Measuring the Cognitive Complexity in the Comprehension of Modular Process Models [original]

WINTER et al.: COGNITIVE COMPLEXITY IN MODULAR PROCESS MODELS 1

Measuring the Cognitive Complexity in the

Comprehension of Modular Process Models

Michael Winter, R¨

udiger Pryss, Thomas Probst, Julia Baß, and Manfred Reichert

Abstract—Modularization in process models is a method to

cope with the inherent complexity in such models (e.g., model

size reduction). Modularization is capable to increase the quality,

the ease of reuse, and the scalability of process models. Prior

conducted research studied the effects of modular process models

to enhance their comprehension. However, the effects of modular-

ization on cognitive factors during process model comprehension

are less understood so far. Therefore, this paper presents the

results of two exploratory studies (i.e., a survey research study

with N = 95 participants; a follow-up eye tracking study with

N = 19 participants), in which three types of modularization

(i.e., horizontal, vertical, orthogonal) were applied to process

models expressed in terms of the Business Process Model and

Notation (BPMN) 2.0. Further, the effects of modularization on

the cognitive load, the level of acceptability, and the performance

in process model comprehension were investigated. In general, the

results revealed that participants were confronted with challenges

during the comprehension of modularized process models. Fur-

ther, performance in the comprehension of modularized process

models showed only a few significant differences, however, the

results obtained regarding the cognitive load revealed that the

complexity and concept of modularization in process models

were misjudged initially. The insights unraveled that the atti-

tude towards the application and the behavioral intention to

apply modularization in process model is still not clear. In

this context, horizontal modularization appeared to be the best

comprehensible modularization approach leading to a more fine-

grained comprehension of respective process models. The findings

indicate that alterations in modular process models (e.g., change

in the representation) are important to foster and enable their

comprehension. Finally, based on our results, implications for

research and practice as well as directions for future work are

discussed in this paper.

Index Terms—Process Model, Modularization, Cognitive Load

Theory, Level of Acceptability, Survey, Study, Eye Tracking

I. INTRODUCTION

The processes, procedures, and operations of organizations

from different domains (e.g., industry [1], healthcare [2])

are usually documented in textual or graphical artifacts (i.e.,

process models). Regarding the latter, information in graph-

ical process models are presented visually with a variety

of symbols as an abstraction of the real world [3]. As a

Michael Winter, Julia Baß, and Manfred Reichert are with the Institute of

Databases and Information Systems, Ulm University, Ulm, Germany; (e-mail:

[email protected], [email protected], manfred.reichert@uni-

ulm.de).

R¨

udiger Pryss is with the Institute of Clinical Epidemiology and Biometry,

University of W¨

urzburg, W¨

urzburg, Germany; (e-mail: ruediger.pryss@uni-

wuerzburg.de).

Thomas Probst is with the Department for Psychotherapy and Biopsy-

chosocial Health, Danube University Krems, Krems, Austria; (e-mail:

[email protected]).

Corresponding Author: Michael Winter

result, an abstract representation of the real world reduces

the risk of cognitive deficiencies (e.g., limited capacity in the

working memory) and, hence, fosters - inter alia - decision-

making as well as communication of underlying information

[4]. However, as a prerequisite for the aforementioned aspects,

and in order to take advantage of process models, it must be

ensured that such models are comprehended properly by all

involved stakeholders [5].

Over the last decade, a lot of research was conducted to foster

our general understanding of working with process models.

Thereby, efforts were put into identifying the factors that have

an effect on the comprehension of process models. In this

context, a distinction is made between two comprehensive fac-

tors affecting the comprehension of such models: 1

objective

properties of a process model (e.g., size of a process model)

must be considered separately from 2

subjective character

traits (e.g., process model expertise) of a model reader.

Regarding 1

objective properties, [6] provides a compre-

hensive overview of forty studies that investigated the com-

prehension of process models. Further, the authors in [7]

evaluated process model related factors and their effects on

process model comprehension, whereas [8] studied the im-

pact of notational deficiencies in a process model on model

comprehension. Finally, the identification of an adequate trade-

off between the size and the structure of process models was

investigated in two studies presented in [9].

In the context of subjective character traits 2

, the work

in [7] evaluated the importance of various character traits

on the comprehension of process models. In addition, the

evaluated character traits from [7] and their effect on model

comprehension were examined in a series of studies described

in [10]. Moreover, the work in [11] presented the first analysis

of cultural dependencies in decision-making in the context of

process model comprehension.

In the recent past, there is an emerging trend in numerous

domains considering an anthropocentric view, in which the

study of the human cognition is taken into account [12].

Especially in the domains of science and technology, the

study of aspects of human cognition (e.g., decision-making,

problem solving) allow the definition of novel approaches in

order to support and obtain an improved performance from

individuals in their work tasks [13], [14]. Therefore, technol-

ogy artifacts (e.g., software code) are considered in detail and

their effects on aspects of human cognition (e.g., reading and

comprehension) are evaluated in order to reveal the inherent

complexity of such artifacts as well as how to positively

reduce this complexity (i.e., cognitive complexity [15]). For

this reason, in the context of process model comprehension,

WINTER et al.: COGNITIVE COMPLEXITY IN MODULAR PROCESS MODELS 2

more emphasis is put on subjective cognitive processes in

respective research. For example, different cognitive strategies

during process model comprehension were defined based on

the results obtained from a large-scale study with over 1000

participants in [16]. Further, [17] researched various cognitive

styles and their applied reading strategies in the comprehen-

sion of process models. Moreover, the works in [18] and [19]

were using eye tracking technologies in order to get insights

about cognitive aspects as well as processes and their effects

on model comprehension.

Usually, process models from organizations in real-life

projects contain a high information density and, hence, vary in

respect to their size and complexity [20]. As a consequence,

this results in additional difficulties for the human cognition

(e.g., limited capacities in the working memory) regarding a

proper and correct comprehension of these process models.

However, an existing approach to tackle this issue and to

relieve human cognition (e.g., reduction of the capacities in

the working memory) is to apply a modularized structure in

these models (i.e., modularization) [21]. In general, modu-

larization describes the concept to decompose a monolithic

structure into smaller independent modules [22]. In terms of

process models, a large process model is modularized into

several smaller modules (i.e., process models), which may

be complete per se as well as independently manageable.

Consequently, the smaller process model modules contain a

lower information density and, hence, inherent complexity is

reduced having a positive effect on human cognition. More

specifically, the positive effects of modularization in process

models are presented in a first review in [23]. In this work, it

was shown that process models can be comprehended better

in a modularized design due to the reduction of the process

model size and complexity respectively. The results obtained

in a study presented from the authors in [24] confirmed prior

results related to modularized process models in general. In-

terestingly, the work [24] revealed that especially for business

practitioners, it is advisable to present the process models in

a non-modularized instead of a modularized representation. A

reason may be that the presentation of smaller process models

impairs the comprehension of process models.

In order to get a better understanding of the effects of mod-

ularized process models on the comprehension of respective

models, a deeper investigation of the effects of modularization

in process models on the human cognition is still missing so

far. In more detail, further research is needed to investigate

the cognitive effects of modularization on the comprehension

of process models. Additionally, the behavioral intention and

performance efficiency during the comprehension of modular-

ized process models may unravel new insights that can foster

their comprehension by the definition of supporting measures

(e.g., comprehension guidelines). Generally, a vast body of

research exists highlighting the benefits of modularization

in process models regarding their comprehension (e.g., [25],

[26], [27]). Thereby, the approach was pursued in previous

research to compare the effects between modularized and non-

modularized process models [28]. As another contribution in

this context, the work at hand presents two exploratory studies

that investigated the effects of three different modularization

types (i.e., horizontal, vertical, orthogonal) on the comprehen-

sion of process models expressed in terms of the BPMN 2.0

from a cognitive point of view. Therefore, the following four

research questions (RQ) were addressed in this work:

RQ 1: Does the use of different modularization types in

process models have an effect on the cognitive load during

the comprehension of BPMN 2.0 process models?

RQ 2: Does an explanation about modularization in pro-

cess models have an effect on the cognitive load during the

comprehension of BPMN 2.0 process models?

RQ 3: Does the use of different modularization types in

process models have an effect on the level of acceptability

during the comprehension of BPMN 2.0 process models?

RQ 4: Does the use of different modularization types in

process models have an effect on the comprehension perfor-

mance of BPMN 2.0 process models?

In order to address the defined RQs, two exploratory studies

(i.e., Study I and Study II) were conducted. Thereby, Study I

was conducted using a survey research design while Study II

was conducted as a follow-up eye tracking study. Regarding

the research questions, RQ 1 was only addressed in Study I,

whereas RQ 4 was only addressed in Study II. RQ 2 and

RQ 3, however, were addressed in Studies I and II. In RQ

1, the cognitive load of participants who were confronted

with three different modularization types (i.e., horizontal,

vertical, orthogonal) during process model comprehension

were assessed. Thereby, the cognitive load is composed of

the dimensions intrinsic, extraneous, and germane cognitive

load. In turn, RQ 2 was concerned with the question whether

an explanation about modularization effects the cognitive load

and its related dimensions (i.e., pre- vs. post-explanation). In

RQ 3, the level of acceptability during the comprehension

of process models was evaluated. Therefore, the perceived

usefulness for understandability (PUU), the perceived ease of

understandability (PEU), the subjective ease of use (SEU),

and the subjective comprehensibility (SC) were evaluated. Fi-

nally, in RQ 4, performance in process model comprehension

(i.e., score, duration, number of fixations, average fixation

duration) of participants with respect to the three different

modularization types was measured in a study relying on eye

tracking technology. Fig. 1 summarizes the addressed research

questions in respective studies.

The structure of this paper is as follows: Section II provides

theoretical background about modularization in process mod-

els. Materials and methods of the two conducted studies are

described in Section III. In Section IV, obtained results of

both studies are presented descriptively, tested for significance,

and discussed. Moreover, Section IV provides limiting factors,

implications for research as well as practice, and future work.

Finally, Section V summarizes the paper.

II. THEORETICAL BACKGROUND

Modularization constitutes a crucial design methodology in

the creation of complex technology [29]. The main princi-

ple of modularization characterizes the decomposition of a

monolithic structure into smaller modules in order to foster

WINTER et al.: COGNITIVE COMPLEXITY IN MODULAR PROCESS MODELS 3

Fig. 1. Addressed Research Questions in Studies I and II

flexibility, reusability, and, primarily, to decrease complexity

in technology systems [30]. Vice versa, the composition of

smaller modules enables the creation of novel technology sys-

tems with an unprecedented inherent complexity (e.g., artificial

intelligence) [31]. A module represents a detachable physical

(e.g., car component) or non-physical (e.g., software code)

construct as a hierarchical part of an entity (e.g., system) [32].

Thereby, modules are subject to clearly defined boundaries

(i.e., non-functional requirements) regarding their functionality

in an entity. Regarding the latter, modularity describes the

characteristic of an entity, which components (i.e., modules)

may be combined or separated during the design phase. The

advantages of a modular structure in such a design are that

these modules are independently manageable, interchange-

able, and complete per se. For this reason, modularization

is widespread and applied in various domains. For example,

in robotic systems, a modular design allows for different

morphologies in problem solving [33], whereas industrial

design uses modularization for combining smaller subsystems

to create larger systems [34]. Another prominent domain of

application of modularization is in the context of process

models [35], [36], [37]. More specifically, a process model or

an aspect thereof (e.g., routine) is depicted into smaller process

models. Therefore, it is frequently used in complex process

models (e.g., reduction of model size) for the purpose of reuse

and a better comprehension of such models [23]. In general,

for the start of the exploration, three different modularization

types (i.e., horizontal [38], vertical [24], orthogonal [39]) that

have become established in the context of process models, are

introduced in the following and, furthermore, were used in the

reported two studies. Note that the work at hand considered

only process models expressed in terms of the BPMN 2.0

and modularization was applied activity-based in all three

modularization types (see Section III-B). Yet, different types of

modularization exist and presented modularization approaches

can also be applied in other process modeling notations (e.g.,

Event-driven Process Chains [25], UML Activity Diagram

[40]) as well as functions (e.g., role-based) [41]. The following

Fig. 2 presents the BPMN 2.0 modeling elements, which are

used in the explanations.

•Activity: An activity is an atomic task and represents a

step in a process.

•Subprocess: A subprocess is an abstracted process step,

which consists of a number of related activities.

•Sequence flow: The sequence flow connect all modeling

Fig. 2. BPMN 2.0 Modeling Elements

elements in a process model and defines the direction of

the process flow.

•Event: An event indicates that something is happening in

the process, which affects its process flow.

A. Horizontal Modularization

In the horizontal modularization type, a process model

is divided into smaller independent, but not self-contained

process models [41]. Thereby, process information are not

abstracted and, hence, are presented fine-grained on one level

(i.e., no hierarchy). Usually, the process flow is oriented along

the defined paths on a horizontal level (i.e., left-to-right).

This modularization type reduces the overall complexity of

a process model and, hence, has a positive effect on process

model comprehension. Furthermore, horizontal modularization

increases reusability as well as maintainability of process

models. In addition, the reusability of the decomposed process

models may foster collaboration through a precise definition

of affiliations. More particularly, through the use of additional

concepts (i.e., pools, lanes) in the process models using

horizontal modularization, the affiliations of the documented

process may be specified more accurately. In BPMN 2.0

process models, horizontal modularization is realized with the

application of specific event types (e.g., link, message events).

For example, when using a link event, the process models are

connected with links, whereas a link represents a one-way

transfer to another link. In general, a link represents a similar

functionality as a GoTo statement known from computer

programming. An example of horizontal modularization in

a BPMN process model is shown in Fig. 3 (a). After the

execution of activity A, the process flow reaches the throwing

link event with label 1. The link event refers to the related

catching link event (i.e., also label 1) and, hence, the process

flow continues horizontally in the other process model. Further

modeling elements are executed according the same principle

up to the end of the process model.

B. Vertical Modularization

In contrast to horizontal modularization, the vertical modu-

larization refers to the decomposition of a process model into

refined subprocesses [42]. More specifically, the inherent high

abstraction level in a process model is reduced and details

of the process model are hidden in underlying subprocesses.

More specifically, juxtaposed to horizontal modularization,

process information are encapsulated and structured in a

vertical hierarchy. On the top level of the hierarchy, the

abstract process model is shown and with increasing hierarchy

depth the activities are defined more precisely. In general,

WINTER et al.: COGNITIVE COMPLEXITY IN MODULAR PROCESS MODELS 4

subprocesses are self-contained process models, but are depen-

dent on the superordinate process model, thus limiting their

reusability. However, a major benefit of using subprocesses

is that the comprehensibility of especially complex and large

process models can be increased [43]. Moreover, although

the reusability is limited, subprocesses reduce redundancies

in a process model and, thus, facilitate model maintainability.

The realization of vertical modularization in BPMN 2.0 is

made with collapsed or expanded subprocesses. The collapsed

subprocess decomposes activities into more fine-grained and

self-contained process models in a vertical downward di-

rection, as known from hierarchical structures. In turn, an

expanded subprocess describes the seamless integration of

the subprocess in the sequence flow of the superordinate

process model. Fig. 3 (b) illustrates a process model with an

expanded subprocess. In this figure, the activity B represents

a subprocesses that triggers the execution of activity X and Y.

More specifically, activity B is the abstract high-level activity

in the hierarchy, which represents vertically the activities X

and Y. After the execution of both activities, the subprocesses

is completed and the process execution continues along the

sequence flow.

C. Orthogonal Modularization

The orthogonal modularization is based on the aspect-

oriented programming paradigm in order to increase modu-

larity in a process model. In this modularization type, the

decomposed process model is separately specified with a

pointcut (i.e., join point) [44]. Generally, orthogonal modular-

ization is mainly used for the decomposition of cross-cutting

concerns, such as privacy or security aspects (e.g., password

inquiry) [41]. Thereby, similar to the other two modularization

types, the size of a process model is reduced having a

positive effect on process model comprehension. Furthermore,

orthogonal modularization increases the maintainability as

well as reusability through the clear separation of cross-cutting

concerns. Orthogonal modularization in BPMN 2.0 process

models is realized in two ways. One way describes the extrap-

olation of cross-cutting concerns within a process in an event

subprocess, similar to horizontal and vertical modularization.

Therefore, specified exception events that could be triggered

at any point in time (e.g., caused by an external event) refer

(e.g., via link event) to the defined event subprocess. The other

way, in turn, is oriented by the adoption of specific notions

(i.e., advice, join point, point cut) known from aspect-oriented

programming [45]. In particular, repeating cross-cutting con-

cerns (e.g., key generation for login) within a process model

are outsourced and depicted in a separate modularized process

model [46]. Further, a notion (e.g., security aspect) is provided

for the outsourced model defining its function. Regarding the

latter, Fig. 3 (c) depicts a process model with orthogonal

modularization. More specifically, before the execution of

activity A and B, process flow triggers the execution of the

cross-cutting concern once for activity A and B. Thereby, the

cross-cutting concern represents the outsourced process model

and, hence, can only be used for the defined function.

(a) Horizontal Modularization

(b) Vertical Modularization

Fig. 3. Modularization Types in BPMN 2.0

III. METHODS AND MATERIALS

A. Participants

In Study I, which addressed RQ 1 and, as did Study II, RQ 2

and RQ 3, a total of 95 students participated. The participants

were enrolled from an entry course in the context of Business

Process Management at Ulm University. 45 participants were

female, 48 male, and 2 others. 52 participants were younger

than 25 and the rest indicated an age between 25 and 35. Based

on information obtained from a demographic questionnaire,

63 participants stated that they already had experience in

process modeling as well as process model comprehension.

The participants were randomly divided into three groups (i.e.,

horizontal, vertical, orthogonal). For the random allocation

of the participants, the randomization function of Google

Forms was used (see Study Design). The group with the

horizontal modularization type consisted of 35 participants,

the group with vertical modularization of 28 and, finally,

the orthogonal group of 32 participants. Table I presents the

baseline comparisons between the three modularization groups

(i.e., horizontal, vertical, orthogonal).

In Study II, which addressed RQ 4 and, as did Study I, RQ

2and RQ 3, 19 participants were invited for the study and all

participants were male. 4 participants indicated an age younger

than 25 and the others had an age between 25 and 35. Three

participants had no experience in the context of process mod-

WINTER et al.: COGNITIVE COMPLEXITY IN MODULAR PROCESS MODELS 5

TABLE I

SAMPLE DESCRIPTION AND COMPARISON OF STUDY IIN BASELINE VARIABLES

Variable Horizontal (N = 35) Vertical (N = 28) Orthogonal (N = 32) p value

Gender N (%)

female 15 (42.86) 13 (46.43) 17 (53.12)

male 18 (51.43) 15 (53.57) 15 (46.88)

other 2 (5.71) 0 (0.00) 0 (0.00) p = .615a

Age N (%)

<25 years 18 (51.43) 19 (67.86) 16 (50.00)

25 - 35 years 17 (48.57) 9 (32.14) 16 (50.00) p = .327a

Experience N (%)

yes 25 (71.43) 21 (75.00) 17 (53.13)

no 10 (28.57) 7 (25.00) 15 (46.87) p = .149a

aFisher’s exact test

eling and comprehension of process models. Furthermore, no

allocation of the participants into modularization groups was

necessary, since all participants followed the same procedure

in Study II (see Section III-D).

B. Materials

Two studies (i.e., Study I and Study II) were conducted.

Thereby, Study I consisted of three parts and the following

process models were used: For Part One (see Section III-

D), 12 different process models expressed in terms of the

BPMN 2.0 were used [47]. The choice to use BPMN 2.0

process models was made for several reasons. BPMN 2.0

is the de facto industry standard for the creation of readily

comprehensible process models and an ISO/IEC 1950:2013

standard [48]. In particularly, BPMN 2.0 serves as a seamless

link between process design (e.g., process documentation) and

implementation (i.e., process automation). Moreover, during

the last decade, a vast body of knowledge evolved, which

has promoted the widespread application of BPMN 2.0 in

practice as well as in research [49]. For each modularization

type (i.e., horizontal, vertical, orthogonal), four process models

needed to be comprehended by the participants. Further,

modularization was applied in the process models activity-

based. More specifically, process models were depicted into

smaller models based on semantically related activities (see

Section IV-G). Thereby, the four process models documented

the following process scenarios: order, refuel, delivery service,

and, finally, credit application. In this context, the modularized

process models had intentionally a low complexity in terms of

model size and structure in order to avoid potential side effects

(e.g., cognitive overload due to model size [50]), which could

have an effect on the outcome of interest (see Section IV-E).

In particularly, the process models were composed of basic

elements of BPMN 2.0 and contained an average number

of 40 modeling elements. In Part Two of Study I, a BPMN

2.0 process model, presented in the respective modularization

type, was shown to the participants, with which the respective

modularization types were described and the application of

modularization in this context was explained. This process

model described a loan process and was created by the

authors in [41]. Regarding the latter, this work provides a

validated conceptual basis for the reduction of complexity in

process models through the accumulation of defined patterns.

Thereby, the presented loan process in this work is kept

simple but consists of the most common modeling elements

of BPMN 2.0 [51]. Hence, the loan process reflected a good

balance of simplicity as well as complexity and was, therefore,

adequate for the use in this study. In the final Part Three of

Study I, a similar loan process from [41] was used in all

three modularization types1. Finally, considered performance

measures in Study I are described in Section III-C and used

instrumentation in Section III-E.

In Study II, which consisted of three parts, the following

process models were used: 81 different BPMN 2.0 process

models with similar model properties as the models used in

Study I documenting 9 process scenarios were used in Part

Two. Particularly, for each modularization type, 27 process

models were created. Thereby, the process models docu-

mented the following process scenarios: powershell, online

shopping, pizza baking, refuel, order, shipping, smartphone

unlock, loan, and, finally, credit card payment. Similar as in

Study I, the process models (i.e., average number of modeling

elements was 25) were kept simple intentionally in order

to ensure that the emphasis can be put on the concept of

modularization. Furthermore, for each process model, four

true-or-false comprehension questions needed to be answered

by the participants. The comprehension questions referred

on process model syntactics as well as semantics2. Finally,

considered performance measures in Study II are described in

Section III-C and used instrumentation in Section III-E.

C. Performance Measures

In the following, the considered performance measures,

which have been used in both studies, are described in detail3.

Study I & Study II:

•Cognitive load: The cognitive load depicts the invested

cognitive capacity of the working memory during a task.

Thereby, the cognitive load consists of the following

dimensions: intrinsic, extraneous, and germane cognitive

load [52]. Thereby, intrinsic load constitutes the com-

plexity of intrinsic information and is affected by existing

1The materials used in Study I are available at: https://drive.google.com/

open?id=1rytYcYS5oZ8HVpWhF0Fdb2sZ9USxVnyW

2The materials used in Study II are available at: https://drive.google.com/

open?id=1XYCbq3Ai8gd-xy7maGTx8nt-YfyEFc7R

3The questions regarding cognitive load and level of

acceptability are available at: https://drive.google.com/file/d/

1q3FmujGhwGkNM8a-oo3QJsNgFDaCaAtW/view?usp=sharing

WINTER et al.: COGNITIVE COMPLEXITY IN MODULAR PROCESS MODELS 6

knowledge and element interactivity (e.g., demand on the

working memory). In turn, extraneous load is affected by

the way information is presented. Finally, germane load

describes the mental effort to process and comprehend

information based on constructed mental models [53]. To

measure the single dimensions related to the cognitive

load, the adapted measurement proposed in [54] was

used in Studies I and II in order to investigate RQ 1

(i.e., Study I) and RQ 2 (i.e, Studies I and II). Thereby,

respective work demonstrated that the application of

the proposed measurement is a validated and reliable

instrument for measuring the cognitive load. Hence, the

measurement can be applied from an informed (i.e., with

prior knowledge) and na¨

ıve point of view (i.e., without

prior knowledge) about the concept of cognitive load. The

single dimensions, which were comprised of several items

(i.e., two for intrinsic, three for extraneous, and germane

cognitive load), had to be rated on a 7-point Likert scale

from strongly disagree (i.e., 1) to strongly agree (i.e., 7).

•Perceived usefulness for understandability (PUU): De-

rived from the technology acceptance model (TAM) [55],

PUU describes the perceived usefulness of a particular

modularization type within a process model in the context

of process model comprehension (RQ 3). Therefore, four

items on a 7-point Likert scale from strongly disagree

(i.e., 1) to strongly agree (i.e., 7) needed to be answered

totaling to a min/max value of (4 x 7). Moreover, the

used measure was evaluated for validity and reliability in

prior research [56].

•Perceived ease of understandability (PEU): Derived from

TAM, PEU characterizes that the use of a particular

modularization type within a process model is associated

with less mental effort (RQ 3). Therefore, four items on

a 7-point Likert scale from strongly disagree (i.e., 1) to

strongly agree (i.e., 7) needed to be answered totaling to

a min/max value of (4 x 7). Moreover, the used measure

was evaluated for validity and reliability in prior research

[56].

The following performance measures were only used in

Study I with respect to RQ 3

•Subjective ease of use (SEU): Derived from PEU, in

SEU, Participants needed to indicate a modularization

type regarding the ease of use. Therefore, based on sub-

jective preferences, all three modularization types were

juxtaposed and the participants chose a modularization

type with the highest intention to use. Accordingly, the

frequency of the respective modularization types was

evaluated.

•Subjective comprehensibility (SC): The most comprehen-

sible modularization type was inquired from the partic-

ipants. Similar as SEU, all three modularization types

were juxtaposed and the participants were asked to indi-

cate the best comprehensible modularization type based

on subjective preferences. Accordingly, the frequency of

the respective modularization types was evaluated.

Finally, the following performance measures were only used

in Study II with respect to RQ 4. Thereby, prior research

demonstrated that considered performance factors were suit-

able in order to evaluate the comprehension of process models

[57]. Furthermore, various parameters can be considered in the

analyses of eye tracking data in the context of process model

comprehension [19]. However, similar research (e.g., [18],

[16] demonstrated that the considered eye tracking measures

were suitable for a first evaluation of the process model

comprehension performance in modularized models:

•Score: Participants needed to answer for each compre-

hended process model four true-or-false comprehension

questions. The comprehension questions referred to the

semantic as well as syntactic dimensions of the process

models. For each correct given answer, a point was

awarded. Particularly, a participant could score a max-

imum of four points per process model.

•Duration: A timestamp was added at the moment partic-

ipants started comprehending respective process models.

After comprehending a process model and answering

respective comprehension questions, another timestamp

was added. This allowed us to measure the duration

needed for comprehension on a fine-grained level.

•Fixation: Fixations constitute eye movements of very low

velocity at a specific point in a stimulus (e.g., image),

in which relevant information is extracted about what

is being looked at [58]. The measuring of the number

of fixations allowed us to make conclusion about the

cognitive load as well as about specific points (e.g.,

process modeling constructs) in the stimulus (i.e., process

model) that may pose a challenge in the comprehension

process for the participants.

•Fixation duration: The fixation duration indicates the

period of time in which the eyes remain still while

looking at a stimulus [59]. During this period of time,

the acquisition of information from the currently viewed

point in a stimulus (i.e., process model) takes place.

Hence, the analysis of the average fixation duration

allowed for additional assumption regarding the cognitive

load during the comprehension of process models [60].

Based on the defined measures, Fig. 4 summarizes the

research models for Study I (a) and Study II (b). More specif-

ically, both research models investigate whether the cognitive

load, level of acceptability, and performance in process model

comprehension is affected by the three modularization types

applied in the process models. In addition, for Study I (see

Fig. 4 (a)), the provision of an explanation about modularized

process models on respective measures was explored.

D. Study Design

The study design is based for both studies accordingly on

the guidelines proposed in [61], which provided all essentials

for studies in computer science.

Study I was an online-based survey (i.e., survey research

design). As an exploratory study, a survey constituted a

suitable methodology for the acquisition of first data regarding

the perception and acceptance of modularization in process

models. Moreover, the possibility of conducting the survey on-

line (i.e., participants were neither spatially bound, nor bound

WINTER et al.: COGNITIVE COMPLEXITY IN MODULAR PROCESS MODELS 7

(a) Research Model - Study I

(b) Research Model - Study II

Fig. 4. Research Models for Studies I and II

by time) allowed us to increase the scope of your study and to

collect a large number of data in a short time. Finally, obtained

data can be examined in more detail in further studies (see

Section IV-G). As prerequisite for participation in Study I, a

mobile device (i.e., laptop, smartphone) was required (see Sec-

tion IV-E). Further, Study I was conducted at Ulm University

in an entry course on Business Process Management. Hence,

all participants of this study were recruited in this course.

Moreover, as an incentive for a conscientious participation, a

bonus point for the later exam of this course was awarded

for all participants, who participated in the study. Before

the study reported in this paper, two pilot studies with four

participants each were conducted. The pilot studies were used

in order to obviate ambiguities as well as misunderstandings.

Furthermore, the overall quality of the study material was

increased and technical functions (i.e., data collection) had

been checked for their proper implementation. In the course,

a web link leading to the online survey was provided to the

participants via a projector. Thereby, the procedure of Study

I, which consisted of three parts, was as follows: For Part

One, participants were led to the survey page (i.e., Google

Forms) by accessing the provided web link [62]. At accessing

the web link, a randomization function, provided by Google

Forms, was used to randomly allocate the participants into one

of three groups (i.e., horizontal, vertical, orthogonal). Then, an

introduction was presented to the participants, outlining the

procedure and the goal of the study. Afterwards, participants

were asked to answer a set of demographic questions (e.g.,

age, gender, expertise in process modeling). After completing

this step, the participants needed to evaluate their assigned

modularization type. More specifically, four modularized pro-

cess models were presented in a successive order (see Section

III-B). For each process model, participants had to answer a

set of questions related to the three dimensions of the cognitive

load (i.e., intrinsic, extraneous, germane) in order to investigate

RQ 1. Afterwards, in Part Two, the allocated modularization

type was exemplified, textual as well as graphical, explaining

the application of modularization in process models to all

participants. Moreover, an additional set of questions capturing

the cognitive load was presented to address RQ 2 (i.e., pre-

vs. post-explanation). Then, the items concerning the perceived

usefulness for understandability (PUU) and the perceived ease

of understandability (PEU) were presented to the participants

with respect to RQ 3. Finally, in Part Three and to further

address RQ 3, all three modularization types were shown and

participants were asked to compare and rank the three modu-

larization types with regard to subjective usefulness (SEU) and

comprehensibility (SC). Additionally, another questionnaire

related to the cognitive load had to be answered. Finally,

participants were able to leave feedback and the study was

finished. The complete execution of Study I took about 20

minutes. Fig. 5 illustrates the design used in Study I. In more

detail, in Part One, RQ 1 (i.e., cognitive load) was addressed.

In Part Two, RQ 2 (i.e., pre- vs. post-explanation) and RQ 3

(i.e., level of acceptability; PUU and PEU) were investigated.

Finally, in Part Three, RQ 3 (i.e., level of acceptability; SEU

and SC) was addressed.

Study II was conducted as a follow-up eye tracking study.

The eye tracking study enabled us to gain first insights

into performance metrics (e.g., duration, number of fixations)

during the comprehension of process models with varying

modularization approaches. Moreover, obtained performance

metrics can be juxtaposed with related cognitive load whether

there might be a correlation between performance metrics,

cognitive load, and the comprehension of process models

resulting in a correlated elevation respective measures with

increasingly complex process models. In Study II, no par-

ticipants were invited which already participated in Study I.

The study was conducted at Ulm University in a designated

eye tracking lab. Prior to this study, two pilot studies with

four participants each were conducted for the purpose of

reviewing the used study material. Due to the device limitation,

only one participant could be evaluated each time and a

session in Study II, which consisted of three parts, was as

follows: In Part One, the study started with welcoming the

participants and explaining the study procedure as well as a

brief oral explanation about modularization in process models.

Afterwards, similar to Study I, the participants were asked to

answer a demographic questionnaire. Then, the participants

were placed in front of the eye tracking device and the device

was calibrated accordingly. Following this, the participants

completed a brief tutorial in order to familiarize them with the

functionality of the eye tracking device. After completing these

mandatory steps, in Part Two, the participants were confronted

with 9 modularized process models. In more detail, for each

WINTER et al.: COGNITIVE COMPLEXITY IN MODULAR PROCESS MODELS 8

Fig. 5. Study Design used in Study I

modularization type (i.e., horizontal, vertical, orthogonal) three

process models were shown. Hence, obtained from the pool

of the 81 process models (27 process models for each mod-

ularization type; see Materials), 3 x 3 process models were

randomly shown to the participants. Furthermore, for each

process model, the participants needed to answer four true-or-

false comprehension questions. After three evaluated process

models, the eye tracking device was calibrated anew in order

to prevent faulty data. In this way, the performance measures

regarding the comprehension performance of modularized

process models were assessed with respect to RQ 4. After

all process models had been evaluated, in Part Three, the

participants needed to answer a questionnaire capturing the

cognitive load (RQ 2), perceived usefulness for understand-

ability (PUU), and perceived ease of understandability (PEU)

(RQ 3). Finally, after the opportunity to leave feedback, the

study ended. The time required for the execution of the study

was approximately 30 minutes. The used design in Study II is

shown in Fig. 6. More specifically, in Part Two, RQ 4 (i.e.,

process model comprehension performance) was investigated.

In Part Three, RQ 2 (i.e., cognitive load) and RQ 3 (i.e., level

of acceptability; PUU and PEU) were addressed.

E. Instrumentation

In general, all materials used in Study I were provided

in Google Forms. More specifically, demographic data (e.g.,

age, gender, experience in process modeling), information

Fig. 6. Study Design used in Study II

related to the cognitive load and the level of acceptability were

collected with questionnaires in Google Forms.

In Study II, demographic data, questions concerning the

cognitive load and level of acceptability were collected with

paper-based questionnaires. Eye movements were captured

with SMI iView X Hi-Speed system. Therefore, the eye track-

ing device was placed in front of a 23” monitor (resolution of

1920x1080, 96 PPI) presenting the respective process models

to the participants. Moreover, to ensure a high data-quality,

a 13-point calibration was performed. Eye movements were

recorded at a sampling rate of 240 Hz. For answering the true-

or-false comprehension questions, participants used a keyboard

with two predefined keys providing the respective answering

options (i.e., ’true’ and ’false’). Eye tracking data collected

during Study II was analyzed and visualized with SMI BeGaze

3.7.59 software. Finally, SPSS 25 was used for all statistical

analyses.

IV. RESULTS

This section presents the descriptive as well as inferential

statistics of the results obtained from Studies I and II.

A. Results of Study I

Table II presents for each process model (i.e., four in

total) mean (M) and standard deviation (SD) regarding the

respective dimension (i.e., intrinsic, extraneous, germane) of

the cognitive load obtained of Part One from Study I. In

general, the dimensions intrinsic and extraneous load are on

a moderate level but the results fluctuate differently for each

process model. However, germane cognitive load reflects an

increased level in all three modularization types. Table III

shows mean (M) and standard deviation (SD) for the cognitive

load as well as the level of acceptability obtained in Part

Two from Study I. In detail, the table shows the value for

WINTER et al.: COGNITIVE COMPLEXITY IN MODULAR PROCESS MODELS 9

the three dimensions after the participants were provided with

an explanation about modularization. Furthermore, perceived

usefulness for understandability (PUU) as well as perceived

ease of understandability (PEU) reflecting the level of accept-

ability of respective modularization type are shown in Table

III. Regarding the cognitive load, the three dimensions show

increased values juxtaposed to the results presented in Table II.

Concerning PUU and PEU, it appears that the participants

were indecisive regarding the benefits of modularization in

process models (i.e., average 16 out of 28 for PUU and PEU).

TABLE II

DESCRIPTIVE RESULTS FOR COGNITIVE LOAD OF PART ONE FROM

STUDY I

Variable Horizontal Vertical Orthogonal

ICL PM 1 1.83 (.82) 2.20 (.90) 1.80 (.81)

ICL PM 2 2.40 (.92) 2.79 (1.24) 2.31 (1.15)

ICL PM 3 3.83 (1.21) 4.57 (1.37) 3.72 (1.22)

ICL PM 4 3.23 (1.20) 3.98 (1.34) 3.33 (1.21)

ECL PM 1 1.99 (.85) 2.17 (.83) 1.94 (.96)

ECL PM 2 2.35 (1.33) 2.61 (1.44) 2.81 (1.36)

ECL PM 3 3.59 (1.35) 4.07 (1.53) 3.75 (1.22)

ECL PM 4 2.86 (1.23) 3.49 (1.48) 3.23 (1.23)

GCL PM 1 4.12 (1.24) 4.69 (1.16) 4.38 (.92)

GCL PM 2 4.30 (1.36) 4.67 (1.30) 4.34 (.99)

GCL PM 3 4.52 (1.05) 5.02 (1.03) 4.56 (1.07)

GCL PM 4 4.39 (1.20) 4.88 (1.02) 4.67 (.95)

Note: ICL = Intrinsic Cognitive Load; ECL = Extraneous

Cognitive Load; GCL = Germane Cognitive Load; PM =

Process Model

TABLE III

DESCRIPTIVE RESULTS FOR COGNITIVE LOAD AND LEVEL OF

ACCEPTABILITY OF PART TWO FROM STUDY I

Variable Horizontal Vertical Orthogonal

ICL 3.70 (1.23) 4.00 (1.29) 3.50 (1.18)

ECL 2.97 (1.02) 3.56 (1.34) 2.95 (1.14)

GCL 4.59 (1.05) 4.87 (1.02) 4.83 (.95)

PUU 1 2.69 (1.23) 3.46 (1.43) 2.78 (1.43)

PUU 2 4.74 (1.34) 4.71 (1.24) 4.50 (1.41)

PUU 3 2.83 (1.10) 3.39 (1.50) 3.03 (1.40)

PUU 4 5.03 (1.36) 5.04 (1.20) 4.91 (1.25)

PUU Sum 15.29 (2.35) 16.61 (2.47) 15.22 (2.61)

PEU 1 4.77 (1.52) 4.86 (1.33) 4.69 (1.23)

PEU 2 2.46 (1.22) 2.96 (1.50) 2.59 (1.29)

PEU 3 4.77 (1.24) 4.50 (1.35) 4.84 (1.30)

PEU 4 2.83 (1.18) 3.07 (1.36) 3.03 (1.23)

PEU Sum 14.83 (2.73) 15.39 (3.08) 15.16 (2.36)

Note: ICL = Intrinsic Cognitive Load; ECL = Extraneous

Cognitive Load; GCL = Germane Cognitive Load;

PUU = Perceived Usefulness for Understandability;

PEU = Perceived Ease of Understandability

Finally, the results (i.e., frequencies and percentages) for

subjective ease of use (SEU) and subjective comprehensibility

(SC) (i.e., subjective level of acceptability) as well as mean

(M) and standard deviation (SD) for the cognitive load of Part

Three from Study I are shown in Table IV. Regarding SEU,

participants indicated that a horizontal modularization reflects

a higher subjective ease of use compared to a vertical and

orthogonal modularization. Furthermore, the results related

to SC were in compliance with the results related to SEU

showing that a horizontal modularization was better compre-

hensible juxtaposed to a vertical and orthogonal modulariza-

tion. Regarding the cognitive load, the three dimensions are in

TABLE IV

DESCRIPTIVE RESULTS FOR LEVEL OF ACCEPTABILITY AND COGNITIVE

LOAD OF PART THREE FROM STUDY I

Variable Horizontal Vertical Orthogonal

SEU N (%)

Horizontal 26 (74.29) 14 (50.00) 14 (43.75)

Vertical 7 (20.00) 14 (50.00) 10 (31.25)

Orthogonal 2 (5.71) 0 (0.00) 8 (25.00)

SC N (%)

Horizontal 26 (74.29) 14 (50.00) 17 (53.13)

Vertical 6 (17.14) 13 46.43) 10 (31.25)

Orthogonal 3 (8.57) 1 (3.57) 5 (15.62)

CLT ICL ECL GCL

4.95 (1.56) 4.42 (1.57) 4.92 (.99)

Note: SEU = Subjective Ease of Use; SC = Subjective

Comprehensibility; CLT = Cognitive Load Theory; ICL =

Intrinsic Cognitive Load; ECL = Extraneous Cognitive

Load; GCL = Germane Cognitive Load

line with the results about the cognitive load obtained in Part

Two (see Table III and represent higher values in general in

comparison to the results of obtained in Part One from Study

I (see Table II).

B. Results of Study II

Table V presents mean (M) and standard deviation (SD)

of the performance results score, duration (s), fixation, and

average fixation duration (ms) of Study II obtained for each

modularization type. Regarding the score, there were only

minimal differences and participants nearly reached the max-

imum score (i.e., max is 4) on average in answering the

comprehension questions. However, between duration and fix-

ation, there were differences between the three modularization

types (i.e., horizontal, vertical, orthogonal). In detail, for

horizontal modularization, participants needed more time and

more fixations during process model comprehension. However,

in vertical modularization, participants were the fastest and

required fewer fixations. Finally, regarding the average fixation

duration, while there were only minimal differences in the

average fixation duration between vertical and orthogonal

modularization, however, it appears that the horizontal modu-

larization indicated longer average fixation durations.

Finally, results representing PUU and PEU (i.e., level of

acceptability) as well as the three cognitive load dimension

(i.e., intrinsic, extraneous, germane) of Part Three from Study

II are shown in Table VI. Regarding PUU and PEU, results

were on a moderate level but, in comparison with PUU and

PEU from Study I (see Table III), they showed lower values.

Moreover, the specific cognitive load dimensions were on a

moderate level, whereas extraneous cognitive load shows an

increased value juxtaposed to intrinsic and germane cognitive

load. In addition, compared to the results regarding the cog-

nitive load of Part Two and Three from Study I (see Table III

and IV), the results are showing lower values.

C. Inferential Statistics

1) Results for RQ 1: To evaluate whether the differences

seen in the descriptive results with respect to RQ 1 reach

statistical significance, analyses of variances (ANOVAs) were

WINTER et al.: COGNITIVE COMPLEXITY IN MODULAR PROCESS MODELS 10

TABLE V

DESCRIPTIVE RESULTS FOR THE PERFORMANCE RESULTS OF PART TWO

FROM STUDY II

Variable Horizontal Vertical Orthogonal

Score

PM 1 3.47 (.77) 3.63 (.60) 3.42 (1.07)

PM 2 3.42 (.77) 3.63 (.76) 3.68 (.67)

PM 3 3.37 (.90) 3.63 (.50) 3.53 (.70)

Duration

PM 1 71.61 (30.44) 64.73 (33.15) 67.06 (15.95)

PM 2 82.73 (37.09) 67.46 (24.70) 76.15 (31.44)

PM 3 83.18 (28.84) 65.74 (20.69) 71.27 (25.05)

Fixation

PM 1 60.84 (27.11) 55.82 (21.15) 56.14 (14.58)

PM 2 76.78 (35.06) 58.82 (19.04) 64.03 (26.38)

PM 3 72.92 (27.54) 55.72 (17.93) 61.57 (19.38

Fix. Dur.

PM 1 238.32 (34.12) 224.85 (29.38) 242.89 (41.60)

PM 2 214.80 (29.30) 220.66 (33.29) 221.52 (27.84)

PM 3 258.54 (41.68) 208.32 (30.94) 201.73 (30.41)

Note: PM = Process Model

TABLE VI

DESCRIPTIVE RESULTS FOR LEVEL OF ACCEPTABILITY AND COGNITIVE

LOAD OF PART THREE FROM STUDY II

Variable Value Variable Value

PUU 1 2.11 (1.52) PEU 1 4.79 (.86)

PUU 2 4.00 (1.00) PEU 2 2.11 (1.20)

PUU 3 2.16 (1.26) PEU 3 4.63 (.83)

PUU 4 4.00 (1.16) PEU 4 2.05 (1.22)

PUU Sum 12.26 (1.97) PEU Sum 13.58 (1.95)

CLT ICL ECL GCL

2.97 (1.17) 3.86 (1.01) 2.58 (.99)

Note: PUU = Perceived Usefulness for Understandability;

PEU = Perceived Ease of Understandability; CLT = Cognitive

Load Theory; ICL = Intrinsic Cognitive Load; ECL =

Extraneous Cognitive Load; GCL = Germane Cognitive Load

performed for all three cognitive load dimensions (i.e., in-

trinsic, extraneous, germane). Moreover, Greenhouse-Geisser

correction was applied if necessary (i.e., significant Mauchly’s

sphericity test). Thereby, one within-subject factor ”model”

(four levels: cognitive load dimension for process model 1

- 4) and one between-subject factor ”modularization” (three

levels: horizontal, vertical, orthogonal) were examined. The

main effect for the cognitive load dimensions for process

model 1 - 4 (ME 1) and for the modularization comparison

(ME 2) were evaluated as well as the interaction effect process

model*modularization (IE). In addition, in the event of signifi-

cance for ME 1, repeated contrasts were employed. Moreover,

in the event of significance for ME 2, Post hoc analyses using

the Bonferroni post hoc criterion were employed. Finally, all

statistical tests were performed two-tailed and the significance

value was set to p <.05. Table VII presents the results with

respect to RQ 1.

Regarding intrinsic cognitive load, ME 1 was significant

and repeated contrasts showed that the second process model

(M = 2.48 (1.11)) had a higher intrinsic cognitive load (p <

.001) than the first process model (M = 1.93 (.85)) and the

third process model (M = 4.01 (1.30)) had a higher intrinsic

cognitive load (p <.001) than the second process model but

the fourth process model (M = 3.48 (1.27)) had a lower in-

trinsic cognitive load (p <.001) than the third process model.

TABLE VII

INFERENTIAL STATISTICS FOR RQ 1

Intrinsic Cognitive Load

ME 1 F(2.81; 258.25) = 131.09 p <.001

ME 2 F(2.00; 92.00) = 4.24 p = .017

IE F(2.81; 258.25) = .74 p = .608

Extraneous Cognitive Load

ME 1 F(2.90; 266.66) = 53.34 p <.001

ME 2 F(2.00; 92.00) = 1.52 p = .223

IE F(5.80; 266.66) = .78 p = .580

Germane Cognitive Load

ME 1 F(2.75; 252.78) = 4.47 p = .006

ME 2 F(2.00; 92.00) = 2.06 p = .133

IE F(5.50; 252.78) = .39 p = .875

Note: ME = Main Effect; IE = Interaction

Effect

Furthermore, ME 2 was significant and Post hoc analysis using

the Bonferroni post hoc criterion for significance indicated that

the means of vertical modularization (M = 3.39 (1.21)) differed

significantly from horizontal (M = 2.83 (1.03); p = .017) and

orthogonal (M = 2.79 (1.10); p = .038) modularization.

Regarding extraneous cognitive load, ME 1 was significant

and repeated contrasts showed that the second process model

(M = 2.58 (1.37)) had a higher extraneous cognitive load (p

<.001) than the first process model (M = 2.02 (.88)) and the

third process model (M = 3.79 (1.37)) had a higher extraneous

cognitive load (p <.001) than the second process model

but the fourth process model (M = 3.17 (1.32)) had a lower

extraneous cognitive load (p <.001) than the third process

model.

Regarding germane cognitive load, ME 1 was significant

and repeated contrasts showed that the second process model

(M = 4.42 (1.22)) did not have a higher germane cognitive

load (p = .676) than the first process model (M = 4.38 (1.13))

but the third process model (M = 4.68 (1.06)) had a higher

germane cognitive load (p = .014) than the second process

model but the fourth process model (M = 4.63 (1.08)) did not

have a higher germane cognitive load (p = .528) than the third

process model.

2) Results for RQ 2: To evaluate whether the differences

seen in the descriptive results with respect to RQ 2 reach

statistical significance, analyses of variances (ANOVAs) were

performed for all three cognitive load dimensions (i.e., in-

trinsic, extraneous, germane). Moreover, Greenhouse-Geisser

correction was applied if necessary (i.e., significant Mauchly’s

sphericity test). Thereby, one within-subject factor ”explana-

tion” (two levels: cognitive load dimension before as well as

after providing an explanation about modularization in process

models (i.e., pre- vs. post-explanation) and one between-

subject factor ”modularization” (three levels: horizontal, ver-

tical, orthogonal) were examined. The main effect for the

explanation (ME 1) and for the modularization (ME 2)

were evaluated as well as the interaction effect explana-

tion*modularization (IE). In addition, in the event of signifi-

cance for ME 2, Post hoc analyses using the Bonferroni post

hoc criterion were employed. Finally, all statistical tests were

performed two-tailed and the significance value was set to p

WINTER et al.: COGNITIVE COMPLEXITY IN MODULAR PROCESS MODELS 11

<.05. Table VIII presents the results with respect to RQ 2.

TABLE VIII

INFERENTIAL STATISTICS FOR RQ 2

Intrinsic Cognitive Load

ME 1 F(1.00; 92.00) = 33.96 p <.001

ME 2 F(2.00; 92.00) = 3.17 p = .047

IE F(2.00; 92.00) = .38 p = .688

Extraneous Cognitive Load

ME 1 F(1.00; 92.00) = 5.87 p = .017

ME 2 F(2.00; 92.00) = 2.44 p = .093

IE F(2.00; 92.00) = 1.54 p = .221

Germane Cognitive Load

ME 1 F(1.00; 92.00) = 5.14 p = .026

ME 2 F(2.00; 92.00) = 1.54 p = .221

IE F(2.00; 92.00) = .76 p = .471

Note: ME = Main Effect; IE = Interaction

Effect

Regarding intrinsic cognitive load, ME 1 was significant

and intrinsic cognitive load was higher (M = 3.72 (1.23)) after

explanation than before (M = 2.98 (.91)). ME 2 was significant

but Post hoc analysis using the Bonferroni post hoc criterion

for significance indicated no significant differences due the

lack of statistical power (i.e., weakly significant global effect

(p = .047)).

Regarding extraneous cognitive load, ME 1 was significant

and extraneous cognitive load was higher (M = 3.14 (1.18))

after explanation than before (M = 2.89 (.89)).

Regarding germane cognitive load, ME 1 was significant

and germane cognitive load was lower (M = 4.53 (.96)) after

explanation than before (M = 4.75 (1.01)).

3) Results for RQ 3: To evaluate whether the differences

seen in the descriptive results with respect to RQ 3 reach

statistical significance, analyses of variances (ANOVAs) were

performed for the four variables (i.e., perceived usefulness for

understandability (PUU), perceived ease of understandability

(PEU), subjective ease of use (SEU), subjective comprehen-

sibility (SC)). Moreover, Greenhouse-Geisser correction was

applied if necessary (i.e., significant Mauchly’s sphericity

test). The between-subject factor ”modularization” had three

levels (horizontal, vertical, orthogonal). The main effect (ME)

”modularization” for each variable was evaluated. In addition,

in the event of significance for ME, Post hoc analyses using

the Bonferroni post hoc criterion were employed. Finally, all

statistical tests were performed two-tailed and the significance

value was set to p <.05. Table IX presents the results with

respect to RQ 3.

TABLE IX

INFERENTIAL STATISTICS FOR RQ 3

ME Part Two of Study I

PUU F(2.00; 92.00) = 2.96 p = .057

PEU F(2.; 92.00) = .34 p = .711

Part Three of Study I

SEU F(2.00; 92.00) = 4.91 p = .009

SC F(2.00; 92.00) = 1.59 p = .209

Note: PUU = Perceived Usefulness for Under-

standability; PEU = Perceived Ease of Under-

standability; SEU = Subjective Ease of Use

SC = Subjective Comprehensibility

Regarding SEU, there was a significant difference between

the modularization types and Post hoc analysis using the

Bonferroni post hoc criterion for significance indicated that

the frequencies between horizontal (N = 26) and orthogonal

(N = 14) modularization differed significantly (p = .007; better

for horizontal modularization).

4) Results for RQ 4: To evaluate whether the differences

seen in the descriptive results with respect to RQ 4 reach

statistical significance, analyses of variances (ANOVAs) were

performed for all four performance measures (i.e., score,

duration, fixation, average fixation duration) for each process

model (i.e., three in total). Moreover, Greenhouse-Geisser

correction was applied if necessary (i.e., significant Mauchly’s

sphericity test). Thereby, one within-subject factor ”model”

(three levels: performance measure of process model 1 - 3)

and its ME was evaluated. In addition, in the event of signif-

icance for ME, repeated contrasts were employed. Finally, all

statistical tests were performed two-tailed and the significance

value was set to p <.05. Table X presents the results with

respect to RQ 4.

TABLE X

INFERENTIAL STATISTICS FOR RQ 4

ME Score

PM 1 F(1.66; 29.93) = .32 p = .688

PM 2 F(1.98; 35.61) = .69 p = .507

PM 3 F(1.62; 29.18) = .60 p = .522

Duration

PM 1 F(1.89; 34.00) = .41 p = .655

PM 2 F(1.99; 35.76) = 2.14 p = .133

PM 3 F(1.97; 35.40) = 4.06 p = .026

Fixation

PM 1 F(1.74; 31.39) = .45 p = .618

PM 2 F(1.81; 32.57) = 4.27 p = .026

PM 3 F(1.88; 33.90) = 4.69 p = .017

Fixation Duration

PM 1 F(1.74; 31.23) = 2.03 p = .153

PM 2 F(2.00; 35.94) = .41 p = .668

PM 3 F(1.68; 30.24) = 23.07 p <.001

Note: ME = Main Effect; PM = Process Model

Regarding duration in the third process model, ME was

significant and repeated contrasts showed that horizontal mod-

ularization (M = 83.18 (28.84)) had a longer duration (p =

.012) than vertical modularization (M = 65.74 (20.69)) but

orthogonal modularization (M = 71.27) did not have a longer

duration than vertical modularization.

Regarding fixation in the second process model, ME was

significant and repeated contrasts showed that horizontal mod-

ularization (M = 76.78 (35.06)) had more fixations (p =

.014) than vertical modularization (M = 58.82 (19.04)) but

orthogonal modularization (M = 64.03 (26.38)) did not have

more fixations than vertical modularization. Regarding fixation

in the third process model, ME was significant and repeated

contrasts showed that horizontal modularization (M = 72.92

(27.54)) had more fixations (p = .006) than vertical modu-

larization (M = 55.72 (17.93)) but orthogonal modularization

(M = 61.57 (19.38)) did not have more fixations than vertical

modularization.

Regarding average fixation duration in the third process

model, ME was significant and repeated contrasts showed that

WINTER et al.: COGNITIVE COMPLEXITY IN MODULAR PROCESS MODELS 12

horizontal modularization (M = 258.54 (41.68)) had a longer

average fixation duration (p <.001) than vertical modular-

ization (M = 208.32 (30.94)) but orthogonal modularization

(M = 201.73 (30.41)) did not have a longer average fixation

duration than vertical modularization.

D. Discussion

In the context of process model comprehension, the pre-

sented two studies investigated the effects of modularization

on process model comprehension from a cognitive point of

view. Generally, the application of modularization in process

models has the purpose to enable a better comprehension of

such models by reducing the overall process model complexity

(e.g., model size reduction [43]). In this context, an emphasis

was put in previous research on the comparison between

modularized and non-modularized process models. Thereby,

many research applied and investigated the effects of a vertical

modularization (i.e., collapsed subprocesses) [28], [23], [24]

on the comprehension of process models. However, other

modularization types were also the subject of research (i.e.,

horizontal [25], vertical [24], orthogonal [27]). Therefore, the

work at hand extends the vast body of research about the

effects of modularization during the comprehension of process

models. In the scope of four research questions (i.e., RQ 1

- RQ 4; see Section I) the effects of three modularization

types (i.e., horizontal, vertical, orthogonal) on process model

comprehension were investigated. Thereby, only modularized

process models (i.e., in absence of related non-modularized

process models) were taken intro consideration (see Sec-

tion IV-G).

First, in RQ 1, we evaluated the effects of different mod-

ularization types (i.e., horizontal, vertical, orthogonal) on the

cognitive load (i.e., intrinsic, extraneous, germane) during the

comprehension of modularized process models. Regarding the

descriptive statistics, intrinsic and extraneous cognitive load

were on a low to medium level (see Table II) for all modu-

larization types. This indicates an average interactivity of the

process model elements (i.e., intrinsic cognitive load) while the

representation of modularized process models was perceived

as appropriate (i.e., extraneous cognitive load). However, the

germane cognitive load was at an above-average level (see

Table II) pointing out that the comprehension of modularized

process models is a complex endeavor and that participants

were confronted with difficulties in handling the information

presented in the process models. As a consequence, emphasis

should be put on methods for efficient information handling

to foster the comprehension of modularized process models

by means of the definition of specific schemata for com-

prehension (e.g., process model comprehension guidelines).

Inferential statistics showed that a vertical modularization had

a significant higher intrinsic cognitive load (significant ME

2) than a horizontal or orthogonal modularization (see Ta-

ble VII). More specifically, the application and comprehension

of vertical modularization in a process model had a higher

inherent level of difficulty regarding the comprehension of

such models. This could be due to the high interactivity of the

model elements or the modularized parts in a process model.

While the process model elements or the modularized parts

in a horizontal and orthogonal modularization are defined in

the scope of model-dependent structures (e.g., pools, events),

however, in vertical modularization, respective model elements

or parts are in the scope of a subprocess. Thereby, the

subprocess could be considered as an additional process model

leading to the effect that two process models instead of one

needed to be comprehended properly. In the context of vertical

modularization, possible approaches in order to decrease the

intrinsic load may be, on one hand, to ensure an appropriate

level of prior knowledge in the comprehension of process

models. On the other hand, through the simplification of the

process model representation by splitting it into short step-by-

step representations. Moreover, as proposed in [24], another

approach would be the integration of the subprocesses (i.e.,

removal of the hierarchy) into the complete process model.

Thereby, modeling elements of the subprocesses are grouped

and highlighted in the model.

Second, in RQ 2, the effects of modularization in pro-

cess models on the cognitive load (i.e., intrinsic, extraneous,

germane) after providing an explanation about respective

modularization types (i.e., horizontal, vertical, orthogonal)

were juxtaposed (i.e., pre- vs. post-explanation). Generally, in

the context of ME 2, no significant differences were found

for extraneous and germane load, but for intrinsic load a

significant difference was found. A reason might be that a

learning effect may occurred in Part Two from Study I, since

the participants already were confronted with their assigned

modularization type in a process model in Part One from

Study I. However, regarding ME 1 (see Table VIII), the results

revealed a significant higher intrinsic as well as extraneous

cognitive load and a significant lower germane cognitive load

after providing an explanation about modularization in process

models to the participants (i.e., Part Two of Study I) compared

to the results related to the cognitive load without any explana-

tion about modularization in process models (i.e., Part One in

Study I). This insight is of particular interest as it seems to be

that the participants misjudged the level of complexity (i.e.,

process model element interactivity (i.e., intrinsic load) and

representation (i.e., extraneous load)) in the comprehension of

modularized process models. More specifically, after providing

an explanation about modularization in process models, par-

ticipants then realized the actual complexity in modularized

models. In other words, the element interactivity (e.g., links

in horizontal modularization; see Section II) and the form of

representation of modularized process models require higher

demands on the working memory. However, another indication

could be that modularized process models were not correctly

or only partially comprehended. With respect to germane

cognitive load, a decrease was observable. This might be

an indication that an explanation about modularized process

models fosters the mental process of comprehending presented

information in modularized process models. Further, the same

could be observed in intrinsic and extraneous cognitive load

in the descriptive results obtained of Part Three from Study

II (see Table VI). With respect to germane cognitive load of

Part Three from Study II, however, the results showed a lower

germane cognitive load compared to the ones of Part One

WINTER et al.: COGNITIVE COMPLEXITY IN MODULAR PROCESS MODELS 13

from Study I (see Table II). An explanation for this might

be that the participants in Study II were confronted with a

comprehension task. In more detail, they needed to answer a

set of comprehension questions for each modularized process

model. Consequently, this may have led to the participants

studying at and comprehending the modularized process mod-

els more effectively, as the objectives between Studies I and

II were different (i.e., pure comprehension vs. comprehension

performance). To sum up, these observations confirm that

an explanation of modularization in process models fosters

the handling of information presented in such models (i.e.,

germane load). In addition, the results indicated, as known

from other research (e.g., [28], [23], [27]) that modularized

process models can be comprehended intuitively. However,

for a correct and complete comprehension, an explanation

about the application of modularization is mandatory. The

reason is that that the results regarding intrinsic and extraneous

load revealed that modularized process models pose specific

challenges regarding their proper comprehension (e.g., higher

demands on the working memory). In general, the results

indicate that when applying modularization in process models

three aspects may be of importance in order to ensure a proper

comprehension of such models: 1

The representation of

modularized process models should be amended accordingly

to make the information presented in modularized models

more receptive (e.g., using colors). 2

The provision of proper

explanations about modularization in process models to enable

and facilitate the conceptualization of memory schemata for

the purpose of a better comprehension of modularized process

models. 3

The definition of an objective why modularized

process models needed to be comprehended.

Third, in RQ 3, the level of acceptability of modularization

in process models was investigated. Therefore, we addressed

the perceived usefulness for understandability (PUU) as well

as the perceived ease of understandability (PEU) in Part

Two of Study I and the subjective ease of use (SEU) as

well as the subjective comprehensibility (SC) in Part Three

of Study I. The results for PUU and PEU were similar

in all three modularization types (i.e., horizontal, vertical,

orthogonal) and were on average (see Table III). That means

that participants were undecided about how modularization

fosters the comprehension of such process models. Moreover,

it appears that the attitude toward using as well as the intention

to use modularization in process models is still unclear and

possibly even questionable. Similar effect was observed by the

authors in [24] in the context of vertical modularization. To

counteract this, it would be beneficial to elaborate the purpose

as well as the benefits of modularization in process models

in more detail. In contrast, considering the results obtained

of Part Three from Study II (see Table VI), the descriptive

results regarding PUU and PEU were lower compared to

the results from Study I (see Table III). Taking into account

that the participants from Study I were confronted with more

information about modularized process models that may have

had a positive effect on PUU and PEU, the results from

Study II revealed as well that the use and the benefits of

the application of modularization in process models is not

clear. Furthermore, regarding SEU, a significant difference

was found (see Table IX) and participants indicated that a

horizontal modularization in process models reflected a higher

subjective ease of use compared to orthogonal modularization.

This can be explained by the fact that the horizontal modular-

ization decomposes a process model in smaller modules (i.e.,

process models) in order to foster the reusability as well as

the comprehensibility in general. At the same time, the decom-

position into smaller modules decreases the inherent process

model complexity that may result in a higher ease of use, since

the comprehension of smaller modules is associated with less

cognitive load. However, note that this aspect, in turn, may

cause opposing effects in the modularization of process models

(e.g., split-attention effect [63]). In general, such impairing

effects should be considered in modularized process models

(see Section IV-G) [43]. Although the inferential statistics re-

vealed no significant differences, however, from the descriptive

statistics the results for SC confirm the observations made

regarding SEU (see Table IV). More specifically, participants

indicated, based on subjective preferences, that a horizontal

modularization in process models is the best comprehensible

juxtaposed to vertical or orthogonal modularization.

Fourth, in RQ 4, the four performance measures (i.e., score,

duration, fixation, average fixation duration) were observed

in an eye tracking study in which all three modularization

types (i.e., horizontal, vertical, orthogonal) were presented to

the participants in a comprehension task. While the results

for vertical and orthogonal modularization were similar, how-

ever, certain horizontal modularized process models showed

significant differences (see Table X). In particularly, partic-

ipants, for certain horizontal modularized process models,

needed more time to comprehend, showed a higher number

of fixations as well as a longer average fixation duration.

These results indicated that participants were confronted with

a higher cognitive load during the comprehension of process

models representing a horizontal modularization. However,

compared to the results of RQ1-3obtained in Study I,

in which horizontal modularization is associated with a low

cognitive load, the results from the eye tracking study were

contradictory related to the cognitive load. In more detail,

participants from Study I indicated that process models with

horizontal modularization were better comprehensible than

process models in vertical or orthogonal modularization (see

Table IV). However, comprehension performance measures

(e.g., comprehension duration) in Study II revealed that par-

ticipants needed more effort to get to grips with horizontal

modularized process models. Nevertheless, the participants

indicated that horizontal modularization in process models

appears to be the best comprehensible modularization type (see

Section IV-A). Therefore, in the light of the results obtained in

Studies I and II, the application of horizontal modularization

in process models may lead to a more fine-grained comprehen-

sion of such models, since it must be ensured that the smaller

process model modules but also the entire process model is

comprehended properly . Further, regarding the achieved score

in the comprehension questions, the obtained results for all

modularization types were 3.50 (i.e., 4 is max) on average

(see Table V). Same es in RQ 1, modularized process models

could be comprehended intuitively [27], [50].

WINTER et al.: COGNITIVE COMPLEXITY IN MODULAR PROCESS MODELS 14

Finally, Tab. XI summarizes our general findings obtained

for each research question (i.e., RQ 1 - 4) in Studies I and II.

E. Limiting Factors

Several limiting factors were encountered during the execu-

tion of Studies I and II. First, the modularized process models

might not be representative. Usually, process models document

procedures of the real world, which are far more complex

(e.g., high information density). However, the used process

models in Studies I and II were kept simple intentionally

(i.e., average number of modeling elements <50). Thereby,

research showed that process model comprehension becomes

error-prone from 50 modeling elements [64]. Consequently,

complex process models make different demands regarding,

for example, the cognitive load and the level of acceptability

juxtaposed to less complex process models. Moreover, second,

the scenarios documented in respective process models repre-

sent another limitation. Most of the scenarios in the process

models used in Studies I and II are common. Hence, an

unfamiliar scenario in a process model might has a negative

effect on process model comprehension in comparison with a

familiar scenario. Third, the inherent difficulty (e.g., process

model complexity, question difficulty) of the study material

may not be appropriate. In detail, the true-or-false compre-

hension questions might be too easy, since the participants

had almost reached the maximum score in answering these

questions. Fourth, another limitation were the participants of

both studies. Specifically, only students with varying expertise

in process modeling (see Table I) were evaluated and, hence,

generalizability is limited. Fifth, the sample sizes limit the

statistical power and there might be significant differences

between Study I and Study II, which we could not detect,

but which might become apparent in larger sample sizes. In

addition, the number of participants in Studies I and II was

not the same and, hence, there was an imbalance. Sixth, as

procedures of Studies I and II were not the same there might

be a potential risk of validity in the comparison of the assessed

performance measures. Seventh, no block randomization was

used in Study I, but only a randomization function provided

by Google Forms. Therefore, no balance in the modularization

groups (i.e., N = 35 for horizontal, N = 32 for vertical, and N =

28 for orthogonal modularization) was achieved and, in addi-

tion, it could not be ensured that all three groups share similar

characteristics (e.g., experience). Eight, in Study I, since

participants were confronted with several modularized process

models, a learning effect could have occurred that affected

results regarding the cognitive load as well as the level of

acceptability. Ninth, although a reduction in the cognitive load

during process model comprehension can be achieved with the

application of modularization (e.g., reduction of process model

complexity), however, other cognitive effects may emerge,

which in turn have a negative effect on the comprehension of

such models. For example, the depiction of a process model

into smaller process models leads to the circumstance that the

attention of an individual must be split between the smaller

modules in order to ensure a proper model comprehension

(i.e., split-attention effect [63]). As a result, the split attention

may cause a higher cognitive load [43]. Tenth, similar as in

ninth, since the participants in Study I (see Section III-D)

could complete the online survey using any mobile device

(e.g., laptop, smartphone), hence this fragmentation constituted

another risk especially in Study I. In more detail, due to the

different screen sizes (laptop vs. smartphone), the process

models could be displayed completely without the need for

further actions (e.g., scrolling), which, in turn, could have

led to an impairment during process model comprehension.

Finally, while results look promising, additional studies are

needed in order to confirm the generalization of the results.

F. Implications

The provided insights have implications for research on

process model comprehension as well as for practice by

investigating the effects of modularization in process models.

For research: With the results from this work as theoretical

foundation, research may focus on the replication of the pre-

sented studies with the use of more complex process models.

More complex process models should have a stronger effect on

the cognitive load (i.e., intrinsic, extrinsic, germane), as more

information needs to be processed in the working memory.

Consequently, with the insights obtained from other studies

(e.g., [24]), concrete efforts can be made in order to reduce the

cognitive load in general, especially when dealing with com-

plex modularized process models. Moreover, changes in the

overall representation based on model design guidelines (e.g.,

[23]) of respective modularization types could be implemented

and their repercussions on process model comprehension or on

the cognitive load could be investigated in additional studies.

Moreover, an emphasis can be put on the intrinsic as well as

extraneous load to achieve a significant load reduction in these

two dimensions. Possible approaches would be to address the

modeling element interactivity (e.g., reduce number of con-

necting elements) or with changes in the overall representation

(e.g., application of colors). Furthermore, the question arises

whether various domain experts (e.g., modeling experts vs.

doctors) perceive modularization in process models differently

in comparison with each other. In addition, the combination of

different modularization types (e.g., horizontal + orthogonal)

might open a novel perception of modularization, having a

divergent effect on process model comprehension, compared

to the previous approaches. The analysis of recorded eye

movement could reveal novel insights. For example, are there

different and common strategies (e.g., back-and-forth saccade

jumps) in the comprehension of modularized process models.

Finally, it would be interesting to investigate whether the

same effects of the three modularization types (i.e., horizontal,

vertical, orthogonal) as presented in this work can be observed

in other modeling notations.

For practice: Prior work already highlighted the benefits of

modularized process models in a practical context [21]. With

the work at hand, we extend existing research in this context

and highlight the challenges of the application of modular-

ization in process models. However, in practice, in order to

make use of the advantages of modularization, efforts should

be made to increase the attitude towards using as well as the

WINTER et al.: COGNITIVE COMPLEXITY IN MODULAR PROCESS MODELS 15

TABLE XI

SUMMARY OF FINDINGS IN STUDIES IAND II

Research Question Denouement

RQ 1: Cognitive Load

Intrinsic (i.e., modeling element interactivity; highest in vertical modularization) and extraneous (i.e., presentation)

load were on a low to medium level, whereas germane load (i.e., mental effort) was above-average indicating that

participants were confronted with challenges in the comprehension of modularized process models.

RQ 2: Cognitive Load

(Post-Explanation)

Intrinsic and extraneous load were significant higher, while germane load was significant lower after presenting an

explanation about modularization. Accordingly, participants misjudged the complexity of modularized process

models, but an explanation about modularization may foster the construction of mental models for the proper

comprehension of presented information in modularized process models

RQ 3: Level of Acceptability Participants were undecided about the benefits of modularization in process models. Amongst all three types,

horizontal modularization appears to be the best comprehensible.

RQ 4: Performance Obtained results were similar, but implied that the application of horizontal modularization in process models led to

a more fine-grained comprehension in general. Further, modularized process models can be comrehended intuitively.

behavioral intention to use modularization in process models.

Moreover, an awareness must be raised to model processes

from the very beginning in a modularized way. Therefore,

modularization in process models must be explained precisely

to practitioners and, accordingly, attention must be paid that

modularization is applied correctly in order to avoid possible

later consequences. In this way, performance and efficiency

in working with process models can be increased due to an

overall lower cognitive load. The two studies revealed only few

significant differences regarding the three modularization types

and, hence, the choice about which type of modularization

to use can be made based on subjective preferences, since

there were only little differences in terms of comprehension

performance. However, the results indicated that horizontal

modularization is a more preferable choice.

G. Future Work

While the results obtained from both studies provide new

exploratory insights about the effects of different types of

modularization (i.e., horizontal, vertical, orthogonal) in pro-

cess models, their generalization needs to be confirmed by

additional studies, e.g., in order to obtain more accurate results

allowing such a generalization, additional studies are needed

either through replication or similar studies in other environ-

ments or with different samples. Regarding the latter, domain

experts from different fields (e.g., physicians, therapists) would

represent an appropriate sample. In this context, it would

be interesting to investigate the influence of process model

expertise in working with modularized models. Moreover,

since process models from the real world usually are more

complex than the process models used in Studies I and II,

further studies may investigate the effects of modularization

in complex real-world process models, which are taken from

organizations. This could include, for example, a comparison

between modularized and non-modularized process models

in order to emphasize the effects of modularized process

models. Other types of modularization (e.g., composition), the

combination of different modularization types (e.g., horizontal

+ orthogonal), or a change in their representation (e.g., design)

could be the subject of further research in order to improve our

understanding of modularization in process models. Further,

modularization in process models and their effects should

also be considered based on other function (e.g., role-based).

Moreover, a special emphasis should be put on the cognitive

load and its related dimensions (i.e., intrinsic, extraneous,

germane) in order to achieve a reduction in the related

dimensions that should lead to a better comprehension of

modularized process models. In this context, the consideration

of other cognitive effects (e.g., split-attention effect, worked-

example effect [65]) will allow for the identification of new

insights, enabling a better assistance (e.g., comprehension

guidelines, tool support) in the comprehension of modularized

process models. In addition, another emphasis will be put

on the analysis of eye tracking measures (e.g., fixation) in

order to examine comprehension strategies (e.g., back-and-

forth saccade jumps). Thereby, fixation time variabilities as

well as scan path patterns may be another indicator for the

determination of the cognitive load in this context. Moreover,

in order to double-check the results, the investigation of the

effects of modularization in other process modeling languages

(e.g., Event-driven Process Chains, UML Activity Diagram)

will be subject of future work. Finally, a similar study is

in preparation regarding modularization using a data-centric

modeling approach [66]. Finally, for the future, we plan the

replication of the presented studies in a real-world scenario.

Therefore, practitioners from industry will be invited and

process models derived from practice are planned to be used.

V. CONCLUSION

This paper presented the effects of three modularization

types (i.e., horizontal, vertical, orthogonal) on the comprehen-

sion of BPMN 2.0 process models. Particularly, in the scope of

four research questions (i.e., RQ1-RQ4), the cognitive load

(i.e., intrinsic, extraneous, germane), the level of acceptability

(i.e., perceived usefulness for understandability, perceived ease

of understandability, subjective ease of use, subjective compre-

hensibility) and the performance (i.e., score, duration, number

of fixations, average fixation duration) in process model com-

prehension were investigated in two exploratory studies (i.e.,

Study I was conducted as a survey research study with N = 95

participants and Study II as an eye tracking study with N = 19

participants) using modularized process models. In general, all

three modularization types had similar effects on the cognitive

load and its related dimensions, the level of acceptability, and

on the performance in process model comprehension. How-

ever, subjectively, the results obtained from the participants

suggested to prefer a horizontal modularization in process

models. Taking a closer look at the results, they revealed

WINTER et al.: COGNITIVE COMPLEXITY IN MODULAR PROCESS MODELS 16

that participants were confronted with different challenges

(e.g., process model elements interactivity, creation of mental

schemata) while comprehending modularized process models.

Furthermore, as an interesting finding found in both studies is

that participants misjudged the inherent level of complexity of

modularization in process models. In more detail, modulariza-

tion was perceived less complex initially (i.e., low cognitive

load). After an explanation about the correct application of

modularization in process models, the participants realized the

true complexity of modularized process models resulting in a

significant higher cognitive load. Moreover, the results showed

that participants were hesitant about the application and related

benefits of modularized process models. Therefore, with this

work, we underline the importance of specific alterations (e.g.,

provision of a thorough explanation) about modularization in

process models and to further study the role of the cognitive

load as well as level of acceptability in this context.

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Michael Winter studied Computer Science at Ulm

University and has been working there as a research

associate since 2015. His main research focus is

on the topic of business process management. He

focuses on the creation and particularly on the com-

prehension of visual process models. In this context,

he applies measurement methods as well as theories

from cognitive neuroscience (e.g., eye tracking, elec-

trodermal activity) and psychology (e.g., Cognitive

Load Theory) to unravel new insights. Therefore, he

developed a conceptual framework to foster and to

assist novices as well as experts in the comprehension of process models. In

addition, he utilizes approaches from different domains (e.g., serious game) in

various studies as well in order to ensure a proper and correct comprehension

of process models.

Prof. Dr. R¨

udiger Pryss studied at the Universities

of Passau, Karlsruhe and Ulm. He holds a Diploma

in Computer Science. After graduating, he worked

as a consultant and developer in a software company.

Since 2008, he has been a research associate at Ulm

University. In 2015, he received a PhD in Computer

Science. In his doctoral thesis, R¨

udiger focused on

fundamental issues related to mobile process and

task support. R¨

udiger was local organization chair of

the BPM’09 and EDOC’14 conferences. Moreover,

he is experienced with teaching courses on database

management, programming, service-oriented computing, business process

management, document management, and mobile application engineering. In

2019, R¨

udiger Pryss was appointed as full professor in medical informatics

at the University of W¨

urzburg.

Prof. Dr. Thomas Probst studied Psychology at Re-

gensburg University. He holds a Diploma in Psychol-

ogy. After graduating, he started his psychotherapy

training and received his certification as cognitive-

behavior therapist in 2013. Between 2013 and 2015,

he worked at Regensburg University (research assis-

tant and deputy head of the psychotherapy outpatient

center). In 2015, he received a PhD in Psychol-

ogy at the Humboldt-University of Berlin. In his

doctoral thesis, Thomas focused on psychotherapy

monitoring, patient-therapist feedback, and decision

support tools. From 2015 to 2016, he was Interim Professor for Clinical

Psychology and Psychotherapy as well as for Clinical Psychodiagnostics at

the University Witten/Herdecke. In 2017, he was Interim Professor at the

Georg-August-University G¨

ottingen and research associate at Ulm University.

At 2017, he was appointed as Professor for Psychotherapy Sciences at the

Danube University Krems, Austria. Moreover, he is experienced with teaching

courses on psychotherapy and psychodiagnostics, psychosomatics, digital

health, quantitative research designs.

WINTER et al.: COGNITIVE COMPLEXITY IN MODULAR PROCESS MODELS 18

Julia Baß studied Media Informatics at Ulm Uni-

versity. In addition to the research on process model

comprehension, a focus of interest of Julia is the

field of psychology. Here, she evaluated the cogni-

tive load, usability, and user experience of learning

management systems. Furthermore, in the context

of eye tracking, she investigated approaches re-

garding pupil-based biofeedback and examined eye-

movements during the comprehension of process

models.

Prof. Dr. Manfred Reichert holds a PhD in Com-

puter Science and a Diploma in Mathematics. Since

2008 he has been appointed as full professor at

the University of Ulm, where he is director of

the Institute of Databases and Information Systems.

Before, he was working as associate professor at the

University of Twente in the Netherlands. There, he

was also a member of the management board of the

Centre for Telematics and Information Technology,

which is one of the largest academic ICT research

institutes in Europe. Manfred’s research interests

include business process management (e.g., adaptive and flexible processes,

process lifecycle management, data-driven and object-centric processes) and

service-oriented computing (e.g., service interoperability, mobile services,

service evolution). He has been PC Co-chair of the BPM’08, CoopIS’11,

EMISA’13 and EDOC’13 conferences, and General Chair of the BPM’09

and EDOC’14 conferences.