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*Corresponding author. E-mail: elias.bengtsson@hh.se
MOTIVATION, LEARNING STRATEGIES AND PERFORMANCE
AMONG BUSINESS UNDERGRADUATES AT UNIVERSITY
COLLEGES IN SWEDEN
Elias BENGTSSON 1, *, Britta TELEMAN2
1School of Business, Engineering and Science, Halmstad University, Kristian IVs väg 3, 30118 Halmstad, Sweden
2Konstfack University of Arts, Cra and Design, LM Ericssons väg, 126 27 Stockholm, Sweden
Received 12 June 2019; accepted 07 September 2019
Abstract. Purpose – is paper brings new material to the understanding of interlinkages between
motivation, learning and performance in academic contexts. By investigating these interlinkages in
a new context – students of business and management at a Swedish university college – it seeks to
answer the following research questions: How do students’ degree and type of motivation relate to
their learning strategies?; how do students’ degree and type of motivation and learning strategies
relate to their academic success?; and how do student characteristics in terms of experience and
gender inuence the nature and strength of these relationships?
Research methodology – e data used in this paper is based on student surveys and a centralised
system of reporting and archiving academic results. e latter contains information on the academic
performance of individual students, whereas the surveys gathered information on the students’ back-
ground characteristics (experience and gender), their motivation for pursuing academic studies and
their learning strategies. e dierence in proportion tests and OLS regressions were then applied
to investigate dierences between student groups and relationships between the dierent variables.
Findings – e ndings reveal that business students are more extrinsically than intrinsically mo-
tivated; that deep learning approaches lead to higher grades for particular examination forms, and
that female students are typically more intrinsically motivated, engage more in deep learning ap-
proaches and perform better than their male counterparts.
Practical implications – e ndings suggest that practitioners in higher education involved with
the business and/or university college students have good reasons to stimulate motivation gener-
ally, and intrinsic motivation in particular. However, this must be accompanied by examination
forms that promote deep learning.
Originality/Value – In contrast to most research, this paper focuses on the interlinkages between
motivation, learning and performance among business students in a university college setting. is
contrasts most research on this topic which tends to be focused on university students, particularly
in the US, in other elds of study or accounting. Moreover, this paper also takes student character-
istics into account and uses a variety of measures to operationalise academic performance.
Keywords: academic performance, university college, business students, learning approaches,
intrinsic motivation, extrinsic motivation.
JEL Classication: A2, A22.
Business, Management and Education
ISSN 2029-7491 / eISSN 2029-6169
2019 Volume 17 Issue 2: 111–133
https://doi.org/10.3846/bme.2019.10512
112 E. Bengtsson, B. Teleman. Motivation, learning strategies and performance among business...
Introduction
e relationship between motivation, learning and academic performance is a widely rec-
ognised topic which has attracted considerable empirical research. is paper investigates
academic students of business and management at a Swedish university college. Swedish
universities and university colleges oering higher studies in business education typically
award two degrees; the Master of Science in Business Administration (MSc Business) and a
more vocationally oriented degree in business called Civilekonom. Besides, there are several
academic institutions oering supplementary specialisations or specic programs. In this
paper, the sample consisted of students from both the MSc Business and the Civilekonom de-
gree, as well as students from a business administration program specialised on construction
and real estate. is context of the paper allows it to oer new insights on the relationship
between motivation, learning and academic performance.
First, there is relatively little research on the link between motivation, learning strate-
gies and academic performance among business students (Vantournout etal., 2012). At the
same time, it has been suggested that business students are a breed of their own in terms
of motivation, which is claimed to be mainly driven by external rewards such as securing
prestigious and high salary jobs (DeMarie & Aloise-Young, 2003; MCEvoy, 2011). is
makes motivation and its impact on education a particularly important topic for business
schools (Debnath etal., 2007). Indeed, the few studies that focused on business students
suggest that their motivation has implications on learning approaches and academic per-
formance (Everaert, Opdecam, & Maussen, 2017; Ariani, 2016; Du, 2004). Second, the
limited research on motivation, learning and performance among business students has
predominantly focused on accounting students (Everaert etal., 2017) and in the US (Du,
2004). Understanding of the topic may, therefore, be particular and specic to those con-
texts. ird, most academic research on student characteristics has analysed how academic
performance diers between genders. e implications of experience are less researched
(Vantournout etal., 2012, Du, 2004), as is the link between these factors and motivation.
Fourth, most research on motivation, learning strategies and performance is based on
university students. Very few examples (e.g. Yu, Zhang, Nunes, & Levesque-Bristol, 2018;
Vantournout etal., 2012) tackle the topic in a university college setting, which itself may
raise particular challenges relating to student motivation for academically (as opposed to
more vocationally) oriented studies. Fih, most research (e.g. Byrne, Flood, & Willis, 2002;
Sadler-Smith, 1996; Trigwell & Prosser, 1991) rely on relatively narrow denitions of aca-
demic performance or single measures, which may omit meaningful relationships between
performance, motivation and learning. Finally, most research on motivation, learning and
performance is relatively dated about the structural changes toward an increasing number
and increasingly diverse body of students that has been ongoing and perhaps reinforced in
recent years, both generally and in Sweden (c.f. European Commission, 2013; UHR, 2016).
ese structural developments pose challenges related to organisation and strategies in
higher education (Bennett, 2003; Bowl & Bathmaker, 2016).
is paper thus brings new material to the research of interlinkages between motiva-
tion, learning and performance by taking into account gender and experience of business
Business, Management and Education, 2019, 17 Issue 2: 111–133 113
university college students. It also uses a variety of performance measures. In line with this,
the paper seeks to answer the following research questions:
– Q1: How do students’ degree and type of motivation relate to their learning strategies?
– Q2: How do students’ degree, type of motivation and learning strategies relate to their
academic success?
– Q3: How do student characteristics in terms of experience and gender inuence the
nature and strength of these relationships?
e remainder of this paper is outlined as follows: First, we provide an overview of the
current understanding of how motivation and learning strategies are related in academic
settings (Section 1). We focus on research from business studies contexts in particular and
develop several hypotheses on the relationships between motivation, learning and academic
performance. Second, issues relating to data and the methodology used to test the hypotheses
are outlined (Section 2). In section 3, we present the results of the empirical analysis and
compare them to prior research. Finally, in conclusion, we discuss our ndings in the light
of the ongoing discussion in higher education, on what type of motivation, learning strate-
gies and examinations one should encourage in order to promote academic performance.
1. Literature review: Motivation, learning and academic performance
is section provides an overview of the literature on motivation (section 1.1), the link
between motivation and learning (section 1.2) and the relationships between motivation,
learning and academic performance (section 1.3). e section uses prior research (empha-
sising prior research based on business students, whenever such research is available) and
deduction to develop some hypotheses that are subsequently empirically tested. A gure that
summarises these hypotheses is provided at the end of the section.
1.1. Motivation
ere are various conceptualisations on how academic students’ motivation inuence their
learning, performance, adjustment and well-being (Vansteenkiste, Zhou, Lens, & Soenens,
2005). One such conceptualisation is the self-determination theory (SDT) which seeks to
explain people’s behaviour by their underlying motivation and the types of goals they pursue
(Deci & Ryan, 2000). SDT distinguishes between two types of goals: intrinsic goals which
are perceived as necessary in their own right, without consideration of any potential rewards
linked to pursuing these goals other than self-fullment (i.e. self-development, health, mental
tness, community contribution, aliation etc.). Extrinsic goals are outward-oriented and
relate to acquiring specic properties or characteristics that are perceived to be important in
the eyes of others (i.e., wealth, fame, power, status, image, etc.) (Kasser & Ryan, 1996; Wil-
liams etal., 2000).
e terminology of extrinsic and intrinsic also provides a terminology to describe various
motivation categories, where the degree of self-determination in the regulation of goals and
behaviour provides a continuum of motivational categories and subcategories. Intrinsic mo-
tivation is marked by pursuing goals that are valued by their signicance without any other
rewards. is also implies that behaviour is self-determined and regulated without external
114 E. Bengtsson, B. Teleman. Motivation, learning strategies and performance among business...
pressures (Deci, 1975). Extrinsic motivation includes a range of subcategories which all re-
late to the pursuit of extrinsic goals, but vary in the extent to which goals and behaviour are
autonomous and whether it is motivated by coercion or external rewards.
e least self-determined form of extrinsic motivation is external regulation, which is
characterised by behaviour in pursuit of some externally dened goal, requirement or reward.
Introjected regulation is associated with pursuing goals to avoid feelings of guilt, self-image
anxiety or to attain self-enhancements such as pride. Identied regulation is the conscious
pursuit of goals that the person views as characterising his or her personality or identity.
Finally, integrated regulation happens when external goals have been fully aligned with one’s
other values and needs. Actions characterised by integrated motivation share many qualities
with intrinsic motivation, although they are still considered extrinsic because they are done
to attain separable outcomes rather than for their inherent enjoyment or feeling of well-being
itself. Intrinsic and extrinsic motivations and goals are not mutually exclusive, but a particu-
lar behaviour or goal may be regulated by both (Deci & Ryan, 1995; Ryan & Deci, 2000).
In research in academic contexts, dierentiating between intrinsically and extrinsically
motivated students is common (Biggs, 2011). Intrinsically motivated students to study their
chosen topics primarily to gain knowledge, understanding and satisfying their natural curi-
osity. In contrast, extrinsically motivated students study to attain other goals than only the
learning itself (Vansteenkiste etal., 2006). Within business and management studies, student
motivation has primarily been used to understand learning approaches or to explain aca-
demic performance (see below). One exception is DeMarie and Aloise-Young (2003) who
compared motivation between graduate students within the business and educational stud-
ies. Business majors were signicantly less likely to explain their choice of studies because
of “interest in the area” or “interest in the classes” and signicantly more likely to say they
picked their major because it would help them “nd a job easily” and lead to a “high salary.”
Arquero etal. (2015) demonstrate similar ndings in a comparative study between nursing
and accounting students. As suggested by McEvoy (2011), these results indicate that busi-
ness students may generally be more extrinsically than intrinsically motivated. Based on this
reasoning, the following hypothesis is proposed:
H1: Business students are more extrinsically than intrinsically motivated
1.2. Motivation and learning approaches
Higher education students’ approaches to learning are oen conceptualised as the interac-
tion between the characteristics of the individual student and their perceptions of courses,
teaching and assessment procedures (Entwistle, 1990). is paper is based on the well-es-
tablished concepts of deep and surface learning approaches (Biggs, Kember, & Leung, 2001;
Entwistle & Tait, 1990). ese are perhaps the most common way to describe and understand
how dierent students learn and study in higher education and are the results of a long pe-
riod of development and renement in the literature.
Surface and deep learning approaches are mutually exclusive (Biggs, 1987). Adopting
a surface approach means focusing on the essentials to complete a task or meet examina-
tion requirements, mostly aiming for avoiding failure at minimum eort. e learning is
Business, Management and Education, 2019, 17 Issue 2: 111–133 115
characterised by memorisation and reproduction of the material, and as a result, students
focus on isolated facts and fail to understand how concepts, themes and similar are related
to each other. Deep learning approaches, on the other hand, are oriented towards a deeper
understanding of the topic and analytical thinking. Such an approach can be characterised as
a personal commitment to learning and an interest in the subject (Biggs, 1987). Students fol-
lowing an in-depth learning approach develop critical thinking and seek to understand how
concepts, themes etc. relate to each other. ey also seek to grasp how they relate to other
areas, experiences or concepts (Ballantine etal., 2008; Du & McKinstry, 2007; Tang, 1994).
e link between motivation and learning approaches is generally conceptualized as fol-
lows: extrinsic motivation is associated with surface learning, while intrinsically motivated
students tend to adopt an in-depth learning approach (Lucas & Meyer, 2005). e latter tend
to be more dedicated and more genuinely engaged in the materials to be learned (Vansteen-
kiste, Simons, Lens, Sheldon, & Deci, 2004).
Does this mean that extrinsic motivation weakens deep learning? Earlier theories seem
to support this notion. External rewards were understood as substitute targets that distract
learners from deep learning (Deci etal., 1999; Luyten & Lens, 1981; Vansteenkiste etal., 2004;
Niemiec etal., 2006). Extrinsic goals were seen as weakening the learning process, by under-
mining self-determination, interest and curiosity (Vansteenkiste, Lens, & Deci, 2006). More
recent evidence, however, suggests that extrinsic motivation, in combination with intrinsic
motivation may promote deep learning (Mo, 2011). Also, extrinsic motivation may trigger
intrinsic motivation and thereby strengthen deep learning (Rassuli, 2012) and lead to better
academic performance (Tasgin & Coskun, 2018). is is also supported by recent research
in a business studies context, where accounting students with high intrinsic and extrinsic
motivation tended to be more engaged in deep learning (Everaert etal., 2017). In a study on
rst-year students in an undergraduate accounting course, intrinsically motivated students
were found to have a slightly higher score for deep learning compared to surface learning
(Du, 2004). Taken together, this leads to the following hypotheses:
H2a: Intrinsic motivation among business students is positively related to deep learning
approaches
H2b: Extrinsic motivation among business students is positively related to surface learn-
ing approaches
H2c: e combination of intrinsic and extrinsic motivation among business students is
positively related to deep learning approaches
1.3. Motivation, learning approaches and academic performance
Studies on the link between learning approaches and learning outcomes are numerous
(Richardson, 2017; Weber & Patterson, 2000; Watkins & Hattie, 1985; Trigwell & Prosser,
1991; Sadler-Smith, 1996; Du, 2004; Sun & Richardson, 2016). In general, the relationship
between deep learning approaches and outcomes (measured as examination scores or self
-perceived learning) is positive (Dong, Bai, Zhang, & Zhang, 2019; Smyth, Mavor, & Pla-
tow, 2017; Chan, 2016; Sakurai, Parpala, Pyhältö, & Lindblom-Ylänne, 2016). For students
that adopt deep approaches to learning, analytical and conceptual thinking skills are more
116 E. Bengtsson, B. Teleman. Motivation, learning strategies and performance among business...
likely to develop, which in turn promote stronger academic performance (Ginns, Martin, &
Papworth, 2018; Hall, & Raven, 2004). In a business studies context, several studies (Abhay-
awansa, Bowden, & Pillay, 2017; Alanzi & Alfraih, 2017; Davidson, 2003) found that deep
learning increases academic performance, whereas surface learning does the opposite. e
latter is also conrmed by Fryer and Ginns (2018) and Teixeira and Gomes (2017). Everaert
etal. (2017) report similar ndings among accounting students, even when controlling for
time spend and ability. However, sometimes, the relationship is perhaps less intense than
expected or even negative (Byrne etal., 2002; Wynn-Williams etal., 2016). is may relate
to examination forms being ill-suited to assess those skills and other abilities associated with
deep learning.
Prior research also demonstrates that the link between intrinsic motivation and academic
performance in higher education is oen positive (Eppler & Harju, 1997; Turner, Chandler,
& Heer, 2009). Taylor etal. (2014) conduct a meta-analysis that concludes that intrinsic
motivation is the only motivation type to be consistently positively associated with academic
achievement, but there are also exceptions (such as Herath, 2015). One reason why more
intrinsically motivated student tend to perform better is that they are more likely to adopt
deep learning approaches (Lange & Mavondo, 2004).
For extrinsic motivation, the evidence is much more mixed. Pintrich etal. (1993) ob-
served no correlation between this type of motivation and course grades. However, research
on business students shows that general motivation aects performance among accounting
students, both in terms of course grades (Doran, Bouillon, & Smith, 1991) and in terms of
applying knowledge on complex case material (Davidson etal., 1996). For university college
students (although not business students), Yu etal. (2018) show that a self-determined (i.e.
intrinsic) motivation to choose a major predicts positive outcomes in university college set-
tings.
Taken together, the above ndings point towards a positive link between learning ap-
proaches and performance on the one hand, and motivation and performance on the other.
Does this mean that more (intrinsically) motivated students tend to adopt more in-depth
learning approaches and thereby achieve higher academic performance (c.f. Biggs, 2001;
Everaert etal., 2017) study on accounting students found that both intrinsic motivation and
extrinsic motivation have a signicant positive inuence on deep learning, which in turn
is positively related to academic performance. Conversely, surface learning was associated
with lower academic performance. Based on this literature, the following hypotheses are
suggested:
H3a: Deep learning is positively related to academic performance among business stu-
dents
H3b: Surface learning is negatively related to academic performance among business
students
H4a: Intrinsic motivation and academic performance in higher education is positively
related to business students
H4b: e combination of intrinsic and extrinsic motivation is positively related to aca-
demic performance among business students
Business, Management and Education, 2019, 17 Issue 2: 111–133 117
1.4. Gender
e topic of dierences in motivation between genders is extensively studied. Severiens and
ten Dam’s (1994) meta-analysis of the topic suggests that males tend to report higher ex-
trinsic motivation or similar conceptualisations than females. Approaches to learning and
gender dierences have also received considerable attention, but ndings are mixed (Lange &
Mavondo, 2004; Crawford & Wang, 2015). Failing to identify gender dierences across learn-
ing approaches is common (Richardson, 1993; Wilson etal., 1996), but some studies suggest
that males tend to adopt surface learning approach to higher extent than female students,
whereas the latter are more likely to adopt deep learning approaches (Sithole, 2018; Gledhill
& van der Merwe, 1989; Bigg, 1987).
Research on dierences between male and female business students suggests a somewhat
dierent pattern. While many studies also fail in establishing gender dierences in learning
approaches (Richardson & King, 1991; Byrne & Flood, 2008), female students in account-
ing have been found to have signicantly higher surface approach scores compared to male
students (Sadler-Smith, 1996; Du, 2004). However, Lange and Mavondo (2004) found that
male accounting students are more likely to adopt surface learning approaches.
Mixed results also characterise research on gender dierences in terms of academic per-
formance. Some research fails to establish any cross-gender patterns (Doran etal., 1991;
Buckless, Lipe, & Ravenscro, 1991; Gist, Goedde, & Ward, 1996; Du, 2004). Most studies
seem to suggest better academic performance among female students (Lipe, 1989; Tyson &
Woodward, 1989), whereas the research that nds better performance among male students
is more limited (M.Y.Koh & H.C.Koh, 1999). Research that examines gender dierences
across both learning approaches and academic performance is more limited. Byrne etal.
(2002) is an exception and report a positive relationship between deep learning approaches
and academic performance for female students. However, any similar relationship for the
male student could not be established. Based on these ndings, the following hypotheses
are developed:
H5a: Deep learning approaches are more common among female students.
H5b: Female students attain better academic results.
1.5. Experience
e general pattern regarding experience and learning approaches suggest that deep learning
approaches increase with experience, whereas surface learning approaches diminish. is is
due to cognitive sophistication or experience in handling complex situations (Biggs, 1987).
Indeed, similar patterns are established in research focusing on learning approaches for busi-
ness students. Du (1999, 2004), Sadler-Smith (1996) and Abhayawansa, Tempone, and Pil-
lay (2012) report that mature students are less likely than younger students to adopt surface
learning approaches. Other studies (Asikainen & Gijbels, 2017) also show that students tend
to adopt more deep learning approaches over time. Despite these ndings, research suggests
that younger business students tend to perform better (Dockweiler & Willis, 1984; M.Y.Koh
& H.C. Koh, 1999) or at least not worse (Bartlett, Peel, & Pendlebury, 1993) than their
118 E. Bengtsson, B. Teleman. Motivation, learning strategies and performance among business...
fellow older students. Other studies, however, report dierent results (Postare, Mattsson,
Lindblom-Ylänne, & Hailikari, 2017; Kyndt, Donche, Trigwell, & Lindblom-Ylänne, 2017).
e impact of academic and work experience on motivation, learning approaches and
academic performance is less studied. Research on grade-school students suggests that intrin-
sic motivation drops as students move up the grades, whereas extrinsic motivation remains
stable (Lepper, Corpus, & Iyengar, 2005). We have not been able to identify any research on
higher education business students. However, one could hypothesise that intrinsic motiva-
tion would increase with experience since experience tends to increase autonomous and
self-regulatory behaviour (Ryan & Deci, 2000). Conversely, adult life may require a focus
on more external rewards, in order to sustain one’s standard of living and in some cases,
support other family members. Although prior ndings are mixed, this paper suggests the
following hypotheses:
H6a: Experience is positively related to deep learning approaches
H6b: Experience is not related to academic performance
2. Data and methodology
2.1. Data sample and collection
e total number of students participating in the data collection amounted to 135. Out of
these, 56% were rst-year students, whereas second-year students represented 44%. ese
gures represent participation rates of 52 and 53% of all business administration students
enrolled. e participating students lled out a survey consisting of 56 questions relating to
their background characteristics (age, experience and gender; 6 questions), their motivation
for pursuing academic studies (28 questions) and their learning strategies (20 questions).
Also, data on academic performance was collected through the Swedish centralised system
of reporting and archiving academic results (“Ladok”). In order to enable a cross-match
between motivation, learning strategies and academic performance, the questionnaire was
not anonymous. However, students were only asked to report their social security system
number (and not their names) and were promised complete condentiality. e question-
naire responses were gathered during classes on four occasions in the 2017 spring semester
(25 February – 14 March).
e age of the students ranged between 19–35, with 76% being 25 or younger at the time
of the data gathering. In terms of experience, students had taken on average, 3.1 semesters in
academic studies and had 2.8 years of work experience. 63.7% of the students were female and
36.3% male, implying that no student considered themselves outside the traditional gender clas-
sication. e distribution of the sample across the background variables is shown in Table1.
2.2.1. Measuring motivation, learning strategies and academic performance
2.2.1 Variables on motivation
Students’ motivation was measured using a Swedish translation of the Academic Motivation
Scale (AMS-C 28) College (CEGEP) version (Vallerand etal., 1993). All items were rated on
a seven-point Likert scale, ranging from “I seldom or never do this” to “I almost always do
Business, Management and Education, 2019, 17 Issue 2: 111–133 119
this” for processing strategies and from “Completely disagree” to “Totally agree” for regula-
tion strategies and motivational regulations. Each question is referring to a particular type
of extrinsic or intrinsic motivation, enabling a calculation of scores for all motivation types
in self-determination theory (Vallerand etal., 1993). Annex A provides a key that shows to
which motivation type the specic questions in the questionnaire are associated.
Based on the survey results, several motivational variables were constructed. e simplest
ones included composite scores for intrinsic and extrinsic motivation by averaging the values
of students’ responses on questions about particular motivational forms (c.f. Vansteenkiste
etal., 2004). A measure of total motivation was also included to capture students’ who may
have the high or low overall motivation, which averages the scores for both motivational
types. Tests of internal consistencies displayed acceptable Cronbach’s alpha values for the
computed variables (ranging between 0.844–0.782), similar to previous studies such as
Vansteenkiste, Zhou, Lens, and Soenens (2005) and Kusurkar etal. (2013).
2.2.2. Variables on learning approaches
Students’ learning strategies were measured using a Swedish translation of the “Revised Two
Factor Study Process Questionnaire” (R-SPQ-2F) (Biggs, 1987; Biggs etal., 2001). R-SPQ-2F
provides scores relating to students’ deep and surface learning strategies. e questionnaire
consists of 20 questions to which answers are provided on a ve-point Likert scale. Students
were asked to indicate how oen they agree with a particular statement or perform a par-
ticular activity, with answers ranging from “I seldom or never do this” to “I almost always/
always do this”.
We included both deep and surface study strategies in the analysis. Omitting any of
these variables would not allow for analysis of whether students who rely heavily on both
approaches have dierent motivational drivers or obtain dierent academic performance.
Vansteenkiste etal. (2005) used a similar approach to create an optimal learning composite
from the scores on the LASSI (Learning And Study Strategies Inventory, Vansteenkiste etal.,
2005). e results from the survey showed acceptable Cronbach’s alpha values for the com-
puted variables (between 0.711–0.641) (c.f. Biggs etal., 2001).
2.2.3. Variables on academic performance
Performance measurement in prior research has oen been operationalised in terms of
grades, or grade point averages, nal exam grades (Elliot, McGregor, & Gable, 1999; Diseth
& Kobbeltvedt, 2010; Byrne etal., 2002; Sadler-Smith, 1996; Trigwell & Prosser, 1991) or
drop-out rates (Bennet, 2003). In this paper, we measure ECTSs earned about the number
of potential ECTSs for each student, based on the courses any particular student had regis-
tered for (P). In addition to total ECTSs, we also include a measure of the relative amount of
high pass grades (HP), and for both these variables we distinguish between ECTSs relating
to written exams (PW and HPW) and ECTSs relating to other forms of examinations (such
as group assignments, essays etc.) (PO and HPO). Since the administrative system does not
dierentiate between those students passing at the rst occasion for examination and those
who retook an exam, this factor cannot be covered in the performance variables.
120 E. Bengtsson, B. Teleman. Motivation, learning strategies and performance among business...
2.2.4. Variables on student characteristics
e survey also included questions on student characteristics: Gender (male, female, other);
academic experience measured as a number of semesters in higher education (AcaExperi-
ence); work experience transformed into a number of full-time years, where all part-time,
seasonal and temporary experiences were included (WorkExperience). Short descriptions of
all variables are provided in Table1.
2.3. Data overview and empirical methods
Table1 also reports sample sizes, means, and standard deviations of the signicant dependent
variables measured. It reveals – among other things – that the students who participated in
the survey were more extrinsically than intrinsically motivated; that they were more prone
Table1. Descriptive statistics
Variables Description Mean Median Min Max Std.
De v. S kew. Ex.
Kurt.
DeepLearning Deep learning approach 28.72 29.00 10.00 45.00 6.52 –0.03 –0.27
SurfaceLearning Surface learning approach 25.08 24.00 11.00 46.00 7.03 0.29 –0.40
IntrinsicMotivation Intrinsic motivation 4.026 4.19 1.50 6.47 1.01 –0.32 –0.40
ExtrinsicMotivation Extrinsic motivation 5.29 5.41 3.08 7.00 0.83 –0.46 –0.03
TotalMotivation (Intrinsic Motivation
+Extrinsic Motivation)/2
4.66 4.76 2.37 6.44 0.80 –0.53 0.14
Male Gender 0.36 0.00 0.00 1.00 0.48 0.57 –1.68
AcaExperience No of semesters in higher
education
3.11 3.00 2.00 10.0 1.27 1.57 5.61
WorkExperience No of years of work
experience
2.76 2.00 0.00 13.0 2.69 1.26 1.54
Pass No. of ECTSs/ Sum of
ECTSs registered for
0.69 0.72 0.18 0.92 0.16 –0.96 0.47
HighPass No. of ECTSs with high
pass/Sum of ECTSs
registered for
0.29 0.29 0.00 0.71 0.20 0.36 –0.93
PassWritten No. of ECTSs from
written exams/Sum of
ECTSs registered for
0.55 0.55 0.08 0.86 0.16 –0.61 –0.03
HighPassWritten No. of hig pass ECTSs
from written exams/Sum
of ECTSs registered for
0.26 0.24 0.00 0.67 0.19 0.41 –0.89
PassOther No. of ECTSs excluding
those from written
exams/Sum of ECTSs
registered for
0.14 0.10 0.00 0.58 0.08 2.69 10.76
HighPassOther No. of high pass ECTSs
excluding those from
written exams/Sum of
ECTSs registered for
0.03 0.03 0.00 0.35 0.04 4.78 33.76
Business, Management and Education, 2019, 17 Issue 2: 111–133 121
to use in-depth study strategies; that their average age was 23; that the students had around
three years of work experience; and that 64% were females.
e hypotheses were tested using standard statistical methods. e dierence in propor-
tion tests was used to determine whether there were any dierences in motivational forms,
and to see whether these dierences also appeared between students based on the control
variables. OLS regressions were applied to investigate the relationships between independent
and dependent variables. All variables were tested for multicollinearity, which led to the ex-
clusion of the variable age due to too strong correlation with work experience. No regressions
displayed heteroscedasticity.
3. Results and analysis
3.1. Dierences in motivation
Beginning with within-group motivational characteristics, Table2 displays dierences be-
tween intrinsic and extrinsic motivation. Dierences for the whole sample and subsamples
based on dummy variables for gender. e results support the hypothesis that business stu-
dents are more extrinsically than intrinsically motivated (H1) for both the whole sample
and for the gender subsamples. External motivation exceeded intrinsic motivation at the 1%
signicance level.
3.2. Motivation and learning
Several regressions were applied to test the hypotheses that link motivation with learning
approaches (see Table3). Regressions 1 and 4 test whether the variables relating to gender
and experience in work or studies aect learning approaches. For deep study learning (Dee-
pLearning), gender was not signicant. Hypotheses H5a – that deep learning approaches are
more common among female students is thus rejected.
e other regressions (2–5) test, whether adding motivational variables enhances the
explanatory power compared to the regressions based on gender and experience only. For re-
gressions seeking to explain students’ degrees of deep learning approaches, regressions 2 and
3 led to considerable increases in explanatory power when adding the motivational vari-
ables IntrinsicMotivation and TotalMotivation to the model. Whereas the baseline regression
Table2. Dierences in intrinsic and extrinsic motivation
Varia b l e Mean ± SD Δ t–value p–value n
Full sample Intrinsic Motivation 4.025 1.009 –1.268*** –11.257 0.000 135
Extrinsic Motivation 5.293 0.832
Male Intrinsic Motivation 3.814 1.115 –1.250*** –6.257 0.000 49
Extrinsic Motivation 5.064 0.843
Female Intrinsic Motivation 4.146 0.928 –1.277*** –9.658 0.000 86
Extrinsic Motivation 5.423 0.802
Note: */**/*** denote signicance at 10%/5%/1% levels
122 E. Bengtsson, B. Teleman. Motivation, learning strategies and performance among business...
(Regression 1) had an adjusted r2 of 0.01, this gure reached 0.41 and 0.24 respectively. Both
these variables were found to be statistically signicant at the 1%, with IntrinsicMotivation
displaying a much stronger impact on DeepLearning.
Taken together, regression 2 lends considerable support to the hypothesis that intrinsic
motivation among business students is positively related to deep learning approaches (H2b).
Similarly, regression 3 suggest that hypothesis 2c – that total motivation among business
students is positively related to deep learning approaches – should be accepted. Finally, expe-
rience in academic studies is negatively related to deep learning, aer the eects on intrinsic
motivation is considered. is lends some support for Hypothesis H6a.
Table3 also shows the results of the test of hypothesis H2a – that extrinsic motivation
among business students is positively related to surface learning approaches. Regression 4
shows that gender can explain the degree to which students engage in surface learning ap-
proaches, although the regression is insignicant at the overall level. However, this changes
when adding the independent variable measuring extrinsic motivation (ExtrinsicMotivation).
is makes the regression overall signicant and increases its explanatory power. Besides, it
shows that extrinsic motivation is signicantly positively related to surface learning approaches.
Table3. Motivation and learning
Dependent var. DeepLearning DeepLearning DeepLearning SurfaceLearning SurfaceLearning
Regression # 1 2 3 4 5
n 135 135 135 135 135
Intercept 29.616*** 13.858*** 10.805*** 25.765*** 17.165***
0.000 0.000 0.001 0.000 0.000
IntrinsicMotivation 4.217***
0.000
TotalMotivation 2.053***
0.000
ExtrinsicMotivation 1.585**
0.033
Male –1.611 –0.092 –0.204 2.716** 3.231**
0.174 0.920 0.846 0.033 0.012
WorkExperience 0.237 0.139 0.283 –0.195 –0.122
0.266 0.396 0.127 0.391 0.589
AcaExperience –0.310 –0.793** –0.618 –0.364 –0.420
0.484 0.023 0.114 0.443 0.370
Overall F–test 1.047 24.275*** 11.663*** 1.869 2.594**
Overall p–value 0.374 0.000 0.000 0.138 0.039
R20.023 0.428 0.264 0.041 0.074
Adj. R20.001 0.410 0.241 0.019 0.045
Note: */**/*** denote signicance at 10%/5%/1% levels
Business, Management and Education, 2019, 17 Issue 2: 111–133 123
3.3. Learning and academic performance
Table4 reveals the empirical results from the regressions of motivation and learning ap-
proaches on academic performance. Initially, in Table4, only gender and experience are
included in the regressions (6–11) on the six dierent variables measuring academic perfor-
mance. A few observations are noteworthy: All regressions are signicant on the overall level;
gender and experience have the highest explanatory power on the variables Pass and Pas-
sOther (.14 and .37 respectively); academic experience is positively related to all measures of
academic performance, whereas work experience is not; and gender matters for all academic
performance variables apart from PassOther and HighPassOther. Tests were also performed
where dummies for academic programs were included, but not succeeding in establishing
statistically signicant results. Taken together, this lends considerable support to hypotheses
H5b and H6b – that female students attain better academic results, and that experience is
not related to academic performance.
In regressions 12–17 reported in table 4, deep learning was added as independent vari-
ables to investigate whether deep learning is positively related to academic performance
(H3a1–2). Deep learning (regressions 13 and 15) is revealed to have a signicant positive
impact on academic performance when measured as the percentage of high passes in gen-
eral and on written exams (HighPass and HighPassWritten), but not for other academic
performance variables. Adding these variables also increases explanatory power, even aer
adjusting for overtting (reected in the adjusted r2). e variables on student characteristics
remain the same, with the male still being signicantly negative for Pass, HighPass, Pass-
Written, HighPassWritten and experience of academic studies for all performance variables.
Table4 also displays the results of the relationship between surface learning and academic
performance. While the signs and signicances for the gender and experience variables re-
main unchanged, surface learning fails to achieve statistical signicance for all academic
performance variables (regressions 18–23). is result oers little support for the hypothesis
that surface learning is negatively related to academic performance (H3b).
Turning to the relationship between motivation and academic performance, Table5 shows
regressions of intrinsic motivation on the six academic performance variables. e results
indicate that the relationship is not particularly strong, but the relationship is statistically
negative for the performance variable Pass. As for the regressions on learning approaches,
gender and academic experience are signicant. ese results strongly reject the hypothesis
that intrinsic motivation and academic performance in higher education is positively related
(H4a1–2).
Similar results were found when analysing the relationship between the overall levels of
motivation with academic performance. In regressions where the independent variable is
Intrinsic Motivation, the results are more striking. In particular, regressions 30 and 32 reveal
that high motivation is signicantly and negatively related to both Pass and PassWritten.
ese results imply a rejection of the hypothesis that high intrinsic and extrinsic motivation
is positively related to academic performance (H4b).
124 E. Bengtsson, B. Teleman. Motivation, learning strategies and performance among business...
Table4. Academic performance and learning approaches
Dependent var. Pass HighPass PassWritten HighPassWritten PassOther HighPassOther
n 135 135 135 135 135 135
Regression # 6 7 8 9 10 11
Intercept 0.509*** 0.222*** 0.487*** 0.223*** 0.021 –0.001
0.000 0.000 0.000 0.000 0.152 0.910
Male –0.080*** –0.113*** –0.084*** –0.112*** 0.004 –0.001
0.001 0.001 0.002 0.001 0.703 0.915
WorkExperience –0.002 –0.001 –0.005 –0.001 0.003 0.000
0.684 0.886 0.311 0.836 0.113 0.764
AcaExperience 0.070*** 0.041*** 0.034*** 0.027** 0.034*** 0.009***
0.000 0.006 0.001 0.033 0.000 0.001
Overall F–test 22.668*** 6.199*** 7.490*** 5.238*** 25.226*** 4.378**
Overall p–value 0.000 0.001 0.000 0.002 0.000 0.006
R20.342 0.124 0.146 0.107 0.366 0.091
Adj. R20.327 0.104 0.127 0.087 0.352 0.070
Regression # 12 13 14 15 16 17
Intercept 0.491*** 0.066 0.508*** 0.085 –0.017 –0.0192
0.0000 0.450 0.0000 0.322 0.533 0.2765
DeepLearning 0.001 0.005** –0.001 0.005* 0.001 0.0006
0.7337 0.038 0.7298 0.063 0.109 0.2283
Male –0.079*** –0.104*** –0.085*** –0.105*** 0.006 0.0063
0.0015 0.0027 0.0021 0.002 0.568 0.9708
WorkExperience –0.002 –0.0021 –0.005 –0.002 0.003 0.0028
0.6625 0.7285 0.331 0.698 0.151 0.8556
AcaExperience 0.069*** 0.038*** 0.034*** 0.029** 0.034*** 0.0345***
0.0000 0.0034 0.0009 0.0238 0.000 0.0004
Overall F–test 16.916*** 5.8579 5.6096 4.8815 19.800*** 3.6618***
Overall p–value 0.0000 0.0002 0.0003 0.0011 0.0000 0.0073
R20.3423 0.1527 0.1472 0.1306 0.3786 0.1013
Adj. R20.3221 0.1266 0.1210 0.1038 0.3595 0.0736
Regression # 18 19 20 21 22 23
Intercept 0.523*** 0.289*** 0.479*** 0.308*** 0.044* 0.007
0.000 0.000 0.000 0.000 0.072 0.647
SurfaceLearning –0.001 –0.003 0.000 –0.002 –0.001 0.000
0.734 0.271 0.859 0.417 0.237 0.512
Male –0.078*** –0.106*** –0.085*** –0.098*** 0.007 0.007
0.002 0.003 0.002 0.005 0.552 0.987
WorkExperience –0.002 –0.001 –0.005 0.002 0.003 0.003
0.667 0.821 0.321 0.757 0.135 0.803
AcaExperience 0.068*** 0.035*** 0.035*** 0.022* 0.034*** 0.034***
0.000 0.007 0.001 0.086 0.000 0.001
Overall F–test 16.915*** 4.962*** 5.584*** 4.458*** 19.333*** 3.378**
Overall p–value 0.000 0.001 0.000 0.001 0.000 0.012
R2 0.342 0.132 0.147 0.147 0.373 0.094
Adj. R2 0.322 0.106 0.120 0.114 0.354 0.066
Note: */**/*** denote signicance at 10%/5%/1% levels
Business, Management and Education, 2019, 17 Issue 2: 111–133 125
Table5. Academic performance and motivation
Dependent var. Pass HighPass PassWritten HighPassWritten PassOther HighPassOther
Regression # 24 25 26 27 28 29
n 135 135 135 135 135 135
Intercept 0.584*** 0.257*** 0.566*** 0.263*** 0.018 –0.006
0.000 0.001 0.000 0.001 0.475 0.707
IntrinsicMotivation –0.020 –0.009 –0.021 –0.011 0.001 0.001
0.085 0.572 0.107 0.513 0.868 0.701
Male –0.087*** –0.116*** –0.092*** –0.116*** 0.004 0.004
0.000 0.001 0.001 0.001 0.687 0.969
WorkExperience 0.071*** 0.038*** 0.037*** 0.028** 0.034*** 0.034***
0.000 0.005 0.000 0.028 0.000 0.001
AcaExperience –0.001 –0.001 –0.004 –0.001 0.003 0.003
0.763 0.914 0.360 0.868 0.117 0.784
Overall F–test 18.017*** 4.705*** 6.346*** 4.019*** 18.786*** 3.300**
Overall p–value 0.000 0.001 0.000 0.004 0.000 0.013
R20.357 0.126 0.163 0.110 0.366 0.092
Adj. R20.337 0.100 0.138 0.083 0.347 0.064
Regression # 30 31 32 33 34 35
Intercept 0.659*** 0.314*** 0.652*** 0.329 0.006 –0.015
0.000 0.004 0.000 0.002 0.856 0.480
TotalMotivation –0.033** –0.020 –0.036** –0.023 0.003 0.003
0.026 0.338 0.028 0.260 0.630 0.466
Male –0.091*** –0.120*** –0.097*** –0.120*** 0.005*** 0.005***
0.000 0.001 0.001 0.001 0.638 0.964
WorkExperience –0.002 –0.001 –0.005 –0.002 0.003 0.003
0.617 0.857 0.266 0.802 0.110 0.743
AcaExperience 0.071*** 0.038*** 0.037*** 0.029** 0.034*** 0.034***
0.000 0.004 0.000 0.024 0.000 0.001
Overall F–test 18.803*** 4.878*** 7.018*** 4.257*** 18.867*** 3.406**
Overall p–value 0.000 0.001 0.000 0.003 0.000 0.011
R2 0.367 0.130 0.178 0.116 0.367 0.095
Adj. R2 0.347 0.104 0.152 0.089 0.348 0.067
Note: */**/*** denote signicance at 10%/5%/1% levels
3.4. Discussion
In terms of the type of motivation that dominates business students, our results support the
hypothesis (H1), as suggested by McEvoy (2011), that business students are more extrinsi-
cally than intrinsically motivated. e results reveal that extrinsic motivation is signicantly
higher at the 1%-level for the full sample and all subsamples based on the gender variable.
ereby the ndings are similar to those of DeMarie and Aloise-Young (2003), who discov-
ered that business students are to a higher degree motivated by career prospects and high
salaries rather than an interest in the area of studies.
126 E. Bengtsson, B. Teleman. Motivation, learning strategies and performance among business...
e results on the link between motivation and learning approach support earlier studies
by Lucas and Meyer (2005) and Vansteenkiste etal. (2004), lending considerable support to
the hypothesis that intrinsic motivation among business students is positively related to deep
learning approaches (H2b). Similarly, extrinsic motivation yields considerable explanatory
power and is signicantly associated with surface learning approaches (conrming H2a).
Combined intrinsic and extrinsic motivation among business students was found to be
positively related to deep learning approaches (suggesting acceptance of H2c), but the eect
from combined intrinsic and extrinsic motivation was lower than for intrinsic motivation
alone. ereby, contrary to earlier studies (Mo, 2011; Rassuli, 2012) including research in
business studies contexts (Everaert etal., 2017), no evidence is found that suggests that com-
binations of motivational forms support learning generally. Our ndings thereby support the
suggestion that high extrinsic motivation does not support intrinsic motivation, but slightly
distracts students from deep learning.
e results show that deep learning approaches lead to better academic performance
when measured as a percentage of high passes in general and on written exams, but not for
other academic performance variables. A potential explanation could be that through other
forms than written exams, these other performance variables are examination forms such as
group assignments, and similar. is may potentially dilute the eects of in-depth learning
approaches. Surprisingly, no relationship between surface learning approaches and academic
performance could be established. Taken together, these results oer little support for the
hypothesis H3b – that that surface learning is negatively related to academic performance,
but at least partially supports hypothesis H3a – that deep learning is positively related to
academic performance among business students, as documented by Davidson (2003).
Turning to the direct link between motivation and academic performance, the negative
albeit mainly insignicant inuence on academic performance from intrinsic motivation
implies a rejection of hypothesis H4a – that intrinsic motivation and academic performance
in higher education are positively related. Our ndings thereby dier from earlier research,
such Turner etal. (2009). Also, our results reveal that high general motivation (extrinsic and
intrinsic combined) is signicantly and negatively related to both Pass and HighPassWritten,
thereby leading to a rejection of the hypothesis that a combined high intrinsic and extrinsic
motivation is positively related to academic performance (H4b). Something that needs to
be acknowledged, however, is the sensitivity related to questions on motivation. It might be
that students answer more in line with how they would like to identify themselves and their
goals, rather than what motivates them. is could potentially mean that students downplay
their level of extrinsic motivation.
Our ndings on the relationship between learning approaches and academic performance
paint a somewhat mixed picture, which itself is not uncommon (Byrne etal., 2002). Our
results thereby share characteristics of the large amount of research that fails to establish
clear links between intrinsic motivation, learning approaches and academic performance (c.f.
Biggs etal., 2001). One explanation could be that the operationalisation of the performance
variables is too crude to capture any distinct eects from dierent learning approaches. An-
other potential and more dismal, the explanation could be that students are assessed and
graded in ways that favour surface learning approaches.
Business, Management and Education, 2019, 17 Issue 2: 111–133 127
Regarding student characteristics, several conclusions can also be drawn from the vari-
ables included in our paper. Beginning with experience, the positive relation between aca-
demic experience and all measures of academic performance suggest that students become
more skilled in the task of accumulating academic credits. It also suggests that there is no
pattern of weak students continuing falling behind, which also could be attributable to weak
students dropping out. In combination with the negative relation between academic experi-
ence and deep learning approaches, our results could suggest that students learn over time
that more surface-type learning approaches are better suited to pass exams, courses or course
modules.
e lack of relationship between work experience and performance counters some prior
research that documents that younger students are performing better than their older co-
students (Dockweiler & Willis, 1984; M.Y.Koh & H.C.Koh, 1999). Our ndings thus coun-
ter both Biggs’s (1987) suggestion that work experience leads to cognitive developments and
skills in handling complex tasks, as well as similar suggestions from prior research in business
studies context (Du, 1999, 2004; Sadler-Smith, 1996). Potential explanations of our ndings
could be that work experiences before academic studies tend to be menial jobs that add a few
skills that are useful in academic studies. Alternatively, students with work experience oen
are older and may have additional obligations outside academic studies (such as children),
which may restrict time available for deep learning approaches.
e variable on gender paints a coherent picture. Female students are more motivated
across almost all motivational variables. ese results contradict those of Severiens and ten
Dam (1994), that suggest that male students have higher extrinsic motivation than their
female co-students. Also, female students were found to adopt deep learning approaches
to a greater extent and surface approaches to a lower extent. Besides, male students per-
formed statistically signicantly worse for most academic performance variables apart from
PassOther and HighPassOther. e latter could relate to courses or course modules without
written exams, oen including group assignments, which may dilute the gender eect. Our
results thus lend support to both general ndings that female students tend to perform better
in general (Gledhill & Van der Merwe, 1989), and in business studies contexts (Lange & Ma-
vondo, 2004). However, these results contrast ndings on accounting students, where males
have been found to attain better academic results (Hasall & Joyce, 1997; Sadler-Smith, 1996).
Conclusions
e ndings presented in this paper are conrming many notions found in prior research,
but also oer new insights. One is formal evidence that business students are more extrinsi-
cally than intrinsically motivated. Another is that deep learning approaches lead to higher
grades for particular examination forms but not for others. On a meta-level, this corresponds
to ndings in prior research, which have yielded mixed results. A dismal explanation could
be that deep learning approaches only are rewarded in particular examination forms (such
as written exams but not on other examination forms). A more positive perspective would
suggest that other examination forms than written exams oen are group assignments, which
may dilute the eects of deep learning approaches.
128 E. Bengtsson, B. Teleman. Motivation, learning strategies and performance among business...
is could also indicate that the relationships between motivation, learning and perfor-
mance are inuenced by other factors, implying that context, student characteristics or person-
ality matter. In this paper, a few such factors are accounted for by including variables on student
characteristics. Variables relating to gender and experience provide essential information on
the relationship between motivation, learning and academic performance. e ndings that
female students are typically more intrinsically motivated, engage more in deep learning ap-
proaches and perform better than their male counterparts are somewhat contradictory to much
research in business contexts. Experience is also taken into account, and the ndings indicate
that academic experience increases academic performance, but no eects can be related to work
experience. is suggests that generic skills learnt outside academia are less useful to attain
academic credits or high grades, but that students indeed learn to master those “specialised”
skills of academic studying through attending higher education. Another explanation could be
that high age (being strongly correlated with work experience) oen implies other obligations,
such as having children or older parents that require support, allowing these students less time
to spend but that they might still study more eciently and/or energetically.
e ndings also yield a number of practical implications. One is that practitioners in
higher education have good reasons to stimulate motivation generally, and intrinsic motiva-
tion in particular. However, this must be accompanied by examination forms that promote
deep learning, such as choosing written exams over multiple-choice exams and consider ways
to assess group assignments that do all students justice.
is paper also points to fruitful areas of future research. e results indicate that the
framing of academic performance, and how this is operationalised, could shed additional
light. One option, albeit time-consuming and technically challenging in most administrative
systems in academia, would be to operationalise performance as actual scores on exams or
similar. Other areas for future research include a more granular approach to the dimensions
covered by this paper’s variables on student characteristics. For instance, work experience
could be dierentiated by types of jobs (whether these are menial or more advanced, or
related to the particular topic studied) and the length of job spells (to dierentiate between
more permanent positions and seasonal jobs). Finally, since this study only covers one cohort
in one university college, there is a risk that the population might not be as representative as
suggested. However, since prior research on this topic mainly focuses on university students
and in the US, this setting oers a complement and might help to paint a broader picture. To
include students in geographically diverse contexts and from dierent years would strengthen
the analysis. Also, conducting cross-sectional studies across topics or disciplines could reveal
interesting patterns in how the link between motivation, learning and performance varies
and is aected by students’ experiences, nature of academic institution (college or university)
or program (more academic or more vocational).
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