Visual storytelling for improving the comprehension and utility in disseminating information systems research: Evidence from a quasi‐experiment

Abstract

Since the start of human civilisation, storytelling has served as an effective medium for disseminating important knowledge within families, communities, and organisations. We make a case for the use of visual storytelling, namely, video stories, to supplement traditional scholarly articles in the Information Systems (IS) discipline, thereby exploring its potential to improve actual and perceived comprehension, perceived utility, satisfaction as well as intentions to cite, share, and accept research. Drawing on cognitive learning theory, the cognitive theory of multimedia learning and the literature on deep processing, we develop our research model, which is based on the model by Jiang and Benbasat (2007). We test our model in experimental settings with 269 research‐oriented students and academics who were randomised into four conditions: (1) reading a text‐based article, (2) reading the script for a video about the article, (3) viewing the video story of the article, and (4) viewing the video story followed by reading the article. Results showed that the article's script was significantly perceived to be the least useful in disseminating research content. The video story and text‐based article were perceived to be equally useful, and supplementing the text‐based article with a video story was perceived to be the most useful. Moreover, the video story and text‐based article supplemented by a video story were of roughly equal effectiveness; yet, the video script was the most effective, and the text‐based article was least effective relative to other formats in disseminating scholarly knowledge. Last, we discuss how to further improve the design of video stories by referring to the critical narrative theory, which has the potential to significantly promote the dissemination of IS scholarly knowledge.

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The current reference for this work is as follows:
Kristijan Mirkovski, David Michael Hull, James Eric Gaskin, and Paul Benjamin
Lowry (2018). “Visual storytelling for improving the dissemination and
consumption of information systems research: Evidence from a quasi-experiment,”
Information Systems Journal (ISJ) (accepted 10-Feb-2019).
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“Visual Storytelling for Improving the Comprehension and Utility in Disseminating
Information Systems Research: Evidence from a Quasi-Experiment”
For review at the Special Issue on “Storytelling and Information Systems” at the Information
Systems Journal.
Kristijan Mirkovski
Deakin University
Faculty of Business and Law
Melbourne, Australia
Email: kmirkovsk2@gmail.com
James Gaskin
Marriott School of Management
Brigham Young University
Provo, Utah, USA
Email: james.eric.gaskin@gmail.com
David Hull
University of Texas at Tyler
Computer Science Department
Tyler, TX, USA
Email: dhull8@outlook.com
*Paul Benjamin Lowry
Virginia Tech
Pamplin College of Business
Business Information Technology
Pamplin Hall, Suite 1007
880 West Campus Drive
Blacksburg, VA 24061
Phone: +1-540-750-0923
Email: Paul.Lowry.PhD@gmail.com
* Corresponding author

Visual Storytelling for Improving the Comprehension and Utility in Disseminating
Information Systems Research: Evidence from a Quasi-Experiment
ABSTRACT
Since the start of human civilisation, storytelling has served as an effective medium for
disseminating important knowledge within families, communities and organisations. We make a
case for the use of visual storytelling, namely video stories, to supplement traditional scholarly
articles in the Information Systems (IS) discipline, thereby exploring its potential to improve
actual and perceived comprehension, perceived utility, satisfaction as well as intentions to cite,
share and accept research. Drawing on cognitive learning theory, the cognitive theory of
multimedia learning and the literature on deep processing, we develop our research model,
which is based on the model by Jiang & Benbasat (2007). We test our model in experimental
settings with 269 research-oriented students and academics who were randomised into four
conditions: (1) reading a text-based article, (2) reading the script for a video about the article, (3)
viewing the video story of the article and (4) viewing the video story followed by reading the
article. Results showed that the article’s script was significantly perceived to be the least useful
in disseminating research content. The video story and text-based article were perceived to be
equally useful and supplementing the text-based article with a video story was perceived to be
the most useful. Moreover, the video story and text-based article supplemented by a video story
were of roughly equal effectiveness; yet, the video script was the most effective and the text-
based article was least effective relative to other formats in disseminating scholarly knowledge.
Last, we discuss how to further improve the design of video stories by referring to the critical
narrative theory, which has the potential to significantly promote the dissemination of IS
scholarly knowledge.
Keywords: visual storytelling, video stories, scholarly knowledge dissemination, cognitive
learning theory, cognitive theory of multimedia learning, deep processing

2
INTRODUCTION
Humans have developed social and neurophysiological capacities with which to process and
disseminate information to communicate with each another using stories. Storytelling forms the
foundation of how we learn about the world around us (Schutz, 1960). Throughout history and in
many cultures and civilisations, storytelling has served as an effective medium for disseminating
important knowledge (Lindesmith & McWeeny, 1994). For example, the Iliad and the Odyssey
are literary monuments depicting the central role of storytelling as a medium for retaining and
disseminating knowledge in Ancient Greece. Storytelling also remains an inherent backbone of
conversation and inquiry within families (Zwack et al., 2016), communities (Ramasubramanian,
2016) and organisations (Dailey & Browning, 2014) in modern society.
Story is an account (written or oral, fiction or nonfiction) focusing on a self-contained
event or series of connected events with the purpose of eliciting a ‘single effect’ (Stein, 1982).
Storytelling is an approach to telling a story that involves the communication of ideas, beliefs,
personal histories and life-lessons (LeBlanc & Hogg, 2006) through accounts of actions
formulated from real or imagined events, characters, a structure of a beginning, middle, and end
and a plot tying all these elements together (Hayne, 2009). Visual storytelling is the telling of a
story enhanced via the use of visual media (e.g. photography, graphics, illustrations and video)
and aural media (e.g. music, voice and sound effects) (Caputo, 2003). Visual storytelling can
invoke an innate, physiological and reflexive response in the form of receiver engagement and
empathy (Ryokai et al., 2002).
Visual storytelling can: (1) facilitate organisational learning (Militello & Guajardo, 2013),
(2) increase information generation and collaborative engagement and track the development
trajectory in cognitively-demanding research disciplines (Rambe & Mlambo, 2014) and (3)
improve the visual memory capacity and writing skills of students (Sarıca & Usluel, 2016).
Koehler et al. (2005) investigate how text and video versions of four different stories influence
individuals’ interest and affect, emotional engagement, recall of information, ability to summarise
main points, judgments of story quality and opinions about content matter. Their results show
that interactions between the aural and visual components of stories told through video lead to
higher interest, affect and emotional engagement and improved ability to summarise main
points, judgments of story quality and opinions about content matter.
A large portion of the human brain is devoted to image processing, visual interpretation
and synthesis (Paivio, 2010). The cognitive science literature confirms that, regardless of
various context-based factors (e.g. idiosyncratic learning experiences), humans still learn, retain
and comprehend best when information is disseminated in a multimedia format (i.e. aural and

3
visual) as opposed to a strictly prose format (i.e. text) (Mayer, 2005a, 1997, 2002). Furthermore,
deep processing (Carroll et al., 1985) is promoted when information is presented in modules or
chunks (Schluep et al., 2003) and in a multimedia format (Mayer, 2003, 2005b). Thus, drawing
on cognitive load theory (CLT), the cognitive theory of multimedia learning (CTML) and the
literature on deep processing, we make a case for the use of visual storytelling to supplement
the traditional text-based academic article in the information systems (IS) discipline. We argue
that video stories that are designed to activate multiple sensory cues and channels would
promote deeper cognitive processing than would traditional text-based manuscripts and would,
in turn, improve research understanding, distribution, acceptance, citation and influence.
Beyond this rationale for enhanced cognition, we offer a pragmatic rationale for the use
of video stories. We note a major shift in academia whereby it is no longer sufficient to advance
one’s career solely on the basis of having published in top academic journals. Nowadays,
researchers are expected to advocate for and translate their work to wider audiences.
Researchers also operate in a world of high specialisation, yet great value is increasingly placed
on interdisciplinary work. In these settings, how can IS researchers keep up with their own area
of specialisation, let alone follow research outside their immediate discipline, which is necessary
to foster interdisciplinary work? How can they better advocate for their work and get it noticed
outside the small circle of similarly-minded individuals? We claim that the use of video stories
provides an answer to these questions.
Thus, there is a pressing need for an alternative way of disseminating research in the IS
discipline, which is optimally aligned with the characteristics of natural human processing and
learning capacities. We provide preliminary evidence that visual storytelling has the potential to
increase impact by improving the consumption, distribution, acceptance and citation of IS
research. In our model, we hypothesised that the content format (i.e. the four treatments listed)
through which scholarly knowledge is disseminated affects the learner’s comprehension and the
perceived usefulness of the research, which in turn leads to satisfaction as well as the intention
to cite, share and accept the research.
The idea of disseminating scholarly knowledge using alternative presentation formats is
not new. Other disciplines, such as management, psychology and computer science, have
embraced alternative approaches for disseminating and consuming scholarly knowledge. For
example, the ACM CHI Conference on Human Factors in Computing Systems is an
international conference on human-computer interaction, which provides video briefs for the
extended abstracts of its publications. We also raise this call with caution because, for more
than 350 years, scholarly knowledge has been communicated more or less effectively through

4
text-based articles following a fairly rigid, vetted template (e.g., Oldenburg, 1665). By no means
is our goal to propose that video stories supplant articles as the sole means for disseminating
scholarly knowledge.
Our paper is organised as follows: we first review relevant literature on storytelling,
human cognitive processing and multimedia instructional design to provide theoretical support
for our research model. We then provide an example of a video story to link our arguments in
support of using visual storytelling for improving the dissemination and consumption of IS
research. Further, we develop a research model, which we test in experimental settings. Finally,
we conclude with a discussion of our findings and the limitations of the current research.
THEORETICAL FOUNDATIONS
Stories and Storytelling
A well-told story features primary characters (i.e. a protagonist and antagonist) and secondary
characters, and aims to achieve unity of effect and create a mood via the mechanism of a
coherent plot (Ackerman, 2003). Traditional oral stories have the following sequence: (1) a
beginning that introduces the primary characters, which can take the form of a human or
another animate actor, an object, a practice, or an idea, (2) a middle that renders an event such
as a conflict between the protagonist and antagonist and (3) an end that reaches a culmination
as a consequence of the latter event (Stein, 1982). Storytelling is an approach to telling a story,
focusing on the elicitation and construction of stories rather than a story as an object (Moezzi et
al., 2017).
Storytelling is emotionally richer and more effective in terms of fidelity, memorability and
information transfer relative to simple gestures and words (Lindesmith & McWeeny, 1994). Well-
told stories are memorable, easy to understand and represent a base for establishing credibility
(Lämsä & Sintonen, 2006). They also create a sense of empathy from a cognitive and emotional
position, which helps individuals understand the experiences of others (Lämsä & Sintonen,
2006). That is, a story allows both the sender and receiver to make sense of otherwise discrete
events by reconstructing a shared version of experience, embedded in an information-rich
context (Rappaport, 2008), which explains ‘the brute facto of the pleasure and universality of
storytelling’ (Dutton, 2009, p. 105).
Thomas (2006) identifies five adaptive functions of storytelling: (1) cognitive devices for
organising memory and knowledge (e.g. sharing of tacit knowledge via stories), (2) tools for
sense-making (e.g. individuals make unpleasant and unrepeatable events in their lives more
meaningful by putting them in an interpretive sequence in the form of an autobiographical story),
(3) tools for communication (e.g. human cooperative communication crucially relies on shared

5
intentionality, which comprises important prosocial motivations and social cognitive skills for
joint attention), (4) bonding mechanisms of community practice (e.g. describing how to fix a
broken car amongst mechanics not only meets their need for technical knowledge, but also their
need for group identity and social status) and (5) vehicles for cultural transmission (e.g.
sacrificial harvest). Hence, the human mind processes storytelling in an empathetic way.
Visual storytelling can evoke emotions, drive deeper engagement and cause more
profound changes in behaviour. It can also help promote self-understanding, expression and
communication of difficult-to-conceptualise issues (Drew et al., 2010). Within experimental
settings, Baggett (1979) revises a text version of a dialogue-less movie (The Red Ballooni) to
the point where participants are unable to match episodes in the film with passages from the
story and vice versa. These structurally-equivalent forms of the story (i.e. video and text
formats) are then deployed in another study of memory, in which Baggett (1979) uses a cued
recall approach and finds that both formats of the story have similar patterns of recall when
respondents are asked to remember particulars of the story immediately following the
experiment. Nevertheless, an analysis of delayed recall one week following the experiment
reveals much better recall performance from respondents who viewed the video format of the
story.
Human Cognitive Processing and Cognitive Load Theory
The architecture of human memory features working memory and long-term memory. Working
memory is a memory system for storing small amounts of information for a very short duration
(Cowan, 2010). It can process only a limited number of novel interacting elements
simultaneously (Miller, 1956). Thus, processing and learning are impeded when working
memory capacity is overloaded in a task, as explained by CLT (Sweller, 1994). By contrast,
long-term memory vastly expands human processing capability using cognitive constructs, also
known as schemas, which incorporate multiple elements of information into a single element
that can be processed with economy within working memory. Long-term memory involves large
amounts of information that are stored semi-permanently (Ericsson & Kintsch, 1995).
CLT distinguishes three forms of cognitive load—intrinsic, extraneous and germane—
that constitute the total cognitive load (see Table 1). These three are cumulative, such that the
total cognitive load cannot exceed the working memory resources available for information
processing and understanding learning material (Van Merriënboer et al., 2003). Hence, to the
extent that extraneous and intrinsic cognitive loads are, respectively, reduced and managed,
working memory resources are available for constructing, elaborating and automating schemas
(Sweller, 1994).

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Table 1. Three Constructs of Total Cognitive Load*
Construct Description
Examples of Cognitive Load (Condition
and Cause)
Intrinsic
cognitive load
Deals with the inherent complexity of
the information being processed;
depends on the degree of interaction
between elements (low-element
interactivity vs. high-element
interactivity) in the disseminated
information; it can be reduced by
simplifying the information
complexity.
Learning to drive a car involves understanding
(1) the necessary pressure to apply to the
brakes to stop; (2) the amount of force
needed to turn the steering wheel in a
particular direction and (3) the required speed
adjustments when driving a car on a rainy day
or in a traffic jam (high-element interactivity
=> high intrinsic load)
Extraneous
cognitive load
Concerned with the instructional
design of the disseminated
information; it can be imposed by the
complexity of the information being
processed, which can be avoided
using effective instructional design; it
can be reduced by use of scaffolding
strategies by which complexity is
gradually introduced into the
instruction
Verbally describing the shape of a square
(rather than simply drawing it) is more difficult
to understand, as the verbal medium is
inefficient for disseminating such information
(high intrinsic load => high extraneous load)
Germane
cognitive load
Concerned with the construction and
automation of schemas; simply
referred to as the working memory
resources dedicated to dealing with
intrinsic cognitive load for processing
information and understanding the
learning material.
Understanding that driving a van is similar to
the known skill of driving a car, but varies with
respect to manoeuvrability (low intrinsic load
=> high germane load)
*Adapted from Sweller (1994).
Deep Processing and Cognitive Theory of Multimedia Learning
Learning is a generative process requiring effort, rather than passively acquiring external
knowledge, in which learners actively construct their own meanings (Osborne & Wittrock, 1983).
Säljö & Marton (1976) propose that leaners construct meaning according to one of two main
approaches to learning: deep processing and surface processing. Learners adopt deep
processing when the main objective is to seek understanding from the author’s meaning and
relate it to one’s prior knowledge and personal experiences. Conversely, learners adopt surface
processing when the main objective is to reproduce information without detailed or further
analysis.
Carroll et al. (1985) argue that deep processing occurs in one of three forms: learning by
doing, learning by thinking and learning by knowing. Learners have the tendency to experiment
with different modes of information processing and knowledge representation rather than
working on the basis of overly structured systematic (step-by-step) instructions. They prefer to
make sense of their learning experience by developing and testing hypotheses (Sweller, 1994),

7
in which they relate their learning experiences to prior knowledge so as to decide how to
conduct certain processes or decide what processes to undergo (Carroll & Mack, 1999).
Learners carefully select relevant information from the material and engage in learning by doing
and thinking, in which they relate new information with existing knowledge. This process helps
learners construct schemas (Carroll & Mack, 1984).
‘Constructivist learning’ describes the learner’s process of (1) selecting appropriate
information from both words and pictures, (2) conceptually organising the words and pictures in
coherent schemas (i.e. internal connections) and (3) integrating constructed schemas with
appropriate prior knowledge (i.e. external connections) (Mayer et al., 1999). Sweller (1988)
further proposes that deep processing takes place when learners actively process information
and create schemas stored in long-term memory. Building on CLT, Mayer (2005a) proposes
CTML. First, CTML assumes that the human cognitive system has two separate channels—a
visual-pictorial channel and an auditory-verbal channel—for representing and handling received
information. Second, it is proposed that each channel in the cognitive system is limited in its
capability to receive and handle information. Third, deep processing is more likely to occur when
related verbal and pictorial schemas are handled in the working memory at the same time
(rather than asynchronously). To further advance the application of CTML, Mayer (2008)
proposes ten principles for multimedia instructional design, which aim to reduce extraneous
processing, manage essential processing and foster generative processing (see Table 2).
Table 2. Ten Principles of Multimedia Instructional Design*
Purpose
Principle
Definition
Reducing
extraneous
processing/
extraneous cognitive
load
Coherence
Reduces use of irrelevant material.
Signalling
Highlights essential material.
Redundancy
Does not add on-screen text to narrated animation.
Spatial contiguity
Places corresponding text and graphics in proximity.
Temporal contiguity
Presents corresponding narration and animation
concurrently.
Managing essential
processing/intrinsic
cognitive load
Segmenting
Presents animation in learner-paced segments.
Pre-training
Provides pre-training in the essential material for a
learner.
Modality
Presents words as narration instead of text.
Fostering generative
processing/germane
cognitive load
Multimedia
Presents words and graphics together rather than
words separately.
Personalisation
Presents words in conversational style rather than
formal style.
*Adapted from Mayer (2008)

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Linking Storytelling, Human Cognitive Processing, Deep Processing and Multimedia
Instructional Design: An Illustrative Example of Video Stories
Drawing on CLT, CTML and the literature on deep processing, we propose that the process of
disseminating IS knowledge would benefit considerably from using visual storytelling in a form
of video stories that supplement traditional text-based articles. Video stories include multiple
sensory cues and channels, which provide extensive active interactions for learners (Eisner,
2008). To illustrate the utility of the video story as an effective tool for communicating research,
we developed an example of a video story for the well-cited academic work by Davis et al.
(1989), ‘User Acceptance of Computer Technology: A Comparison of Two Theoretical Models’.
This video story can be accessed at the following link: http://youtu.be/igXPrDC-RIY (7.56
minutes). We encourage the reader to watch this short video before continuing with the
remainder of this manuscript. We chose this particular article due to its widely-known contentii in
the IS discipline; however, such a video story could be made for any article.iii The script for this
video story example is included verbatim in Appendix A.
In producing this video story, we made a conscious effort to adhere to the ten principles
of multimedia instructional design (see Table 3) (Mayer, 2008; Mayer & Moreno, 2002; Ohlsson,
1993). To illustrate the cognitive impact of the video story, we now invite the reader to recall
specific parts of the story and to consider the following questions: (1) What problem was this
study seeking to address? (2) What was the main motivation for this study? (3) Upon which
model was TAM founded? (4) Which two constructs were balancing on the scales, and which
one was weightier? (5) Consider now a relatively non-critical detail: how long were the
participants trained in the technology? (6) Even more specifically: What was in the hand of the
woman recoiling at the computer desk? (7) Did the ‘like’ and ‘dislike’ thumbs-up and thumbs-
down images invoke affective responses or reflexive thoughts? (8) While recalling information to
address each of these questions, were specific images recalled from the video story? As
theorised by Tulving (1972) and Schank (1999), such images are unconsciously indexed as
touch-points for future recall. Finally, (9) while watching the story, were parts of the video
(images, narration or latent messages) consciously (or subconsciously) connected to personal
life experiences, as theorised by Schank (1990)?

9
Table 3. Application of the Ten Principles of Multimedia Instructional Design*
Purpose
Principle
Application of the Principles in the Multimedia
Presentation
Reducing
extraneous
processing/
extraneous
cognitive load
Coherence
Fundamental postulates of TAM were narrated, and essential
words were highlighted.
Signalling
Relevant facts about TAM were emphasised visually and
vocally.
Redundancy
Essential material about TAM was presented only through
narration and graphical animation; on-screen text following the
narration (captioning) was avoided.
Spatial contiguity
Where appropriate, printed words were placed in proximity to
the graphical animations.
Temporal contiguity
Narration and relevant graphical animation were
synchronised.
Managing
essential
processing/
intrinsic
cognitive load
Segmenting
The narrated animation about TAM was divided into 31
learner-paced segments, consisting of one to three sentences
with durations of 8 to 12 seconds.
Pre-training
The TAM video story began with a brief overview of the main
goals and motivations. Additionally, most IS scholars were
already familiar with TAM.
Delivery control**
The video story consisted of seven sections (Introduction and
Staging, Theoretical Foundations, Research Questions,
Methodology, Findings, Implications and Conclusion) marked
with hyperlinks, allowing learners to skip sections that the user
regards as irrelevant or navigate directly to those of interest.
Modality
Graphics with spoken text, rather than printed text, were used.
Fostering
generative
processing/
germane
cognitive load
Multimedia
Graphics with printed words were used together rather than
separately.
Personalisation
Direct quotes from the TAM article were reworded to be more
conversational and to better suit the spoken (rather than the
printed) form of the narration.
* Adapted from Mayer (2008); ** Principle added to the ten principles of multimedia instructional design by
the authors of the present manuscript.
THEORETICAL DEVELOPMENT
Research Model
The purpose of our study is to make a case for the use of visual storytelling to supplement the
traditional text-based article. We provide a basic model and empirical study to provide
preliminary evidence to show that video stories can yield significant benefits and even
competitive advantages (e.g. more citations and general influence) for authors who adopt this
approach. We draw on CLT, CTML and the literature on deep processing to develop our
research model, illustrated in Figure 1, which is based on the model developed by Jiang &
Benbasat (2007). We expect that these factors will jointly promote scholars’ intentions to cite

10
and share an article or, if the article is being reviewed for a journal, to agree to accept the article
for publication. If these factors and relationships hold, then at a minimum, we will have made an
empirical case for researchers to devote resources (energy, attention) to further explore the
merits of this approach to communicating scholarly knowledge.
Figure 1. Research Modeliv
Hypotheses Development
Content Format
CTML proposes that the use of cognitive processing capacity can be economized when
both visual and aural (i.e. processing phonological information) channels of working memory are
utilised (Mayer, 2005b). The modality principle of CTML states that essential processing is
properly managed when the words accompanying visuals are presented as narration rather than
as caption text (Mayer, 2009). The effect is to mitigate the intrinsic load and thereby enable
more working memory processing to be dedicated to generative processing. Similarly, the
multimedia principle of CTML states that generative processing occurs when words and
graphics are presented together rather than separately (Mayer, 2005a), which reduces the
germane cognitive load by deploying both the visual and aural channels simultaneously. Thus,
designing content as a video story can mitigate intrinsic load and, we argue, foster generative
processing.
Cairncross & Mannion (2001) claim that the nonlinear navigational features of many
multimedia packages provide greater freedom and control over learning. That is, learners can
choose the sequence in which sections are presented and control the pace of the presentation,
concentrating on the material they find unfamiliar or interesting and skipping the material that is
understood or irrelevant. Therefore, the control for the delivery of information in video stories
can mitigate the extraneous cognitive load, which enhances intrinsic processing so as to foster

11
generative processing.
Video stories provide many ‘touch points’ (Schank, 1999), allowing learners to connect
the disseminated information with their own life experiences. These touch points provide a quick
indexing method for storing and retrieving episodic (i.e. story-based) information from memory—
a process that is far more powerful than storing and retrieving semantic (i.e. written) information
(Tulving, 1972). Thus, the use of video stories provides a more memorable and relatable
learning experience than static text and thereby aligns with the objective of fostering generative
processing as a means to enhance learning outcomes.
We propose that text-based articles would benefit from visual storytelling, especially
when the learner is allowed to control the delivery of information. In this way, learners can
subconsciously make predictions (hypotheses) about the meaning of certain concepts when
watching the video story. Then, when reading the text-based article, they can make further
interpretations based on their own testing of those implicit hypotheses. Consequently, a learner
can construct a subjectively more-accurate schema of the key concepts from the learning
material and gain a better actual and perceived understanding of the material when consuming
scholarly knowledge using text-based articles and video stories together. Thus, we predict:
H1a. The use of a video story to supplement the text-based article will result in learners’
greater objective comprehension when compared to using only the text-based article,
the video story or the script.
H1b. The use of a video story to supplement the text-based article will result in learners’
greater perceived comprehension when compared to using only the text-based article,
the video story or the script.
Video stories can facilitate deep processing, which involves learning by doing, learning
by thinking and learning by actively (and subconsciously) making and testing hypotheses about
the meaning of key concepts. Thus, video stories will—to a greater extent than for static text—
help a learner gain confidence regarding the understanding of the learning material, thereby
resulting in the higher perceived usefulness of the disseminated information (Jiang & Benbasat,
2007). Moreover, due to the delivery control feature of video stories, learners are able to select
the sequence, pace and tone at which information is being presented, which enables them to
learn actively and construct more accurate schemas of the key concepts, perceiving greater
usefulness of video stories for the understanding of the material (Mayer, 2005b). For example,
video stories can provide a visual walk- and talk-through of new methodological approaches,
which increases the learner’s perceived usefulness of the newly learned methodological
approach. Whereas, written abstracts or text alone cannot offer the engaging and clear
communication of methodological approaches provided by video stories.

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Fiske (1993) argues that story processing relies on predefined existing story knowledge
structures; that is, learners store recurring story content and episodes with causal associations
as event prototypes or causal knowledge structures in the long-term memory. As a result,
individuals attempt to understand incoming story information by relating it to the previously
constructed prototypes or structures from their personal experiences (Schank & Abelson, 1995).
In this process, learners not only attempt to understand how and why a story evolves, but are
also likely to imagine themselves in the same situation (i.e. mental simulation), creating a
cognitive construction of hypothetical scenarios (Taylor & Schneider, 1989). In this mental
simulation, learners engage in a ‘convergent process in which all mental systems and capacities
become focused on the events occurring in the narrative’ (Green & Brock, 2000, p. 701).
Escalas et al. (2004) introduced the similar concept of ‘being hooked’ to describe the degree of
involvement of a consumer watching ads on television. They argue that stories can lure
individuals, making them experience what the story’s characters feel, even immersing them in
the story’s world.
We argue that the use of a video story will create place-holders (Ohlsson, 1993), touch
points (Schank, 1999), and ‘hooks’ (Escalas et al., 2004), which can then be reaffirmed by the
article. When learners relate their ‘existing knowledge’, they feel that the learning material has
been useful (i.e. has utility). Thus, video stories establish the information as ‘existing
knowledge’, after which the article requires the learner to draw on that existing knowledge to
make sense of complex information. Hence, we hypothesise:
H1c. The use of a video story to supplement the text-based article will result in greater
perceived utility when compared to using only the text-based article, the video story or
the script.
Comprehension, Utility and Satisfaction
One of the goals of learning is knowledge acquisition (Hui et al., 2008). Keller (1983)
suggests that learning satisfaction is directly related to the learner’s perceptions and feelings
about learning effectiveness or outcomes. Norman & Spohrer (1996) argue that learning
effectiveness is one of the most salient factors for predicting learner satisfaction. A relatively
learnable course results in student satisfaction because students overcome challenges during
the process of acquiring knowledge. This is consistent with the logic of the theory of planned
behaviour, that perceived behaviour control is positively correlated with attitudes towards an
activity (Ajzen, 1991). Thus, we expect a positive relationship between comprehension and
satisfaction.
When learners feel that they have successfully understood a concept, they are far more
satisfied than when they feel that they have not understood it (Choi & Johnson, 2007). This

13
relationship between perceived comprehension (or self-efficacy) and satisfaction is particularly
salient in a learning context (Klassen & Chiu, 2010). Thus, when learners think that they
understand, they are more satisfied than if they believe that they have failed to understand. The
reason for this relationship is that comprehension grants learners perceived control over the
concept they are learning, and this control is an innate human desire, which, when fulfilled, is
satisfying (Choi & Johnson, 2007). We therefore hypothesise:
H2. Learners’ perceived comprehension of the information has a positive influence on
their satisfaction regarding the learning material.
Yield shift theory (YST) defines the satisfaction response as a valence affective arousal
with respect to an object that has reference to a state or an outcome desired by an individual
(Briggs et al., 2008). It draws on five assumptions and two propositions, positing that
satisfaction responses are a function of the perceived shifts in yield for the active goal set.
According to YST, the satisfaction response is a ‘multiplicative function of the utility ascribed to
the goal and the assessed likelihood of attaining it’ (Briggs et al., 2008, p. 277). That is,
individuals feel neutral when outcomes match expectations or desires. They experience
satisfaction when outcomes exceed expectations or desires and dissatisfaction when outcomes
are lower than expectations or desires.
In the IS literature, Bhattacherjee (2001a, b) has found that usefulness drives
satisfaction because when people use IT, they have certain expectations that they hope will be
fulfilled by their use. When those expectations are met (i.e. the IT was useful), people’s needs
are fulfilled. And the fulfilment of needs is intrinsically satisfying (Choi & Johnson, 2007).
Following a similar line of reasoning, Wolfinbarger & Gilly (2001) point out that the availability of
current and relevant information on a webpage increases users’ perceived usefulness, which in
turn has a positive influence on satisfaction and loyalty. Greater usability leads to minimal
searching costs, reducing potential errors and increasing user satisfaction with the use of a
website (Alba & Cooke, 2004).
Roszkowski & Soven (2010) investigate the relationships between the amount learned,
information usability and satisfaction with learning among first-year college students
participating in an evaluation of an orientation training programme. The results show a strong
correlation between self-assessed learning and perceived usefulness information, whereby
usefulness was almost as good as the amount learned in predicting satisfaction with learning.
Thus, we posit that when learners feel that the learning material has high utility, they are more
satisfied than when they deem it to be of low utility.
H3. Learners’ perceived utility of information has a positive influence on their satisfaction

14
with the learning material.
Intentions to Cite, Share and Accept
Expectancy disconfirmation theory posits that an individual’s intention to continue using
IT is largely dependent on three variables: (1) the user’s level of satisfaction with the IT, (2) the
extent of the user’s confirmation of expectations and (3) the extent of the user’s confirmation of
post-adoption expectation (i.e. perceived usefulness) (Bhattacherjee, 2001a, b; Bhattacherjee &
Premkumar, 2004). The IT adoption literature has provided abundant evidence to show that
perceived usefulness is the most important determinant of users’ adoption intention (Davis et
al., 1989; Taylor & Todd, 1995; Venkatesh & Davis, 2000). Furthermore, Bhattacherjee (2001b)
has shown that satisfaction with prior use of online banking was the strongest predictor of users’
continuance intention.
Lustria (2007) asserts that higher website interactivity leads to greater comprehension of
the content, which in turn increases positive attitudes towards the webpage. In a similar manner,
learners develop a positive intention to cite, share and accept a particular research study
because they have positively evaluated it and have found it useful. Thus, by nature, they seek to
share that value, as it positively reflects them. Therefore, when academics genuinely
understand an article, find it useful in advancing their individual or group work and feel satisfied
with the achievements of the article, they are more likely to cite this article in their current or
future work, share it with colleagues (where appropriate) or accept the article in the case of peer
review.
When individuals perceive that they understand something, they feel they have mastery
or control over it. Social display of control is a way of increasing people’s social standing among
their peers (Carver & Scheier, 1982). Thus, academics are more likely to share, cite or accept
an article that they think they understand because they both identify with it and want it to reflect
on their identity (Kirk et al., 2015). Conversely, if they cannot understand an article, they are
less likely to share it with others because they might be required to engage in conversations
about it, for which they are unprepared. They are also less likely to cite it because they cannot
leverage information in the article that they have not understood (e.g. trying to use a tool that an
individual does not understand how to use). Last, reviewers are unlikely to accept an article they
cannot understand because if they cannot understand it, others are unlikely to understand it,
which means that it is therefore not ready for publication. Thus,
H4. Learners’ perceived comprehension of a research study has a positive influence on
their intentions to cite, share and accept that research.
Liaw (2008) posits that perceived usefulness and perceived satisfaction contribute to

15
learners’ behavioural intentions to use an e-learning system. Following the same line of logic,
when individuals perceive something as useful, they want to share it with others because they
see value in it (Moldovan et al., 2011). When sharing that value with others, individuals attribute
that value to their own social identity (Kelley, 1967), thereby boosting their social standing
among their peers. They are also more likely to cite an article that proves useful than an article
that proves otherwise. Finally, one of the criteria for acceptance in any quality scholarly outlet is
a meaningful contribution. Thus, a useful article (i.e. it makes a contribution) is more likely to be
accepted than one that is perceived as lacking usefulness (i.e. no meaningful contribution). We
thus predict:
H5. Learners’ perceived utility of a research study has a positive influence on their
intentions to cite, share and accept that research.
If individuals are satisfied with a product or service, they are more likely to share it with
others because they anticipate that others will also share in their positive experience (Anderson,
1998). Sharing positive experiences is an innate human social desire that allows individuals to
feel as though they have contributed to society (Froh et al., 2010). Johnson & Oppenheim
(2007) argue that academics are more likely to cite others with whom they have social
closeness, implying that citers cite others because they feel comfortable with their work. Hence,
they are also more likely to cite an article that is satisfying, rather than one that is unsatisfying,
because in order for it to be satisfying, it must have fulfilled some need (Choi & Johnson, 2007).
If it is not satisfying, then it cannot have fulfilled the need required to merit citation. Similarly,
they will also not accept an unsatisfying article because it does not meet the basic requirement
of satisfying some need (Davis, 1971). Thus,
H6. Learners’ satisfaction towards a research study has a positive influence on their
intentions to cite, share and accept that research.
METHODOLOGY
Statistical Methods
The conceptual model consists of two parts, as illustrated in Figure 2. The first part, the analysis
of variance (ANOVA), will determine whether and to what extent the delivery format of the
content affects objective comprehension and perceived utility. In particular, we test our model by
conducting a quasi-experiment with research-oriented students and academics, who were
randomised into four conditions: (1) reading a traditional article, (2) reading a script of an article
(see Appendix B), (3) viewing a video story of the script and (4) viewing a video story and then
reading the associated article. In Figure 2, the line emanating from the content format does not
imply a causal and correlative relationship that could be tested with regression. Instead, it

16
simply implies that the differing treatments (content formats) will result in differing levels of utility
and comprehension. This is a difference of means in the outcome variables, which can be
appropriately tested using ANOVA, in which the factoring variable is the content format (four
treatments), and the dependent variables are utility, perceived comprehension and objective
comprehension.
Figure 2. Statistical Approaches to Test the Conceptual Model
The second part, involving structural equation modelling (SEM), will determine whether
and to what extent perceived comprehension and perceived utility affect positive intentions (to
cite, share and accept), partly through satisfaction. We expect satisfaction and intentions to be
strongly influenced by the participant’s perceived level of comprehension, regardless of the
actual comprehension. That is, if readers think that they have understood an article, they will be
more satisfied and will be more likely to cite and share as well as to accept it under peer-review
conditions. Whether readers have actually understood the article is irrelevant to their research
evaluations and intentions.
Sample and Design
We used Amazon Mechanical Turk™ (aka mTurk) to collect anonymous responses from a
highly-filtered demographic. We followed the latest procedures on effectively conducting
research on mTurk, including careful demographic screening and the use of attention traps in
the survey, which improved the data quality (Lowry et al., 2016). Our filters required participants
to adhere to the following requirements:
• be at least 24 years old

17
• speak English at a highly proficient level (85% minimum proficiency)
• be research-oriented students or academics
• use a large-screen device (not a smartphone) while participating in the study
By using these filters, as well as attention traps in our survey, we obtained 269 usable
responses and filtered out 557 ineligible attempts. We designed a four-treatment quasi-
experimental study in which participants were randomly assigned to one of four treatment
groups based on presented research materials: 1) video only (n = 69), (2) script only (n = 81),
(3) article only (n = 52) and (4) video and article (n = 67). Notably, the script-only condition
involved the written script used to produce the video. The participants were verified for eligibility
and were then presented with one of these four treatments at random. They comprised 58%
male and 42% female. About 54% reported moderate engagement with research in their day-to-
day work, and 22% and 23% reported low and heavy engagement, respectively.
The presented research material was from a recent (at the time of data collection),
intellectually challenging JAIS article by Lowry et al. (2015), which was scrubbed of any
identifying information, including the journal in which it had been published (see http://youtu.be/-
1FsG0o0pOc for the scrubbed version). We chose this article especially because it is non-visual
in nature, it was forthcoming at the time of the experiment and, thus, was familiar to very few
academics. Moreover, because it is much lengthier, much more complex, and much more
challenging to summarise than simpler articles, it was just the kind of article many academics
might hesitate to translate into video form. We followed all the previously discussed principles in
producing the script and video. 94.4% and 95.9% of the participants reported being unfamiliar
with the article and with the authors, respectively. The script for the video story used in the
experiment is included verbatim in Appendix B.
Measurement
Our model includes two original measures (i.e. objective and perceived comprehension) and six
adapted measures (i.e. perceived utility, intention to cite, intention to share, intention to accept
and satisfaction). Further information about the constructs measured is included in Appendix C.
Objective comprehension refers to a learner’s objective understanding of the content of the
material, which is based on the key points of the target material. Perceived comprehension is
defined as the learner’s perceptions of the extent to which he or she understood the content of
the material, which was specifically developed for this study. Objective comprehension versus
perceived comprehension was used because differences regarding format should be able to
deliver the information more or less effectively. The only way to objectively test this is with
objective comprehension questions (e.g. a quiz). Questions about perceived comprehension

18
would not be adequate for determining actual comprehension. Perceived utility, which is
adapted from Jiang & Benbasat (2007), is defined as a learner’s perceptions of the extent to
which the materials (e.g. text-based article, video story etc.) are useful for helping him or her
understand the underlying content.
Satisfaction, which we adapted from Bhattacherjee & Premkumar (2004), refers to a
learner’s emotional state following the use of one of the four different content formats for
disseminating scholarly knowledge. Intention to cite is defined as the extent to which users feel
that they would cite the material (Galletta et al., 2004; Galletta et al., 2006). Intention to share
refers to the extent to which users feel that they would share the material (Galletta et al., 2004;
Galletta et al., 2006). Intention to accept is defined as the extent to which users feel that they
would accept the research material as reviewers for a peer-reviewed journal (Galletta et al.,
2004; Galletta et al., 2006).
Prior to performing the actual experimental manipulations, we gathered a number of
demographic/control variables so that we could test for counter-explanations for our model
results. Specifically, we examined whether gender, age, review experience, academic position
or research engagement level had any meaningful differences in terms of positive intentions. In
all cases, there were no significant differences in positive intentions across groups.
After the participants underwent their experimental treatment, they were asked to
complete several post-test measures: objective comprehension (i.e. whether they actually
understood some of the basic points of the article), perceived comprehension, perceived utility,
satisfaction, intentions to cite, intentions to share and intentions to accept for publication under
peer-review conditions.
RESULTS
We generally followed the model and testing approach developed by Jiang & Benbasat (2007)
to test the effects of content format on the critical antecedents (i.e. comprehension and utility) of
our dependent variables (i.e. intent to cite, share and accept). To do so, we used ANOVA to test
the manipulations for objective comprehension and perceived utility regarding the impact of
content format. Furthermore, we used SEM to test the causal model regarding the relationships
between perceived comprehension, perceived utility, satisfaction, and intentions (to cite, share
and accept). Table 4 and Figures 3 and 4 summarize the results for our ANOVA testing. Tables
5, 6, and 7 and Figure 6 summarize the results from our SEM analysis.
ANOVA Tests on Experimental Manipulations
Table 4 summarises the results of our ANOVA testing, in which we examined how the four
format manipulations influenced perceived utility and objective comprehension.

19
Table 4. ANOVA Results
Construct
Sum of Squares
df
Mean Square
F
Sig.
Utility
Between groups
10.612
3
3.537
6.742
.000
Within groups
139.036
265
0.525
Total
149.648
268
Objective comprehension
Between groups
21.709
3
7.236
5.978
.001
Within groups
320.797
265
1.211
Total
342.506
268
Based on the format manipulations, there were significant differences in perceived utility
and objective comprehension. A post-hoc Bonferroni’s test, which is included in Appendix D,
showed that the script format was significantly less helpful than the other formats, indicating that
an extended abstract was perceived as least useful on its own in disseminating the research
content. The video and article formats were perceived to be equally useful, and the presence of
both was perceived to be the most useful. In terms of objective comprehension, the article
format was significantly less effective than the other three formats, with video alone and video
and article together roughly equal in effectiveness; the script was the most effective, although
these latter differences were not statistically significant. No significant differences were
observed between the format types regarding perceived comprehension (ANOVA p-value =
0.647). Figure 3 and Figure 4 depict the relationships between the chosen format, average
perceived utility and objective comprehension outcomes.
Figure 3. Relationship between the Chosen Material Format and Perceived Utility

20
Figure 4. Relationship between the Chosen Material Format and Objective Comprehension
Pre-Analysis and Factorial Validity
We validated our measurement model before assessing the causal relationships. For the
exploratory factor analysis (EFA), we chose maximum likelihood as the extraction method and
Promax with Kaiser normalisation as the rotation method. The rotation converged in five
iterations. As shown in Table 5, the EFA showed excellent discriminant validity with no
substantive cross-loadings, convergent validity with strong primary loadings (Hair et al., 2010)
as well as strong reliability (α > 0.700) (Nunnally et al., 1994).
Table 5. Exploratory Factor Analysis (Loadings < 0.200 Suppressed)
Latent
Construct
Items
Factor with Resulting Cronbach’s Alphas
α =.885
α =.824
α =.909
α =.919
Perceived
utility
PUtil_1
.823
PUtil_2
.797
PUtil_3
.881
Perceived
comprehension
CompP_1
.785
CompP_2
.646
CompP_3
.890
Intentions to
cite, share,
accept
ITC_1
.679
ITC_2
.775
ITC_3
.830
ITS_1
.779
ITS_2
.753
ITS_4
.716
ITA_1
.625
ITA_2
.464
ITA_3
.686
Satisfaction
Sat_1
.753
Sat_2
.882
Sat_3
.866
Sat_4
.848

21
The confirmatory factor analysis (CFA) also exhibited excellent discriminant validity (see
Table 6), with all inter-factor correlations less than the square root of AVE (Fornell & Larcker,
1981), with the MSV less than the AVE, as well as convergent validity (all AVEs > 0.500) (Kline
et al., 2011) and strong reliability (all CRs > 0.700) (Malhotra, 2008). Positive intentions were
modelled as a multidimensional second-order reflective factor. As shown in Table 7, the model
fit statistics for the CFA were also excellent, with all measures meeting appropriate thresholds
set by Hu & Bentler (1999).
Table 6. CFA Correlation Matrix and Validity Measures*
Construct
CR
AVE
MSV
ASV
(1)
(2)
(3)
(4)
Utility (1)
.886
.723
.412
.379
.850
Satisfaction (2)
.920
.743
.543
.434
.604
.862
Intentions (3)
.942
.844
.543
.449
.601
.737
.919
Comprehension (4)
.830
.620
.444
.417
.642
.628
.666
.788
*The underlined and bolded numbers on the diagonal are the square root of the AVE.
Table 7. Summary of Model Fit
Measure
Value
Chi-square, df, p-value
206.4, 143, < 0.001
GFI
0.926
CFI
0.981
PCFI
0.820
NFI
0.941
TLI
0.977
PCLOSE
0.899
RMSEA (low – high)
0.041 (0.028–0.053)
SRMR
0.034
SEM Analysis
The causal model was tested using an AMOS SEM model with latent factors instead of factor
scores in path analysis. Because the full measurement model was used in the causal model and
the degrees of freedom did not change from the measurement to the structural model, the
model fit statistics remained unchanged. The findings fully support our conceptual model;
however, only a 90% confidence level was applicable to the relationship between perceived
utility and positive intentions. The predictive power of the model proved strong, with 46% and
62% of the variance in the endogenous variables being explained by their predictors. The
results are depicted in Figure 5.

22
Figure 5. SEM Results
***p < 0.001; †p < 0.100; R2 is on the top right of the endogenous variables.
The results shown in Table 8 indicate that perceived comprehension and perceived
utility have significant effects on satisfaction, with path coefficients of 0.408 (0.001 significance
level) and 0.342 (0.001 significance level), respectively. Thus, H2 and H3 are supported.
Similarly, perceived comprehension, utility and satisfaction positively influence positive
intentions to cite, share and accept, with path coefficients of 0.279 (0.001 significance level),
0.129 (0.001 significance level) and 0.483 (0.001 significance level), respectively, supporting
H4, H5 and H6. Furthermore, although the differences in objective comprehension and
perceived utility were significant with regards to content format, they were not significantly
different in the manner theorised. Thus, we did not find support for H1a, H1b and H1c. The
results show that the script-only treatment was significantly worse in terms of its influence on
objective comprehension than the other treatment formats. In addition, the article-only treatment
resulted in significantly lower perceived utility relative to the other formats.
Table 8. Summary of Resultsv
Hypothesis
Evidence
Support?
H1a. Objective comprehension
Script only: significantly worse than others**
No
H1b. Perceived comprehension
No significant differences
No
H1c. Perceived utility
Article only: significantly worse than
others***
No
H2. Perceived comprehension

satisfaction
β=0.408***
Yes
H3. Perceived utility  satisfaction
β=0.342***
Yes
H4. Perceived comprehension  intentions
β=0.279***
Yes
H5. Perceived utility  intentions
β=0.129†
Yes
H6. Satisfaction  intentions
β=0.483***
Yes
***p < 0.001; **p < 0.01; †p < 0.100

23
DISCUSSION
In this study, we sought to present a theoretical and empirical case around the notion that IS
researchers and the IS discipline could benefit from supplementing their traditional text-oriented
research articles with video stories. Video stories provide a rich and concise mode for
disseminating information in an engaging, relatable and impactful way. They enable a summary
of the critical points retained by learners, encouraging them to either consciously or
unconsciously seek the relatedness of the disseminated message with their relevant prior
experiences (Mayer, 2008). Therefore, video stories have a great potential to expand the reach
of IS research, including increasing research citations, influence and acceptance.
The video story in our study adopts a third-person narrator perspective in telling the story
of the logic that Davis followed in proposing the TAM. It does so by use of the personalization
principle of CTLM for fostering generative processing whereby we paraphrase Davis’s quotes
and arguments. We note that this falls short of the use of first-person accounts, which are
classic devices in fiction. However, the storytelling embedded in our video story is more along
the lines of a biography of a deceased subject, where the author does not have access to the
subject’s inner monologue (i.e. a first-person perspective) and must tell the story by relying upon
what others said about the subject. This illustrates the effect of our use of paraphrasing and
thereby represents an initial effort to invoke storytelling techniques.
The non-significant results for H1 might imply that paraphrasing alone is not sufficient to
invoke storytelling. This finding prompted us to identify alternative, evidence-based ideas
regarding how to improve the effectiveness with which video stories disseminate their meaning.
Critical narrative theory (CNT) (Voithofer, 2004) might offer an alternative theoretical lens to
explain the non-significant results and to improve multimedia learning experiences by optimising
the story structure. CNT assumes that story structures are not static, implying that stories evolve
over time as social, cultural, historical and technical factors (e.g. media convergence) emerge,
change, dissipate or merge (Voithofer, 2004). Thus, one should account for the story structure
to make multimedia materials more effective at disseminating information. Drawing on learning
theories, multimedia instructional design approaches and cultural theories of pedagogy, CNT
provides a conceptual framework for designing and evaluating educational multimedia
materials, focusing on multiple elements of the story structure, including (1) genre, (2) story, plot
and subplot, (3) space, place and setting, (5) time, (6) character and characterisation, (7) point
of view/focalisation, (8) complication/crisis and (9) resolution and coda.
The proposed implications of CNT can improve the story structure in video stories, thus
enriching the learning experience and more-effectively disseminating the meaning of the story.

24
For example, the complication/crisis implication suggests the use of problem-centred
instructions with complications or crises that are culturally relevant (i.e. authentic) to the lives of
learners. The rationale is that a content design that encourages users to develop a thoughtful
solution from different perspectives and with varied approaches would enhance the degree to
which the video stories are perceived as being more engaging, relatable and impactful.
Questions about the complication/crisis implication that can guide the story structure include: (a)
What information is provided to complicate the story? (b) How are learners guided to make
meaning from the complications? (c) What is the relationship between the story crisis and
conflicts and the complications encountered in the lives of learners? (d) What story paths are
eliminated with the introduction of a complication or crisis? (e) How is crisis used to support the
story? Finally, (g) how does the crisis setup and transition to the end and resolution of the story?
See Appendix E for further details regarding the implications for structure in video stories.
Despite the non-significant results, certain aspects of our findings indicate that
leveraging video stories might have the potential to yield positive results in relation to the
dissemination and consumption of IS scholarly knowledge. In our experiment, we note that the
content format makes a difference in objective comprehension and perceived utility, that
perceived utility and perceived comprehension both strongly affect satisfaction and that
satisfaction strongly affects intentions to cite, share and accept. In terms of perceived utility, the
video story’s script was perceived as the least useful content format for consuming scholarly
knowledge. Text-based articles and video-story formats were perceived as equally useful;
however, the presence of both (i.e. video story followed by the manuscript) was perceived as
most useful. The script may have been perceived as least useful because it contained the least
amount of information and did not incorporate any of the visual or aural cues. The article also
did not have these cues, but it did comprise more information than the script. Having both the
video and the article provided the most information and included the most cues for remembering
the information. This finding is in line with the multimedia principle of CTML suggesting that
words should be presented together with graphics (rather than separately) to foster generative
processing (Mayer, 2008).
Regarding objective comprehension, the text-based article was the least effective
format. The video story and the text-based article supplemented with the video story were
almost equal in effectiveness, and the video story’s script was the most effective format for
objectively understanding scholarly knowledge among respondents. While the script was
perceived as least useful, it was objectively most useful for comprehension. This is perhaps
because it was shortest and required the least time commitment, therefore resulting in the least

25
information overload. This is congruent with the CLT assumption about total cognitive load; that
is, to the extent that extraneous load is minimized and essential load is managed, more
processing capacity within working memory is available for constructing, elaborating and
automating schemas (Sweller, 1994). Where the design of the artefact economizes on the use
of working memory, participant can readily remember the sparse information available. It is
critical to emphasise that even though the script was objectively most useful, it was not
perceived as useful at all. This is an important difference because it is this perceived usefulness
that leads to satisfaction (and, ultimately, intent to share etc.).
Limitations and Future Research
Here, we discuss the limitations of our study and the directions for future research. The first
limitation relates to the design of the video stories. Case-based reasoning theory complements
CLT and CTML by proposing that individuals solve novel problems by (1) retrieving former
experiences from long-term memory, (2) interpreting a new situation by trying to find
associations with former experiences and (3) adapting a former solution to make it relevant to
the needs of a new situation (Kolodner, 2014, 1993; Schank, 1990). Cognitive learning
assimilation theory (Ausubel et al., 1968) proposes that the use of concept maps aids
comprehension. Therefore, future studies should focus on exploring how to integrate evidence-
based concept maps in the design of video stories, which can enhance the comprehensibility of
the disseminated message and positively contribute to an individual’s generative processing.
A second limitation of our study is also related to the design of the video story. Besides
being comprehensive and relevant, video stories should also attract users’ interest to be
considered effective. Prior research has repeatedly confirmed that an individual’s degree of
interest has a powerful influence on (1) attention (Hidi et al., 2004), (2) goals (Harackiewicz &
Durik, 2003) and (3) learning capacity (Harackiewicz et al., 2002). Individual interest, which is
defined as the psychological state of engaging with ideas over time (Hidi & Renninger, 2006),
includes affective and cognitive development (Hidi & Berndorff, 1998). Learners progress from
situational interest (which is always motivational) to individual interest (Hidi & Renninger, 2006)
as a result of both cognitive evaluation and emotional (i.e. affective) arousal. Hence, future
studies should attempt to use emotional (e.g. affective arousal cues) devices, which can prompt
a cognitive evaluation as well as affective arousal in producing individual interest.
A third limitation relates to the design and setting of the experiment. We expect future
research to conduct more robust experimental designs that will consider additional scenarios,
such as video, article and script, to investigate whether they will provide further insights.
Moreover, future studies should consider running an experiment in real-life settings to better

26
control the quality of their sample population.
CONCLUSION
In this paper, we considered and empirically tested the merits of the video story as a companion
to traditional scholarly articles. To support our arguments, we reviewed theories and research
on deep processing and the use of multimedia instructional material. We drew upon the
principles of CLT and CTML and the literature on deep processing to develop a research model,
which we tested using data from 269 research-oriented students and academics who
participated in our experiment. The model was moderately supported, suggesting the potential
upside of expending the extra effort to produce such videos.
We acknowledge that the strength and robustness of the paper medium for
disseminating scholarly knowledge has been demonstrated over the last 350 years of scientific
research publishing. The benefits and contributions of this vetted genre are virtually
undisputable and cannot be supplanted. However, the barriers to successfully establishing
video stories as a viable format are diminishing because of the rapid advances in audio-visual
technologies, storage and bandwidth capacities, public virtual infrastructure and the global
acceptance of user-generated open content. The latter, coupled with the difficulty experienced
by researchers in keeping up with the ever-growing body of academic articles and their need for
greater academic impact and interdisciplinary work, suggests a propitious setting in which to
innovate and begin taking advantage of opportunities to enhance IS scholarly articles with video
stories.
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i http://www.imdb.com/title/tt0048980/
ii We use this well-known study, rather than an original study, to practice what we preach by reducing
extraneous processing by highlighting the essential material and message of the current study and by
minimising the novelty of the content in the illustrative example. However, if we were to present an

31
unfamiliar study (for example, one of our own works in progress or recent publications), this would
present a competing message to the viewer, therefore undermining the primary message we are trying to
disseminate, which is all about video stories.
iii For example, the video http://youtu.be/LZQIDkA-ke0 summarises the main points of the MIS Quarterly
article by Lowry et al. (2013), which discusses journal rankings.
iv Note that the lines for H1a–c represent the effect that the varying treatments (e.g. video story vs. text-
based article) have on utility and comprehension. These will be tested via ANOVA for differences of
means rather than through regression or correlation techniques.
v See Appendix D for additional details regarding pairwise comparisons.

Using Supplementary Video Stories for Improving the Dissemination and Consumption
of Scholarly Knowledge: Evidence from a Quasi-Experiment
ONLINE APPENDIX A. SCRIPT FOR ILLUSTRATIVE EXAMPLE OF VIDEO STORY
To derive our example video story, we sought to understand the critical path of the story in the article by
Davis et al. (1989). All non-essential elements were subsequently dropped from the story. We largely
quote and paraphrase the original authors where possible. Direct quotes are indicated in the story. Each
of the 31 parts corresponds to one of the 31 segments in our video story. To apply the story to the
imagery of the video, we recorded the audio for each of the 31 segments separately and then attached
the audio clips to the corresponding scenes. This approach frees the creator from having to record the
entire narration in a single take and then attempt to time the imagery around that single audio narration (a
painful and difficult task). Our piecemeal approach also allows for relatively easy editing—audio clips and
scenes can be added, removed and updated without affecting the rest of the story elements. This makes
revision requests more feasible.
1. This is a research brief for the original 1989 Management Science article, which discusses the
merits of the technology acceptance model in comparison with the more general theory of
reasoned action.
2. Investment in business technology is inherently risky. Their complexity, scope and cost all add up
to a major investment of both time and money that is not even guaranteed to work.
3. Often times when the project is actually completed, it still is underutilised or resisted by end
users. This ‘resistance to end-user systems by managers and professionals is a widespread
problem’ that needs to be addressed.
4. Why is there so much resistance to the use of new technology? What makes us so averse to
change? These are compelling and difficult questions that need investigation, but existing
research has fallen short of conclusiveness. ‘Understanding why people accept or reject
computers [and related technologies] has proven to be one of the most challenging issues in
information systems research’.
5. In this study, we hope ‘to better predict, explain and increase user acceptance’ through a ‘better
understand [ing of] why people accept or reject computers’.
6. Furthermore, this research addresses the ability to predict peoples’ computer acceptance from a
measure of their intentions and the ability to explain their intentions in terms of their attitudes,
subjective norms and perceptions of the technology.
7. The results of this study ‘suggest the possibility of simple but powerful models predicting user
acceptance of technology, with practical value for evaluating systems and guiding managerial
interventions aimed at reducing the problem of underutilised computer technology’.
8. This model, called the technology acceptance model, is a powerful framework that can be used to
help both researchers and practitioners learn how to develop better information systems.
9. In order to understand TAM, we must first look at the theory of reasoned action, which is ‘a widely
studied model from social psychology, which is concerned with the determinants of consciously
intended behaviours’. ‘TRA is a general model, and as such, it does not specify the beliefs that
are operative for a particular behaviour. Researchers using TRA must first identify the beliefs that
are salient for subjects regarding the behaviour under investigation’.
10. From an IS perspective, a particularly helpful aspect of TRA is its assertion that any other factors
that influence behaviour do so only indirectly by influencing attitudes or subjective norms.
11. In 1986, Fred Davis introduced an adaptation of TRA, the technology acceptance model
(Gendler), which is specifically intended to explain computer use behaviour.
12. The goal of TAM is to provide an explanation of the determinants of computer acceptance that is
general and capable of explaining user behaviour across a broad range of end-user computing
technologies and user populations.
13. This study is motivated by four main research questions. First, how well do intentions predict
actual use?
14. Second, how well do TRA and TAM explain intentions to use a system?
15. Third, do attitudes mediate the effect of beliefs on intentions?
16. And fourth, is there some alternative theoretical formulation that better accounts for observed

2
data?
17. In order to assess TAM, we gathered data from 107 full-time MBA students who were invited to
use a new computer technology.
18. At the beginning of the semester, the students were given one hour of training and orientation on
the use of the new technology.
19. At the end of this training, we administered the first wave of a questionnaire containing measures
of the TRA and TAM variables. Fourteen weeks later, we administered the same questionnaire
and additionally inquired regarding their actual use of the new technology.
20. Our results yield three main insights concerning the determinants of computer use: first, people's
computer use can be predicted reasonably well from their intentions.
21. Second, perceived usefulness is a major determinant of people's intentions to use computers.
22. And third, perceived ease of use is a significant secondary determinant of people's intentions to
use computers.
23. ‘What do our results imply for managerial practice? When planning a new system, IS practitioners
would like to be able to predict whether the new system will be acceptable to users, diagnose the
reasons why a planned system may not be fully acceptable to users and to take corrective action
to increase the acceptability of the system in order to enhance the business impact resulting from
the large investments in time and money associated with introducing new information
technologies into organisations’.
24. While attempts to solve this problem have been made with the use of early warning techniques,
there ‘has been the lack of good predictive models’ that can be used to help improve these
techniques.
25. Rapid prototypes, user interface management systems and video-recorded mock-ups are
increasingly being used to create realistic ‘facades’ of what a system will consist of, at a fraction
of the cost of building the complete system.
26. Though, this raises the question of whether brief exposure to a prototype system is adequate to
permit the potential user to acquire stable, well-formed beliefs.
27. Our findings indicate that, after just one hour of training and orientation, people formed general
perceptions of a system's usefulness that were strongly linked to use intentions, and their
intentions were significantly correlated with their future actual use of the system.
28. Our findings have implications for improving user acceptance as well. Many designers believe
that the key barrier to user acceptance is the lack of user friendliness of current systems, and
thus, increasing a system’s ease of use is the key to success.
29. Yet our data indicates that, although ease of use is clearly important, the usefulness of the
system is even more important and should not be overlooked. Although users may be willing to
tolerate a difficult interface in order to access functionality that is very important, no amount of
ease of use will be able to compensate for a system that doesn't do a useful task. Such an
implication should help guide software design decisions. However, future research is needed to
further test the generality of the trade-off between usefulness and ease of use.
30. ‘Overall, research in this direction should yield practical techniques to evaluate and improve the
acceptability of end-user systems’.
31. ‘The ability to take robust, well-formed measures of the determinants of user acceptance early in
the development process is undoubtedly going to have an impact on our ability to weed out bad
systems, refine the rest, and generally cut the risk of delivering finished systems that get rejected
by users’.

3
ONLINE APPENDIX B: SCRIPT OF VIDEO STORY IN THE EXPERIMENT
This script was developed based on a summary of the recent article by Lowry et al. (2015)
1. Information systems designers and publishers are keenly interested in how to retain users.
Accordingly, information systems researchers are eager to supply theories to explain and predict
users’ intentions to continue to use information systems.
2. Now, many different theoretical approaches have been taken to predict continuance intentions,
however, these existing models often focus on users’ extrinsic motivations, such as desires for
productivity, efficiency, and general utility, but fail to fully explain the range of intrinsic and
extrinsic motivations that influence continuance intentions.
3. Intrinsic motivations in particular have been shown to be a strong predictor of meaningful user
outcomes, such as continuance intentions, as well as satisfaction, and perceived performance.
4. Differentiating between users’ intrinsic and extrinsic motives—and the stimuli that fulfil these
motives—is particularly relevant for encouraging positive user interactions.
5. These ideas are also highly pertinent to the newer idea of gamification that is starting
revolutionise systems design.
6. To identify key differences between intrinsic and extrinsic motivators, several studies have
extended extrinsic motivation models or created new models to address users’ intrinsic
motivations. However, models predicting intrinsic motives of system use often ignore extrinsic
motives. As far as we know, no study has proposed a model that can account for the effects that
these normally conflicting motives have on a user’s satisfaction, continuance intentions, and
evaluations of system performance.
7. Additionally, most studies do not conceptualise the different types of intrinsic motivation – for
example, hedonic motives like pleasure versus intrinsic motives like learning – and they do not
measure the successful fulfilment of intrinsic motivations independently of that of extrinsic
motivations.
8. Existing models also often fail to account for user expectations, which are a key component of all
interactions and are directly predicted by motivations. So, to address the issue of nomological
completeness, researchers must also consider the role of expectations in system interactions.
9. The existing underdeveloped constructs and models potentially confound research on system use
and thus make such studies difficult to interpret or at least difficult to generalise across various
types of systems and interactions.
10. This gap in the literature also holds back the theoretical and empirical advancement of
gamification and information systems design.
11. Seeking to fill this gap, Bhattacherjee and Premkumar successfully predicted continuance
intention in 2004, but did so in an extrinsic-only context. To fill the remainder of the gap, we build
on their model to propose a new theoretical model, the multi-motive information systems
continuance model (or MISC), which explains and predicts the discrete cognitive processes
through which systems fulfil a range of motives and expectations and how this fulfilment leads to
continuance intentions.
12. The MISC model also accounts for design-related constructs that have the potential to contribute
to or confound any study on system use, namely: design aesthetics, perceived ease of use, and
design-expectations fit which will each be discussed in turn.
13. But first, to better explain continuance intentions we developed and tested the MISC model in a 3
by 3 experiment involving three primary motives and expectations: hedonic (via joy), intrinsic (via
learning), and extrinsic (via usefulness). We tested these three across three different information
systems contexts: online gaming to capture hedonic, online recreational learning to capture
intrinsic, and online paid work to capture extrinsic. Let’s dive a little deeper.
14. The MISC model is built upon expectation disconfirmation theory, or EDT, and the B&P model.
Theories and literature around EDT are plentiful. Sometimes referred to as met-expectations,
expectancy violation, or expectation-disconfirmation, these theoretical models concern whether
an experience conforms to one’s expectations or if those expectations are disconfirmed or
violated.
15. Most studies using an expectancy-confirmation or expectancy-disconfirmation paradigm argue
that an individual’s expectations largely determine his or her overall satisfaction with something,
such as a person, service, or product – and in our context, an online interaction with a user

4
interface.
16. ‘Expectations’ refers to one’s beliefs about future events. By nature, the human mind projects and
considers future scenarios to anticipate required actions, for both physical and social survival.
17. ‘Disconfirmation’ is the extent to which an event is evaluated as either exceeding or falling short
of expectations. ‘Positive disconfirmation’ results when perceived performance exceeds
expectations, thereby causing satisfaction. Whereas, ‘negative disconfirmation’ occurs when
performance falls below expectations, causing dissatisfaction.
18. In 2004, Bhattacherjee and Premkumar published an EDT-based model to explain changes in
beliefs and attitudes toward information technology use. We extend the B&P model and its
measurement approach to build the MISC model because the B&P model offers a parsimonious
means of capturing and explaining expectations, disconfirmation, and related constructs across
multiple periods of time. These multiple time periods are essential for assessing disconfirmation
after assessing expectations.
19. Unlike EDT, the focus of the B&P model is to explain continuance intentions, which is also our
phenomenon of interest. The B&P model also measures and explains effects over multiple
periods. In period one they train and survey participants regarding a particular software
application. Then in period two, after participants have used the software, they are surveyed
again, but this time regarding the extent to which their expectations were either met or
disconfirmed.
20. While the B&P model has its uses, it shares two shortcomings common to most EDT-based
models: namely, an inability to predict disconfirmation sufficiently, and a failure to take into
account the full spectrum of motivations that drive information system use.
21. To address these limitations, MISC builds upon the B&P model by adding three more
expectations as predictors of disconfirmation, and by including multiple motivation-related factors
to the model.
22. The MISC model leverages Design Expectations Fit (DEF), Ease of Use, and Design Aesthetics
as additional predictors of Disconfirmation. DEF is the extent to which the design of the software
matches the expected interaction. For example, if, prior to using some software, you expect to be
able to have fun interactions with it, but the software is designed for productivity rather than fun,
then the DEF is low. In this case, the DEF would be much higher if you had expected to interact
productively with the software. Applications with designs that match the expectations of the user
will be preferred to those, which do not match the users’ expectations.
23. Ease of use is a common construct in information systems research that represents the degree to
which you believe using a system will be free of effort. Applications that are easy to use will be
preferred over those that require more effort.
24. Design Aesthetics refers to the appropriateness and professionalism of the user interface.
Aesthetic, or appealing, interfaces are more likely to be preferred over unappealing ones.
25. Beyond these three new predictors of disconfirmation, we also unmuddy the waters by splitting
beliefs and disconfirmations into their naturally occurring disparate parts, including Hedonic,
Intrinsic, and Extrinsic components. When we learn about a new software or information system,
we may have expectations or beliefs that fit into one of these three categories. For example, we
may expect the software to be pleasurable on some level, uplifting to a certain degree, and, to
some extent, useful for accomplishing something.
26. In all existing models, these three different components are mooshed together, resulting in the
observed poor ability to predict disconfirmation. But when we separate them, we are able to
predict hedonic disconfirmations with hedonic expectations (or beliefs), intrinsic disconfirmations
with intrinsic expectations, and extrinsic with extrinsic. Taken together, these should also predict
evaluations of the interactions far better, as well as the user’s intention to continue using the
software.
27. To test this model, we randomly assigned each of the 550 participants to one of nine groups in a
3 by 3 design that matched one of the three expectations (hedonic, intrinsic, or extrinsic) with one
of three designed interfaces (hedonic, intrinsic, or extrinsic). In each of the nine groups, we
primed participants to expect a hedonic, intrinsic, or extrinsic system interaction, but then we
randomly assigned them to a system with hedonic, intrinsic, or extrinsic design (regardless of
their primed expectation).
28. After priming, we assessed expectations. Then the participants interacted with their randomly

5
assigned system. After the interaction, the participant was surveyed again to assess
disconfirmation, as well as continuance intention.
29. The design and data enabled us to analyse three separate models, one each for hedonic,
intrinsic, and extrinsic contexts. The results show profoundly more power to predict
disconfirmation than existing models. While existing models such as the B&P model achieve an
R-square of only 0.09, our three separate models have R-squares ranging from 0.28 to 0.43.
Each of these models was also able to predict substantial variance in continuance intentions, with
R-squares between 0.54 and 0.66.
30. Additionally, we found that expectations actually play a fairly minor role in predicting
disconfirmation, even when split into the three separate models. Instead, in the context of system
use, our three other predictors (DEF, ease of use, and design aesthetics) are far better predictors
of disconfirmation.
31. Interestingly however, ease of use is a good predictor only for extrinsically motivated interactions;
design aesthetics is a good predictor only for hedonically motivated interactions; while design
expectations fit is a critical predictor of disconfirmation for all types of expected system
interactions.
32. These findings lead us to a revised final model of MISC, in which we replace the three types of
expectations with the three more potent predictors.
33. To summarise, building on work by Bhattacherjee & Premkumar in 2004, we develop and test the
MISC model as a comprehensive model for explaining and predicting how a range of motives and
expectations influences user satisfaction and continuance intentions for multiple types of
information systems that have been designed with various intents.
34. Among many other findings, our analysis reveals that design-related constructs affect
performance beliefs differently depending on system intent and user motives and expectations.
This suggests that system designers can leverage the MISC model to learn where to focus their
efforts as they design specific systems with specific intents.
35. Nevertheless, we show that a user’s motives do not always match the intent of a system’s design,
which increases the need for systems to be designed to accommodate multiple motives.
36. Additionally, many findings are consistent across all types of systems, suggesting that certain
design constructs are universally essential.
37. The MISC model also provides a foundation for extending a wide range of research in human-
computer interaction and for revisiting prior research to examine the effects of multiple types of
motivation in established systems-use theories.

6
ONLINE APPENDIX C: MEASUREMENT OF CONSTRUCTS AND EXPERIMENT DESIGN
Measurement of constructs
Construct
Prompts and Items
Notes
Perceived
Utility (PU)
Adapted from
Jiang &
Benbasat
(2007)
Please indicate your agreement with the following: ‘The
media format of the research materials…’
1. ‘…was helpful for me to evaluate the research’.
Defined as the user’s
perceptions of the
extent to which the
materials (e.g., article,
video etc.) are useful
for helping them
understand the
underlying content.
2. ‘…was helpful in familiarising me with the research’.
3. ‘…was helpful for me to understand the research’.
Comprehension
(perceptual)
1. I clearly understand the main points of the research.
Defined as the user’s
perceptions of the
extent to which they
understood the content
of the material.
2. I clearly understand the problem(s) this research is
trying to address.
3. I clearly understand the main findings of the research.
Comprehension
(objective)
Original
measure based
on key points of
the target
materials.
1. Which of the following was not one of the three
motivation types discussed in this research? (extrinsic,
intrinsic, hedonic, autonomic) (autonomic)
Defined as the user’s
objective
understanding of the
content of the material
(correct answers are
shown in parentheses).
2. Past studies have failed to sufficiently predict which of
the following: Satisfaction, disconfirmation, continuance
intention, ease of use (disconfirmation)
3. Which was the better predictor of disconfirmation:
expectations or design expectation fit? (DEF)
4. What percent of the variance (R-square) in
disconfirmation was the B&P model able to explain? (9%)
5. Which other three predictors are better at predicting
disconfirmation than expectations? (DEF, ease of use,
and design aesthetics)
6. Whom are the practical implications of this article most
valuable for? (System designers)
Intention to Cite
(ITC)
Adapted from
Galletta et al.
(2004); Galletta
et al. (2006)
1. If this research were in my area, I would be likely to
cite it.
Intention to cite is
defined as the extent
to which users feel
they would cite the
material.
2. This research is highly citable in its research area.
3. This research would make for a useful reference in its
research area.
Intention to
Share (ITS)
Adapted from
Galletta et al.
(2004); Galletta
et al. (2006)
1. If I knew someone in the same research area as this
study, I would share this research with them.
Intention to share is
defined as the extent
to which users feel
they would share the
material.
2. I would not hesitate sharing this research.
3. This research could be very useful for others in this
research area.
Intention to
Accept (ITA)
Adapted from
Please complete the following: ‘If I were reviewing this
research as a peer reviewer at a top journal, I would…’
1. ‘…likely to give it a positive review’.
Intention to accept is
defined as the extent
to which users feel
they would accept the
2. ‘…I would be more likely to accept it than reject it’.

7
Galletta et al.
(2004); Galletta
et al. (2006)
3. ‘… I consider that this research deserves a chance at
acceptance in a high-quality publishing outlet’.
research material as a
reviewer at a peer-
reviewed journal.
Satisfaction
(Bhattacherjee
& Premkumar,
2004)
1. I am ___ with my experience reviewing this research.
Changed framing from
‘website’ to ‘research’.
2. Extremely displeased... Extremely pleased.
3. Extremely frustrated... Extremely delighted.

4. Extremely discontented... Extremely contented.
5. Extremely dissatisfied... Extremely satisfied.
Controls and Demographics:
• Age: continuous scale
• Gender: male/female
• Position: student, assistant, associate, full
• Editorial experience: none, as reviewer only, associate, senior, EIC
• Year PhD obtained (or anticipated): years from 1950 to 2020
• Research engagement: no research, low research, moderate, heavy
• How long did it take to review the provided materials?: continuous scale from 0 to 15
• Had you read this article prior to today?: yes/no
• Do you personally know the authors of the provided research study?: yes, no
• Have you published or had a paper accepted in JAIS?: yes/no
• Is the provided research study in your personal area of research interest?: yes/no
• Research area: Information Systems, Strategy, Marketing, Organisational Behaviour, Other
Management, Other Non-management
Experiment design
This empirical study was performed in August 2015. Each participant was paid US$ 3.50 and the
average time to complete the task was 15.48 minutes. To ensure credibility and quality of responses in
our experiment, we set eligibility criteria, which were enforced by the mTurk selection process. The
eligible participants for our experiment were either PhD students, adjunct faculty/part-time, clinical
teaching faculty/lecturer (non-tenure track), assistant professor/ lecturer (tenure track), associate
professor/senior lecturer (tenure track), full professor, university administrator, or master level students
(research oriented). A large percentage of the respondents were master’s students (research oriented),
PhD students, and adjunct faculty/part-time. Furthermore, most of the respondents reported that they
have a moderate research engagement and they are located in the United States of America.
Moreover, we further filtered out about 50% of the respondents due to insufficient research
activity. Of the remaining sample, 54% reported moderate engagement with research in their day-to-day
work, and 22% and 23% reported low and heavy engagement, respectively. We also included eligibility
questions, attention traps and times (e.g. too fast resulted in disqualification) to filter out unengaged
responses in our survey to further increase the credibility and quality of responses in our experiment.

8
ONLINE APPENDIX D: FURTHER DETAILS ON BONFERRONI’S TESTS
Multiple paired comparisons Bonferroni’s tests
To compare the efficacy of each format type, we conducted a post-hoc Bonferroni’s test using the Utility
as the dependent variable and format type as the factoring variable. The results of this test are shown
below. The first table below (ANOVA) shows that there is a significant difference is utility when comparing
media format.
ANOVA (Utility)
Sum of Squares
df
Mean Square
F
Sig.
Between Groups
10.612
3
3.537
6.742
.000
Within Groups
139.036
265
.525
Total
149.648
268
The second table below shows the multiple comparisons between formats. The p-value for the
difference between formats (with regards to Utility) is shown in the Sig column. We have used a dark
highlight to indicate the pairs that are significantly different at the 90% confidence level. Notice that in all
cases, the script is significantly different than the other formats.
Multiple Comparisons
Dependent Variable: Utility (Bonferroni)
(I) Format (J) Format
Mean
Difference
(I-J)
Std. Error
Sig.
95% Confidence Interval
Lower Bound
Upper Bound
Video
Script
.33952*
.11866
.027
.0241
.6550
Article
.01072
.13302
1.000
-.3429
.3643
Both
-.18154
.12424
.871
-.5118
.1487
Script
Video
-.33952*
.11866
.027
-.6550
-.0241
Article
-.32880
.12871
.067
-.6709
.0133
Both
-.52106*
.11962
.000
-.8390
-.2031
Article
Video
-.01072
.13302
1.000
-.3643
.3429
Script
.32880
.12871
.067
-.0133
.6709
Both
-.19226
.13387
.913
-.5481
.1636
Both
Video
.18154
.12424
.871
-.1487
.5118
Script
.52106*
.11962
.000
.2031
.8390
Article
.19226
.13387
.913
-.1636
.5481
*. The mean difference is significant at the 0.05 level.
The means plot below shows that the script is significantly less useful than the other formats in
terms of utility.

9
We conducted a similar test for comparing the objective comprehension between media formats.
The global test shown below in the ANOVA table indicates that significant differences do exist between
formats when it comes to objective comprehension.
ANOVA (Correct)
Sum of
Squares
df
Mean Square
F
Sig.
Between Groups
21.709
3
7.236
5.978
.001
Within Groups
320.797
265
1.211
Total
342.506
268
The post-hoc Bonferroni’s test confirms that significant differences locally between pairs exist for
the article. These differences (significant at the 95% confidence level) have been highlighted in the table
below.
Multiple Comparisons
Dependent Variable: Correct (Bonferroni)
(I) Format (J) Format Mean Difference
(I-J)
Std. Error Sig.
95% Confidence Interval
Lower Bound
Upper Bound
Video
Script
-.18894
.18025
1.000
-.6681
.2902
Article
.62207
*
.20205
.014
.0850
1.1592
Both
.03310
.18871
1.000
-.4685
.5347
Script
Video
.18894
.18025
1.000
-.2902
.6681
Article
.81102
*
.19551
.000
.2913
1.3307
Both
.22204
.18170
1.000
-.2609
.7050
Article
Video
-.62207
*
.20205
.014
-1.1592
-.0850
Script
-.81102
*
.19551
.000
-1.3307
-.2913
Both
-.58898
*
.20334
.025
-1.1295
-.0485
Both
Video
-.03310
.18871
1.000
-.5347
.4685

10
Script
-.22204
.18170
1.000
-.7050
.2609
Article
.58898
*
.20334
.025
.0485
1.1295
* The mean difference is significant at the 0.05 level.
The figure below visualizes these differences between media formats in terms of objective
comprehension.

11
ONLINE APPENDIX E. CRITICAL NARRATIVE THEORY’S IMPLICATIONS FOR STORY
STRUCTURE IN VIDEO STORIES*
Narrative
Structure
Element
Implications for Story Structure in Video Stories
Logic
Questions to Consider
Genre
Considering what genres are
embedded into a video story
allows one to integrate the
larger social and cultural
stories
(a) Do the characters, settings, story
patterns, subject matter, and plots create
expectations with which the reader is
familiar or with which the reader is too
familiar, leading to uncritical acceptance of
the learning story?
Story, plot, and
subplot
Synchronising subplots with
the main plot to increase
learners’ motivation and
curiosity
(a) Are there any nondeigesic elements
(e.g. music, sound effects, and animations)
that distract from the plot by presenting plot
structures that do not fit within the genre,
characters, and locations of the story?
(b) Can the learner be presenting with the
next episode while providing closure to the
previous episode?
(c) How can you motivate the learner by
creating curiosity about the next event? (d)
Are there ways to contradict by presenting
inconsistencies in the story structure to
motivate the learner?
Space, place, and
setting
Creating contexts for learners
to allow them to invest in a
particular learning space,
providing the simulation of
multiple virtual and real
spaces
(a) How does the story give meaning to
space, place, and setting?
(b) Who defines the borders of space,
place, and setting?
(c) Who and what can cross the borders
and under what conditions?
(d) What rules govern who exists inside and
outside a space, place, and setting? (e)
What is the relationship between the
learner's physical space and virtual space?
Time
Representing time as
numerous intersecting,
overlapping events that take
on various meanings and
multiple interpretations from
the aspects of both telling a
story and understanding a
story
(a) What is the relationship between the
learner's experience of time and story's
representation of time?
Character and
characterisation
Using of compelling and
socially and culturally diverse
characters
(a) Can the learner be included as a
character in the story?
(b) Are characters simple and unchanging
or do they evolve and change over time?
(c) Do characters reinforce stereotypes of
race, class, ability, gender, ethnicity, ability,
region, and profession?

12
(d) What is the relationship between the
language that a character uses and
learners’ perceptions of that character? (e)
What is the relationship between the
evolution of the characters and the
development of the plot and story?
Point of
view/focalisation
Considering the perspective
in which information is
presented to account for the
reliability of the story
information
(a) Whose perspectives are highlighted in
the story and whose are omitted?
(b) What are the characters and the
learners allowed to see?
(c) To what extent can the learner
determine the point of focalisation?
(d) What forms of focalisation are possible
in an online learning story?
Complication/crisis
Using problem-centred
instructions with problems
(i.e. crisis, complications) that
are culturally relevant (i.e.
authentic) to the life of the
learners; thus, engaging
learners to approach a
thoughtful solution from
different perspectives and
with varied approaches
(a) What information is provided to
complicate the story?
(b) How are learners guided to make
meaning from the complications?
(c) What is the relationship between the
story crisis and conflicts and the
complications that are encountered in the
lives of learners?
(d) What story paths are eliminated with the
introduction of a complication or crisis?
(e) How is crisis used to support the story
(g) How does the crisis setup and transition
to the ending and resolution of the story?
Resolution and
coda
Setting the context of the
story for the learner to reflect
on what he/she learned and
the resolutions and solutions
that he/she developed to
solve the learning problems;
thus, connecting the learning
story to the larger story in the
lives of the learner
(a) What are the unresolved complications
and crises in the learners’ lives?
(b) What is the relationship between how
the story resolves the crisis/complication
and how learners are able to resolve the
complications in their own lives?
(c) Are the resolution and story complex
enough so that learners are encouraged to
reflect on the story?
(d) How does the resolution and coda
connect to the lives of the learners?
(e) How relevant is the story resolution to
the initial problem/complication of the
learning story?
*Adapted from Voithofer (2004).

13
REFERENCES FOR APPENDICES
Bhattacherjee, A. & Premkumar, G. (2004) Understanding changes in belief and attitude toward
information technology usage: A theoretical model and longitudinal test. MIS Quarterly,
28, 229-254.
Davis, F. D., Bagozzi, R. P. & Warshaw, P. R. (1989) User acceptance of computer technology:
A comparison of two theoretical models. Management Science, 35, 982-1003.
Galletta, D. F., Henry, R., McCoy, S. & Polak, P. (2004) Web site delays: How tolerant are
users? Journal of the Association for Information Systems, 5, 1-28.
Galletta, D. F., Henry, R. M., McCoy, S. & Polak, P. (2006) When the wait isn't so bad: The
interacting effects of website delay, familiarity, and breadth. Information Systems
Research, 17, 20-37.
Gendler, T. S. (1998) Galileo and the indispensability of scientific thought experiment. British
Journal for the Philosophy of Science, 49, 397-424.
Jiang, Z. & Benbasat, I. (2007) The effects of presentation formats and task complexity on online
consumers’ product understanding. MIS Quarterly, 31, 475-500.
Lowry, P. B., Gaskin, J. & Moody, G. D. (2015) Proposing the multimotive information systems
continuance model (MISC) to better explain end-user system evaluations and continuance
intentions. Journal of the Association for Information Systems, 16, 515-579.
Voithofer, R. (2004) Teaching computers to tell learning stories: Using critical narrative theory
to frame design and evaluation strategies for online educational experiences. Journal of
Educational Multimedia and Hypermedia, 13, 47-72.
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