The Relationship Between Perceptions of Academic Quality and Approaches to Learning: Implications for Principles of Marketing Instructors

INTRODUCTION

A primary purpose of marketing education research is the discovery of methods and tools designed to enhance student learning and improve educational outcomes. To propose positive pathways to learning, for example, research in the marketing domain has primarily focused its recommendations on the mode of knowledge acquisition commonly referred to as a meaning or deep orientation (Dahl et al., 2018; Diamond et al., 2008). Yet, often overlooked are the variety and range of learning approaches students actually employ to prepare and complete their degree requirements, that is, to effect their learning. To realize the benefits of proposed advances in pedagogy, it is imperative first to understand how students learn (Abushina, 2026; Byrne et al., 2002; Byrne & Willis, 2008).

Over the past several decades, researchers in higher education have conceptualized and operationalized distinct approaches to studying/learning.^[1] Gaining the most attention are the popular batteries created and refined by Entwistle and colleagues (N. Entwistle et al., 1979; N. J. Entwistle et al., 2000, 2013; N. J. Entwistle & Ramsden, 1983), which focus on three approaches to learning: 1) the deep approach, centered on understanding the meaning of course materials and seeking to relate ideas to prior knowledge; 2) the surface approach, associated with studying without reflection or purpose, primarily to memorize materials only to satisfy assessment; and 3) the strategic approach, striving to achieve the highest performance possible, while being mindful of assessment criteria, and organizing/managing study time and physical workspace (Baeten et al., 2010; Byrne et al., 2002; Ramsden, 1979; Richardson, 2010).

For some time, little evidence existed regarding the learning approach preferences of business students in general. The accounting discipline led the way in treating approaches-to-learning research as a priority topic, with its output becoming quite prolific in the early 2000s. Accounting educators have explored learning approaches among their students at the introductory course level (Hall et al., 2004), for majors in several European nations (e.g., Byrne et al., 2002; Teixeira et al., 2013), as a response to interventions to promote deep learning and greater analytical thinking (English et al., 2004; Hall et al., 2004), and to compare differences between the genders (Byrne & Willis, 2008; Paver & Gammie, 2005). Those researchers, along with others across business disciplines, advocate a critical need for students to adopt deep approaches to learning (e.g., Dahl et al., 2018; Dyer & Hurd, 2016). Yet, little attention is given in such studies, marketing education included, regarding the relationships between the specific learning approaches students are putting into practice and the implications of such behaviors for those students, their instructors, and for undergraduate business programs in general.

Further, another topic rarely addressed in the extant business education literature is how students’ perceptions of the academic quality of their program influence such learning-approach behaviors (Faranda et al., 2021). To explore this question would provide greater insight into the relational nature of learning, that is, between learning-approach preferences and the learning environment. A student’s decision to use one learning approach over another is dependent on the context, the content, and the demands of specific tasks to be completed within that environment (Nulty & Barrett, 1996; Ramsden, 1979; Richardson, 2005). It would be beneficial to understand better whether specific learning approaches are associated with more positive (or, more negative) views by students of their course of business education study. Such insight is particularly relevant for marketing instructors, who are responsible for designing and implementing course structures, assignments, and assessments that may either reinforce or reshape students’ preferred approaches to learning. Drawing upon students presently enrolled in the Principles of Marketing course (exclusively for junior-level business majors), the present exploratory study’s purpose is to examine these participants’ approaches to learning and to explore possible relationships with their perceptions of the quality of their common, prerequisite program of study, and to determine what implications such findings may hold for marketing educators. Therefore, the premise of this paper is that understanding how students approach learning in Principles of Marketing can inform more intentional and effective instructional design within the discipline.

LITERATURE REVIEW

Student Approaches to Learning

Marton and Säljö (1976) initially identified two distinct levels of processing of information from learning materials: surface-level and deep-level. Surface-level processing is characterized by a reproductive conception of learning, which emphasizes the superficial properties of the material and signals a strategy centered on rote learning (Richardson et al., 2007). In contrast, in deep-level processing the student focuses on the intentional content of the learning material with the aim to comprehend its meaning (Richardson, 2005). Subsequently, other researchers began to term these levels of processing as “approaches to learning” (e.g., N. Entwistle et al., 1979; N. J. Entwistle, 1991; N. J. Entwistle & Ramsden, 1983; Prosser & Trigwell, 1990). Subsequent scale-development efforts generally shared a common result, in that Marton and Säljö’s (1976) deep- and surface-processing levels did not capture all distinctive types. Namely, there was evidence of an achieving dimension, which conveyed behaviors such as intentionally organized study and the need for high academic achievement (e.g., Biggs, 1987a, 1987b; N. Entwistle et al., 1979). Entwistle and his colleagues came to refer to this third approach to learning as strategic-level learning.

Deep Approach to Learning

The deep approach is evidenced by the intention to understand content by relating ideas to previous knowledge and experience, seeking patterns and underlying principles, and critically evaluating logic and argument (Burton & Nelson, 2006; N. Entwistle, 1997; N. J. Entwistle et al., 2000). Other defining features are engagement with ideas and enjoyment of intellectual challenge (N. J. Entwistle & Peterson, 2004) as well as synthesizing concepts (Khong & Tanner, 2024). Additional behaviors associated with deep learning include taking the time to reflect on assigned reading materials, to ponder the reasons behind arguments and getting “hooked” on academic topics to the point of wishing to study them further (N. J. Entwistle et al., 2013).

Several business disciplines have explored the enhancement of deep learning among their students. Accounting educators have conducted studies on instructor efforts, via changes to curriculum design and the immediate learning environment, to enhance use of deeper approaches (English et al., 2004; Hall et al., 2004). In marketing education, researchers have enacted classroom activities to enhance problem-solving skills (Diamond et al., 2008), the development of reflective learning and higher-order critical thinking (Dahl et al., 2018; Dahl & Peltier, 2025), and the application of self-directed learning to promote effective, lifelong learning (Ahmed & Canning, 2024; Boyer et al., 2014). Marketing scholars have also highlighted important pathways to enhance deep learning, advocating, for example, experiential learning Koernig et al., 2024; McGrath, 2023; Story et al., 2020; Wu et al., 2025, collaborative learning (Shah et al., 2025) and the association of music/songs to marketing concepts (Bryant & Reilly, 2024; Rich & Dingus, 2024).

While the higher education literature presents a strong argument in favor of the relationship between deep learning and positive learning outcomes (K. Wilson & Fowler, 2005), some have identified equivocal outcomes in these relationships, as well (Diseth & Martinsen, 2003). This raises the question of whether a sole focus on promoting and facilitating deep learning among business students in general, and marketing students in particular, is the most beneficial way to effect favorable educational outcomes.

Surface Approach to Learning

The surface approach is characterized as a superficial and reproductive conception of learning, indicated by reliance on memorizing with no attempt to reach understanding of key concepts or to make any connections between them (Kamberi, 2025; Kember, 2016). Relying on study activities associated with surface learning often leads to a lack of engagement with the subject (Hall et al., 2004), impedes development of desirable competencies, and, relative to deeper learning approaches, is associated with poorer-quality learning outcomes and lower academic performance (Byrne et al., 2002; Diseth & Martinsen, 2003; Duff et al., 2004). Several specific behaviors associated with surface learning include a haphazard, non-structured approach and falling behind in day-to-day work, the latter of which creates the fear of a time constraint for assignment completion and exam preparation (Faranda, 2015). The results of Ballantine et al.'s (2018) study associated the surface approach with higher levels of cheating.

Strategic Approach to Learning

The strategic approach is characterized by a fervent awareness of assessment criteria, and a strong motivation to outperform others and attain the highest possible marks (N. J. Entwistle & Peterson, 2004). Strategic learners are systematic and organized in their learning efforts, make themselves aware of what instructors deem important, and closely monitor whether their work output meets instructors’ requirements (N. J. Entwistle & Ramsden, 1983). An activity identified with strategic learning is to prepare one’s learning space, for example, with a “clean” desk and the presence of items like notes, books, calculator, etc. neatly organized at the onset of a study session. Actions associated with the creation of note-making tools, such as highlighting, typing up outlines of course material, and the creation of homemade flashcards are also indicative of strategic learners (Faranda, 2015).

Researchers in business education have detected positive associations between students’ use of the strategic approach and their performance on various assignments, projects, and exams (e.g., Byrne et al., 2002; Duff, 2003) and on GPA attainment (Rodriguez, 2009). Faranda et al. (2021) found a positive relationship between senior-level marketing majors who most favored the strategic approach and their satisfaction ratings with their own academic performance in the major. Ballantine et al. (2018) found that undergraduate business students adopting the strategic approach reported the least likelihood of cheating.

Overview of the Three Approaches to Learning

The deep approach is emphasized across business disciplines as the most important to encourage, foster, and develop in our students. However, the strategic approach’s impact has often been overlooked, even dismissed at times as less than desired by several of these same researchers (Byrne & Willis, 2008; Flood & Wilson, 2008; Teixeira et al., 2013), even when their findings showed it to represent students’ preferred mode of learning. Given its connection with positive performance outcomes, the strategic approach has gained some acknowledgment as favorable and beneficial to learning (Ballantine et al., 2018; Faranda et al., 2021; Richardson, 2013). As such, we believe it plays an important role in influencing a student’s perception of the quality of their academic program and their satisfaction with their own academic achievement.

RESEARCH QUESTIONS

As students move through their undergraduate programs, they adopt learning styles related to their major discipline of study (Nulty & Barrett, 1996). Adoption of a preferred learning style is not absolute, but more a tendency, influenced both by relatively stable factors (e.g., personal-difference characteristics; long-term experience with a particular course delivery mode) and by more transient objectives (e.g., last-minute cramming for an exam; managing a heavier-than-usual workload) (p. 333). Higher education research has supported the notion that the teaching and learning environment of the liberal arts and social sciences typically fosters cultivation of deep approaches among its students to a greater degree than for students of business (Kember et al., 2008; Smith & Miller, 2005). The strategic approach was most preferred in a recent study of marketing majors (Faranda et al., 2021) and by Backhaus and Liff (2007) among management students. For accounting students, the strategic approach emerged as that most utilized, per studies by Byrne and Willis (2008) and Flood and Wilson (2008), while for Hall et al. (2004) and Jackling (2005) the accounting students surveyed most often adopted surface learning approaches. There is no body of consistent findings of learning-approach preferences among the various business disciplines, in comparison with each other or with marketing alone. To address this gap, we pose the following, two-part research question:

Research Question 1a: What are the preferred approaches to learning adopted by business majors?

Research Question 1b: Are there detectable differences in such preferences between marketing majors and students in the several, other business disciplines?

Within their educational experience, factors influencing students’ learning-approach behaviors include – among others – workload, teaching quality and supportiveness, usefulness of course materials, assessment modes employed, and relevance to professional practice. It is the students’ interpretation of these contextual factors incorporated by instructors and the program of study “which triggers the effects of the learning environment” (Baeten et al., 2010, p. 248). When taken together, these specific factors constitute a general indicator of perceived academic program quality (Ramsden, 2003) and wield a strong influence over students’ chosen approaches to learning. Moreover, higher education researchers have uncovered reciprocal ties between students’ learning behaviors and their perceptions of the academic environment (Lizzio et al., 2002; Richardson, 2006; Sun & Richardson, 2016). The array of factors influencing perceptions of the learning environment may lead to differing judgements of academic program quality among students in the varying business disciplines. For marketing students, such judgements of program quality might be uniquely linked to their own pattern of learning-approach preferences, yielding relationships differing from those detected among, say, finance students. Therefore, for upper-level marketing students and their program of study, it would be beneficial to understand the nature of such relationships and whether they differ from (or resemble) those of students from other business disciplines. This leads us to our second research question:

Research Question 2: What is the relationship between student perception of the academic quality of the business program of study and the adoption of approaches to learning?

METHOD

Participants, Survey Instrument, and Data Collection

Data were gathered from upper-level business majors at a mid-sized, public university in the mid-Atlantic region of the United States. Specifically, junior-level students enrolled in the Principles of Marketing course designated solely for business majors, served as the population of interest. Principles of Marketing was chosen because it is the most popular marketing course taught at the university level for both marketing majors and many other business students, and it is a common focal course for pedagogical research in our field (e.g., Al-Fattal, 2025; Lincoln & Frontczak, 2008; Story et al., 2020). For marketing majors, Principles serves as the foundational requirement for most other marketing courses in the academic program. It is highly likely that a significant majority, and possibly all, marketing professors teach a “Principles of Marketing” course at some point in their academic careers.

Prior to enrollment in the Principles course, these students completed 10 business core, prerequisite classes. At the time of the study, they were also concurrently enrolled in three, upper-level “Principles of” courses, namely Finance, Management, and Operations. The survey was conducted within two weeks of final examinations, by which time the students had submitted and received grades for all major course deliverables. Several instructors for the course allowed the researchers to administer the paper-and-pencil survey, in class. Data were gathered over a period of four semesters from 12 sections. Class members were informed that their participation was entirely voluntary. As an incentive, these students were informed that for each fully completed questionnaire, a donation of $.25 would be made by the researchers to the regional food bank. Few students declined participation, and 755 surveys were administered. In addition to some basic demographic information, the survey instrument also asked students to denote their major.

Survey Instrument and Measures

Approaches to Learning

The survey incorporates the 52-item Revised Approaches to Studying Inventory (RASI), which measures a student’s preference for a particular approach to learning/studying (N. J. Entwistle et al., 2013). When data are gathered via the RASI, they consistently result, via exploratory factor analysis (EFA), in the emergence of the three learning dimensions of deep, strategic, and surface, with little variation to the loading pattern of its 13 subscales on these dimensions (e.g., Brown et al., 2015; Byrne et al., 2004; Richardson, 2010). The RASI contains five subscales for the strategic approach and four each for the deep and surface approaches. Each subscale is comprised of four items. Ratings for each of the 13 subscales are summed, which yields scores with a range of 4 to 20. To enable comparison, the summated scores for each of the three learning approaches are divided by the number of constituent subscales to yield scaled mean values. Participants were instructed to think of their overall experience in the business courses taken to date when indicating their relative disagreement/agreement with the 52, first-person comments about studying, with 1 = Disagree to 5 = Agree. The midpoint of the scale (3) is defined as “Unsure.”^[2] Minor modifications were made to the RASI to “translate” British-influenced educational terminology to American usage (e.g., “lecturers/tutors” to “instructors/professors”). The subscales of each of the three approaches to learning, along with sample items, are given in Table 1.

Academic Program Quality

To assess perceptions of program quality, the survey included the Wilson et al. (1997) version of the Course Experience Questionnaire (CEQ). The intent of the CEQ is to measure students’ perceptions of the quality of both their degree programs and the teaching delivered in them, rather than for individual classes or instructors (Lizzio et al., 2002). The CEQ consists of 36 items grouped into six, component subscales corresponding with key aspects of effective academic programs and teaching, such as defining clear goals and standards and engaging in appropriate assessment of student performance. The CEQ utilizes a five-point scale, anchored by Definitely disagree (1) to Definitely agree (5). Item scores for each subscale are summed, then divided by the number of items comprising a particular component. An additional assessment of the overall perceived quality of their experience in the business program to date is operationalized as a composite variable comprising the mean values of the six subscales of the CEQ. Table 2 presents the six subscales of the CEQ and the number of items which fall within each subscale, along with two, item examples of each.

Measures of Program Satisfaction and Achievement Satisfaction

Wilson et al. (1997) supplemented the CEQ measure with an additional, standalone item, not falling within any of the six components of program quality described above (as presented in Table 2). This item, using the same five-point scale and dubbed “Academic Program Satisfaction,” asks students to provide an overall rating of their satisfaction with the program of study. This item is utilized to validate the CEQ scale via correlations between it and each of the mean values of the six CEQ components (Diseth et al., 2006; Richardson, 2010). Also adopted from Entwistle et al. (2013) is a single, 9-point item which asks the student to rate objectively “your assessed work overall in your business courses, so far,” with 1 = Rather badly and 9 = Very well. This item is referred to as “Satisfaction with Academic Achievement in the Business Program.”

RESULTS

Sample Characteristics

The surveys of 13 participants were eliminated due to extensive incompleteness or paying no heed to multiple, reverse-worded items used as attention checks. This left a useable sample of 742 respondents. Of these, 457 (61.7%) were male and 283 (38.2%) were female, indicative of the general ratio of male-to-female students among business majors at this institution. Two individuals chose not to disclose their gender. Participants’ ages ranged from 19-30 (M = 20.6; SD = 1.03). Within this final sample, there were occasions when a participant skipped an item in mid-measure. For all such missing items, the mean value of the scale replaced the missing value (Tabachnick & Fidell, 2007).

Reliability of Measures

Cronbach’s (1951) coefficient alpha was used to assess the internal consistency of the RASI and CEQ measures. For the deep and strategic approaches and their constituent subscales, all values exceeded the preferred .70 threshold (Nunnally, 1978), with the reliability score for the surface approach (α = .68) slightly below that mark (see Table 3). Values from the present study compare favorably with similar studies (e.g. Byrne et al., 2004; Richardson, 2010; Teixeira et al., 2013). EFA was used to determine the RASI factor structure. Also consistent with prior studies, three factors emerged, aligned with the three learning approach scales of the RASI (i.e., Deep; Strategic; Surface). Additionally, the value of the primary loading coefficient for each of the 13 subscales is associated with the learning approach expected.

Descriptive statistics and psychometric properties of the CEQ are shown in Table 4. Coefficient alpha values for each subscale, except for “good teaching” (α = .69), exceed .70. Because the CEQ, with its six components, is frequently explained by a single, underlying dimension, it can be regarded in such cases as an indicator of the perceived academic quality of a given field of study (K. L. Wilson et al., 1997). Academic Program Satisfaction, measured by the five-point validation item, was positively correlated with each CEQ component, as well as with Academic Program Quality (all relationships at the p < .001 level). These correlations accord concurrent criterion validity for the CEQ as an indicator of students’ perceptions of the academic program quality of the prerequisite business core and their present experience in the Principles courses.

Learning Approach Preferences

Research Question 1a examines the learning-approach preferences of junior-level business majors, nearing completion of their Principles of Marketing course. Scaled mean scores for the three learning approaches, along with their constituent subscales (Table 3), show that these students engage in the strategic approach most frequently (14.84), followed by the deep approach (13.60), and least, the surface approach (12.89). All pairwise comparisons of these values are statistically significant (p < .001).

Research Question 1b asks whether students in the specific majors offered within the business program would exhibit differing preference patterns for the three learning approaches. For all majors, except Economics, the same order of preference occurs as for the sample as a whole: Strategic, Deep, Surface. It should be noted that the number of participants denoting Economics as their major was 14, by far the smallest of the university’s seven business disciplines. Table 5 displays the mean values of the three learning approaches for all majors, in addition to those for the majors combined, excepting marketing. T-tests were performed to detect any differences between marketing majors and all the other majors combined, for the three learning approaches. None of those three tests approached significance.

The findings for Research Question 1b obviate any need to split or divide any portion of the study participants into groups (based on major) for any subsequent analysis we present. The rank order of learning-approach preference – Strategic, Deep, Surface – is essentially invariant among students of the several major fields of study. Thus, the remaining analysis, which includes all study participants, can be regarded as applicable and relevant to business educators in general. While we will direct the implications of the study to instructors of marketing principles and their students, the entire marketing program can benefit from its findings.

Correlation analysis between the nine-point item, Satisfaction with Academic Achievement in the Business Program (M = 6.11), and the three learning approach scores supports this preference ranking. All three correlations with the learning approaches were significant, with the strongest relationship between achievement satisfaction and the strategic approach (r = .36, p < .001). The relationship with the surface approach is also significant, but inverse to satisfaction with academic achievement (r = -.24, p < .001). The association with the deep approach is the smallest of the three, yet also significant (r = .10, p < .01). A regression analysis of the satisfaction measure on the three learning approaches (F = 60.62, Adjusted R² = .195, p < .001) alters the picture slightly. The strongest predictor of these students’ assessment of their academic achievement in the business program to date is the strategic approach (β = .197, t-value = 10.05, p < .001). However, both the surface approach (β = -.024, t-value = -3.62, p < .001) and the deep approach (β = -.017, t-value = -2.78, p < .01) display a negative relationship. This mixed result for the deep approach calls into question the relationship of it to these students’ views of their academic success at the mid-point of their quest toward earning an undergraduate degree in business. The two findings regarding the surface approach support the notion that considerable reliance on it results in negative perceptions of academic achievement.

Academic Program Quality and Learning Approaches

Research Question 2 investigates relationships between learning approaches and student perceptions of the quality of the business program to date. As shown in Table 6, correlation coefficients between components of the CEQ and the students’ scores for the strategic approach and the deep approach are each positive, with 10 of those 12 relationships reaching significance. These findings provide evidence that these students’ perceptions of the academic quality of their program are positively related to the use of strategic and deep approaches. In addition, correlations were negative between the surface approach and all CEQ items, and at levels of magnitude generally greater than with the other two learning approaches, with that of “appropriate workload” as the strongest (r = -.56). Also, correlation coefficients of the composite measure of Academic Program Quality with each of the three learning approaches mirror the relationships found for the individual CEQ components. These results echo Richardson’s (2010) remark “…that students’ perceptions of academic quality are associated more with the absence of undesirable approaches to studying than with the presence of desirable approaches” (p. 198).

Canonical correlation analysis provides further insight into the relationships between academic program quality and learning approaches. From the two sets of variables that comprise these measures, canonical variate pairs are derived, with the first pair serving to maximize the association between the two sets of variables. Subsequent pairs also maximize the association between the two sets of variables, while controlling for the effects of all prior pairs. Here, the maximum number of canonical variates that may be derived is three, due to the three RASI scales. Because the statistical model is symmetric in the two sets of variables, there is no stipulation that either set be considered explanatory with the other designated as the response set. Determining which canonical functions should be retained and interpreted followed Hair et al.'s (1998) procedure. First, all three pairs extracted are statistically significant, and thus subject to further analysis (Table 7, Panel A). A multivariate model of all canonical functions (Table 6, Panel B), tested simultaneously with the assessment of each individual function, is significant for all test statistics (p < .0001), confirming the results shown in Panel A.

Next, the practical significance of each variate pair is considered, via the magnitude of the canonical relationships, to determine the contribution of the findings toward increasing understanding of the research problem under study (Hair et al., 1998). The value of Wilks’ Lambda (Λ = .498) “measures the proportion of the variance in one set of variables that is not shared with the second set of variables” (Richardson, 2010, p. 193). Then 1 – Λ (.502) indicates the proportion of variance shared by the two sets of variables. Per Cohen’s (1988) criteria, this value constitutes a large effect. When the squared value of the pair’s canonical correlation meets a minimum of 10% (Tabachnick & Fidell, 2007), further support for the practical significance of a variate pair is present. This r² value indicates the proportion of shared variance not yet explained by any previous (and, larger pairs). In Table 7, Panel A, the intercorrelations of the three significant pairs of variates represent shared variance of .402, .121, and .054, respectively, supporting the dismissal of the third pair of covariates from further consideration.

In conjunction with any assessment of practical significance, results from canonical correlation should render viable interpretation. Canonical cross-loadings indicate the correlation of each of the original observed variables of a given set with the canonical variate of the opposite set of variables (Table 8). This resembles multiple regression; however, here, each variable of one set is correlated to the variate of the other set, rather than a single variable. Such cross-loadings provide a greater understanding of the underlying structure of the relationships between the two sets of variables than provided by the correlation analysis in Table 6. A +/- .30 criterion for significance at the p < .05 level may be applied to the values of these cross-loadings. However, due to the large size of the present sample, this should be regarded as a very conservative guide; thus, values in the low-to-mid .20s can be considered significant at this level of alpha (Hair et al., 1998).

As displayed in the top portion of Table 8, each of the six CEQ components is significantly, positively related to the first learning approach variate (LearnApp1), with the highest loadings exhibited by “appropriate workload” (r = .55) and “clear goals and standards” (r = .40). The values for the other four CEQ components all meet or exceed .30. For the second learning-approach variate (LearnApp2), only “emphasis on independence” (r = .24) and “generic skills” (r = .22) can be regarded as moderately significant.

Turning to the cross-loadings of the three learning approaches (bottom portion of Table 8), the largest correlation to the CourseExp1 variate exists with the surface approach and at -.61, this association is an inverse one. The positive cross-loadings for deep approach (r = .23) and for strategic approach (r = .22) are notably less strong and are better regarded as approaching significance at the p < .05 level. For learning-approach ties to the second course experience variate (CourseExp2), the cross-loadings of the deep and strategic approaches are likely significant at the .05 level (r = .27 and r = .24, respectively), while that of the surface approach is not (r = .09).

In sum, both variate combinations offer insights into these students’ perceptions of the academic quality of their business program of study to date. Regarding the first variate pair, program quality, as indicated by each component of the CEQ measure, is positively related to these study participants’ preferred approaches to learning (i.e., strategic and deep). However, perceptions of academic program quality are most influenced by the absence of surface approaches to learning (Faranda et al., 2021; Richardson, 2010). While the relationships presented with respect to LearnApp2 and CourseExp2 are modest, several are noteworthy: The pattern of canonical loadings for both variates differs from those of the first variate pair. With CourseExp2, students’ positive evaluations of the program quality elements “emphasis on independence” and “generic skills” are positively related to their preferences for the use of deep and strategic learning approaches, with little bearing on the presence or absence of surface approach usage.

DISCUSSION AND IMPLICATIONS FOR MARKETING EDUCATORS

With few exceptions (e.g., Backhaus & Liff, 2007; Faranda et al., 2021) business educators have focused predominantly on implementation of the deep approach, with limited consideration for the strategic approach. Thus, a contribution of the present study’s results is evidence of the prevalence and preference of Principles of Marketing students’ usage of the strategic approach and of its positive relationships to satisfaction with academic performance and to the learning environment within their required course program. With no preferential differences found between marketing majors and those participants across the other business disciplines combined, these findings also contribute to our understanding of business students’ learning approaches in general and should serve as a means by which to improve their learning experience. Additionally, we can focus implications of the study and any associated recommendations specifically to instructors of Principles of Marketing courses.

An additional noteworthy outcome of this study is the significance of these students’ preference for the strategic approach over that of activities which promote deep learning. While both were positively associated with academic program quality, even more influential to that relationship with learning-approach choice is the negative tie with surface approaches to learning. Findings of business students’ preferences for the strategic approach have at times been lamented as contrary to the favored learning objectives of higher education (Byrne et al., 2009; Teixeira et al., 2013). We regard that view as shortsighted. Most often, the usage of the deep approach follows closely to that of the strategic approach and significantly above that of engagement in surface learning.

The study’s finding of these students’ preference for the strategic approach over the deep approach runs counter to similar studies in the arts and sciences. However, researchers of approaches to learning have not offered much to explain why this contrast exists. Biglan (1973) offered some insights via his framework of subject matter in different academic areas. Business disciplines are categorized as entailing the practical application of their subject matter more so than the liberal arts and the hard sciences. With the possible exception of engineering fields, business education is more tools-oriented and more applied than other areas of undergraduate studies. Moreover, it has been argued that the primary charge of marketing educators is to prepare students “to be productive performers in business and organizations” (Schlee & Harich, 2010, p. 341), which has become more challenging and opportunistic in response to the impacts of AI on teaching and learning (Barger et al., 2025; Grewal et al., 2025; Narang et al., 2025; Seif, 2026). In project-based courses, for example, the better that marketing students perform on such tasks, the greater their chance for compiling a record of in-class achievements to showcase for potential employers. The priorities of these students and the nature of the academic environment found in business education likely influence preference for the strategic approach.

There appears to be no downside to participants’ strong preference for the strategic approach. The various facets of strategic learning are reflective of behaviors which contribute to academic performance and to sound preparation for initial (and continued) success in the workplace. In addition to the marketing discipline, we encourage all business educators to acknowledge the positive impacts of students’ reliance on strategic learning behaviors – namely, higher levels of academic achievement – and adopt more practices that enhance and reinforce their usage, subsequently lessening students’ reliance on surface approaches. This should not come at the expense of teaching methods which promote and develop deep learning. Rodriguez (2009) regards both approaches to be indispensable and complementary. He commends deep learning as reflective of students’ learning goals, the desire to increase their knowledge and develop key skills, and regards strategic learning as more indicative of the performance goals formed to prove the mastery of that knowledge and the acquisition of those skills. In sum, the use of deep approaches to learning facilitates academic achievement via strategic learning behaviors.

Because Principles of Marketing is often the first exposure that business students have to the discipline, these instructors face the dual challenge of building foundational marketing knowledge, while also motivating a diverse group of majors. Among these students, those who adopt a strategic learning approach are especially driven by achievement motives and often view high grades as indicators of future success. By recognizing these tendencies, instructors can design curricula that reinforce students’ desire for high performance and channel it into meaningful engagement with core marketing concepts.

This study contributes to marketing pedagogy by challenging implicit assumptions about disciplinary differences and encouraging more integrative, evidence-informed course design. Table 9 presents recommendations for supporting strategic learners in a Principles of Marketing course, organized around six themes: motivation, clarity, preparation, feedback, collaboration, and empathy. These themes draw on prior marketing education research examining instructor behaviors associated with outstanding teaching (e.g., Faranda & Clarke III, 2004; Granitz et al., 2009). The instructional strategies are embedded within foundational marketing concepts such as segmentation–targeting–positioning (STP), the marketing mix (4Ps), and consumer decision-making processes. Positioning these pedagogical supports within core disciplinary content encourages students to simultaneously optimize course performance while developing deeper conceptual understanding of marketing. Although the recommendations and examples in Table 9 focus on introductory marketing topics, the strategies are adaptable to other marketing courses, including marketing research, services marketing, and marketing analytics. Together, these directed actions will help instructors balance students’ orientation for high marks with the goal of cultivating long-term understanding of marketing principles.

The results of our analysis of the CEQ and its components also provide guidance to lessen students’ reliance on surface approaches to learning. Each of the six scales of the CEQ was strongly, negatively correlated with surface approach learning (Table 6). Thus, the upgrading of any one component would lead to a lessening of reliance on surface learning; however, the marketing program of study needs a starting point in any efforts to improve students’ perceptions of the learning environment. According to Lizzio et al. (2002) insufficient attention to “appropriate workload” and “appropriate assessment” most consistently lead students toward adoption of surface approaches to learning. For our study, only these two CEQ components were negatively related to the LearnApp2 covariate. Garver et al. (2025) suggest that perceived high workload impacts Gen Z students’ ability to work (outside school) and offset expenses and debt. Improvements in workload balance and assessment practices would offer the greatest chance for initial success because of the level of change that would be required. Other components (e.g., students’ perceptions of good teaching throughout the department; steps to raise students’ analytical and problem-solving skills) would likely need a longer time frame to enact. Lizzio et al. (2002) did not treat lightly such long-run goals, but rather pointed out that “early improvements in one domain of the learning environment will…create momentum to continue efforts with more challenging tasks,” (p. 45).

LIMITATIONS AND SUGGESTIONS FOR FUTURE RESEARCH

The learning approach/academic quality relationships presented in this cross-sectional study are correlational in nature and do not address any underlying causal ties. By use of canonical correlation, neither variable battery (RASI, CEQ) was treated as independent nor criterion. However, within the extant literature, the direction of such relationships has primarily focused on the impact of learning approaches on perceptions of academic program quality. Yet, several researchers have posed the question of potential bidirectionality. Baeten et al. (2010) commented that it is the student’s perception of instructor delivery and assessment modes and of course program design which “triggers the effects of the learning environment,” (p. 248) and wields strong influence over students’ chosen approaches to learning. Richardson (2006; Sun & Richardson, 2016) found reciprocal ties between students’ learning behaviors and their perceptions of the academic environment. More research needs to be conducted to explore the directionality and causality of these relationships.

Students’ overall program perceptions were assessed during their concurrent enrollment in Principles of Marketing, which may introduce a recency effect whereby the current course experience might disproportionately influence their responses. To mitigate such effects, participants were instructed at multiple points throughout the survey instrument to respond in terms of the sum total of all business courses taken to date, which for these students totaled 10 classes completed prior to the Principles course.

An important extension of this work would be to investigate the longer-term implications of learning approaches, particularly with respect to knowledge retention and sustained learning outcomes. Longitudinal research tracking students over time would provide insight into whether the observed associations persist beyond the immediate instructional context. For instance, it would be useful to understand if differences emerge across learning approaches in terms of the retention of learning outcomes (e.g., Freshman to Senior, Recent Graduate to 5+ years Post-grad) and the potential transfer across deep, strategic, and surface learning preferences.

Finally, while the key measures employed here (namely the RASI and the CEQ) demonstrate utility across disciplines due to the general nature of their measurement items, refinement of these measures may be a fruitful extension of the approaches to learning framework. The development and validation of instruments specific to particular disciplines may provide more nuanced insights into how learning approaches and perceptions of program quality operate within particular academic contexts.

CONCLUSION

Ultimately, fostering use of strategic learning behaviors among marketing students presents an opportunity to raise their performance level and to improve our own classroom efforts as individual educators. To ensure that students develop a strong knowledge base in the marketing discipline, become proficient at the skills sought by employers, and develop critical, lifelong learning skills, teaching approaches which incorporate the fostering of strategic behaviors will enhance student achievement. In turn, this success will reflect on the quality of individual instruction and also contribute to the achievement of marketing department program goals.

Note

The authors report there are no competing interests to declare.