Student Thinking Styles and Course Engagement in Online Versus Face-to-Face Marketing Courses

Abstract

As online course delivery becomes an ever-increasing modality in marketing education, it is essential that marketing educators understand the unique factors that drive student course engagement in online versus face-to-face courses. This article develops and empirically tests a conceptual model grounded in the theory of mental self-governance which suggests different student thinking styles play varying roles in driving course engagement and satisfaction in online and face-to-face courses. The conceptual model is empirically tested by analyzing survey and objective learning achievement (final grade) data collected from 194 students enrolled in online marketing courses and 232 students enrolled in face-to-face marketing courses. Results indicate that delivery modality influences the effect distinct thinking styles have on course engagement. Interestingly, the analysis suggests that course engagement has a greater positive impact on final grades for students enrolled online compared with those enrolled in face-to-face marketing courses. The implications of these findings are discussed, offering valuable insights for educators navigating the evolving landscape of marketing education.

Keywords

learning styles learning approaches and issues marketing education issues structural equation modeling (SEM)methodology undergraduate education level/type of education online/distance education level/type of education student motivation

In the dynamic landscape of higher education, traditional pedagogical paradigms are constantly challenged by innovative modes of instruction and learning. A significant transformation in recent decades has been the integration of online asynchronous courses, introducing unprecedented flexibility and accessibility for students (Wu & You, 2022). While distance education and online courses have historically been popular among non-traditional student populations, there is a growing interest among traditional-age students in enrolling in online courses (McMurtrie, 2023). A 2022 study revealed a significant shift, indicating that the percentage of traditional-aged undergraduate students favoring completely or mostly face-to-face courses dropped from 65% to 41% post-pandemic (Robert, 2022). More students now choose to enroll in online courses as a preference rather than out of necessity, marking the transition of online courses from niche offerings to mainstream options for on-campus students.

As universities seamlessly integrate online asynchronous instruction alongside traditional face-to-face classes, a profound understanding of the factors influencing student engagement in these diverse settings becomes paramount. The distinctions between these learning environments are central to our investigation. Online asynchronous courses, distinguished by their flexibility and accessibility, represent a departure from the structured, in-person interactions of traditional face-to-face instruction. Understanding which type of student can thrive in online versus face-to-face classes is crucial, contributing not only to the broader discourse on effective pedagogy but also addressing a central issue in contemporary higher education. In this pursuit, our study focuses on the relationship between student thinking styles, conceptualized by Sternberg (1997), and course engagement, as elucidated by Handelsman et al. (2005), within the realm of marketing education.

Student engagement within individual courses stands as a paramount predictor of academic achievement, often referred to as the “holy grail of learning” (Sinatra et al., 2015, p. 1). In the field of marketing education, research highlights that highly engaged students tend to achieve superior grades in principles of marketing courses and show a greater mastery of course learning objectives (Hopkins et al., 2020). While the significance of student engagement is acknowledged, the advent of fully online course delivery in marketing prompts a crucial question for educators: What type of student would be particularly engaged in online versus face-to-face classes? This question gains urgency due to challenges in online course delivery, including limited peer-to-peer interaction, reduced satisfaction rates, and increased course dropout rates (Palvia et al., 2018).

The juxtaposition of online asynchronous and face-to-face teaching modalities offers a unique opportunity to explore how student thinking styles may interact with these distinct learning environments. While prior research has investigated the influence of various course design factors on student engagement (e.g., Francescucci & Rohani, 2019; Northey et al., 2015), the role of thinking styles in shaping students’ active involvement in coursework remains relatively underexplored, particularly within the marketing discipline. This presents an important opportunity for marketing educators as student thinking styles can be easily assessed, and students can be advised on modality decisions based on such assessments.

This study investigates the intricate relationship between a student’s thinking style, their level of course engagement, and overall course satisfaction. We posit that the strength of the link between thinking styles and course engagement, as well as satisfaction, will vary depending on the course delivery modality—whether face-to-face or online. With data collected from 194 students enrolled in online asynchronous marketing courses and 232 students in traditional face-to-face marketing courses, this quantitative research study utilizes multi-group structural equation modeling to empirically test the conceptual model.

Our study reveals that different student thinking styles indeed impact course engagement differently based on delivery modality. These findings have implications for marketing educators as they are encouraged to develop tailored instructional strategies that cater to diverse thinking styles, with the goal of fostering higher engagement levels which lead to improved learning outcomes. The findings also emphasize that course engagement is particularly important in online learning environments. In such environments, our results suggest that student course engagement has a particularly strong positive relationship with academic achievement.

Background

The concept of student course engagement has emerged as a pivotal and extensively studied phenomenon within the realm of higher education. In the pursuit of enhancing pedagogical best practices and improving student outcomes, academics have extensively investigated the multifaceted dimensions of student engagement. One seminal work that has significantly contributed to our understanding of this construct is Handelsman et al.’s (2005) comprehensive model of course engagement. In their study, Handelsman and colleagues conceptualize student engagement as multifaceted construct consisting of cognitive, emotional, and behavioral facets. This framework emphasizes the interconnectedness of these dimensions, highlighting how they collectively shape students’ experiences in the classroom. By delineating the various components of engagement, Handelsman et al. (2005) provided researchers and educators with a structured lens through which to assess and improve the quality of student learning experiences.

A myriad of studies in marketing education have sought to explore the outcomes of student course engagement (e.g., Dickinson-Delaporte et al., 2020; Hopkins et al., 2020; Merkle et al., 2022; Taylor et al., 2011), and this research has consistently shown that engaged students are more likely to exhibit higher levels of motivation and academic achievement. Engaged students are also more inclined to persist in their studies and develop a deeper understanding of course content. Such findings have fueled a growing interest among marketing academics to investigate what instructors can do in terms of course design and infusing active learning activities to enhance course engagement (e.g., Dickinson-Delaporte et al., 2020; Merkle et al., 2022; van Esch et al., 2020). Other research has examined the effect individual factors such as student self-efficacy (Hopkins et al., 2020) and motivation (Taylor et al., 2011) have on levels of course engagement. Despite these recent pedagogical insights, a clear understanding of what type of student is best positioned for success in different types of learning environments (i.e., face-to-face versus online) is still lacking.

When considering the cultivation of engagement, it is important to acknowledge that students are not homogeneous in their mental processes, and their pattern of course engagement may be a function of individual differences in cognition. Understanding how students’ heterogeneity is related to course engagement can help marketing educators design curricula online and offline that facilitate broad student learning. In this research, we examine the engagement patterns of marketing students based on their innate thinking styles (Sternberg, 1990).

Thinking Styles

Rooted in the theory of mental self-government, thinking style, or cognitive style is not what defines a person but instead reflects the different ways individuals use information to organize and govern themselves (Sternberg, 1997). Thinking style is defined as a person’s preferred way of using their cognitive information-processing abilities (Sternberg, 1997). In other words, thinking style is not the same as cognitive ability but rather represents the way a person prefers to integrate knowledge. It is important to note that individual thinking styles are neither inherently good nor bad, and the relative strength of style preferences varies across individuals. Some individuals strongly gravitate toward specific preferences, while others exhibit weaker preferences (Sternberg & Zhang, 2005).

Sternberg’s theory of mental self-government is structured around the three branches of the United States government system: judicial, executive, and legislative. This categorization of thinking styles is rooted in the idea that these branches were developed as “external reflections of ways people can organize or govern themselves” (Sternberg & Zhang, 2005, p. 247). Again, these thinking styles are not indicative of abilities but rather preferences, and they do not imply a right or wrong style. Preferences for a particular thinking style are acquired through socialization and, while relatively sticky, can be contextually dependent (Sternberg, 1997; Sternberg & Zhang, 2005). Moreover, individuals often exhibit a combination of the three thinking styles, rather than a strict adherence to one.

Judicial Thinking Style

The judicial thinking style pertains to individuals who relish making judgments. Judicial thinkers enjoy evaluating rules and procedures and have a proclivity “for tasks, projects, and situations that require evaluation, analysis, comparison-contrast, and judgment of existing ideas, strategies, projects, etc.” (Sternberg & Zhang, 2005, p. 247). They prefer addressing each problem using a unique yet preexisting solution. For instance, judicial students might prefer assessing facts and procedures and derive satisfaction from critiquing and offering opinions. In the context of marketing education, students with a judicial thinking style may excel in tasks that require in-depth critical analysis, such as evaluating the merits of a market research plan or case study analysis. Finally, judicial thinkers take pleasure in assessing the strengths and weaknesses of others’ arguments and thus enjoy observing and evaluating debating parties (O’Hara & Sternberg, 2001).

Executive Thinking Style

The executive thinking style aligns with individuals who embrace structure. Executive thinkers are comfortable with receiving clear instructions and adhering to rules and procedural tasks. They prefer solving problems with pre-established or pre-structured solutions. Learning experiences that are highly structured, with explicitly outlined activities and assessments, resonate with learners exhibiting an executive thinking style (Sternberg & Zhang, 2005). In marketing education, students with an executive thinking style may excel in tasks requiring project management, marketing plan execution, and campaign implementation. They may thrive in group projects where expectations are explicit, and roles are clearly defined (O’Hara & Sternberg, 2001). Finally, executive thinkers prefer to implement others’ ideas and thus may thrive in environments where they are required to analyze and implement prior marketing campaigns in a new context.

Legislative Thinking Style

Conversely, the legislative thinking style is characterized by individuals with a creative inclination. Legislative thinkers thrive in situations where pre-structured solutions are absent. They prefer to analyze and structure problems independently, allowing them to craft innovative and original solutions. Legislative thinkers cherish the autonomy to decide what needs to be done and relish identifying potential courses of action as opposed to being given prescriptive instructions (Sternberg & Zhang, 2005). In the context of learning environments, students with a legislative thinking style prefer to design their own learning paths and resist being confined to predetermined courses of action. They favor a less structured educational environment and gravitate toward opportunities for invention and idea generation. In essence, legislative thinkers seek autonomy in determining what and how they learn and are assessed.

We propose that students with specific thinking styles will be more engaged in and satisfied with face-to-face versus online marketing classes. Specifically, we argue that judicial thinkers, who enjoy assessing the merits of debating parties, will show higher engagement levels and greater satisfaction with face-to-face compared with online courses. Conversely, executive thinkers, who prefer structure and well-outlined plans, will demonstrate greater engagement and higher satisfaction with online rather than face-to-face courses. In addition, the creative legislative thinkers are expected to be more engaged in and satisfied with face-to-face courses due to the opportunities for spontaneity and creativity offered by such environments. Finally, we hypothesize that this varying level of engagement and satisfaction will, in turn, influence their academic achievement in the class (i.e., final grade). The conceptual model depicting these relationships is presented in Figure 1.

Figure 1.

Conceptual Model Linking Student Thinking Style to Course Engagement, Satisfaction, and Learning Achievement.

Thinking Styles, Course Engagement, and Satisfaction

The choice between asynchronous online and traditional face-to-face marketing courses presents university students with distinct learning environments. Online asynchronous courses afford students the flexibility to engage with course materials and assignments at their own pace and convenience, transcending geographical constraints and accommodating those unable to attend in-person sessions (LaTour & Noel, 2021; Northey et al., 2015; van Esch et al., 2020). While offering students the flexibility in terms of when to engage with course materials, online asynchronous courses are often rigid in terms of how students can engage. Driven by best practices, online asynchronous courses offer students clear guidelines, firm deadlines, and timely, personalized one-on-one instructor feedback (Vlachopoulos & Makri, 2019).

Traditional face-to-face marketing courses, on the contrary, foster real-time group interactions, enabling instant feedback from multiple sources including from both peers and instructors. In addition, face-to-face courses can be highly adaptive as instructors can adjust to the changing needs of their students in real-time. For example, during an in-class lecture or exercise an instructor can spend more time or focus on topics that are of particular interest to the audience. This environment is particularly conducive to dynamic discussions, and in-person networking opportunities. In sum, there are important structural differences between face-to-face and online asynchronous courses that likely resonate differently with different learners.

Individuals with a judicial thinking style tend to prefer tasks that involve evaluation, critique, and the comparison of varying viewpoints. The live and spontaneous unstructured learning environments available in the face-to-face classroom facilitate discussion and debate. This environment may be attractive to judicial thinkers as it offers them the opportunity to observe, evaluate, and rate different points of view in real-time. Conversely, in online marketing courses, where opportunities for spontaneous debates and real-time interactions may be limited, judicial thinkers may find it challenging to evaluate diverse viewpoints and engage in the kind of intellectual discourse they thrive on. Consequently, this reduced capacity for comparison and real-time debate may lead to lower levels of engagement in online versus face-to-face courses (Handelsman et al., 2005). Moreover, their preference for analytical and critical thinking may make them more discerning consumers of education, potentially rendering them less satisfied with the format of online courses that may not align with their learning style.

Hypothesis 1 (H1): Students with a judicial thinking style will be (a) less engaged in and (b) less satisfied with online compared with face-to-face marketing courses.

Conversely, we predict students with an executive thinking style will exhibit higher levels of engagement and satisfaction with online compared with face-to-face marketing courses. This prediction is rooted in the works of Sternberg (1997) who identifies executive thinkers as those who are characterized by efficient decision-making and goal-oriented problem-solving. Extensive research in the field of online education has shown that self-directed learners, which align closely with executive thinking styles, tend to excel in virtual learning environments (Garrison, 2003). Moreover, a study by Shea and Bidjerano (2012) found that self-regulated learners, akin to the executive thinking profile, reported higher levels of effort and cognitive presence in online courses. Given the inherent structure associated with online courses and executive thinker’s preference for structure and clear directions, we posit that students with this cognitive disposition will find online marketing courses more conducive to their learning preferences, leading to increased engagement and satisfaction when compared with traditional face-to-face formats.

Hypothesis 2 (H2): Students with an executive thinking style will be (a) more engaged in and (b) more satisfied with online compared to face-to-face marketing courses.

The legislative thinking style, characterized by a preference for tasks that involve creativity and ideation (Sternberg, 1997), may interact differently with the inherent structure of online courses compared with traditional face-to-face courses. Face-to-face courses may better suit students with a preference for spontaneous, unstructured learning environments, such as legislative thinkers, who tend to be highly creative (Groza et al., 2016). While online courses offer great consistency and the opportunity for self-directed learning, they are structured by nature and involve firm deadlines and a predetermined stable learning path. Such structure may pose challenges for students with legislative thinking styles who like to decide what to do and how to do it (Dziuban et al., 2015). This mismatch between the cognitive preferences of legislative thinkers and the relatively structured nature of online courses could potentially lead to lower levels of engagement and decreased satisfaction in online compared with face-to-face classes.

Hypothesis 3 (H3): Students with a legislative thinking style will be (a) less engaged in and (b) less satisfied with online compared with face-to-face marketing courses.

Existing literature consistently affirms a positive association between student course engagement and their overall satisfaction with that course (e.g., Cheng & Chau, 2016; Gray & DiLoreto, 2016; Lopez-Fernandez & Rodriguez-Illera, 2009). This well-established theoretical connection is underpinned by the premise that students, when choosing to invest their cognitive and emotional resources in a course (i.e., engagement), do so with the anticipation that such engagement will contribute to an enhanced learning experience, ultimately aligning with their expectations and yielding satisfaction (Gao et al., 2020). In essence, students make a conscious decision to engage in a course, guided by their belief that the effort expended in terms of energy and attention will be instrumental in realizing their educational objectives and, consequently, result in satisfaction with the course. Regardless of whether courses are delivered in traditional face-to-face settings or via an asynchronous online modality, the theoretical framework linking engagement to satisfaction remains relevant and powerful. Hence, we propose the following hypothesis:

Hypothesis 4 (H4): Regardless of delivery modality, course engagement is positively related to course satisfaction.

Learning Achievement

The marketing education literature consistently supports the positive association between student engagement and learning achievement, as reflected in final grades (e.g., Hopkins et al., 2020; Northey et al., 2015, van Esch et al., 2020). While a positive relationship between student engagement and grade is expected here, we do predict delivery modality to play an important boundary condition in relationship. In traditional face-to-face courses, the structured nature of in-person meetings fosters constant interaction opportunities among students and instructors, both inside and outside the classroom. Often such interactions occur spontaneously before, during, or after class meetings. These informal interactions and associated support mechanisms are recognized contributors to enhanced learning achievement (Anderson, 2008). For example, a student who is unclear about a given topic may ask a peer prior to class for clarification. Thus, for students enrolled in face-to-face courses, factors other than their engagement in the actual course likely play an important role in their learning achievement.

Conversely, while online courses empower students with autonomy over their learning pace, opportunities for impromptu extracurricular learning opportunities are limited. The absence of forced physical interactions in the online environment, as provided in face-to-face courses, also diminishes opportunities for spontaneous and informal contact and related support. Thus, we contend that in the realm of online marketing courses, where students navigate a more self-directed learning path, the impact of course engagement on learning achievement is heightened compared with traditional face-to-face settings. Considering this, we posit that course engagement will exert a greater positive effect on learning achievement (grade) in online marketing courses compared to their face-to-face counterparts.

Hypothesis 5 (H5): Course engagement will have a greater positive effect on learning achievement (grade) for students enrolled in online compared to face-to-face marketing courses.

Method

Students enrolled in introductory-level undergraduate marketing courses composed the study’s sample. The sample includes students enrolled in nine sections across five academic semesters. These nine sections include three face-to-face sections and six fully online asynchronous sections. During the first week of each class, students in the respective courses completed a survey that contained the thinking style questionnaire as well as some demographic questions. During the final week of the course, a second survey was administered that contained the course engagement, satisfaction, and word-of-mouth scales. The two waves of survey data were matched using an identifier populated in the survey response software. After official term grades were posted, the students’ final grades (points earned out of 1,000) were matched to the two waves of survey data. The final complete and matched data set includes 194 students in online asynchronous marketing courses and 232 students in face-to-face marketing courses. Each respondent in the final data set is unique (i.e., no student was included in more than one section).

To measure thinking styles, we used Sternberg and Wagner’s (1991) Thinking Style Inventory scale. Student course engagement was measured using the multifactor scale created by Handelsman et al. (2005), and satisfaction was measured by adapting the scale of Oliver and Swan (1989). As control variables, respondents were asked about their major and year in school (1 = 1^st year, 2 = sophomore, 3 = junior, 4 = senior). In the analysis, the student’s major was coded as follows: (1 = marketing major, 0 = not a marketing major). Finally, as discussed previously, an objective measure of student course achievement (i.e., points earned out of 1000) was gathered through the course learning management system. Table 1 contains a list of all measurement items collected across the two waves of survey data collection.

Table 1.

Measures and Factor Loadings.

Measurment Items		λ
Judicial Thinking Styles
1	I like situations where I can compare and rate different ways of doing things.	.73
2	I like to check and rate opposing points of view or conflicting ideas.	.60
3	I like projects where I can study and rate different views or ideas.	.77
4	When working on a task, I can see how the parts relate to the overall goal of the task.	.62
Executive Thinking Style
1	I enjoy working on things that I can do by following directions.	.79
2	I like to figure out how to solve a problem following certain (definite) rules.	.71
3	I like projects that have a clear structure and set plan and goal.	.75
4	I like to follow definite rules or directions when solving a problem or doing a task.	.77
Legislative Thinking Style
1	When faced with a problem, I use my own ideas and strategies (ways) to solve it.	.75
2	I like to play with my ideas and see how far they go.	.73
3	I like problems where I can try my own ways of solving them.	.79
4	When working on a task, I like to start with my own ideas.	.70
College Student Course Engagement
Skills
1	Making sure to study on a regular basis	.79
2	Putting forth effort	.67
3	Staying up on the readings	.68
4	Looking over class notes to make sure I understand the material	.72
5	Being organized	.58
6	Taking good notes	.75
7	Listening/reading carefully	.75
Emotional
1	Finding ways to make the course material relevant to my work or life	.82
2	Applying course material to my work or life	.81
3	Finding ways to make the course interesting to me	.80
4	Really desiring to learn the material	.83
Participation
1	Reaching out to the professor when I don’t understand something	.67
2	Having fun while completing course assignments	.77
3	Helping fellow students	.66
Performance
1	Getting a good grade	.87
2	Doing well on the tests/quizzes	.81
3	Being confident that I can learn and do well in the class	.83
Satisfaction
1	displeased me / pleased me	.89
2	disgusted with / contented with	.87
3	very dissatisfied with / very satisfied with	.92
4	did a poor job for me / did a good job for me	.90
5	poor choice in taking this course / wise choice in taking this course	.85
6	unhappy with / happy with	.88
7	bad value / good value	.88
8	very unfavorable / very favorable	.93

We first fit the data to a confirmatory factor analysis (CFA) to confirm the psychometric properties of the measure. The full data set including both the face-to-face and online samples fit the measurement model well, χ² (613) = 1,200, comparative fit index (CFI) = .94; IFI = .95; Tucker–Lewis index (TLI) = .94; root mean square error of approximation (RMSEA) =.047. All items loaded significantly on their designated constructs and all but one standardized factor loading were above the generally accepted standard (0.6). In addition to the factor loadings, evidence of convergent validity was assessed by calculating the Average Variance Extracted (AVE) and alpha reliability of each construct. Table 2 includes the means, standard deviations, AVEs and alpha reliability measures of each construct and correlations among the constructs.

Table 2.

Correlation Matrix (F2F Sample [n = 232] Below Diagonal, Online Sample [n =194] Above).

p < .05. **p < .01.

As an initial test of the study hypotheses, we fit the full data set (including both the face-to-face and online samples) to the hypothesized structural model (see Table 3). This analysis confirms the proposed factor structure is an appropriate representation of the data. The overall model fit statistics are satisfactory, χ2 (719) = 1,731; CFI = .91; increment fit index (IFI) = .91; TLI = .90; RMSEA = .058. In terms of main effect relationships, the path from judicial thinking style to course engagement (β = .21, p < .01) is positive and significant. For this baseline analysis which fits the data from both face-to-face and online samples, no other path between any thinking style and course engagement or satisfaction is significant. The path from course engagement to satisfaction (β = .69, p < .01) is positive and significant lending initial support to H4. Results from the single-group analysis also yields a positive and significant (β = .34, p < .01) path between course engagement and final grade. Recall, Hypothesis 5 predicts course engagement will have a greater positive effect on learning achievement for students enrolled in online compared with face-to-face courses; thus, further analysis is required to support this prediction.

Table 3.

Path Estimates—Full Sample (n = 426).

Dependent	Course engagement	Satisfaction	Final grade
Independent	β	β	β
Judicial Thinking	.21**	–.04	–.03
Executive Thinking	.07	.05	.11*
Legislative Thinking	.02	–.07	–.05
Course Engagement		.68**	.34**
Satisfaction			–.15*
Marketing Major	.20**	.04	–.01
Year in School	.19**	.02	–.01
R²	.13	.48	.08

Note. Standardized path coefficients reported.

p < .05. **p < .01.

To assess the moderating effect of delivery modality, we first fit a multi-group structural equation model. This multi-group model involves fitting the face-to-face sample to the model and then fitting the online sample to a second model. We then conducted pair-wise parameter comparison tests by calculating the critical rations (CR) of the differences between the two respective parameters. Table 4 contains the results of the multiple-group structural equation model while Table 5 contains the results of the significant critical ratio comparison tests.

Table 4.

Multi—Group Path Estimates—F2F and Online Student samples.

Dependent	F2F sample (n = 232)			Online students (n = 194)
Dependent	Course engage.	Sat.	Final grade	Course engage.	Sat.	Final grade
Independent	β	β	β	β	β	β
Judicial Thinking	.24**	–.13**	.04	.11	.16**	–.05
Executive Thinking	–.03	.08	.06	.17*	–.01	.09
Legislative Thinking	.04	–.04	–.09	.02	–.15**	–.04
Course Engagement		.66**	.19		.67**	.46**
Satisfaction			–.13			–.18
Marketing Major	.22**	.07	.02	.06	–.01	–.04
Year in School	.06	.05	.04	.04	–.10	–.16**
R ²	.11	.45	.04	.05	.51	.17

Note. Standardized path coefficients reported.

p < .05. **p < .01.

Table 5.

Pairwise Parameter Comparison (F2F Model Estimates Online Model Estimates).

Path	F2F path	Online path	Critical ratio
Path	β	β	Critical ratio
Executive→Thinking Style Course Engagement	–.03	.17	(1.83)*
Marketing→ Major Course Engagement	.22	.06	(1.73)*
Judicial→Thinking Style Satisfaction	–.13	.16	(2.94)**
Year in School→Satisfaction	.05	–.10	(1.98)**
Course Engagement→Final Grade	.19	.46	(2.15)**
Year in School→Final Grade	.04	–.16	(2.38)**

Note. Only statistically significant critical ratio tests reported.

p < .10. **p < .05.

In terms of the impact of judicial thinking style on course engagement (H1_a), the path for the face-to-face group is significant and positive (β = .24, p < .01) and the path for the online group is insignificant (β = .11, p > .10). While the face-to-face path is stronger, the CR for the difference of the parameter estimates failed to reach significance (p > .10). Thus, support for H1_a is mixed. Interestingly, counter to the prediction of H1_b the impact of judicial thinking style on course satisfaction is positive for the online group (β = .16, p < .01) and negative to the face-to-face group (β = −.13, p < .05; pair-wise difference CR: 2.94, p < .01). This suggests that while judicial thinkers may be more engaged in face-to-face courses, they are significantly less satisfied in face-to-face compared to online courses.

In support of H2_a, the impact of executive thinking style on course engagement is significantly stronger for the online group (β = .17, p < .01) compared with the face-to-face group (β = −.03, p >.10; pair-wise difference CR: = 1.83, p < .10). The analysis fails to support H2_b as executive thinking style has no significant effect on satisfaction for either group. The analysis also fails to support H3_a as legislative thinking style has no effect on course engagement for either group. In terms of the impact of legislative thinking style on satisfaction, in initial support of H3_b, the path for the online group has stronger negative impact (β = −.15, p < .01) than the path for the face-to-face group (β = −.04, p > .10). However, the CR for the difference of the parameter estimates failed to reach significance (p > .10). The multi-group analysis lends additional support to H4 as the course engagement satisfaction path is significant and positive for both the face-to-face and online groups.

Finally, H5 predicts that course engagement will have a greater positive effect on learning achievement for online versus face-to-face learners. The analysis supports this prediction as the path from engagement to learning outcome is significantly stronger for the online group (β = .46, p < .01) compared to the face-to-face group (β = .19, p > .10), (C.R. difference parameter estimates = 2.17, p < .01). Interestingly, the model explained a greater proportion of the variance in learning outcome for the online group compared to the face-to-face group (R²: online = 17%, F2F 4%). Collectively, these results suggest that course engagement is particularly important in predicting leaning outcome (as measured as an objective final grade) for students in online versus face-to-face marketing courses.

Discussion and Implications for Marketing Educators

The ubiquity of online course options shows no signs of diminishing in the foreseeable future. Students will likely continue to demand choice regarding delivery modality regardless of whether they are “traditional” on-campus residential students or true distance learners living far from campus (McMurtrie, 2023). Concurrently, the significance of course engagement for student success remains a steadfast reality. In light of these considerations, the study’s findings hold substantial implications for educators, instructional designers, and administrators seeking to optimize student experiences and outcomes in both face-to-face and online marketing courses. Recognizing that different thinking styles have a differential impact on engagement and satisfaction across different delivery modalities underscores the need for tailored instructional strategies. At a minimum instructors should be mindful that students have diverse cognitive preferences and thus, offer an array of instructional materials and assessments that align with varying thinking styles. Such an inclusive course design approach can contribute to increased satisfaction and engagement, by catering to students with distinct thinking styles.

To effectively utilize the insights from our research, instructors should consider implementing the Sternberg and Wagner’s (1991) Thinking Style Inventory scale at the beginning of the term to assess students’ thinking styles. This proactive approach can offer valuable insights into the diversity of thinking styles present in each course, whether it is delivered in person or online. Armed with this knowledge, instructors can highly tailor their instructional strategies to better accommodate the varying cognitive preferences of students, thereby enhancing engagement and overall learning outcomes.

Furthermore, program administrators can leverage the Thinking Style Inventory to provide personalized guidance to students regarding the most suitable learning modalities for their cognitive preferences. By offering informed recommendations grounded in an understanding of individual thinking styles, advisors can help students select learning environments that best align with their cognitive strengths. This tailored approach can enhance the fit between students’ learning preferences and their educational experiences, ultimately leading to increased engagement, satisfaction, and academic success.

As predicted, the findings of this study reveal that judicial thinkers tend to show higher levels of engagement face-to-face compared with online marketing courses. If possible, judicial thinkers should be encouraged to take face-to-face courses. In addition, this implies that an objective in designing online asynchronous courses is to replicate the real-time discourse and interaction available in face-to-face settings. Thus, online marketing courses should incorporate opportunities for debate and discussion among both students and instructors. Implementing features such as video discussion boards, peer review activities, and course polling are a few examples of tactics that can be leveraged to effectively engage judicial thinkers in an online learning environment.

Moreover, the study’s results also serve as a cautionary note for educators teaching face-to-face courses, as judicial thinkers expressed lower levels of satisfaction in face-to-face compared with online courses. One possible explanation for this surprising finding could be that judicial thinkers, who inherently possess a critical and judgmental nature (Sternberg, 1997), may have higher expectations regarding opportunities to interact and critique in face-to-face settings as opposed to online environments. Instructors should take these findings into consideration and provide judicial thinkers with ample opportunities to engage in discourse, challenge ideas, and compare various perspectives. Creating an environment that fosters discussion and encourages the exploration of contrasting viewpoints would be beneficial not only for judicial thinkers in online courses but also for those enrolled in face-to-face marketing courses.

Next, the support for the predicted positive impact of executive thinking style on engagement in online courses underscores the necessity for deliberate and highly structured online course design (Castro & Tumibay, 2021). While integrating elements that cater to diverse thinking styles in online classes is paramount, it is equally crucial to ensure that these elements are clearly outlined and well-structured. Whether it’s a discussion forum aimed at engaging judicial thinkers, or a creative multimedia-based project designed for legislative thinkers, all components should include clear instructions and well-defined learning objectives. Embracing a structured yet interactive approach empowers marketing educators to craft online learning experiences that resonate with executive thinkers, resulting in heightened engagement levels and improved learning achievement.

Recognizing the negligible impact of legislative thinking style on engagement in both modalities calls for targeted interventions to enhance the involvement of these students. Legislative thinkers prefer little structure and desire the opportunity to be highly creative and spontaneous. Most university courses, whether delivered face-to-face or online, however, are decidedly structured. This disconnect between what traditional teaching generally rewards and the desires of legislative thinkers can be difficult for instructors to navigate. Sternberg and Zhang (2005) go as far as to suggest that many instructors view these students as “pains in the neck.” (p. 247). Nevertheless, educators and instructional designers should explore innovative approaches, such as incorporating practical applications or case studies, to captivate the creative “outside the box” legislative thinkers and promote their engagement. Online instructors should be particularly cognizant of the negative relationship between legislative thinking style and satisfaction among online students. Again, when making course design considerations, online courses should offer legislative thinkers some latitude to design their learning path.

One of the study’s most important findings is that course engagement has a significantly stronger effect on learning achievement in online versus face-to-face courses. While course engagement holds importance for all college students, the study’s findings emphasize its heightened significance for those enrolled in online courses. This distinction is likely attributed to the limited opportunities for informal peer-to-peer and student-faculty interactions outside the structured course format within online education. Given this, instructors should consider strategies such as requiring participation in online office hours and mandating synchronous meetings with teammates to mimic the interactions experienced by face-to-face students. These efforts can help bridge the gap and enhance the overall learning experience for online learners, making their educational journey more fulfilling and effective.

Limitations and Directions for Future Research

While the present study contributes valuable insights into the influence of thinking styles on face-to-face and online course engagement and satisfaction, it is not without limitations. First, the study’s focus on marketing courses within a specific educational context may limit the generalizability of the findings across marketing academia and across other disciplines. The sample consisted of students enrolled in introductory marketing courses and consisted of a heavy majority of non-marketing majors. Exploring the applicability of the proposed model in upper division marketing courses where most students are marketing majors, and in theory more engaged in the topic, could provide a more comprehensive understanding of the role of thinking styles in course engagement.

Next, the study solely focused on how delivery modality influences the impact students’ innate thinking styles have on course engagement and satisfaction. This relatively narrow focus leaves room for exploration of additional variables that may contribute to these outcomes. For instance, student characteristics like prior academic experience and technological proficiency may interact with student thinking style influencing levels of course engagement and satisfaction. Congruity between instructor and student thinking style, class size, instructor experience, and teaching materials may also be important factors influencing course engagement. In sum, additional boundary conditions can be explored to unveil a more nuanced understanding of the complexities influencing student engagement.

As an additional direction for future research, investigating the effectiveness of interventions tailored to different thinking styles across different delivery modalities could be a fruitful avenue. Our results show that student thinking styles do matter in terms of course engagement and satisfaction and ultimately learning outcomes. Designing and implementing unique instructional strategies in online and face-to-face learning environments that cater to specific cognitive preferences may enhance course engagement and satisfaction. This type of research can help provide practical insights for educators and institutions. Marketing educators and researchers alike should carefully consider modality when crafting such strategies, as our findings suggest that delivery modality is an important boundary condition in this relationship. While this study advances our understanding of the interplay between thinking styles and course engagement, addressing these limitations and pursuing future research directions will contribute to a more comprehensive and nuanced comprehension of the complex factors influencing student experiences in online and face-to-face marketing courses.

Finally, the study’s finding regarding the amplified impact of course engagement on learning achievement in online versus face-to-face courses presents opportunities for future research. As educators increasingly navigate the landscape of virtual learning environments, researchers should delve deeper into the specific strategies that yield optimal engagement and, consequently, enhanced learning outcomes. Future research endeavors could explore the nuanced dynamics of interactive platforms, collaborative projects, and feedback mechanisms, assessing their differential effectiveness and potential synergies. In addition, investigating the role of emerging technologies and innovative pedagogical approaches in bolstering online course engagement could provide valuable insights. While course engagement is important for all learners, our findings suggest it is especially critical for those in an online environment.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Mark D. Groza

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