickly respond to changes in customer needs in the social media environment
PSMA5 This brand has the capacity to adjust the scale of its response to changes in the social media environment as needed (e.g., the firm being able to build a significant presence on Snapchat when customers’ preferences shift from Facebook to Snapchat)
PSMA6 This brand has the capacity to adjust the scale of its response to changes in customer needs in the social media environment as needed (e.g., the ability to respond to 1 customer post or 100 customer posts in a day if needed)
Change seeking
CS1 I like trying new things rather than continue doing the same old things
CS2 I like to experience novelty and change in my daily routine
CS3 I like a job that offers change, variety and travel, even if it involves some danger
CS4 I am continually seeking new ideas and experiences
CS5 I like continually changing activities
CS6 When things get boring, I like to find some new and unfamiliar experience
CS7 I prefer an unpredictable way of life to a routine way of life
Customer engagement
CE1 My interaction with this brand makes me feel valuable
CE2 I feel I have a special bond with this brand
CE3 I feel I have a personal connection with this brand
CE4 I feel I have a special relationship with this brand
Customer-based brand equity
CBBE1 It makes sense to buy the products or use the services of this brand instead of any other brand, even if they are the same
CBBE2 Even if another brand has the same features as this brand, I would prefer to buy the products or use the services of this brand
CBBE3 If there is another brand as good as this brand, I prefer to buy the products or use the services of this brand
CBBE4 If another brand is not different from this brand in any way, it seems smarter to purchase the products or use the services of this brand
Level of Social Media Use
LSMU1 Social media has been used by many salespersons in our company.
LSMU2 Social media is widely recognized among our salespersons
LSMU3 Social media is used by our salespersons almost every day.
Customer Motivation
CM1 I am satisfied with the experience of using SNSs
CM2 I am pleased with the experience of using SNSs
CM3 My decision to use SNSs was a wise one
CM4 My feeling with using SNSs was good

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Figure 1. Conceptual model.
Figure 1. Conceptual model.
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Figure 2. Figure 2. Structural model (Bootstrapping).
Figure 2. Figure 2. Structural model (Bootstrapping).
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Table 1. Construct Reliability and Validity.
Table 1. Construct Reliability and Validity.
Items Loadings α CR AVE VIF
CBBE1 0.901 0.916 0.941 0.798 3.017
CBBE2 0.890 2.894
CBBE3 0.903 3.120
CBBE4 0.879 2.565
CE1 0.742 0.801 0.87 0.626 1.461
CE2 0.758 1.571
CE3 0.839 1.873
CE4 0.821 1.659
CM1 0.876 0.905 0.933 0.778 2.583
CM2 0.905 3.060
CM3 0.885 2.673
CM4 0.861 2.377
CS1 0.774 0.903 0.924 0.634 2.093
CS2 0.805 2.287
CS3 0.873 3.328
CS4 0.802 2.235
CS5 0.735 2.137
CS6 0.819 2.450
CS7 0.757 1.936
PSMA1 0.668 0.889 0.915 0.645 1.623
PSMA2 0.784 2.055
PSMA3 0.834 2.406
PSMA4 0.854 2.593
PSMA5 0.866 2.682
PSMA6 0.795 2.143
Notes: α: alpha value (reliability); CR: composite reliability; AVE: average variance extracted; CBBE: customer-based brand equity; CE: customer engagement; CM: customer Motivation; CS: change seeking; PSMA: perceived social media agility
Table 2. Discriminant validity - Fornell – Larcker criterion.
Table 2. Discriminant validity - Fornell – Larcker criterion.
CBBE CE CM CS PSMA
CBBE 0.893
CE 0.607 0.791
CM 0.682 0.477 0.882
CS 0.757 0.621 0.674 0.796
PSMA 0.629 0.503 0.580 0.543 0.803
Table 3. Discriminant validity – Heterotrait - monotrait ratio (HTMT).
Table 3. Discriminant validity – Heterotrait - monotrait ratio (HTMT).
CBBE CE CM CS PSMA
CBBE
CE 0.701
CM 0.748 0.557
CS 0.828 0.718 0.741
PSMA 0.694 0.585 0.629 0.594
Table 4. R-square, Q²predict, and f-square.
Table 4. R-square, Q²predict, and f-square.
R-square Q²predict f-square
CBBE 0.606 0.592
CE 0.424 0.416
CM 0.519 0.513
CE → CBBE 0.150
CM → CBBE 0.247
CM → CE 0.000
CS → CE 0.208
CS → CM 0.380
PSMA → CBBE 0.097
PSMA → CE 0.057
PSMA → CM 0.135
Table 5. Hypothesis Test.
Table 5. Hypothesis Test.
Path Β Mean T-Statistics P Value Decision
H1 PSMA → CBBE 0.253 0.255 5.156 0.000 Supported
H2 PSMA → CE 0.229 0.234 4.330 0.000 Supported
H3 PSMA → CM 0.304 0.303 6.639 0.000 Supported
H4 CE → CBBE 0.291 0.290 7.052 0.000 Supported
H5 CM → CBBE 0.397 0.394 9.208 0.000 Supported
H6 CM → CE 0.018 0.015 0.286 0.775 Rejected
H7 CS → CE 0.484 0.485 8.092 0.000 Supported
H8 CS → CM 0.509 0.510 12.414 0.000 Supported
Model summary: SRMR = 0.058, NFI = 0.851
Table 6. Mediation.
Table 6. Mediation.
Mediation
Path Β Mean T-Statistics P Value Decision
H9 PSMA → CM → CBBE 0.120 0.119 5.767 0.000 Supported
H10 PSMA → CE → CBBE 0.067 0.068 3.536 0.000 Supported
Table 7. Direct, Indirect, and Total Effect.
Table 7. Direct, Indirect, and Total Effect.
Path Β Mean Standard deviation T statistics P values
Direct Effect
PSMA → CBBE 0.253 0.255 0.049 5.156 0.000
Indirect Effect
PSMA → CM → CBBE 0.120 0.119 0.021 5.767 0.000
PSMA → CE → CBBE 0.067 0.068 0.019 3.536 0.000
Total Effect
PSMA → CBBE 0.441 0.444 0.047 9.427 0.000
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