The Effect of Resistance to Change on Students' Acceptance in a

Flipped Classroom Course

Christin Voigt, Kristin Vogelsang

and Uwe Hoppe

University of Osnabrück, Katharinenstr. 1-3, 49074 Osnabrueck, Germany

Keywords: TAM2, Flipped Classroom, Digital Education, Inertia, Resistance to Change, Quantitative Research.

Abstract: Digital technologies are increasingly used in higher education in so-called blended learning courses. A

growing popular course concept is the Flipped Classroom (FC). In an FC, knowledge is acquired at home and

deepened in the in-class time with the teacher. Compared to traditional teaching concepts, FC courses are

considered particularly effective in terms of learning success. However, the transformation to FC-concepts is

a big change for learners and students and often combines with a resistance to change. In this study we

investigate the effect of resistance to change (inertia) on the acceptance of Flipped Classroom courses from

the students' perspective. Teachers can use this knowledge to increase the attractiveness of FC.

1 INTRODUCTION

The use of digital technologies in university teaching

enables new forms of teaching and learning. Teachers

expect positive effects from so-called blended

learning courses (Lehmann et al. 2015). One variant

of blended learning is the Flipped Classroom (FC)

(Bergmann and Sams 2012). In the FC courses,

knowledge acquisition takes place at home. For this

purpose, digital technologies such as learning videos

or online quizzes are provided. The consolidation of

knowledge then takes place in the classroom

supervised by the teacher. Many studies prove the

positive influence of FC, especially in the area of

active, independent work and learning success

(Abeysekera and Dawson 2015; Sun et al. 2018),

(Voigt et al. 2020).

Despite its popularity, the FC concept is still

relatively new. For the students, it represents a clear

departure from the previous, lecturer-centered

teaching-learning arrangement. The success of an FC

depends largely on the acceptance of the stakeholders

involved. First studies use modifications of the well-

known Technology Accteptance Model according to

Davis (Davis 1986) to measure the acceptance of the

overall FC construct (Vogelsang and Hoppe 2018). In

contrast to the original variant, which focuses on the

use of a single technology, the entirety of the learning

http://orcid.org/0000-0003-2503-3207

unit represents the technology object in this case. The

TAM is suitable for measuring the attitude of the

target group towards certain aspects (such as the

quality of outcomes or the influence of social peers)

in connection with the use of the learning concept.

One aspect that has not yet been included in the

investigations is the resistance of students to accept

new concepts. This resistance is called inertia in

science and is an important and at the same time

difficult to grasp effect variable (Polites and

Karahanna 2012). Inertia is often an initial reaction to

changes. Only through the reduction of the resistance

to change a real acceptance of new technology

concepts such as the Flipped Classroom can take

place. Therefore, it is to be assumed that inertia

reduces acceptance. In this research, we want to close

the research gap mentioned above. The present paper

therefore aims to answer the following research

question (RQ):

RQ: What influence has the inertia on the

acceptance of a Flipped Classroom from the

students' perspective in our course?

To answer the research question, we have

developed a quantitative questionnaire, which is

tested in an FC course in the Master of Business

Informatics.

Voigt, C., Vogelsang, K. and Hoppe, U.

The Effect of Resistance to Change on Students’ Acceptance in a Flipped Classroom Course.

DOI: 10.5220/0010283800150022

In Proceedings of the 13th International Conference on Computer Supported Education (CSEDU 2021) - Volume 2, pages 15-22

ISBN: 978-989-758-502-9

In a first step, we conduct a reliability and a factor

analysis to measure the suitability of the selected

items and constructs. Subsequently, we conduct a

regression analysis to show the interdependencies.

The resulting model provides researchers with

information about the connection between attitude-

related acceptance decisions and the resistance of the

target group, which is difficult to observe. Practical

implications for lecturers and program coordinators

can be derived from the model, which can lead to

acceptance-promoting measures.

2 THEORETICAL

FOUNDATIONS

Acceptance is used to measure the assent and

agreement of an individual, e.g. with a technology.

The best known model that deals with the explanation

and measurement of technology acceptance is the

Technology Acceptance Model according to Davis

(Davis 1986).

The TAM is based on the Theory of Reasoned

Action (Fishbein and Ajzen 1975). The essence of

this theory is that a behavioural intention precedes the

actual behaviour. Based on this assumption, human

actions can be explained and predicted to a certain

degree. The TAM assumes that the intention to use a

system (intention to use) precedes the actual use. The

intention to use (IU) is explained in the original

acceptance model by two predictors (perceived ease

of use and perceived usefulness). Especially its easy

comprehensibility and high validity have contributed

to its wide dissemination (Vogelsang et al. 2013).

A large branch of research is concerned with

investigations on the validity of the TAM in different

contexts such as the working world (Venkatesh et al.

2012) or the university (Park 2009). In the course of

time numerous extensions of the TAM have been

developed. Often external variables such as age or

experience of the users are used (Venkatesh et al.

2003). In addition, the model is extended by factors

from other models in order to increase the accuracy

of the information. One such extension is the TAM2

(Venkatesh and Davis 2000). In this model the core

dimensions are preceded by predictors from the area

of cognitive-instrumental and social components.

The TAM and its extensions are still valid today.

It is also often used to explain the intended use of

scenarios in which technology plays an important

role. For example, acceptance studies are used to

measure the acceptance of blended learning events

(Abeysekera and Dawson 2015; Padilla-Meléndez et

al. 2013). Here, the intention of use is not aimed at the

use of the technology alone, but rather at the overall

concept.

While the acceptance studies focus on the

presence of positive reinforcing factors, there are of

course also factors whose presence can weaken the

intention to use the technology. The inertia or

resistance to change is a difficult construct to grasp,

as it is primarily expressed through strongly anchored

attitudes. Inertia or resistance to change describes

remaining in old habits even though better

alternatives are available. Inertia research has its

origin in consumer research, whereby inertia in this

environment means remaining loyal to a brand

instead of choosing new, different or even better

alternatives (Murray and Häubl 2007). The

investigation of the phenomenon takes place in

different scenarios. For example, the influence of

inertia on the use of IS (information systems) (Polites

and Karahanna 2012) is investigated. Inertia is based

on individual habits.

Inertia expresses itself by remaining with familiar

structures and thus counteracts acceptance. However,

the influence of inertia on user acceptance has not yet

been investigated. In general, the negative aspects of

changing a learning scenario are rarely discussed

(Vogelsang et al. 2019).

3 RESEARCH MODEL AND

SAMPLE

TAM2 (Venkatesh and Davis 2000) was chosen as

the initial model for the investigation. Only a few

modifications are necessary to adapt to the present FC

scenario. First, all external variables were removed

from the model. We assume a high homogeneity of

the selected sample with respect to experience, age

and voluntariness. As predictors, output quality (OQ)

was chosen as the cognitive and subjective norm (SN)

as the social component. Output quality measures the

students' attitude towards improvements through the

new concept and its advantages. The subjective norm,

on the other hand, measures the influence of fellow

students with regard to the new concept. The image

used in TAM2 was excluded, since an image gain of

the students via a course to be taken voluntarily is not

considered relevant. Instead, the other cognitive

components, job relevance and result

demonstrability, were adapted to the university

context and replaced by the construct of expected

effort used in the Unified Theory of Acceptance and

Use of Technology (Venkatesh et al. 2003). It can be

CSEDU 2021 - 13th International Conference on Computer Supported Education

assumed that students compare exactly the effort of

the course with the expected results. Therefore, it is

assumed that this construct is more applicable than

the presentation of the success, which only becomes

visible as the exam result at the end of the course.

Thus, we use a model that examines the influence

of perceived ease of use (EoU) and perceived

usefulness (PU) on the intention to use (UI), as well as

the effect of EoU on PU. Predictors of PU are,

according to the theoretical basis, the output quality

(OQ), the subjective norm (SN) and the expected effort

(EE). The influence of the inertia (RC) on the intention

of use and the perceived usefulness should also be

tested. In addition, we want to test whether these

predictors EE, OQ and SN have an effect on the inertia.

In our questionnaire, the individual factors are each

measured with three to four items. Figure 1 shows all

influences to be tested in our research model.

Figure 1: Research Model.

The following hypotheses can be derived from this

research model: Hypotheses H01 a) to H01 c) address

the connections from the original TAM model.

H01a) In our FC course, the perceived usefulness

affects the intention of use.

H01b) The perceived ease of use has an effect on

the intention of use in our FC course.

H01c) The perceived ease of use affects the

perceived usefulness in our FC course.

H02 a) to H02 c) test the effects of TAM2.

H02a) The subjective norm affects the perceived

usefulness in our FC course.

H02b) The expected effort affects the perceived

usefulness in our FC course.

H02c) The result quality has an effect on the

perceived usefulness in our FC course.

Hypotheses H03a) to H04c), on the other hand,

address the inertia in FC:

H03a) Inertia affects the perceived usefulness in

our FC course.

H03b) In our FC course, inertia affects the

intention of use.

H04a) In our FC course, the results quality has an

effect on the inertia.

H04b) The expected effort has an effect on the

inertia in our FC course.

H04c) The subjective norm has an effect on the

inertia in our FC course.

For the purpose of data collection, a written,

voluntary and anonymous survey of students was

conducted in the winter semester 2017/18 at the last

session of the course "Project Management", which is

designed as a Flipped Classroom. Within this course,

students will be provided with audio-supported slides

as video clips during the online time. Based on this,

activating methods such as case study work and

literature discussions will be carried out during the

attendance time to deepen the students' knowledge.

A total of 40 students took part in the survey.

Among the participants, 62% were male and 26%

female. 95% of all participants took part in the lectures

in the classroom, 82.5% in the guest lecture and 70%

used the group work. The most frequent reason given

for non-participation was the overlap with other

courses, with 7.5% of participants. 5% had parallel

work placements or jobs and 2% of the students stated

that they did not participate in at least one of the

classroom sessions due to the increased workload.

The survey contains 23 quantitative questions,

which were assigned to the seven different factors.

The data evaluation consists of two steps: In the

first part, the survey is verified for statistical quality

criteria, in particular reliability and validity. For this

purpose, the reliability analysis and the factor

analysis are carried out and the theoretical framework

is adapted to the results. In the second step, the

significant correlations are examined by means of

regression analyses to test the influences shown in the

above research model. A total of four regression

models are used with the dependent variables:

intention to use, perceived usefulness, perceived ease

of use and inertia.

The Effect of Resistance to Change on Students’ Acceptance in a Flipped Classroom Course

4 MODEL VERIFICATION

The first step of the analysis aims at the composition

of the questionnaire. First, the factors are verified and

adjusted by means of reliability analysis. In the course

of the reliability analysis, two items are removed that

were originally assigned to the factor perceived

usefulness. Cronbacs Alpha is thus 0.770. Table 1

shows the Cronbachs alpha and the item scale

correlation for all items after removing the two items.

Table 1: Cronbachs Alpha.

Factor

Item

Cronbachs

Alpha, if Item

is left out

Item Scale

Correlation

Intention to

use (IU)

IU1 0.736 0.280

IU2 0.746 0.100

IU3 0.741 0.180

Inertia (RC)

RC1 0.737 0.275

RC2 0.713 0.540

RC3 0.769 0.439

RC4 0.703 0.678

Items

Perceived

usefulness

(PU)

PU1 0.734 0.292

PU3 0.742 0.195

Items Ease of

Use (EoU)

EoU1 0.720 0.481

EoU2 0.721 0.489

EoU3 0.730 0.364

Output

Quality (OQ)

OQ1 0.726 0.423

OQ2 0.738 0.229

EE1 0.749 0.020

Expected

Effort (EE)

EE2 0.748 0.127

EE3 0.741 0.232

RC5 0.728 0.366

PU2 0.740 0.228

Subjective

Norm (SN)

SN1 0.715 0.619

SN2 0.728 0.419

SN3 0.727 0.407

The designations of the items refer to the final

order after factor analysis.

For further analysis, all items were coded

uniformly. For example, item RC3 was coded

negatively and PU2 and PU3 positively. Furthermore,

factor analysis was used to confirm the seven factors

IU, PU, EoU, OQ, EE, SN and RC, which are each

measured with 3 to 5 items. Figure 2 shows all new

item-classifications.

Figure 2: Factor Analysis.

As shown in Table 2, the intention to use is

measured by the willingness to reapply such a

learning concept in our FC course.

Table 2: Items Intention to Use.

Items:

Intention to

Use (IU)

Formulation

IU1

I would choose to attend such a learning

concept again.

IU2

I would like to see more learning concepts

of this kind.

IU3

I would also take other courses with such

learning concepts.

The perceived usefulness, on the other hand, is

characterized by an expected improvement in one's

own exam preparation and an expected increased

learning success due to the Flipped Classroom. The

final items are shown in table 3. During factor

analysis, PU2 was reassigned to the perceived

usefulness.

Table 3: Items Perceived Usefulness.

Items:

Perceived

Usefulness

(PU)

Formulation

PU1

My learning success is increased by such

a learning concept.

PU2

I have the feeling that such a learning

concept negatively influences my learning

success.

PU3

I feel badly prepared for the exam because

of the learning concept.

The items of the factor perceived ease of use is

shown in table 4 and asked whether the various

elements of online time were easy for students to use.

There were no changes during factor analysis for

EoU.

CSEDU 2021 - 13th International Conference on Computer Supported Education

Table 4: Items Ease of Use.

Items: Ease

of Use (EoU)

Formulation

EoU1 I find it easy to use the video tutorials.

EoU2

I consider the videos to be simple to

handle

EoU3

I find it easy to work with the videos the

way I want to.

After factor analysis, the expected effort is

measured by the perceived appropriateness of the

cost-benefit ratio, a perceived increase in the

workload due to the use of the Flipped Classroom, as

well as by the workload compared to other learning

concepts that are not designed as FC courses as shown

in table 5.

Table 5: Items Expected Efford.

Items:

Expected

Effort (EE)

Formulation

EE1

The amount of work and the result of the

learning concept are appropriate.

EE2

If all lectures were designed in this way,

the study effort would be too high for me.

EE3

I believe that such a learning concept

increases my study workload.

The factor subjective norm, on the other hand, is

shown in table 6 measured by the attitude of the

fellow students towards the Flipped Classroom as

well as by the general image of such a learning

concept.

Table 6: Items Subjective Norm.

Items:

Subjective

Norm (SN)

Formulation

SN1

Most of my fellow students think that it is

good to participate in such a learning

concept.

SN2

My fellow students support the

participation in such a learning concept.

SN3 My friends like the learning concept.

Table 7 shows the items of OQ. Since EE1 was

ordered from OQ to EE in the course of the factor

analysis, OQ is only measured with two items.

Table 7: Items Qutput Quality.

Items:

Output

Quality (OQ)

Formulation

OQ1

I believe that the learning concept

improves the teaching in general during

studies.

OQ2

I am aware of the advantages of the

learning concept in general.

The inertia is shown in Table 8 and is

characterized by the perceived stress and by

remaining in the status quo. Item RC5, which was

originally assigned to the effort expectation, was

added.

Table 8: Items Inertia.

Items Inertia

(RC)

Formulation

RC1 I prefer the familiar to new structures.

RC2

When I hear that innovations are pending,

I feel stressed.

RC3 I am open to innovation.

RC4 I rely on established concepts.

RC5

The thought of the new learning concept

has stressed me.

These newly sorted factors form the basis for

further analysis.

5 REGRESSION ANALYSIS

In the second part of the statistical analysis, the

regression models are assessed to reveal significant

influences. Based on the previously formulated

hypotheses, a total of three models could be estimated

with the dependent variables: Perceived usefulness,

intention to use and inertia. All factors that have an

effect on the ten percent or lower significance level in

at least one of the two regressions are presented. The

parameters, significance levels and T-values of all

regression models are shown in Table 9 - 11.

The perceived usefulness is shown in table 9. It

determined to be five percent significant in our FC

course and can be explained by   3.588 

0.220  0.253  0.318.

The Effect of Resistance to Change on Students’ Acceptance in a Flipped Classroom Course

Table 9: Regression model for PU.

Factor

Perceived Usefulness

(PU)*





 0.259  4.073

∝

Coef.



Constant p < 0.01 3.588 3.744

Perceived Ease of Use

(EoU)

p < 0.10 0.220 1.824

Output Quality (OQ) p > 0,10 - -

Expected Effort (EE) p < 0.10 0.253 1.940

Subjective Norm (SN) p > 0,10 - -

Inertia (RC) p < 0.05 -0.318 -2.278

Significance of the regression model: ** p < 0.01 * p < 0.05

As shown in table 9, it is positively influenced by

the perceived ease of use and the expected effort, and

negatively influenced by the inertia. The strongest

influence on PU is the inertia. The subjective norm

and the output quality, on the other hand, have no

significant influence on the perceived usefulness in

our FC course.

Moreover, the regression model shown in table 10

is used to determine the intention to use and applies

to the one-percent significance level. It has the largest





of all estimated models and can be determined by

  3.737  0.398  0.302 

0.384.

Table 10: Regression model for IU.

Factor

Intention to Use

(IU)**





 0.376,  7.218

∝

Coef.



Constant p < 0.01 3.737 4.092

Perceived Usefulness

(PU)

p < 0.05 0.398 2.566

Perceived Ease of Use

(EoU)

p < 0.05 0.302 2.470

Inertia (RC) p < 0.01 -0.384 -2.841

Significance of the regression model: ** p < 0.01 * p < 0.05

The strongest influence on the IU is the perceived

usefulness, which positively influences IU in addition

to the perceived ease of use. On the other hand, the

presence of inertia reduces the intention to use.

Finally, a regression model could also be

estimated for inertia as a target variable for the five

percent significance level. This allows it to be

determined by:   2.190  0.344 and is

only negatively influenced by the output quality.

Table 11: Regression model for RC.

Factor

Inertia (RC)*





 0.100,  4.209

∝

Coef.



Constant p < 0.05 2.190 2.518

Output Quality (OQ) p < 0.05 -0.344 -2.052

Significance of the regression model: ** p < 0.01 * p < 0.05

No significant influence on the inertia could be

demonstrated for the quality of the result and the

subjective norm. Overall, our model is able to explain

37% of the effects on the intention of use.

6 HYPOTHESIS TESTING

First of all, it becomes apparent that all relationships

contained in the original TAM model can also be

observed in our FC. Hence, the perceived usefulness

has a positive effect on the intention to use. The

hypothesis H01a) can be confirmed. The perceived

ease of use also has an influence on the perceived

usefulness and the intention to use: the higher EoU is,

the higher is IU and PU in our FC course. Thus,

hypotheses H01b) and H01c) are both supported.

However, the relations we suspect from

hypotheses H02a) to H02c) cannot be completely

confirmed. Only the expected effort affects the

perceived usefulness as predicted, but not the output

quality and the subjective norm. The higher the

expected effort is, the higher is the perceived

usefulness. As shown in Table 5, EE is to be

interpreted as a fair cost-benefit ratio and as an effort

perceived as appropriate for the course. In our Flipped

Classroom, however, the subjective norm has no

influence on PU. Thus, the hypotheses H02a) and

H02c) are rejected, while H02b) can be confirmed.

The inertia also has an effect on both the

perceived usefulness and the intention to use. Hence,

a high general resistance to change leads to a lower

intention to use the teaching concept, the hypotheses

H03a) and H03b) are both supported. The inertia can

in turn be explained by the output quality: A higher

output quality reduces the inertia. Hypothesis H04b)

can be confirmed. Thus, the higher the generally

known advantages and the importance of the teaching

concept for the study, the lower the resistance to

change. While the output quality in the FC model

without inertia would has no influence on the

perceived usefulness and the intention of use in our

course, in the acceptance model shown above it has

an indirect effect on IU and PU via the inertia.

CSEDU 2021 - 13th International Conference on Computer Supported Education

Hypotheses H04a) and H04c), on the other hand,

must be rejected, since there was no significant

influence of the expected effort and the subjective

norm on the inertia. Thus, the subjective norm has no

effect in our FC course as a whole.

7 IMPLICATIONS

From the regression models determined in Chapter 5,

an acceptance model of our Flipped Classroom course

can be derived, which describes the influences of the

students' intention to use a new teaching concept such

as FC. The acceptance model is shown in Figure 3.

In the teaching context, our model means that

students are not dependent on the opinions of their

fellow students when deciding to take part in an FC.

Figure 3: Acceptance Model.

More important are expected effort and the

expected output quality. The general, positive

assumptions about the concept reduce resistance and

negative feelings towards the change. For teachers

and program coordinators it is therefore important

that the advantages of an FC can be communicated

and promoted. These measures lead to an increased

intention to use the concept by students through the

predictors of perceived usefulness and ease of use. As

described in Chapter 4, this perceived usefulness

refers to the individual advantages for exam

preparation and the expected learning success. Such

an increase in learning success in FC has already been

observed in previous studies using activating methods

and improved feedback (Giannakos et al. 2014). In

order to increase the perceived ease of use, which

influences the perceived usefulness and thus also the

intention to use, the university could produce

explanatory videos that make it easier for students to

use digital media.

8 CONCLUSION

In the acceptance model of our FC course, we have

shown that the students' intention to use the new

technologies in education can be predicted by the

perceived usefulness and perceived ease of use.

Social components, on the other hand, have no

influence - in addition to the results of TAM2. Davis

already shows that the influence of the subjective

norm decreases over time (Venkatesh and Davis

2000). Since our examined sample is a master course,

an identical effect seems logical. Thus, the opinion of

other students does not influence the decision to

attend such a teaching concept. Cognitive

components are the main focus of this model to

explain the use of FC. In our FC course, the output

quality plays a special role, since it can reduce the

influence of the inertia. A positive ratio of effort to

benefit also promotes acceptance. This shows that

students are quite willing to achieve a lot if the effort

leads to a positive result.

Especially in times of the Corona Pandemic,

where many digital teaching materials have been

created, the future integration of a Flipped Classroom

is particularly useful. Because in such blended

learning concepts, the newly created digital media

can continue to be used profitably, when face-to-face

teaching is possible again. Both at this point in time

and at times when exclusive online teaching is

integrated, student acceptance is of central

importance for the success of studies. With our

model, we therefore aim to contribute towards

understanding resistance to change in order to

facilitate the integration of such teaching concepts

today and in future.

The limitations of our acceptance model are

initially to be found in the small sample of 40

students. Furthermore, the results are case-related and

apply to the Flipped Classroom concept presented

above. FCs can be designed differently, which could

have an influence on the statistical results and must

be considered in the interpretation. Furthermore, the

low 



value of the regression model for RC suggests

that there are other factors affecting the inertia that

we have not considered. Therefore, there is no claim

to completeness of the model. Future research could

on the one hand verify whether our acceptance model

can be confirmed in other Flipped Classrooms with

different conditions and especially with and with a

larger number of participants and on the other hand

supplement missing influencing factors of the

resistance to chan. In addition, it could be examined

whether the influence of the subjective norm depends

on how far the students have progressed in their

The Effect of Resistance to Change on Students’ Acceptance in a Flipped Classroom Course

studies, and also whether the subjective norm is also

absent in earlier courses, for example in the

Bachelor’s degree. We, therefore, call for further

research analysing the connection between TAM and

digital course concepts.

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