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The Impact of Mental Representations on ICT-Related Overload
in the Use of Mobile Phones
Accepted for publication in Journal of Management Information Systems
—Working Paper Version—
Carol Saunders (corresponding author)
Northern Arizona University, W. A. Franke College of Business
PO Box 5638, Flagstaff, AZ 86011, USA
carol.saunders@nau.edu
Martin Wiener
Bentley University, Information and Process Management Department
175 Forest Street, Waltham, MA 02452, USA
mwiener@bentley.edu
Sabrina Klett
Friedrich-Alexander University Erlangen-Nuremberg, Chair of IT Management
Lange Gasse 20, 90403 Nuremberg, GERMANY
sabrina.klett@fau.de
Sebastian Sprenger
Friedrich-Alexander University Erlangen-Nuremberg, Chair of IT Management
Lange Gasse 20, 90403 Nuremberg, GERMANY
sebastian.sprenger@fau.de
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The Impact of Mental Representations on ICT-Related Overload
in the Use of Mobile Phones
ABSTRACT
ICT-related overload, or the emotional and cognitive state that occurs when individuals
are unable to efficiently retrieve and process information delivered by, or associated with, ICT, is
an epiphenomenon. While prior research tends to ascribe this phenomenon to the amount of
information delivered, this study presents and provides significant empirical support for an
expanded cognitive perspective of ICT-related overload, which views individuals’ information-
processing capabilities as being reliant on differences in mental representations associated with
cultural, demographic, and experiential factors. Specifically, based on a survey with 1,004
mobile phone users, we find that (1) polychronic individuals experience less ICT-related
overload than monochronics; (2) memories of past emotional and cognitive overload increase
ICT-related overload; and (3) age has inverse effects on different overload dimensions.
Altogether, our findings challenge myths about information overload and multitasking, support a
multidimensional conceptualization of ICT-related overload, and suggest ways that managers
can reduce overload and leverage polychronicity.
Keywords: ICT-related overload, communication overload, feature overload, information
overload, mental representations, polychronicity, age, memories of past overload, mobile phone
use.
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The Impact of Mental Representations on ICT-Related Overload
in the Use of Mobile Phones
INTRODUCTION
There has long been more information available than the human mind can grasp. Whether
the information is recorded on papyrus rolls, parchment manuscripts, hand-printed documents or
printing-press runs, complaints about “too many books” echo across the centuries [12]. With the
‘explosion’ of the Internet and related information and communication technologies (ICT), the
situation has worsened and the amount of digital data available on the Internet every day reaches
mind-boggling proportions: The 4.4 zettabytes (where a zettabyte [ZB] is 2 to the 70th power) of
information available currently is estimated to multiply tenfold to 44 ZB by the end of 2020 [1].
Humans are also constantly bombarded with information that is linked to the use of modern ICT
such as mobile phones (e.g., [2,11,30,42,85]) and that comes to them in many different forms
including emails, social media, text messages, and websites. “These new media are being used to
create data smog, storage gluttony, and, in general, IT-related overload” [72:96].
The focus of this study is on overload that results from ICT use, referred to as ICT-
related overload (which is a more broadly defined view of IT-related overload that also includes
communication technologies). We define ICT-related overload as the emotional and cognitive
state that occurs when an individual is unable to retrieve and process the information delivered
by, or associated with, ICT within the required time limit that is needed for task completion
[45,73]. Clearly, a number of different ICT can deliver information. In this study, we are focused
on a particular type of ICT—mobile phones. Mobile phones are omnipresent in our daily lives
and are used by people of all ages. Unlike other types of ICT (e.g., desktop computers and
laptops), people tend to carry mobile phones and interact with them virtually all the time and
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often use them for a variety of purposes: e.g., making calls, texting, browsing the Internet,
emailing, taking photos, and playing games. Mobile phones thus represent a critical technology
in the context of ICT-related overload.
Some individuals can become overloaded with the amount of information they receive in
steady streams from their mobile phones. However, not all individuals are overloaded with the
same amount of information [72]. Consider the example of a top-level manager who receives
hundreds of emails via her mobile phone every day, though does not become overloaded,
whereas a ‘nine-to-five’ office worker may feel overloaded when receiving a dozen emails per
day. In other words, some individuals are better than others in using their cognitive resources to
process information, or have learned heuristics to reduce their processing requirements [6].
Against this backdrop, we argue that is time to stop blaming overload on the sheer
amount of information that is available. Rather, in this study, we attribute ICT-related overload
to differences in mental representations across individuals, which affect their ability to process
information. Here, we propose that cultural, demographic, and experiential factors can help
explain differences in individuals’ information-processing capabilities, and thus in their
perception of ICT-related overload in regard to mobile phones. In particular, our study uses a
cognitive lens based on cognitive psychology to explore how polychronicity (an individual’s
cultural preference for conducting multiple tasks in parallel), age (demography), and memories
of past overload (experience) are related to an individual’s perception of ICT-related overload.
The study is structured as follows: We first introduce the concept of ICT-related overload
and review the related literature. Next, we develop the research model and hypotheses, describe
the methodology, and present the results of our data analyses. Finally, we discuss the theoretical
and practical implications of the study results.
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THEORETICAL BACKGROUND
ICT-Related Overload
To conceptualize and operationalize ICT-related overload, we draw upon Karr-
Wisniewski and Lu’s [45] three dimensions of technology overload: information, (system)
feature, and communication overload. Information overload occurs “when an individual’s
information processing capabilities are exceeded by the information processing requirements”
[45:1062]. Feature overload is the state that occurs when the technology an individual has to use
to complete a task is too complex for the task and/or the individual [45]. Communication
overload is the state when an individual is unable to process the information that is received from
another person or process [45].
The most commonly applied of Karr-Wisniewski and Lu’s [45] three dimensions is
information overload. Frequently, information overload is considered to be based only on the
amount of information that is received (e.g., [11,21]). Studies measuring information overload
assume it occurs when individuals are faced with an increasing number of alternatives and/or
information dimensions per alternative [82], or have to process varying numbers of cues or data
items [21]. In this study, we adopt a more nuanced cognitive perspective that is based on how
individuals process information. This perspective is based on an input-processing-output model
[75], which incorporates emotions as well as cognitions and views individuals’ information-
processing capabilities as being reliant on their mental representations.
Input-Processing-Output (IPO) Model
Input: Adopting a cognitive perspective, the IPO model of ICT-related overload assumes
that individuals do not fully process all information that they receive (see Figure 1). Rather
incoming sensory inputs are initially filtered on the basis of their pertinence to the individual
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[29]. The selection of pertinent inputs occurs after the pattern-recognition stage [29,61]. Input
deemed pertinent enters working memory and the individual may become aware of it as a result
of the activation of the appropriate areas of memory. While the terms ‘working memory’ and
‘short-term memory’ are often used interchangeably, we view working memory as comprised of
short-term memory stores (i.e., phonological loop [verbal], visual-spatial sketchpad), an episodic
buffer, and a processing component that makes use of short-term memory [4,5,26,48]. In
particular, the central executive supervises and manipulates working memory through the use of
attention-related processes [26]. Working memory keeps track of multistep processes and
different task sets, as well as performance effectiveness [53]. Pertinent inputs are encoded and
temporally stored in working (i.e., short-term) memory, which is limited in capacity and the
length of time that it can store inputs. In contrast, the more permanent part of memory, called
long-term memory, stores a large number of past emotions and life-long experiences in the form
of organized mental representations [25,48]. These memories are encoded into long-term
memory through a chain of biochemical and cellular processes [48].
When individuals cannot differentiate and select pertinent information, it becomes an
information-processing problem [62], which is consistent with Weick and Sutcliffe’s view that
overload occurs because of an individual’s “inability to make sense of demands, capabilities and
context as well as the data” [92]. Thus, ICT-related overload does not just occur because
individuals are faced with vast amounts of information [75]. Rather, individuals can forestall
becoming overloaded by filtering out and rejecting those inputs that are not pertinent before they
are ever subjected to a deeper level of processing.
(Insert Figure 1 about here)
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Processing: Once inputs have been selected for processing by an individual on the basis
of their pertinence, they are processed, encoded with emotional tags [18], and stored in long-term
memory. In some cases, individuals revert to heuristics to simplify information processing [58].
Emotion can either help or hinder the processing of pertinent inputs. For example, people have
been found to remember their emotional reactions to financial information better than the actual
numbers [70]. In another example, virtual teammates in an experimental discussion gave more
cognitive attention to factual and normative information that agreed with their pre-discussion
choices [58]. They were more emotionally aroused by the information and appeared to savor the
realization that their teammates agreed with their way of thinking. In contrast, when facts and
normative statements challenged their pre-specified preferences, they initially expended
cognitive resources trying to figure out what the information meant, but subsequently did not
give the incongruent information any attention.
An emotional tag, which may reflect either a positive or negative emotion, is attached to
an event or concept [18]. The event or concept is then associated with prior experiences that are
stored in the individual’s mental representations in long-term memory. Memories of past
experiences are activated to appraise new inputs [18]. If the emotional tag of an input matches
that emotional tag of a related experience stored in an individual’s mental representations, it is
said to be congruent. If there is a mismatch and the input is incongruent with what is stored in
memory, it will not be processed as efficiently as congruent input, if at all.
Mental representations used for input processing differ from individual to individual due
to a number of factors including the individual’s cultural values, demographics, and past
experiences. For example, to describe the “temporal personality” of individuals as well as
cultures, Hall [38] developed the culturally-based concept of polychronicity (and the contrasting
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concept of monochronicity). Polychronicity is defined as “the extent to which people (1) prefer
to be engaged in two or more tasks or events simultaneously, […] and (2) believe their
preference is the best way to do things” [14:51]. Because of their preference for switching across
multiple tasks, polychronic individuals can be expected to process inputs very differently than
monochronic individuals, i.e., people who prefer to do one thing at a time. Similarly, prior
research relates age and past experiences to differences in mental representations across
individuals [88]. (We elaborate on this in the subsequent Hypotheses Development section.)
Output: ICT-related overload has been associated with numerous cognitive and
emotional symptoms. Cognitive symptoms of overload are manifested in the short term by errors
in processing, suboptimal decisions, shedding of tasks, deferring of choice, increased decision
times, increased variability in search strategies, or by otherwise adapting the load to make it
more manageable for the person’s limited attentional resources or expertise (e.g., [16,63]).
Emotional overload symptoms include stress, frustration, distractibility, inner frenzy, and
impatience (e.g., [56,78]). Longer term, sustained cognitive efforts and emotional consequences
can result in mental exhaustion [78].
In summary, our study focuses on a particular type of overload, ICT-related overload
with mobile phones, which is comprised of three dimensions: communication, feature, and
information overload [45]. It explores the impact of differences in mental representations across
individuals resulting from polychronicity (culture), age (demographics), and memories of past
emotional and cognitive overload (experiences) on their perception of ICT-related overload.
HYPOTHESES DEVELOPMENT
The culture in which people are raised affects, and even determines, their thought
processes [65]. Hall, an anthropologist, discovered key cultural values such as perspectives on
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time when he studied Native Americans who typically have polychronic cultures [38]. Thus,
cultures can transmit their values about polychronicity. Further, these values differ not only
across tribal, national and organizational cultures, but also across individuals within cultures
[14]. This is because individuals draw from different cultures to shape their own individual
culture [44] and can “espouse national cultural values to different degrees” [81:680].
Unlike monochronic individuals, polychronic individuals prefer to do several things at a
time [15]; they are more strongly oriented toward the present and feel themselves less bound to a
timetable or a procedure than monochronic individuals. They view time as an inexhaustible
resource and interpersonal relations are at least as important for them as the work to be
performed. This is not the case for monochronic individuals, whose sometimes extreme
concentration or dedication to one particular task relegates interpersonal communication to a
position of secondary importance, either temporarily or more permanently.
According to König and Waller [51], the concept of polychronicity is closely related to
the concept of multitasking, which refers to “the behavioral aspect of polychronicity” (p. 174).
Similarly, Bluedorn [14:107] notes that “multitasking shares elements in common with
polychronicity”. It thus appears that polychronic individuals are well equipped to deal with the
demands of using modern ICT and processing the increasing volumes of information that they
deliver. Polychronics even may resort to concurrent media exposure, which allows them to
become actively engaged in one media (e.g., chatting with friends on Facebook) while being
passively engaged in another (e.g., watching movies on their mobile phone) [80]. The
proliferation of mobile phones, in particular, may be increasing the incidence of multitasking
[77,85]. Hence, polychronics may feel that using ICT, most notably mobile phones, allows them
to efficiently handle multiple tasks simultaneously [66].
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Multitasking has been characterized in many ways, often without considering conceptual
nuances such as the importance of task switching. Since people really cannot cognitively execute
two tasks at one time (e.g. [47,48]), task switching offers a way of understanding how cognitive
multiple tasks might be performed very quickly in “twitch speed” (a phrase coined by Prensky
[65]). It also offers a way of understanding how polychronic individuals may be better at
switching from one task to another. One way of characterizing multitasking is to “represent it
along a continuum in terms of the time spent on one task before switching to another” [76:1819].
At one end of the continuum, concurrent multitasking is viewed as performing two or more tasks
at basically the same time because the task-switching intervals are extremely short—perhaps
milliseconds. At the other end of the continuum, sequential multitasking involves much longer
intervals before switching tasks. Being able to easily switch among tasks is viewed as a desirable
trait since it is perceived as a means of accomplishing many goals within a certain period of time
[35], viewing the same situation from different perspectives [53], and using existing technologies
in creative ways [32].
Successful task switchers likely encoded a positive tag to an event in the past when they
accomplished their goals by switching across multiple information-processing tasks. They
therefore may perceive less, or even no, ICT-related overload. This is consistent with prior
research, which finds that people perceive less role overload as their polychronicity scores
increase (e.g., [19,46,52]). Polychronic individuals may perceive that they are more able than
monochronic ones to manage multiple roles by segmenting sets of role expectations into separate
time blocks, combining activities, and reacting efficiently to multiple demands on their time
[19,46]. Even though role overload is not the same as ICT-related overload, there are similarities
in that polychronics also may perceive that they can better handle multiple roles as well as
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multiple tasks through the use of ICT. Such perceptions are subjective in nature and may serve
the self-fulfilling prophecy biases [57]. Consequently, polychronic individuals can be expected
to experience less ICT-related overload than monochronic individuals. Hence, we propose:
Hypothesis 1. Polychronicity is negatively related to ICT-related overload.
Age is a demographic that has been found to affect individuals’ reactions to using new
technologies [55]. Negative reactions include ICT-related overload and the allied concept of
technostress [19,28,67,84,85,86,91]. In this regard, some empirical studies find a positive
relationship between age and overload (e.g., [8,59]) and technostress (e.g., [79,91]). Tu et al. [89]
suggest that this positive relationship might be explained in several ways: older people are more
rigid in their thinking than younger people; they also display higher resistance to change in
general; and, because one’s learning capacity decreases with age, ICT usage leads to higher level
of stress for older people compared to younger people. Relatedly, cognitive functions such as
speed of processing, memory, reasoning, and dual-task performance may deteriorate with age
making it more difficult to process ICT-related cognitive loads. Thus, older people may
experience more stress [67], and hence, ICT-related overload.
Further, members of the Net Generation born from 1980 to present, which includes
Millennials, have been found to ‘media multitask’ more often than members of older generations
[20,32,43,90], and consequently may be able to process information more efficiently. This is
consistent with prior research, which suggests that the Net Generation finds multiple task
combinations significantly easier than older generations [20,65]. Put differently, task
combinations, especially when switching across multiple media, seem to place less cognitive
demands on younger individuals, who thus may feel less overloaded.
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In contrast, other studies find that older individuals experience less technostress [66,87],
or perceived overload [40], than younger individuals in a work setting. In particular, Ragu-
Nathan and colleagues suggest that older individuals possess the maturity to handle technostress
[66]. Their greater organizational tenure provides them with greater organization-specific
experience to use a given technology or endows them with more power to choose a technology
[87]. Also, older workers have been found to process the greater amounts of information that
they request [88]. Additionally, Reinecke et al. [67] argues that older people may not be as
subject to the social pressures to always be connected as younger individuals. Hence, older
people who are not as worried about missing out may have less communication and information
load to handle and, ultimately, perceive less ICT-related overload.
Against this backdrop, while it could be argued that age is both positively and negatively
related to ICT-related overload, we find the arguments about the poorer cognitive abilities of
older people to handle efficiently the multiple task combinations associated with the use of
mobile phones to be especially compelling. For example, the many features available on mobile
phones lend themselves to switching back and forth across multiple tasks over extremely short
periods of time. Further, in comparison to older users, younger users tend to use these features
significantly more [20], which may improve their ‘media-multitasking’ skills and thus reduce the
likelihood of them feeling overloaded. Hence, we hypothesize:
Hypothesis 2. Age is positively related to ICT-related overload.
Perceptions of ICT-related overload may also be influenced by memories of past
experiences in dealing with ICT. Specifically, the memories of past overload stored in an
individual’s long-term memory carry negative emotional tags [18]. For example, if individuals
receive an input that reminds them of an unpleasant situation in the past when they made a lot of
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mistakes because they were unable to properly use a technology or handle the information
delivered, they will have memories of cognitive overload stored in their mental representations in
long-term memory [75]. Similarly, if individuals were stressed or frustrated in the past when
dealing with a new technology or the information it delivered, they would have stored memories
of emotional overload with negative tags in their mental representations [75]. These stored
memories are likely to negatively impact their use of another (similar) technology. For example,
existing studies (e.g., [72,74]) report a significant and positive relationship between memories of
past overload from ICT use and perceptions of overload from requests to use a new ICT (e.g., a
video-contact technology designed to deliver health and banking information). This suggests
that—regardless of whether the past experience that was negatively encoded as ICT-related
involved mobile phones or not—an individual currently facing an overload situation involving
mobile phones may experience associative activation related to the ICT used in the earlier event.
This is because of connections between the two types of ICT that exist in this individual’s mental
representations. Hence, this individual may be prone to experiencing ICT-related overload.
In other words, if individuals were able to successfully process a mental load in the past,
they will not have a memory of overload associated with that mental effort. However, individuals
may have a negatively tagged memory of an event when they were not able to handle the mental
load required to use a new technology and/or process inputs delivered by this technology. Here,
prior research (e.g., [3,5,83]) suggests that processing such inputs would have created a mental
load, which required the expenditure of mental resources to reduce. The mental load would have
varied as a function of the individual’s abilities, experience and expertise [75]. If an individual’s
mental resources were drained in trying to process the mental load, the individual may have
experienced ICT-related overload. That experience would have activated an alteration of the
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individual’s mental representations associated with the unsuccessful processing of the input. For
example, the individual might remember the frustration in being unable to process the mental
load (past emotional overload) and/or the negative consequences of not adequately processing
the input and making a poor decision (past cognitive overload). This suggests:
Hypothesis 3. Memories of (a) past cognitive overload and (b) past emotional overload
are positively related to ICT-related overload.
Our research model and hypotheses are presented in the figure below.
(Insert Figure 2 about here)
METHODOLOGY
Instrument Development
To test our research model, we developed a survey instrument. Besides providing their
age, survey participants were asked about their cultural preferences with regard to time (i.e.,
polychronicity) as well as their past and present experiences of ICT-related overload. Adding to
this, participants were surveyed on their use of mobile phones and other socio-demographic
information (e.g., education level, employment status, and monthly net income). The survey
instrument was implemented with the use of Questback’s Online EFS Survey software.
Measurement items for the dependent and independent variables were derived from
existing scales, which had been shown to have good psychometric properties in previous studies,
and adapted to the context of our study (i.e., mobile phone use). Specifically, to measure our
dependent variable, ICT-related overload, we adopted the three overload dimensions defined by
Karr-Wisniewski and Lu [45]: communication overload, feature overload, and information
overload. We added items to insure that each dimension contained a cognitive and emotional
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component of ICT-related overload [72,74]. The overload dimensions (i.e., communication,
feature and information) were employed to model ICT-related overload as a reflective-formative
second-order construct [9]. With regard to the independent latent variables, polychronicity was
measured using the “Inventory of Polychronic Values (IPV)” [13], which reflects the level of
individuals’ (subjective) polychronic cultural identity. Items for memories of both past cognitive
overload and past emotional overload were adapted from Rutkowski et al. [74]. These items
reflect (negative) memories associated with the use of an ICT that individuals adopted in the
past, and which are since then embedded in their mental representations in long-term memory.
All latent variables were measured with multiple reflective items on seven-point Likert scales.
The original items were translated into German, as the survey population was German-
speaking. We pretested the survey instrument with ten individuals (two experienced researchers,
five PhD students, and three students). The pretest resulted in minor adaptations in item
wordings. Pretest participants were not included in the main sample. An overview of the
constructs and measurement items (in English) is provided in Table A1 in the Appendix.
We included several control variables in our research model (see also Figure 2 above) to
account for rival explanations: gender, mobile phone type (whether the survey respondent uses a
smartphone or a regular mobile phone), use context (whether the respondent uses her or his
mobile phone primarily for professional or private purposes), as well as three proxies for use
amount in terms of communication (average number of calls the respondent makes or receives on
her mobile phone per day), features (number of mobile phone features the respondent uses, such
as calling, texting, surfing, social networking, etc.), and information (average number of emails
the respondent receives on her mobile phone per day).
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Data Collection
The survey instrument was administered over the Internet to improve accuracy and
efficiency in data collection and analysis. The survey was available online for three months
(from September to November 2014). Survey respondents were recruited through multiple
channels (e.g., email, face-to-face, invitation letter, social media). For example, the survey link
was posted on Twitter, Facebook and professional network websites (e.g., XING). In addition,
email invitations were sent to employees of local companies, and more than 500 invitation letters
were sent to diverse residential areas. Survey participants were required to use a mobile phone on
a regular basis. In total, 1,377 individuals accessed the start page of the online questionnaire, and
1,004 individuals completed the questionnaire. This corresponds to a completion rate of 72.9%.
In terms of gender, our data sample was 42.2% female and 57.8% male respondents. The
age of the respondents ranged from 17 to 74 years with a mean age of 25.07 years (standard
deviation: 8.702). Furthermore, the educational level of the survey participants can be described
as high, with virtually all participants holding at least a high-school diploma (97.8%) and almost
half of them also holding at least a bachelor degree (43.2%). On average, survey participants
started to use ICT at the age of 13 (mean: 13.6; standard deviation: 6.869).
Reliability and Validity
We used the Partial Least Squares Structural Equation Modeling (PLS-SEM) path-
weighting scheme to assess the reliability and validity of the measurement model. Content
validity was assured by selecting well-established measurement items from previous studies as
well as by consulting experienced researchers in a pretest (see above) [17]. To ensure item
reliability, we removed a total of eight items from the model due to low loadings. The loadings
of all remaining items are above the recommended threshold of 0.7 (e.g., [7,41]), except for two
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polychronicity items with loadings slightly below 0.7 (see Table A1 in the Appendix). Construct
reliability can be assessed with the composite reliability measure [33]. All constructs exceed the
recommended threshold of 0.7 (see Table A2 in the Appendix). Moreover, for each construct, the
average variance extracted (AVE) is greater than 0.5, establishing convergent validity [33]. Each
construct also shares more variance with its assigned items (see diagonal values in Table A2)
than with any other variable (see off-diagonal values) [33], indicating discriminant validity [41].1
Additionally, each within-construct item loads highly on the construct it is supposed to measure,
and cross-loadings are lower than within-construct loadings.
To assess the validity of the second-order construct, ICT-related overload, we followed
the process described by MacKenzie et al. [54]. First, we evaluated the AVE for each first-order
dimension (communication, feature, and information overload); second, we calculated Edwards’
[31] adequacy coefficient (R2a) for ICT-related overload. The AVEs (see above) and the R2a
value (0.731) are above the recommended threshold of 0.5, which means that the majority of the
variance in the first-order dimensions is shared with the second-order construct [54].
Common Method Variance and Multicollinearity
As recommended by Podsakoff et al. [64], we employed techniques to reduce the risk of
common method variance already during the design of our survey instrument. Specifically, we
counterbalanced the order of the measurement items of the independent and dependent variables
to “control for priming effects, item-context-induced mood states, and other biases related to the
question context or item embeddedness” [64:888]. Furthermore, to assess common method
variance, we performed Harman’s single-factor test [64]. Neither did a single factor emerge from
1 While the constructs of past cognitive overload and past emotional overload show discriminant validity, they are
still relatively highly correlated (as in earlier studies), indicating the co-presence of emotions with cognitive
processing. Similarly, two of the three overload dimensions (communication and information) are relatively highly
correlated, suggesting that communication overload may contribute to information overload, and vice versa.
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an exploratory factor analysis, nor did one general factor account for more than 50% of the total
variance. (One factor explained 28.3% of the variance in the data.)
In addition, we conducted a full collinearity test by calculating the variance inflation
factor (VIF) values for all independent and dependent variables in our research model [50]. The
highest VIF value was observed for memories of past emotional overload (1.969). This means
that VIF values for all variables were clearly below the most conservative VIF threshold of 3.3
[50]. Considering all model variables, a full collinearity test is also an effective and conservative
alternative for the identification of common method bias [49]. Taken together, the conducted
tests indicate that both common method variance and multicollinearity are unlikely to be a
serious concern in our study.
ANALYSIS & RESULTS
We used SmartPLS 3.0 [69], a popular software application for PLS-SEM, to transform
our research model into a structural equation model. PLS-SEM path modeling is an appropriate
choice when the research focus is on predicting key target constructs [34,37,68]—ICT-related
overload in the case of our study. Also, compared to Covariance Based Structural Equation
Modeling (CB-SEM), PLS-SEM “is more robust with fewer identification issues, works with
much smaller as well as much larger samples, and readily incorporates formative as well as
reflective constructs” [37:143]. In addition, it is the preferred method in situations where CB-
SEM’s distributional assumptions cannot be met [37,68]. As recommended by Hair et al. [37],
we tested the model with a bootstrap size of 5,000 subsamples.
Test of Research Model
As shown in Table 1 below, the PLS-SEM analysis results provide support for three of
the four hypothesized relationships. As expected, polychronicity (H1) has a significant and
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negative effect on ICT-related overload (ß = -0.130, t-value = 4.762, p < 0.001), whereas
memories of both past cognitive overload (ß = 0.178, t-value = 3.872, p < 0.001) and past
emotional overload (ß = 0.175, t-value = 3.593, p < 0.001) have a significant and positive effect
on overload, providing support for H3a and H3b. In contrast, age (H2) is not significantly related
to ICT-related overload (ß = -0.033, t-value = 0.923). Adding to this, three control variables
(communication-related use amount, gender, and mobile phone type) are significantly and
positively related to ICT-related overload (see Table 1), suggesting that women and smartphone
users are more likely to experience overload.
The model explains roughly 20% of the variance in ICT-related overload (R2 = 19.7%).
The strength of the significant main effects was assessed using the effect size f2 as [R2 included –
R2 excluded] / [1 – R2 included] [22,23]. The effect sizes are as follows: polychronicity (f2-value
= 0.019), memories of past cognitive overload (0.015) and past emotional overload (0.015).
(Insert Table 1 about here)
To account for the multidimensionality of the ICT-related overload construct [45] and the
potential existence of one-dimensional effects that may mask other effects [cf. ,19], we repeated
the PLS-SEM analysis with the three dimensions of overload (communication, feature,
information) as dependent variables. This allowed us to explore a different model setup, as also
recommended by Ringle et al. [68]. The results of the post-hoc analysis are presented next.
Post-Hoc Analysis
The post-hoc analysis results confirm the significant effects of three of the four main
independent variables. Polychronicity is significantly and negatively related to communication,
feature, and information overload. Similarly, memories of both past cognitive and past emotional
overload are significantly and positively related to all three dimensions of ICT-related overload.
20
Interestingly, age shows significant but inverse effects on different overload dimensions. While
age is negatively associated with communication and information overload, it is positively
associated with feature overload (see Table 2). This finding helps explain why age did not show
a significant overall effect on ICT-related overload in the main analysis.
(Insert Table 2 about here)
The post-hoc analysis results also reveal some nuanced differences in significance
patterns. For example, while both gender and mobile phone type are significantly and positively
related to all three dimensions of ICT-related overload, communication-related use amount is
significantly and positively related to only one dimension (communication overload), which
seems to explain the overall significant effect observed in the main analysis (see Table 1 above).
DISCUSSION
The study at hand draws on an input-processing-output (IPO) model and uses survey data
from 1,004 individuals to provide significant empirical support for an expanded cognitive
perspective of ICT-related overload. This perspective acknowledges the multidimensionality of
the overload concept and views individuals’ information-processing capabilities as being reliant
on individual differences in mental representations associated with cultural values
(polychronicity), demographics (age), and experiences (past emotional and cognitive overload).
In the following, we discuss the theoretical and managerial implications of our study as well as
the study limitations along with promising avenues for future research.
Theoretical Implications
The main theoretical contributions of our study are twofold: First, the study results show
that ICT-related overload is not only a function of the amount of ICT use and the information
delivered through ICT, but also a function of differences in mental representations across
21
individuals resulting from cultural, demographic, and experiential factors. Specifically, our
results suggest that people with higher levels of polychronicity are less likely to experience ICT-
related overload than are people with higher levels of monochronicity (i.e., lower levels of
polychronicity). Relatedly, we find that unpleasant emotional and cognitive memories of ICT use
in the past are likely to lead to feelings of overload related to the current use of ICT. Our study is
among the first to significantly link ICT-related overload with individuals’ cultural preference
for multitasking or task switching (i.e., polychronicity) as well as their experience of past
cognitive and emotional overload. The study results thus imply that future research on the
antecedents of ICT-related overload needs to adopt a broader perspective that takes into account
differences in information-processing capabilities across individuals. As shown in this study,
such differences may stem from cultural, demographic, and experiential factors. A promising
avenue for further research may thus be to examine the influence of other related factors on
individuals’ processing capabilities, such as temporal pace (cultural values), education level
(demographics), gender (demographics), and level of task expertise (experiences).
Second, the study results highlight the importance of considering the multidimensionality
of the overload concept, especially in relation to age. To our knowledge, the study is among the
first to use a nuanced view of ICT-related overload that incorporates three overload dimensions
(communication, feature, and information), as well as emotional and cognitive components. By
measuring ICT-related overload as a second-order construct, we allow the dimensions to interact
with one another. Further, while our findings about the effects of polychronicity and memories of
past overload on ICT-related overload are consistent for both the second-order construct (main
analysis) and the three separate dimensions (post-hoc analysis), differences emerge when looking
at the impact of age on the different overload dimensions.
22
In particular, the results of our post-hoc analysis indicate that older mobile phone users,
as we anticipated, are more likely to feel overloaded from the phone’s features than are younger
users. The analysis results thus suggest that older people find it harder than younger ones to
explore and use the plethora of options available on mobile phones, which are now more than
just devices for calling someone. One possible explanation for this finding is that older mobile
phone users may have started using ICT such as desktop computers when available features were
still low in number and commands were entered in response to ‘esoteric’ prompts. On the other
hand, younger users are only familiar with ICT that are designed with a wide range of features,
which can be selected through user-friendly interfaces such as icons. Another explanation is that
older users may lack media-multitasking skills, which occur at task-switching intervals of ‘twitch
speed’ (i.e., milliseconds), making it harder for them to use the various features of their mobile
phone simultaneously. In contrast, because of their media-multitasking skills, younger users may
be able to easily switch across multiple tasks in order to leverage the different features of their
mobile phones [32,90]. Yet another explanation is that as younger people have grown up with
modern ICT, they more likely have used a wider variety of ICT [39] and so may find mobile
phones to be similar to other ICT that they have already mastered. In other words, since many of
the younger users have virtually always experienced technology as part of their daily lives, they
might use an assimilation process to learn the new technology as opposed to the more difficult
accommodative process older people are likely to employ [39]. Further, because of their
proficiency and familiarity with ICT from an early age, younger people may have few or even no
memories of situations when they were overloaded from using ICT.
Nevertheless, the results of our study also suggest that younger mobile phone users are
not immune from being overloaded. In other words, they may not be as expert at cognitive
23
multitasking as Net Generation advocates would have us believe [43]. Specifically, we find that
younger users are significantly more likely than older users to experience overload from
information and communication messages delivered through ICT. Spink et al. [80] offer a
potential explanation for this finding: They argue that younger people may think that the plethora
of ICT devices facilitates multitasking. This would imply to them that the devices help make
them even better at something at which they already excel. However, when multitasking,
younger users likely experience reaction-time switching costs (e.g., [20,43]). Greater switching
costs have been associated with a number of factors including slower associative retrieval of task
sets and information from memory, a greater number of interferences in completing multiple
tasks over a period of time, and enhanced task complexity requiring more cognitive effort
[60,71]. Here, one may argue that the task-switching costs in ‘media multitasking’ are
particularly high because users develop a monitoring habit which entails checking mobile phone
content every 30 seconds or less [71]. These short checking intervals are more likely to carry
high switching costs as the users switch frequently from one task to another. Consequently,
younger users who are ‘media multitaskers’ are not able to handle incoming messages and
communications as efficiently as they think they can, potentially creating situations of
information and communication overload for them.
On a related note, the results of our study imply that ‘media multitasking’ may be very
different from other types of multitasking. For example, differences in multitasking, especially in
terms of the length of the task-switching interval and use of media, help explain why our results
suggest that women are more likely to get overloaded despite “the general assumption that
women are superior multitaskers” [27; cf. 32]. In particular, women may be experts at sequential
multitasking (e.g., watching small kids while cooking a meal and questioning a teenager about
24
his homework assignment) where the task switching intervals are longer compared to ‘media
multitasking’. Still another potential explanation could be that women are not engaging in
concurrent multitasking involving primarily cognitive tasks [43]. Rather, women’s multitasking
may be more social in nature and not mediated by mobile phones or other ICT; that is, their
multitasking actually may involve switching across social tasks similar to those studied by Hall
[38] and Bluedorn [14,15]. In particular, Hall [38] considers polychronic time to be ‘female
time’ and in the cultures he studied women were probably expected to be more polychronic. For
Bluedorn [14], polychronicity is more about the sequencing of tasks over a single day or a few
hours, rather than the speed of their performance. Ironically, Bluedorn did not find conclusive
support for gender being related to polychronicity. At any rate, future studies should be more
cognizant of task-switching intervals and frequency. Further, they should explore how ‘media
multitasking’, as a special type of multitasking, might differentially impact perceptions of
overload across gender, as well as age.
Moreover, our study also identifies several additional factors that affect an individual’s
perception of ICT-related overload. For instance, the study results reveal that mobile phone type
is significantly and positively related to all three dimensions of ICT-related overload. This
finding is consistent with earlier findings by Holton and Chyi [40] who find that iPhone users
perceive more overload with news than users of other types of mobile phones. The overload
reported in our study could be related to the plethora of features that are available on iPhones and
other smartphones but not on regular mobile phones, as also indicated by the positive correlation
between mobile phone type and feature-related use amount (see Table A2 in the Appendix).
Finally, by considering the different dimensions of the overload concept, our study
provides new insight on the link between ICT use amount and ICT-related overload. In
25
particular, while the amount of communication-related ICT use is only significantly related to
communication overload, the feature-related use amount is significantly related to both feature
and information overload. However, we observe no significant relationship between the
information-related use amount and any of the three overload dimensions. This is consistent with
our premise that information overload is not just about amount, but also reflects the individual’s
ability to process information. Against this backdrop, future research should not constrain itself
to the information dimension of ICT-related overload in general, and to the amount of
information input in particular. Instead, we encourage more refined theory development and
additional empirical studies in order to explain the nuances of the theoretical relationships found
in this study and to better understand the phenomenon of ICT-related overload as a whole.
Managerial Implications
The results of our study also provide several important practical implications for
managers. First, they point to the importance of providing adequate training on the features of
new technology. Given the study results, such training seems to be particularly relevant for older
employees who are more prone to experiencing feature overload. The training could make them
feel more technically competent and reduce their technostress [86]. Furthermore, such training
may help alleviate negative memories associated with ICT use in the past, and thus reduce the
likelihood of employees perceiving ICT-related overload in general.
Second, managers should consider establishing office norms to reduce communication
and information overload situations, especially to ‘protect’ younger employees. For example,
counteracting digital natives’ tendency to respond to email and chat messages immediately, such
norms may create an environment where communications delivered by ICT need to be responded
to within one workday.
26
Third, managers could also use polychronicity by taking into account their employees’
cultural preferences when assigning tasks. For example, polychronic individuals could be in
charge of communication-laden tasks, whereas monochronic individuals could be assigned to
tasks where heavy concentration is needed such as writing reports or engaging in deep analysis.
Fourth and finally, managers may want to ensure that new technologies are designed in a
way that reduces the risk of ICT-related (feature) overload. For instance, the design of a newly
introduced enterprise software application may offer the use of an ‘expert’ profile (including all
software features) as well as the use of a ‘basic’ profile (restricted to key features). Depending on
their age and experience with similar ICT, users could then receive a recommendation and
decide on whether to use the expert or the basic version of the software.
Limitations and Future Research
The study findings should be interpreted with several limitations in mind. First, our study
was conducted on a large sample of Germans. A number of studies have found significant
differences in polychronicity across cultures. For example, Germans have been found to be less
polychronic than Americans [24]. We thus suggest replicating our study in other (national)
cultures. Second, although our data sample included 335 participants older than 34 (the oldest
participant was 74), the survey population was relatively young on average. Also, almost half of
the survey participants had a high education level (43.2 % of the participants held at least a
bachelor degree). Therefore, repeating our study with an older and less educated population may
reveal additional insights. Third, in addition to multi-indicator latent variables, our research
model also contained variables measured through single indicators (e.g., age), which tend to
increase the model’s full collinearity [36,50]. As noted in the Methodology section, we therefore
conducted a full collinearity test [50]. The VIF values for all variables in the model were found
27
to be lower than 3.3, suggesting that model-wide collinearity was not a problem in our study
[50]. Fourth, even though our data analysis revealed several significant effects on ICT-related
overload, the size of these effects was rather weak. Thus, future research is needed to confirm
and better understand the observed relationships. Fifth, as with many survey-based research
studies, our study included constructs that are difficult to operationalize, such as past memories
of overload embedded in mental representations in long-term memory and individuals’
perceptions of ICT-related overload (as opposed to actual overload). In this regard, a promising
avenue for future research would be to use tools from cognitive neuroscience, or other cognitive
testing, to further examine cognitive and emotional aspects of ICT-related overload. Sixth, prior
research argues that it is impossible for individuals to cognitively engage in multiple tasks at one
time (e.g., [43,47,74]). Indeed, frequent task switching (i.e., concurrent multitasking) places high
demands on mental resources [48] and can result in (cognitive) overload as the brain shifts
between competing inputs [10]. Therefore, a multitasking paradox exists in which polychronics
with their preference for switching across multiple tasks may perceive that they are able to
effectively use ICT and process the delivered information even though the cognitive reality may
be that they are not able to do so and that they are actually overloaded to the same extent as
monochronics. In this regard, it would be interesting to incorporate psychological tests in future
studies to measure potential differences between perceived and actual ICT-related overload.
Relatedly, in the long term, polychronic people may still experience high stress or burnout
because they mistakenly think that they always can use task switching (over various intervals of
time) to handle their frenetic world. We thus encourage future research to conduct longitudinal
studies that explore the long-term effects of ICT use on overload.
28
CONCLUSION
ICT-related overload is an epiphenomenon of the digital age. While prior research tends
to ascribe this phenomenon to the increasing amount of information delivered by ICT, the study
at hand offers an expanded cognitive perspective of ICT-related overload that focuses on
individuals’ information-processing capabilities and thus goes beyond the ‘amount’ argument
and the inherent focus on information overload. Our study contributes to the existing literature
by linking differences in mental representations to cultural, demographic, and experiential
factors, as well as by providing empirical support for the, partly inverse, effects of
polychronicity, age, and past overload on ICT-related overload and its sub-dimensions. In
conclusion, it is our hope that the study results will inspire future research on ICT-related
overload and prove to be valuable in finding ways to reduce the negative consequences of
overload both now and in the future.
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FIGURES & TABLES
Figure 1. IPO model of ICT-related overload
Figure 2. Research model
Pertinent
Information
Inability to
Process
Note: Arrows represent flow and not causality.
Processing
Differences in mental frameworks due
to an individual’s:
• Cultural values (polychronicity)
• Demographics (age)
• Experiences (memories of past
cognitive and emotional overload)
Output
ICT-related overload
• Communication
• Feature
• Information
Input
Delivered
by ICT
Polychronicity
Age
Memories of
past emotional overload
ICT-related
overload
Communication
Feature
Information
Control variables
Gender
Mobile phone type
Use amount & context
Memories of
past cognitive overload
H1 (-)
H3a (+)
H3b (+)
H2 (+)
39
Table 1. PLS-SEM analysis results
Dependent variable
ICT-related overload
Control variables/constructs
ß (t-value)
Gender
0.123 (4.221***)
Mobile phone type
0.159 (3.778***)
Use amount (communication)
0.068 (2.124*)
Use amount (features)
0.043 (1.014)
Use amount (information)
0.025 (0.745)
Use context
-0.014 (0.380)
H1: Polychronicity
-0.130 (4.762***)
H2: Age
-0.033 (0.923)
H3a: Memories of past cognitive overload (PCO)
0.178 (3.872***)
H3b: Memories of past emotional overload (PEO)
0.175 (3.593***)
R2
19.7%
Notes: N = 1,004. Significant effects in boldface. * p < .05; ** p < .01; *** p < .001 (two-tailed test).
Table 2. Post-hoc analysis results
Dependent variables
Comm. overload
Feature overload
Information overload
Control var./constructs
ß (t-value)
ß (t-value)
ß (t-value)
Gender
0.125 (4.081***)
0.090 (2.944**)
0.100 (3.343***)
Mobile phone type
0.113 (2.768**)
0.132 (2.881**)
0.160 (3.979***)
Use amount (comm.)
0.090 (2.851**)
0.020 (0.593)
0.059 (1.931)
Use amount (feat.)
0.083 (1.807)
-0.093 (2.109*)
0.098 (2.382*)
Use amount (info.)
0.035 (1.126)
-0.023 (0.846)
0.044 (1.226)
Use context
-0.045 (1.433)
0.037 (0.917)
-0.022 (0.612)
H1: Polychronicity
-0.137 (4.833***)
-0.063 (2.046*)
-0.125 (4.461***)
H2: Age
-0.107 (3.272**)
0.148 (3.614***)
-0.097 (3.209**)
H3a: Memories of PCO
0.133 (2.715**)
0.149 (2.861**)
0.173 (3.663***)
H3b: Memories of PEO
0.107 (2.245*)
0.221 (3.833***)
0.133 (2.756**)
R2
15.3%
18.0%
19.4%
Notes: N = 1,004. Significant effects in boldface. * p < .05; ** p < .01; *** p < .001 (two-tailed test).
