AN0184340243;jeb01apr.25;2025Apr11.02:31;v2.2.500
Role of marketing agility capability in reactance and student engagement with new industry technologies
In the rapidly changing business landscape, technological advancements position marketing technologies as pivotal components of successful businesses. However, notable evidence of student reactance exists that affects students' engagement and diminishes their competitiveness in the professional arena. This study examines student reactance and highlights the role of marketing agility capabilities in enhancing student engagement. The results obtained through PLS-SEM indicate the mediating effect of reactance on student engagement and reveal the vital role of marketing agility capabilities in mitigating the negative effects on student engagement. This study contributes valuable insights into the complex dynamics of student reactions to marketing technology in educational contexts, underscoring digital fluency and marketing agility.
Keywords: Technology; reactance; marketing agility capability; engagement; digital fluency; coercion
Introduction
The dynamic field of marketing is undergoing a transformation driven by rapid advancements in digital technologies (Katsikeas et al., [22]). These digital shifts are opening unprecedented avenues for marketers to leverage innovation and resulting opportunities to impact a business's success. A vital component of any marketing practice today centers on digital technology, whether it is e-commerce, online search, websites, social media, or mobile marketing (Labrecque et al., [24]). Therefore, knowledge of new industry technology has clear implications for marketing students' career success and is a definite priority among scholars and professionals (Ferrell & Ferrell, [14]). Despite the need to acquire technological knowledge and skills in industry technology, marketing students perform substantially worse than students from other disciplines in demonstrating the use of technology for marketing activities (Kotler et al., [23]). Marketing educators are currently facing significant challenges as they strive to achieve the goals of a digital technology-based curriculum (Faranda et al., [13]).
Additionally, research has identified a gap in entry-level employee competencies and industry needs and recognizes this workplace-readiness gap as one of the most serious problems facing the business landscape (Mishra et al., [29]). Specifically, ThinkLA (Consortium of Advertising Agencies) reportedly found it the most challenging to hire the most qualified business graduates to fill positions that require the use of digital media and technologies. An invaluable student success activity is a learning activity that emphasizes the practical application of industry technology. However, prior research findings indicate that despite the potential benefits, certain students may resist learning new technologies for various reasons, ultimately limiting their ability to fully leverage the learning opportunities presented (Mishra et al., [29]; Pan, [31]). Only 17% of the marketing industry hiring executives expressed confidence in the entry-level applicant pool's understanding of the industry technology (Rohm et al., [35]). Consequently, equipping students with the necessary skills to succeed both academically and professionally becomes a significant challenge for educators.
In the rapidly changing landscape of the marketing profession, recent technological advancements have become integral to marketing activities (Mathur, [26]). It is crucial that new marketing graduates seamlessly integrate new marketing technologies into their skill sets to thrive in the highly competitive work environment. As a result, students' reactance comes into focus when they experience threats to their freedom, choices, or autonomy. We develop a conceptual framework using a combination of dynamic capabilities theory and reactance theory along with technological determinism to address issues of reactance and perceived coercion and increase the engagement and professional success of students in courses involving learning technology through marketing agility capabilities. This study examines the following three research questions:
RQ1: How do digital fluency and perceived coercion affect student reactance and student engagement with experiential learning of industry technology? RQ2: How does student reactance account for student engagement with experiential learning activities involving industry technology? RQ3: What role does marketing agility capability play in student engagement considering student reactance?In recent times, digital fluency has become recognized as an essential skill set that involves the extent to which an individual generates and reformulates information and to effectively, creatively, and productively, leverages digital technologies to achieve desired outcomes (Wang et al., [43]). In educational contexts, coercion can manifest as students feeling compelled to engage in activities or use tools without a sense of choice. Accordingly, perceived coercion refers to the imposition of forceful compliance and restrictive requirements in learning activities that limit students' sense of autonomy (Lojdová & Vlčková, [25]). Students' psychological resistance or reactance grounded within the reactance theory framework refers to a negative psychological reaction that manifests when students perceive that their freedom to make choices is restricted or controlled by others, such as instructors or institutional requirements, and behave against what is expected of them (Ball & Goodboy, [3]). Student reactance can lead to negative reactions, adversely influencing student engagement with experiential learning of new industry technologies. Student engagement in learning activities refers to the degree of attention, effort, participation, curiosity, interest, and passion demonstrated by a student (Chiu, [6]) that makes their education more impactful and applicable to real-world situations. In education settings where hands-on skills or industry readiness are emphasized, engagement is especially crucial, as it enables students to develop practical abilities and adapt to new tools or technologies confidently. Therefore, engagement is not just about keeping students attentive; it shapes how well they understand, retain, and apply what they learn, laying a strong foundation for academic and professional success.
Marketing agility capability reflects the extent to which a student can acquire information and skills to address marketing opportunities for career success (Zhou et al., [48]). It creates enthusiasm, optimism, and urgency to gain proficiency in professional technology (Poolton et al., [33]). Hence, we argue that marketing agility capability reflects the ability to learn about the broader business environment within which professionals operate and respond proactively. It reflects an anticipatory ability of a business entity to discern the current and potential marketplace to consistently sense and act on events and trends (Morgan et al., [30]). Thus, marketing agility capabilities provide a novel means through which student engagement in learning activities and student readiness for the workplace can be achieved. There is also a paucity of insights about marketing agility capabilities, resulting in the Marketing Science Institute considering the examination of marketing agility as a critical research priority for 2018–2020. Furthermore, few insights appear in the literature that investigate the role of producing students who are technologically agile in diminishing student reactance and enhancing student engagement (Taylor, [40]).
"Agility," in general, is a broad term for adaptability and quick thinking, applicable in a variety of disciplines and distinctive contexts, such as accounting, technology, leadership, and shopping. Agility is also characterized in terms of leadership agility demonstrated through leaders' enhanced sensemaking abilities and their capacity to navigate dynamic, disruptive, and complex changes in both external and internal environments (Tandon et al., [38]). Agility in business has been applied to define and shape various terms and frameworks that guide organizational strategies, operations, and culture. For example, accounting agility represents the ability of accounting professionals to quickly and effectively adapt to changes in financial environments, regulations, and organizational needs emphasizing sense-making and responding (Yigitbasioglu, [45]). Technology agility refers to a firm's capacity to swiftly adapt to and respond to technological advancements and changes (Dunlop-Hinkler et al., [12]). Shopping agility involves flexibility, decision-making skills, and resourcefulness for optimizing the shopping experience. In the context of businesses and operations, the concept of agility has extended its influence to areas such as manufacturing, supply chain management, and marketing (Asseraf et al., [2]). Marketing agility highlights the ability of firms to recognize opportunities in a dynamic environment and to quickly adapt their marketing tactics accordingly (Zhou et al., [48]). Marketing agility stands apart as a unique concept, distinct from other related ideas in marketing and associated disciplines, and is a dynamic capability (Kalaignanam et al., [21]; Zhou et al., [48]). While marketing agility is widely acknowledged as a critical factor in achieving marketing excellence, the current dynamic business environment calls for more studies that emphasize its role in the literature (Kalaignanam et al., [21]).
Overall, this study makes three significant contributions. First, with very few review studies on students' marketing agility and psychological reactance (Amarnath & Jaidev, [1]), this study examines how reactance accounts for student engagement with experiential learning of industry technology. By integrating reactance theory from the field of psychology with technological determinism, this study demonstrates that the effects of digital fluency and perceived coercion on engagement can be explained through student reactance. Second, grounded in dynamic capabilities theory from the field of strategic marketing management, this study examines conditions of marketing agility capabilities under which reactance-induced failures in student engagement can be reduced. Third, this study extends the knowledge about student engagement with experiential learning opportunities by examining its antecedents, the mediating factor, and the moderating factors, thereby offering intriguing insights about the enhancement of student engagement.
Theoretical background
Technological determinism, reactance, and dynamic capabilities theory
Technology for marketing activities includes various tools that help businesses plan, execute, and analyze marketing campaigns. This study emphasizes learning industry-standard technology commonly used by marketers to perform their activities, such as creating content for websites and blogs, advertising development, creating email campaigns, optimizing search engine visibility, marketing on search engines, marketing on social media, and visualizing and analyzing consumer data for effective marketing decisions. The commonly used technology for marketers includes web-based technology, such as content management systems (e.g. Wix, WordPress, Squarespace, or Magento) that enable marketers to develop, manage, and optimize marketing content to improve brand awareness and search engine visibility. The tools such as SEMrush and Google Analytics support search engine optimization activities and analyses of website traffic and performance. Technology facilitating email marketing campaigns includes web-based platforms such as Mailchimp and Constant Contact. Furthermore, creating ads for search engines, social media, and websites efficiently is facilitated through web-based platforms, monetization tools, and content creation software, such as Google Ads, Facebook Ads Manager, Canva, and Adobe Creative Cloud. Data visualization and business intelligence technology, such as Looker Studio, are useful to marketers who can create customizable reports facilitating ways to optimize marketing campaigns. Comprehensive marketing web-based technology, Wishpond, enables businesses to develop landing pages, design websites and popups, run contests and promotions, and track and evaluate visitor behavior to effectively manage marketing efforts, gaining insights from website traffic and increasing engagement with customers.
With advancements in technology and the availability of web-based platforms and software, marketers now have access to essential tools that enable them to perform tasks more efficiently and demonstrate the tangible value of marketing to a business. Hence, knowledge and application of these are crucial to marketing students' success in the industry. Advances in technology influence skills, business strategies, and user behavior (Dafoe, [10]). Changing technologies are one of the many factors that change the marketing processes and are integral to connecting marketers with consumers, such as e-commerce and mobile commerce (Dholakia et al., [11]). Technological determinism emphasizes the view that technology is a vital governing factor in processes of social change and transforming societal practices, consumption, and norms (Dafoe, [10]). Espousing this perspective, the position of "technological determinism" within the marketing field suggests that technology drives innovative marketing practices, and thus new technological developments enhance the impact of marketing on business performance (Grewal et al., [15]). Consequently, to remain competitive in the work environment, it is critical for students to gain knowledge and skills in leading technologies (Crittenden et al., [8]).
This study considers digital fluency within the framework of technological determinism to highlight the role technology plays in developing new marketing mixes, strategies, and programs grounded in technology. However, a considerable gap exists between the expectations of the marketplace and students' marketing technology preparedness (Mishra et al., [29]). Therefore, this study examines the theory of psychological reactance in conjunction with technological determinism to explain student reactance and engagement with learning activities involving technology. Within the context of classroom learning activities, students perceive assignment requirements as coercive restrictions to their freedom and behaviors (Mirick, [28]). Reactance theory offers a systematic framework for examining student perceptions of coercion in learning activities involving the use of new technology.
The dynamic capabilities theory has been extensively applied in research exploring the financial implications of marketing (Teece et al., [42]). The theory posits that developing capabilities in response to the dynamic marketing environment is critical for addressing the evolving technological landscape (Teece, [41]). In this study, marketing agility capabilities are conceptualized as the critical capabilities in marketing that enable marketers to adapt their efforts to quickly and effectively respond to changes in the technological environment, learn new technologies, and integrate them into their marketing efforts (Zhou et al., [48]). Marketing agility emphasizes proactivity and implies active research to understand current and potential needs in the technologically advancing marketplace.
Hypotheses development
Relationships of digital fluency and perceived coercion with student reactance
Contemporary literature conceptualizes digital fluency as an individual's ability to generate and reformulate information and to effectively use digital technologies (Wang et al., [43]). It includes proficiency in articulating, expressing, producing, and generating information by reformulating knowledge with respect to using technology. Technological determinism theory offers an organized conceptual framework for understanding students' behavior in the use of new technologies. Digital fluency underscores an individual's knowledge, skills, and responses to the required use of digital technologies (Bologa et al., [4]). Students experience reactance as they examine ways to reclaim their freedom and resist an advocated behavior (Mirick, [28]). Research has shown students' reactance to cell phone policies in the classroom and their enactment of uncivil classroom behaviors (Tatum et al., [39]). Consistent with prior studies, it is expected that students will experience higher reactance when they believe a threat exists to their freedom leading to negative responses, anger, and noncompliance. The reactance process is not induced when students have greater digital fluency, as it adds clarity and confidence in their skill and use of technology. We therefore hypothesize in alternative one-tailed form that:
H1a:
Digital fluency is negatively related to student reactance.
H1b:
Digital fluency is negatively related to students' perceived coercion.
H1c:
Perceived coercion is positively related to student reactance.
Relationships of reactance, digital fluency, and perceived coercion with student engagement
With prior perceptions about the marketing field, marketing students infer their attitudes toward learning new industry technology in the classrooms. As instruction in the classroom conflicts with the students' attitudes toward industry technology, students perceive a threat to their sense of choice and freedom (Mirick, [28]). Psychological reactance theory provides theoretical insights into understanding students' reactance toward experiential learning of industry technology. However, more often it is found that students express reactance in complying with performing such experiential learning activities (Ball & Goodboy, [3]). In accordance with the psychological reactance theory, when students perceive that their prior attitudes toward marketing technology are being challenged, they are more likely to experience reactance. Moreover, recent studies found evidence that instructions in classrooms can trigger student reactance (Zhang & Sapp, [46]).
Student engagement is a multi-dimensional construct that consists of behavioral, emotional, cognitive, and agentic components. As students are coerced into performing assignments that introduce new technology and require them to adapt to learning marketing in a newer and unfamiliar way, student reactance is more likely to be evoked. Reactance represents an individual's negative psychological response to defy a persuasive effort in which the individual is compelled and then acts opposite to what is expected (Rafael & Lopes, [34]). Studies report that an individual responds to a perceived threat to freedom in the form of attitude changes. Hence, a student is more inclined to oppose the instructors' directions, and this behavior can be expressed as an attempt to circumvent the instructions (Mirick, [28]; Zhang & Sapp, [46]). We, therefore, hypothesize that:
H2a:
Reactance is negatively related to student engagement.
H2b:
Digital fluency is positively related to student engagement.
H2c:
Perceived coercion is negatively related to student engagement.
Mediating role of student reactance
Since digital fluency implies skills and capabilities to properly use digital technologies (Ball & Goodboy, [3]), it also represents the responses of students required to use technology in their learning activities (Wei et al., [44]). When students perceive their abilities to be strong in using new technologies in their assignments and consider themselves to be capable of applying their ideas through the new technology, then negative reactions are subdued. Students do not perceive a threat to their freedom, and student reactance and resistance will not be aroused. Prior studies have also found that any perceived threat to student autonomy could provoke reactance that in turn influences a student's attitude toward compliance with classroom learning activities and engagement (Zhang & Sapp, [46]).
Student engagement reflects students' active involvement in their learning activities consisting of behavioral, emotional, cognitive, and agentic aspects of engagement (Zhang et al., [47]). Emotional engagement encompasses affective responses toward peers, instructors, and learning activities, particularly emotions such as happiness, boredom, anxiety, and excitement (Chiu, [6]). On the other hand, cognitive engagement reflects students' mental efforts to finish their tasks using a self-regulated strategic approach to learning rather than superficial learning strategies and agentic engagement involves students' active contribution to their learning and teaching (Merkle et al., [27]). Consequently, with greater digital fluency, students are better able to grasp new technology and its features to complete their assignments and learning activities. Accordingly, negative student reactions are not evoked, and student engagement increases. However, greater perceived coercion elicits student reactance, as students perceive autonomy-impinging persuasive attempts in relation to an instructor's role-related power over students (Zhang & Sapp, [46]) triggering students' negative reactions that diminish student engagement. Therefore, we hypothesize that:
H3a:
Students' reactance mediates the relationship between digital fluency and student engagement.
H3b:
Students' reactance mediates the relationship between perceived coercion and student engagement.
Moderating role of marketing agility
Marketing agility enables students to learn about new technologies marketers use to execute their strategies, learn about trends, and address the changes in the marketplace. Thus, students who have high marketing agility actively use new technology to finish their learning activities and assignments. As a result, reactance declines. Students with marketing agility capability are better able to integrate marketing knowledge and new technologies emerging in the marketing field. Consequently, students effectively learn new features of the technology and its functions and are successful in completing their assignments. Therefore, the usage of technology for an assignment is not disturbing, irritating, or frustrating for students, and student reactance decreases. In addition, student perceptions of coercion also decline, as they are more inclined to learn new technology and proactively integrate it into finishing their assignments.
Marketing agility endows individuals and organizations with the means for trial and error learning, prompt response to the changing marketing landscape, and making sense of the market to adjust effectively (Homburg et al., [20]). Despite the key role of marketing technology in improving the effectiveness of marketing programs, students exhibit reactance (Ball & Goodboy, [3]). Therefore, the marketing agility capabilities of students increase students' abilities to learn and commit to gaining knowledge and skills in new technologies and trends in the marketing field. Consequently, students' levels of attention, effort, and interest in working on learning activities and assignments, ultimately, achieving learning goals are exhibited, leading to greater student engagement. Therefore, we hypothesize that:
H4a:
Marketing agility moderates the relationship between digital fluency and reactance, such that the digital fluency-reactance relationship is strengthened when marketing agility is high.
H4b:
Marketing agility moderates the relationship between digital fluency and perceived coercion, such that the digital fluency-perceived coercion relationship is strengthened when marketing agility is high.
H4c:
Marketing agility moderates the relationship between perceived coercion and reactance, such that the perceived coercion-reactance relationship is weakened when marketing agility is high.
H4d:
Marketing agility moderates the relationship between reactance and student engagement, such that the reactance-student engagement relationship is weakened when marketing agility is high.
Research method
Sample and data collection
This study focuses on student perceptions of their abilities to learn new technologies developing in the marketing field and the work environment influencing student engagement in the classroom. Undergraduate marketing students taking a digital marketing course at a public university in the southeastern region of the United States participated in this research. Since this study involves human research participants, prior approval was obtained from the Institutional Review Board of the university at which the study was conducted. First, a pretest was conducted to evaluate the psychometric properties of the measures, and then a main study was conducted. The participants were provided with appropriate informed consent at the beginning of the questionnaire.
Pretest
The pretest conducted with 54 undergraduate junior-level marketing students also included a check for potential sources of social desirability bias (Crowne & Marlowe, [9]) and common method bias (Podsakoff et al., [32]). The psychometric properties of the measures were evaluated by assessing the reliability and validity of the measures, and based on the results, measures were refined (Hair et al., [16]). Consistent with previous studies, we included a six-item scale to assess social desirability bias (Strahan & Gerbasi, [37]). The evaluation of correlations between the social desirability construct and each of the focal constructs of this study revealed that the correlations were not statistically significant, indicating that social desirability bias is not likely to affect the results of this study. Thus, social desirability bias is not a concern in this study. The pretest also assessed the common method bias using Harmon's single factor test (Chang et al., [5]). The analysis showed none of the factors account for the major portion of the variance in the study, thus suggesting that common method bias is not an issue in this study (Merkle et al., [27]).
Main study
The main study was also conducted with undergraduate junior-level marketing students taking a digital marketing course at a public university in the southeastern region of the United States who responded to an online survey administered through the Qualtrics online delivery platform. Consistent with existing studies on student perceptions, data were collected via online surveys (Merkle et al., [27]), which yielded 172 responses. The survey instrument included questions adapted from prior studies that were pretested. The sample of the main study specifically consisted of 54% females and 46% males, with 86% students in the age range of 20 to 25 years of age. 14% of respondents belonged to ages ranging from 26 to 35 years. Table 1 presents the demographic information of the respondents.
Table 1. Demographic profile of the respondents.
| Demographic profile | Frequency | Percent |
| Gender | ||
| Female | 93 | 54 |
| Male | 79 | 46 |
| Total | 172 | 100 |
| Age | ||
| <20 | 0 | |
| 20–21 | 142 | 82.56 |
| 22–23 | 4 | 2.33 |
| 24–25 | 2 | 1.16 |
| 26–27 | 11 | 6.40 |
| 28–29 | 9 | 5.23 |
| 30> | 4 | 2.33 |
| Total | 172 | 100 |
| Class status | ||
| Freshman | 0 | 0 |
| Sophomore | 0 | 0 |
| Junior | 164 | 95.35 |
| Senior | 8 | 4.65 |
| Total | 172 | 100 |
Measures
The constructs were measured using multi-item scales adapted to the context of this study from prior research. All the measures were assessed using a 7-point Likert scale from "strongly disagree" to "strongly agree" anchors. The measures for digital fluency include four items and were adapted from Wei et al. ([44]). Accordingly, the digital fluency items assess the knowledge and skills associated with new technology in the marketing field. The measures of marketing agility were adapted from Zhou et al. ([48]) that reflect the learning and understanding of the new technology marketers use in their work environment. Reactance was measured with a four-item scale adapted from Shoenberger et al. ([36]) that reflects the extent to which students are irritated, angry, disturbed, or displeased with the assignments requiring the usage of new technology. Perceived coercion assessed the student's perception of their choice to use technology in completing an assignment and was measured using a four-item scale adapted from Rafael and Lopes ([34]). The 11-item student engagement scale was adapted from Zhang et al. ([47]) to capture the behavioral, emotional, cognitive, and agentic aspects of engagement (Chiu, [6]). The measurement items for the constructs of this study are provided in Table 2.
Table 2. Constructs and measurement items.
| (1) Digital fluency Adapted from: Wei et al. (2020); 1 = Strongly disagree and 7 = Strongly agree I am able to quickly pick up new features of technology used in my assignment. I am able to use the new technology functions to articulate my assignment. I often explore new features of the technology used in my assignment. I am able to apply my ideas via the new technology used in my assignment. |
| (2) Marketing agility Adapted from: Zhou et al. (2019); 1 = Strongly disagree and 7 = Strongly agree I am good at learning about new technology marketers use in the work environment. I am good at understanding trends in the marketing field. I am good at learning how to use technologies marketers use in their profession. I am good at understanding job requirements for marketers in the changing technology landscape. I am good at integrating market and technology information. I can respond to the changing marketing field by learning new technology. I can adjust to learning new technology to match the job needs of the marketplace. I am quick to learn new technology as it becomes available to marketers. |
| (3) Perceived coercion Adapted from: Rafael and Lopes (2019); 1 = Strongly disagree and 7 = Strongly agree I am not forced to use technology for an assignment. It is my choice to use technology in an assignment. The assignment requires the use of technology. I am free to choose using technology for completing an assignment. |
| (4) Reactance Adapted from:(Shoenberger et al., 2021); 1 = Strongly disagree and 7 = Strongly agree The usage of technology for an assignment is not disturbing to me. The usage of technology in an assignment is welcomed. The required usage of technology in an assignment is pleasing to me. The usage of technology for an assignment is not irritating for me. |
| (5) Student engagement Adapted from: Zhang et al. (2020); 1 = Strongly disagree and 7 = Strongly agree I look over notes between classes to make sure I understand the material. I take good notes in class. I do my best to understand important concepts. I take the initiative to ask questions when I don't understand the material. I complete assignments on time. I participate actively in group discussions. I answer questions asked by the teacher in class. It is important to obtain knowledge that develops techniques. I am good at learning the material in this class. I do my best to get good grades in this class. I am confident that I can learn enough to do well in this class. |
Model
To evaluate the hypotheses, PLS-SEM technique was employed, as it allows for assessing a complex set of theoretical relationships and facilitates the simultaneous analyses of the relationships among independent, dependent, mediating, and moderating variables within a single conceptual model (Hair et al., [19]). PLS-SEM was performed using SmartPLS 4.0 (Hair et al., [17]).
Measurement model
Using the confirmatory composite analysis approach (Hair et al., 2021), a technique analogous to CFA for CB-SEM, the measurement model was assessed. This technique consists of an examination of the composite reliability, item loadings, average variance extracted (AVE), and discriminant validity (Hair et al., 2021). The descriptive statistics and correlations for all the latent variables are presented in Table 3.
Table 3. Descriptive statistics and correlations.
| Mean | SD | (1) | (2) | (3) | (4) | (5) | |
| (1) Digital fluency | 6.34 | 0.64 | 1.00 | ||||
| (2) Marketing agility | 5.86 | 0.84 | 0.47 | 1.00 | |||
| (3) Perceived coercion | 2.38 | 1.11 | −0.62 | −0.20 | 1.00 | ||
| (4) Reactance | 1.77 | 0.85 | −0.76 | −0.44 | 0.66 | 1.00 | |
| (5) Student engagement | 5.84 | 0.93 | 0.49 | 0.56 | −0.38 | −0.55 | 1.00 |
The item loadings were above 0.69 and statistically significant, with composite reliability in the range between 0.73 and 0.95, exceeding the recommended threshold (Hair et al., 2021). The AVE values were computed to be in the range of 0.65–0.89, providing evidence of reliability and convergent validity among the measures in the study. In addition, discriminant validity was assessed by comparing the AVE values of every construct in the study with the shared variances among the constructs, and the AVE values exceeded the squared correlations, providing strong evidence of discriminant validity (Hair et al., 2021). Table 4 presents each construct's reliability, AVE values, and interfactor correlations to provide measures of convergent validity and discriminant validity.
Table 4. Reliability, convergent validity, and discriminant validity.
| Composite reliability | Average variance extracted (AVE) | (1) | (2) | (3) | (4) | |
| (1) Digital fluency | 0.73 | 0.65 | ||||
| (2) Marketing agility | 0.95 | 0.74 | .54 | |||
| (3) Perceived coercion | 0.82 | 0.66 | .74 | .18 | ||
| (4) Reactance | 0.86 | 0.77 | .61 | .46 | .73 | |
| (5) Student engagement | 0.94 | 0.89 | .58 | .59 | .31 | .63 |
Structural model
The assessment of the structural model first involved examining multicollinearity among the constructs of this study. All the constructs' variance inflation factors were below 3.0 indicating that multicollinearity is not affecting the results (Hair et al., [16]). Following the assessment of multicollinearity, path coefficients and their statistical significance were examined by performing the PLS bootstrapping procedure that involved 5,000 samples to produce bias-corrected confidence intervals for each coefficient. Table 5 presents the results of the structural model.
Table 5. Structural model path analysis.
| Path | Effect | Hyp. | Supported? |
| Direct effects | |||
| Digital fluency → Reactance | −0.45*** | H1a | Yes |
| Digital fluency → Perceived coercion | −0.78*** | H1b | Yes |
| Perceived coercion → Reactance | 0.33*** | H1c | Yes |
| Reactance → Student engagement | −0.41*** | H2a | Yes |
| Digital fluency → Student engagement | 0.08 | H2b | No |
| Perceived coercion → Student engagement | 0.01 | H2c | No |
| Indirect effects | |||
| Digital fluency → Reactance → Student engagement | 0.19*** | H3a | Yes |
| Perceived coercion → Reactance → Student engagement | −0.14*** | H3b | Yes |
| Moderating effects | |||
| Marketing agility × Digital fluency → Reactance | −0.08*** | H4a | Yes |
| Marketing agility × Digital fluency → Perceived coercion | −0.14*** | H4b | Yes |
| Marketing agility × Perceived coercion → Reactance | −0.19*** | H4c | Yes |
| Marketing agility × Reactance → Student engagement | −0.12** | H4d | Yes |
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