Investigating consumers’ cognitive, emotional, and behavioral engagement in social media brand pages: A natural language processing approach

Purpose Consumers rapidly assess packaging through a mix of conscious and unconscious processes, which play a crucial role in shaping their engagement and purchase decisions. The purpose of this study is to explain how visual realism versus caricature in ingredient-based front-of-pack illustrations differentially activates dual cognitive–affective processing routes and shapes multidimensional consumer engagement. Design/methodology/approach Across four complementary studies, this study investigates how illustration type influences preference (behavioral engagement), visual attention (cognitive engagement), emotional intensity (emotional engagement), cognitive load (cognitive engagement) and implicit evaluations (emotional engagement). First, a within-subject behavioral experiment assessed preferences for realistic versus caricatured illustrations across two product categories. Next, neurometric and non-neurometric tools employing eye-tracking, electroencephalography and implicit association tests measured visual attention, cognitive load, emotional intensity, implicit evaluation through emotional association, during exposure to stimuli. The design ensured methodological triangulation, allowing for rich, converging insights into how different illustration styles function as visual heuristics in consumer engagement. Findings Challenging prior findings, the results reveal that caricatured ingredient illustrations evoke positive cognitive, emotional and behavioral engagements than realistic illustrations. By positioning these diverse measures within a single consumer engagement framework, the present research contributes novel empirical insights to the consumer behavior literature by elucidating how ingredient-based illustrations impact consumer engagement, especially in online shopping contexts where quick, affect-driven judgments dominate. Practical implications The research also offers novel managerial implications for product and brand managers; by emphasizing how caricatured ingredient-based illustrations may strengthen the connection between ingredient and its perceived authenticity, enhancing consumer engagement through more intuitive and emotionally engaging visuals. Originality/value The present research offers a novel examination of how FOP ingredient illustrations, realistic versus caricatured, function as visual heuristics in consumer engagement, framed through dual process theory and neuroscientific evidence. While prior studies emphasize realism as a marker of credibility, the findings challenge this assumption, showing that caricatured illustrations evoke stronger emotional and cognitive responses. By integrating behavioral and neuroscientific methods, the study provides new empirical insights into how illustration styles shape emotional, behavioral and cognitive engagements particularly in online shopping environments. From a managerial perspective, the results highlight how caricatured visuals can enhance intuitive comprehension and emotional resonance, reinforcing ingredient benefit associations in consumers’ minds.

Purpose Brands are investing heavily in content marketing within digital communication channels, yet there is limited understanding of the effectiveness of this content on consumer engagement. This paper aims to examine how consumer engagement with branded content is created through consumer-initiated online brand communities (OBCs) and brand-initiated digital content marketing (DCM) communications. Self-brand connections are examined as an important antecedent to the cognitive, affective, behavioural and social dimensions of consumer engagement and the subsequent impact of engagement on loyalty is explored across these two channels. Design/methodology/approach A survey approach was used with two consumer samples for one focal retail brand, namely, a consumer-initiated OBC (Facebook) and email subscribers of the retail brand’s DCM communications. A multi-group analysis of structural invariance procedure was used to comparatively examine the formation of engagement for consumers within the OBC and DCM channels. Findings This study demonstrates the different ways in which engagement forms across different digital communication channels. Self-brand connection (SBC) was found to strongly drive behavioural, cognitive, affective and social engagement. The cognitive, affective and behavioural engagement was found to mediate the self-brand connection and consumer loyalty relationship. Overall, this relationship was most strongly and significantly mediated by affective and cognitive engagement within the OBC channel when compared to the DCM channel. Research limitations/implications The findings of this study should be interpreted with several limitations in mind. First, the research was conducted within the confines of one OBC, within one social networking site platform characterised by self-selected membership based on a passion and immersion with the brand. This means that consumers within the OBC were highly connected to one another and the retail brand and highly socialised in-group norms and mores. This type and intensity of connection may not be the case for all forms of OBCs. Second, this study was limited to one retail brand, from one brand category. Future research should examine OBCs across a range of utilitarian and hedonic brands to comprehensively contextualise the dimensions of engagement. Third, the data for this study was cross-sectional. The use of netnographic analysis and qualitative interviews across a range of OBCs would support the triangulation of the findings of this research, especially with regard to the narrative that consumers’ express when discussing how their SBC manifests through the dimensions of engagement. Fourth, this study explored a single antecedent of engagement, namely, self-brand connections. Future research may consider how SBC operates in conjunction with other complementary factors to enhance consumers’ affective, cognitive, social and behavioural engagement such as brand awareness, satisfaction and participation/interactivity. In addition, future research could examine an expanded array of engagement outcomes such as purchase intention, the share of wallet and reputation. Finally, future research should examine the operationalisation and validation of the dimensions of engagement using multiple competing scales to assess the suitability of these engagement scales across multiple brand categories and contexts. Practical implications Given the increasing investment in branding within social media and the fragmentation of brand communications across multiple communications platforms, the management of effective brand communications remains a significant challenge. This study found that the relationship between self-brand connections, affective, social, behavioural and cognitive engagement and loyalty was context-specific and moderated by a digital communication channel (OBC vs DCM email marketing), thus providing insights as to the effectiveness of OBCs and DCMs as two tools for enhancing consumer loyalty. Originality/value This study makes a novel contribution to the engagement literature by examining the antecedent role of self-brand connections in predicting consumers’ engagement; the moderating role of digital communication platforms (OBC vs DCM) on the formation of cognitive, affective, behavioural and social engagement; and the mediating effect of these dimensions on loyalty.

This article reports on original research investigating the pivotal role that teachers play in student engagement, using a tri-dimensional framework. This framework identifies how teachers’ pedagogical choices impact student engagement in ways that influence students’ external behaviours, internal emotions and internal cognitions. A questionnaire was developed to explore secondary teachers’ (n = 223) perceptions of pedagogies that support students’ behavioural, emotional and cognitive engagement in the classroom. Findings revealed that female participants placed higher importance on pedagogies that support students’ cognitive and behavioural engagement, and participants with leadership roles placed higher importance on pedagogies that support students’ cognitive and emotional engagement. Also emerging from the research was a negative correlation between the importance teachers placed on pedagogies that support cognitive and behavioural engagement and their school’s ICSEA value (the measure of socio-educational advantage in Australian schools). Overall, results support the tri-dimensional framework of student engagement utilised in this study and provide a robust framework for future research to further explore teachers’ pedagogical choices and how these choices impact student engagement.

This study aims to examine the effects of behavioral and emotional engagement on cognitive engagement among elementary school students using structural modeling. Data were collected from 240 students in grades 4-6 using a 15-item Likert-scale questionnaire. Confirmatory Factor Analysis (CFA) was used to test construct validity and reliability, followed by Structural Equation Modeling (SEM) to examine interdimensional relationships. The results showed that behavioral engagement had a positive effect on both emotional and cognitive engagement, while emotional engagement also positively influenced cognitive engagement. However, the effects were relatively small, and emotional engagement did not significantly mediate the relationship between behavioral and cognitive engagement. These findings emphasize the role of behavioral and emotional engagement in enhancing cognitive engagement, while suggesting that other contextual factors should also be considered. The proposed model demonstrated a good fit with the data and provides a basis for understanding student engagement in primary education.

The present study aimed to examine whether and to what extent university student online learning performance was influenced by individual-technology fit (ITF), task-technology fit (TTF), environment-technology fit (ETF), and whether the influence was mediated by their behavioral, emotional, and cognitive engagement. A theoretical research model was developed by integrating the extended TTF theory and student engagement framework. The validity of the model was assessed using a partial least squares structural equation modeling approach based on data collected from 810 university students. Student learning performance was influenced by TTF (β = 0.25, p < 0.001), behavioral engagement (β = 0.25, p < 0.001), and emotional engagement (β = 0.27, p < 0.001). Behavioral engagement was affected by TTF (β = 0.31, p < 0.001) and ITF (β = 0.41, p < 0.001). TTF, ITF, and ETF were observed as significant antecedents of emotional engagement (β = 0.49, p < 0.001; β = 0.19, p < 0.001; β = 0.12, p = 0.001, respectively) and cognitive engagement (β = 0.28, p < 0.001; β = 0.34, p < 0.001; β = 0.16, p < 0.001, respectively). Behavioral and emotional engagement served as mediators between fit variables and learning performance. We suggest the need for an extension to the TTF theory by introducing ITF and ETF dimensions and demonstrate the important role of these fit variables in facilitating student engagement and learning performance. Online education practitioners should carefully consider the fit between the individual, task, environment, and technology to facilitate student learning outcomes.

We often read or hear about how important engagement is, most likely because it has been positively linked to student “achievement, satisfaction, and persistence” (Kuh, 2009, p. 694). This is often the case not only in face-to-face learning settings, but also in online and blended learning settings. However, engagement means different things to different people in different contexts. In fact, Axelson and Flick (2011) contended, “few terms in the lexicon of higher education today are invoked more frequently, and in more varied ways, than engagement” (p. 38). Numerous types of engagement exist—behavioral engagement, civic engagement, community engagement, cognitive engagement, emotional engagement, learning engagement, school engagement, and student engagement— just to name a few. Clearly, engagement is a construct with varying definitions and typologies (Coates, 2007) depending on myriad contextual factors. Therefore, it is important for designers, researchers, and practitioners, when referring to engagement, to provide their definition of this construct. In this column, I introduce several scholarly definitions of engagement.Much of today’s literature about student engagement stems from Astin’s research and theory of student involvement (Axelson & Flick, 2011), as well as Kuh’s (2003) definition of student engagement, and Astin’s and Kuh’s leadership, along with numerous other researchers, on the development, creation, and administration of the National Survey of Student Engagement (“NSSE timeline: 1998–2009,” para. 3). Astin (1984/1999) conceptualized the theory of student involvement to consist of “five basic postulates” (Astin, 1984/1999, p. 519) which are:He developed this theory because of “the tendency of academicians to treat the student as a kind of ‘black box’” (Astin, 1984/1999, p. 519). However, it is Kuh’s definition that is most often cited in the higher education literature. According to Kuh (2003), student engagement is “the time and energy students devote to educationally sound activities inside and outside of the classroom, and the policies and practices that institutions use to induce students to take part in these activities” (p. 25).Other researchers, such as Fredricks, Blumenfeld, and Paris (2004) and O’Brien and Toms (2008), have also sought to define engagement, resulting in a more complex understanding of this concept. For example, Fredricks et al. (2004) conducted a literature review of school engagement and found that it is “multidimensional” (p. 61), consisting of behavioral, emotional, and cognitive engagement, as the following summarizes:An important aspect of the three dimensions of engagement is the frequent overlap of these constructs, as well as others that help define engagement.The multifaceted nature of engagement is also evident in a study by O’Brien and Toms (2008), whose purpose was to define engagement vis-à-vis technology through a “critical multidimensional literature review and exploratory study” (p. 938). In their study, they determined that engagementis a process that consists of a:In their model (see Figure 1), it is clear that the level of intensity of engagement varies within the period of engagement. They also discovered three “threads” of engagement based on sensual, emotional, and spatiotemporal attributes. These are depicted in Table 1.Clark and Mayer (2016) have focused their description of engagement on two particular forms that they have found to be pertinent to e-learning: behavioral and psychological engagement. They explained, “Behavioral engagement refers to overt actions taken by a learner during a lesson intended to improve learning” (Clark & Mayer, 2016, p. 219), whereas, “psychological engagement promotes learning that helps learners to achieve the instructional goal by engaging in relevant cognitive processing during learning” (Clark & Mayer, 2016, p. 219). Behavioral engagement involves what learners do (e.g., read, click, answer, type, etc.) and psychological engagement consists of the “mental activity that promotes achievementof the learning objectives” such as “mentally organizing the material into a coherent structure” (Clark & Mayer, 2016, p. 223). For Clark and Mayer (2016), two critical features of engagement are that: (1) “psychological not behavioral engagement … leads to learning” (p. 220) and (2) psychological engagement may occur with or without behavioral engagement.Designing instruction to foster student engagement, therefore, should involve at minimum, careful thought about how students are to interact with the content, with their peers, and with their instructor(s) with attention to the mental activity (psychological engagement) the interaction(s) will promote, as well as what they are expected to do (behavioral engagement). Engagement is a complex, multidimensional concept that requires some unpacking when we refer to it—and its benefits— in teaching and learning.

A learning environment’s quality has crucial influence on a student’s engagement. In this study, we utilized a structural equation modeling approach to explore the structural relationships between students’ perceptions of an online learning environment and their online learning engagement during China’s COVID-19 school closure period by focusing on an online learning environment and the specific features that facilitate student engagement. The online learning environment was conceptualized as a multidimensional structure consisting of four elements: pedagogy, social interaction, technology, and the consideration of home learning conditions. Student engagement was conceptualized as a multifaceted construct comprising behavioral, emotional, and cognitive engagement. The results showed that teaching presence significantly predicted deep behavioral engagement (β = 0.246), emotional engagement (β = 0.110), and cognitive engagement (β = 0.180). Social presence significantly positively predicted cognitive engagement (β = 0.298) and emotional engagement (β = 0.480), whereas its effect on behavioral engagement was not significant. The perceived ease of technology use significantly predicted only emotional engagement (β = 0.324), and the family learning presence significantly predicted only behavioral engagement (β = 0.108). The results also indicated that emotional and cognitive engagement had indirect effects on the predictive power of the online learning environment for behavioral engagement. These findings provide valuable guidelines and effective strategies for teachers and parents to design suitable online learning environments to enhance K–12 student engagement.

This study aims to examine the influence of academic self-efficacy, perceived usefulness of online learning systems, and teaching presence on student engagement (behavioural, emotional, and cognitive engagement) and student satisfaction with online learning. Data were collected from undergraduate students who experienced a fully online learning process during the COVID‐19 pandemic. Based on social cognitive theory, the relationships among the personal and environmental influences on student behaviour and outcomes were examined using structural equation modelling. The results indicated that academic self-efficacy had significant direct relationships with behavioural engagement and emotional engagement, while perceived usefulness significantly influenced emotional engagement and cognitive engagement. Furthermore, teaching presence significantly influenced all engagement dimensions. Student satisfaction was significantly and directly influenced by behavioural engagement and emotional engagement, but not by cognitive engagement. Finally, the mediation role of each engagement dimension is proven in this study. This study was conducted in Egypt; thus, it contributes to add an empirical evidence regarding online student engagement and satisfaction in the context of a developing country.

Objectives This study aimed to analyze the structural relationships among online course organization, student engagement and perceived learning outcome in university online learning environment. Methods The survey data were collected from a total of 359 students who took the 100% online course offered by K University in the fall semester of 2021. The structural equation modeling was conducted to examine the relationships among course organization, behavioral, emotional, cognitive engagement and perceived learning outcome. Results The findings showed that online course organization had significant effects on behavioral engagement and perceived learning outcome. Behavioral engagement had a positive(+) effect on emotional and cognitive engagement, and emotional engagement had a positive(+) effect on cognitive engagement. In addition, emotional engagement was found to mediate the relationship between behavioral, cognitive engagement and perceived learning outcome. Conclusions This study attempts to broaden the understanding of student engagement reflecting multidimensional concepts by identifying the interrelationships among behavioral, emotional and cognitive engagement. Also, the results of this study can provide implications for teaching strategies to promote student engagement in online learning.

ABSTRACTContextualised in the AI–supported English‐speaking learning, this study examined the roles of AI affordances in influencing EFL learners' emotional, cognitive, and behavioural speaking engagement, and explored the moderating roles of gender and learner types (on‐campus vs. on‐job) in influencing AI‐supported English‐speaking engagement. Data collected from 332 Chinese EFL learners (159 on‐campus and 173 on‐job learners) were analysed by using structural equation modelling. Results indicated that Chinese EFL learners perceived AI affordances to be significant in influencing their emotional, cognitive and behavioural engagement in practicing their spoken English. The results from the PLS‐SEM model revealed that AI affordances accounted for 54.7%, 52.4% and 56.0% of the variance in emotional engagement, cognitive engagement and behavioural engagement, respectively. Learner type was not found to significantly moderate the relationships between AI affordances and speaking engagement. Gender was found to be a significant moderator for the AI affordances–behavioural engagement and AI affordance–cognitive engagement relationships. These findings enrich existing literature about AI–empowered speaking engagement and provide practical implications for English teachers to design effective speaking‐teaching models.

Lack of student engagement in online learning is reported as the major challenge contributing to poor academic performance and completion rates. When transforming an in-person undergraduate remote sensing course to online, this study implemented interactive storytelling lecture trailers (ISLTs) as a tool to effect changes in the realms of behavioral, cognitive, emotional, and student-instructor engagement. We collected survey data to examine students’ own perception of how ISLTs impacted their online learning, and analyzed students’ course participation and performance on tests. Results indicated that ISLTs enhanced some aspects of students’ behavioral engagement such as page views, effectively engaged students’ emotions when viewing ISLTs, and improved student-instructor engagement. Regarding cognitive engagement, ISLTs were able to improve short-term learning skills like remembering and applying levels of thinking. A majority of students recognized that ISLTs enhanced their learning experience and made learning more accessible, while a few considered them burdensome and overwhelming. However, there was no clear evidence indicating that ISLTs enhanced participation or promoted students’ emotional engagement in the follow-up lectures. Further, the improvement of student-instructor engagement we observed through quantitative data analysis lacked representative qualitative support. In summary, this study demonstrates the utility of ISLTs as an online learning engagement tool for stimulating students’ interest and improving their performance in lower levels of cognitive thinking. Further work is required to explore ways to further enhance students’ participation and emotional engagement throughout the semester and confirm the usefulness of ISLTs for student-instructor engagement.

BackgroundThis research explores the relationships between the educational environment, student engagement, and academic achievement in Health Professions Education (HPE) , specifically examining the mediating role of engagement.MethodsThe study used cross-sectional design, and data were collected from 554 HPE students via self-report questionnaires. The Dundee Ready Education Environment Measure (DREEM) assessed the educational environment while the University Student Engagement Inventory measured learning engagement across the behavioral, emotional, and cognitive dimensions. Academic achievement was measured using cumulative GPA. Relationships between study variables were analyzed using path analysis.ResultsPath analysis demonstrated that four educational environment subscales directly affected emotional engagement (48% variance explained). Students’ perception of learning and academic self-perceptions influenced behavioral engagement (28% variance explained), while cognitive engagement was influenced by academic self-perceptions (39% variance explained). GPA was positively influenced by behavioral and cognitive engagement but negatively by emotional engagement. Cognitive and behavioral engagement mediated the relationship between students’ academic self-perceptions and academic achievement.ConclusionsStudents’ perceptions of the educational environment significantly influenced emotional engagement, followed by cognitive and behavioral engagement. Cognitive and behavioral engagement directly affected academic achievement and mediated the relationship between the educational environment and academic achievement.

The COVID-19 pandemic had forced medical students to study at home, transitioning to an emergency remote learning mode of instruction. Its impact on students was unknown and likely to be of concern. Therefore, this study assessed cognitive, emotional and behavioural engagements of medical students during emergency remote learning, and examined its associations with regard to their age, gender, stages of study and ethnic groups. A self-administered questionnaire was distributed to undergraduate medical students at one public medical school in Malaysia. Emergency remote learning was conducted via Microsoft Teams (synchronous) and web resources (asynchronous). The questionnaire consisted of four sections: demographic background, emotional, behavioural, and cognitive engagements with emergency remote learning. Three hundred twenty-nine students (n = 329) completed the questionnaire. The three engagement dimension scores were 3.36/4.00 (behaviour – act), 3.16 (cognition – think) and 3.07 (emotion – feel), respectively. There was a significant difference between the engagement dimension scores (paired data), implying that what students feel, think and act on emergency remote learning did not seem to align. Next, engagements of these students were not significantly associated with their age, stages of study, and ethnic groups, but male students had higher dimension mean scores for cognitive and emotional engagements. Emergency remote learning had a considerable impact on student engagements. The study calls for continuing efforts in improving effectiveness and equity in learning engagements among medical students in the post-pandemic era.

This study investigates the relationship between democratic classroom environment and student engagement and its three dimensions: cognitive engagement, emotional engagement and behavioral engagement. Previous research has extensively reported about the positive relationship between democratic classroom environment and student engagement. However, these studies have evaluated only the relationship between democratic classroom environment and student engagement neglecting the three dimensions. This study contributes to this gap by examining the three dimensions as well. Thus, the paper had two aims: First to investigate the relationship between democratic classroom and the three dimensions of classroom engagement: behavioral engagement, emotional engagement and cognitive engagement and second, to examine the moderating role of teacher between democratic classroom and student engagement. A survey questionnaire was utilized to collect data from secondary school teachers. Since the study was based on correlation method, therefore, regression analysis were conducted to test the hypotheses of the study and to analyze the relationship between the variables. The findings of the study showed that there is a strong positive correlation between democratic classroom environment and student engagement and its three dimensions: behavioral, emotional and cognitive. The study also discovered that teacher moderates the relationship between democratic classroom environment and student engagement. On the basis of the results, the paper concludes that teacher plays an important role in the behavioral, emotional and cognitive engagement of students in the teaching and learning process.

This study examined how students in Technical and Vocational Education and Training programs engage during pre-laboratory idle time, a recurring but often overlooked phase of laboratory instruction. The investigation aimed to generate empirical insights into engagement patterns and to propose the Pre-laboratory Idle-Time Engagement Survey as a pedagogical tool for structuring this period more effectively. Conducted in two Philippine state universities, the study involved thirty-five undergraduates enrolled in laboratory-intensive programs-Bachelor of Technical-Vocational Teacher Education and Bachelor of Technology and Livelihood Education. Participants completed a validated sixty-five item engagement scale measuring cognitive, behavioral, emotional, instructor presence, environmental conditions, peer influence, motivational expectations, and TVET-specific engagement. Descriptive and correlational analyses were employed to identify the relative strength and interrelationships of these dimensions. Results showed that behavioral engagement and instructor presence consistently ranked highest, highlighting the importance of structured preparation and visible teacher support. Cognitive engagement also emerged as an important dimension, whereas emotional engagement and motivational expectations showed greater variability, reflecting uneven affective and motivational readiness. Moderate correlations were observed among peer influence, environmental conditions, and core engagement dimensions, underscoring the role of both social dynamics and logistical factors. Synthesizing these findings, the Pre-laboratory Idle Time Engagement Survey was developed as a practice-oriented guide that aligns each engagement dimension with concrete instructional strategies such as peer-led tasks, motivational prompts, and guided tool preparation. By reframing idle time as a purposeful instructional phase rather than passive waiting, this study demonstrates how the Idle-Time Engagement Matrix can help educators enhance readiness, sustain motivation, and optimize student performance in competency-based laboratory learning environments.