ArticlePDF Available

Information overload in the information age: a review of the literature from business administration, business psychology, and related disciplines with a bibliometric approach and framework development

Authors:
  • Aschaffenburg University of Applied Sciences

Abstract

In the light of the information age, information overload research in new areas (e.g., social media, virtual collaboration) rises rapidly in many fields of research in business administration with a variety of methods and subjects. This review article analyzes the development of information overload literature in business administration and related interdisciplinary fields and provides a comprehensive and overarching overview using a bibliometric literature analysis combined with a snowball sampling approach. For the last decade, this article reveals research directions and bridges of literature in a wide range of fields of business administration (e.g., accounting, finance, health management, human resources, innovation management, international management, information systems, marketing, manufacturing, or organizational science). This review article identifies the major papers of various research streams to capture the pulse of the information overload-related research and suggest new questions that could be addressed in the future and identifies concrete open gaps for further research. Furthermore, this article presents a new framework for structuring information overload issues which extends our understanding of influence factors and effects of information overload in the decision-making process.
ORIGINAL RESEARCH
Information overload in the information age: a review
of the literature from business administration, business
psychology, and related disciplines with a bibliometric
approach and framework development
Peter Gordon Roetzel
1,2
Received: 23 May 2017 / Accepted: 25 June 2018 / Published online: 6 July 2018
The Author(s) 2018
Abstract In the light of the information age, information overload research in new
areas (e.g., social media, virtual collaboration) rises rapidly in many fields of
research in business administration with a variety of methods and subjects. This
review article analyzes the development of information overload literature in
business administration and related interdisciplinary fields and provides a com-
prehensive and overarching overview using a bibliometric literature analysis com-
bined with a snowball sampling approach. For the last decade, this article reveals
research directions and bridges of literature in a wide range of fields of business
administration (e.g., accounting, finance, health management, human resources,
innovation management, international management, information systems, market-
ing, manufacturing, or organizational science). This review article identifies the
major papers of various research streams to capture the pulse of the information
overload-related research and suggest new questions that could be addressed in the
future and identifies concrete open gaps for further research. Furthermore, this
article presents a new framework for structuring information overload issues which
extends our understanding of influence factors and effects of information overload
in the decision-making process.
Keywords Information processing Information management strategies
Information overload Literature review Bibliometric literature analysis
&Peter Gordon Roetzel
peter.roetzel@bwi.uni-stuttgart.de
1
University of Stuttgart, Keplerstr. 17, 70174 Stuttgart, Germany
2
University of Applied Sciences Aschaffenburg, Aschaffenburg, Germany
123
Business Research (2019) 12:479–522
https://doi.org/10.1007/s40685-018-0069-z
1 Introduction
Information overload is a decisive factor driving negative ‘‘work environments
[that] are killing productivity, dampening creativity, and making us unhappy’’
(Dean and Webb 2011). Losses arising directly or indirectly from information
overload are estimated at $650 billion worldwide each year (Lohr 2007)—an
amount that equals the gross domestic product of Switzerland in 2015 (United
Nations Statistic Division 2016).
Information overload occurs when decision-makers face a level of information
that is greater than their information processing capacity, i.e., an overly high
information load (Schroder et al. 1967; Eppler and Mengis 2004), but the
phenomenon is not confined to the modern world. As Blair (2012) noted in her
review article, even in the thirteenth century, scholars complained of ‘‘the key
ingredients of the feeling of overload which are still with us today: ‘the multitude of
books, the shortness of time and the slipperiness of memory’’’ (Blair 2012, p. 1).
Two radical innovations supported the rapid increase in the availability of
information and the decrease in information search-related costs: Gutenberg’s
printing innovations and the rise of information technology (IT). Before these
radical innovations, the issue of information overload was limited to a wealthy and
privileged elite. In particular, the rise of IT and the use of internet services have
resulted in an expansion of information overload-related problems for all social
ranks. In ancient and medieval times, the nobility and academics almost exclusively
faced information overload-related problems, as Blair (2012) and Levitin (2014)
suggested.
Information (over-)load research peaked in the 1980s and 1990s; interest in this
topic quieted down in the 2000s (Eppler and Mengis 2004; Ding and Beaulieu 2011;
Lewis 1996; Edmunds and Morris 2000; Feather 1988) and languished in the 2010s.
In retrospect, research found many impacts and implications of information load and
developed countermeasures. However, a quarter century after interest in information
load research peaked, the information load of managers in day-to-day operations has
quadrupled. Thus, in the information age, information overload research in new areas
(e.g., social media, virtual collaboration) seems to be rising rapidly (Dean and Webb
2011; Hemp 2009; Kolfschoten and Brazier 2013; Shapiro and Varian 2013).
But since the often-cited literature review of Eppler and Mengis (2004),
1
no
study has yet focused on offering a comprehensive and overarching literature review
regarding information overload. The prior literature offers some discipline-specific
literature reviews, which allow in-depth insights and an understanding of
information overload in each discipline: marketing and organizational science
(Klausegger et al. 2007), healthcare management (Hall and Walton 2004), business
informatics conferences (Melinat et al. 2014), technology-based education (Shri-
vastav and Hiltz 2013), general management (Jackson and Farzaneh 2012), and
business-related psychotherapy (Case et al. 2005). But an actual review of
information overload in today’s information age is still missing.
1
Google Scholar: 1302 cites [1.5.2018].
480 Business Research (2019) 12:479–522
123
This literature review aims to close this gap and present a comprehensive and
overarching literature review for 2004–2017. This paper contributes to prior
research in four ways. First, this paper presents an actual and comprehensive
overview regarding the information overload literature in a wide range of fields of
business administration (e.g., accounting, finance, health management, human
resources, innovation management, international management, information systems
(IS) management, marketing, manufacturing, and organizational science). This
overview can be a foundation for research in business administration with a focus on
information overload issues. This review addresses a limitation noted by Eppler and
Mengis (2004), namely that research focusing on information overload from other
perspectives (e.g., psychology, health care, and mass communication) is not
addressed adequately. As Webster and Watson (2002) noted, review articles are
critical to strengthening research itself. Therefore, this paper also identifies the
theoretical basis and the method (e.g., experiment, survey) of the papers to ensure
comparability. Second, this paper structures prior studies and identifies some
avenues for further research. Third, this paper addresses interdisciplinary papers,
links fields of research that remain broadly isolated, and answers the call for
research from Eppler and Mengis (2004), who stated that ‘‘the overload problem
calls for interdisciplinary approaches as many of the open research questions in this
field cross traditional disciplinary boundaries’’ (p. 341). Fourth, this paper provides
a new framework for structuring information overload issues and extends our
understanding of influence factors and effects of information overload in the
decision-making process. This paper develops a thorough framework that spans
from the starting situation, to the information search and selection via information
processing, to decision-making, and to the ex post consequences of the decision.
The remainder of this paper is organized as follows: In the next section, I provide
the theoretical basis for information overload and develop a working definition. The
subsequent section presents the methodology, including the literature collection
process of the bibliometric analysis and a sample description. The fourth section
provides descriptive results of the bibliometric analysis. In the final sections of this
paper, I present and discuss our results and draw conclusions.
2 Working definition of information overload
A widely used standardized definition of information overload is still missing.
Eppler and Mengis (2004) listed seven definitions of information overload in the
business research literature. Similar to business research, prior research on
information processes suffers from a lack of standardized definitions across
different disciplines (Edmunds and Morris 2000; Meadow and Yuan 1997). A
necessary starting point for this study is a working definition of information
overload. This situation prevails in the 2000s (Hadfi and Ito 2013). Thus, a working
definition of information overload is needed.
In information overload situations, a decision-maker faces what Herbert Simon
called ‘‘a wealth of information [which] creates a poverty of attention and a need to
allocate that attention efficiently among the overabundance of information sources
Business Research (2019) 12:479–522 481
123
that might consume it’’ (Simon 1971, pp. 40–41). While research has been aware of
this phenomenon since the 1960s (Eppler and Mengis 2004; Bawden and Robinson
2009), the information age has significantly increased the amount of information
available: ‘‘The information age is drowning us with an unprecedented deluge of
data’’ (Levitin 2014). Shenk (1997) described this phenomenon as data smog, the
‘muck and druck of the information age’’ (Shenk 1997, p. 31). Today, decision-
makers can acquire additional information easily (e.g., via management information
systems), and the cost of additional information is very low compared to the cost in
the pre-IT age (Levitin 2014; Shapiro and Varian 2013). For example, in pre-IT
times, any calculation, evaluation, or determination of key performance indicators
(KPI) entailed costs to pay employees to perform these calculations. In addition, the
acquisition of further management accounting information or reports took time.
Currently, a calculation that required several days in pre-IT times can be performed
by a management information system (MIS) within seconds (Levitin 2014).
Although decision support IS and the acquisition of information developed
rapidly, the decision-maker’s cognitive capacity did not. Simon and Newell (1971)
stated that limited short-term or working memory and limited information
processing capacity per time unit are two decisive factors explaining why
decision-makers cannot incorporate an overly high level of information given
limited time.
2
While pre-IT decision-makers could evaluate the acquired informa-
tion while further analysis was being pursued, today, additional information is
available in a minimum of time. Hence, decision-makers may face situations in
which they receive much more information than they are able to evaluate.
All prior approaches to information overload share the fact that a level, or a
certain set of information, serves as the final straw. To simplify, I refer to this level
or set of information a ‘‘point’’ (in the style of mathematical analysis) because in a
function a point is represented by an X(the information level—as the independent
variable) and a Y(decision-making performance—as the dependent variable).
Considering the simple two-dimensional relationship between the information input
as the independent variable (e.g., information load, information provided, informa-
tion received) and the decision-making performance as the dependent variable, the
decision output will improve between zero and the particular point at which the
human information processing capacity is reached. Beyond that point, the decision-
maker is being asked to handle more information than possible due to his/her
limited information processing capacity. At this level of abstraction, all approaches
to information load name this state ‘‘information overload’’. The underlying
function that describes the relationship between information input and decision
output diverges across the approaches. The prevailing view interprets this
relationship as an inverted U curve (Driver and Streufert 1969; Driver et al.
1990; Schroder et al. 1967) (see Fig. 1).
Another approach is to consider information complexity in addition to the
amount of information (Bawden and Robinson 2009; Eppler and Mengis 2004). IS
2
The limitation of time is a theoretical construct drawing on infinite long-term memory, as Simon and
Newell (1971) noted. Extreme examples might exist for managerial decisions in which unlimited time is
given, but usually time is a decisive factor in managerial decision-making.
482 Business Research (2019) 12:479–522
123
research studies address the issue of information complexity with a special focus on
information quality (e.g., Doll and Torkzadeh 1988 or Burton-Jones and Straub
2006). If information complexity is high, the decision-maker’s information
processing capacity might be reached well before the point when only the amount
of information is used.
Following this view, at least two possible overarching explanations exist for this
turning point (Schroder et al. 1967). First, from a cognitive viewpoint, decision-
makers cannot use more information than his/her limited information processing
capacity, stop acquiring information, and make a decision based on the limited
information that they have (bounded rationality (Simon 1955)). This sequence
assumes that the decision-maker is able to stop information acquisition. However,
situations could occur in which stopping would not be possible, e.g., during a
meeting.
Second, from an equipment-related viewpoint, the inverted U curve could be
explained by limited resources (e.g., time or budget). If a resource that is decisive
for decision-making (e.g., time or budget) is limited, the available information
cannot be used efficiently. For example, an auditor who is strictly limited for time
(to audit) and budget (for size, quality, etc., of his/her auditing team) could face
situations where cognitive information processing is not the limitation, but rather
his/her resources. Similar situations are imaginable for the context of incomplete
contracts.
Limitations stemming from individual characteristics or resources are two sides
of the same coin. In situations in which individual characteristics are the decisive
driver of information overload, resource limitations might not be reached or could
be negligible. By contrast, in situations where resource limitations dominate,
individual characteristics might not be reached or may be negligible.
However, the prior literature on information overload focuses on cognitive issues
in particular. A relatively recent literature review on information overload by Eppler
and Mengis (2004) shows an omission in the research that differentiates between
cognition-related and resource-related information overload. Although little
research is available on the resource-related issues of information overload, we
L* Informaon Load
Decision Making
Performance
D*
Fig. 1 Information and decision-making performance
Business Research (2019) 12:479–522 483
123
do know that time is a decisive factor in decision-making and that time pressure can
decrease decision-making performance due to information overload (Pennington
and Tuttle 2007; Schick et al. 1990). However, the modeled time pressure in both
experiments (Pennington and Tuttle 2007; Schick et al. 1990) allows decision-
makers to read or analyze all information; thus, they are overwhelmed by the
quantity of information. To return to the auditor example, this modeling of time
pressure means that the auditor is able to read all expense vouchers, statements of
account, etc., but is overwhelmed by the quantity of information. Instead, taking a
resource viewpoint, expense vouchers, statements of account, etc., would be so
numerous that the auditor (and his or her team) could not read all of them due to
limited resources. Research is lacking that models such business situations.
Furthermore, the lack of theoretical foundation to unite both business decision-
making and information processing indicates a theory deficit.
Thus, I use the following working definition of information overload:
Information overload is a state in which a decision maker faces a set of
information (i.e., an information load with informational characteristics such
as an amount, a complexity, and a level of redundancy, contradiction and
inconsistency) comprising the accumulation of individual informational cues
of differing size and complexity that inhibit the decision maker’s ability to
optimally determine the best possible decision. The probability of achieving
the best possible decision is defined as decision-making performance. The
suboptimal use of information is caused by the limitation of scarce individual
resources. A scarce resource can be limited individual characteristics (such as
serial processing ability, limited short-term memory) or limited task-related
equipment (e.g., time to make a decision, budget).
3 Methodology
To investigate the body of literature on information overload, I conducted a
bibliometric analysis following the procedure of Schaltegger et al. (2013). The
scope of the following literature review on information overload encompasses all
business administration studies that deal explicitly with information overload.
Following the procedure of Schaltegger et al. (2013), I started with a snowball
sampling (Biernacki and Waldorf 1981). The bibliography on information overload
was compiled beginning with the papers identified in Eppler and Mengis (2004).
3
I
did not include working papers, reports, books, and conference proceedings—with
one exception: regarding IS research, which highlights conferences, I included peer-
reviewed papers that were presented at the four major conferences on information
3
Eppler and Mengis (2004) reported a methodological limitation: They did not consider relevant articles
that addressed information overload situations but used labels other than the four terms ‘‘information
overload’’, ‘‘information load’’, ‘‘cognitive load’’, and ‘‘cognitive overload’’. Possible alternative labels
might be ‘‘data smog, information fatigue/overkill/overabundance/breakdown/explosion/deluge/flood/
stress/plethora, document tsunami, sensory overload’ (Eppler and Mengis 2004, p. 329). Using snowball
sampling avoids this methodological limitation.
484 Business Research (2019) 12:479–522
123
systems (ICIS, ECIS, AMCIS, HICSS). This way, 489 journal articles and 6 IS
conference papers were collected. To enlarge the bibliography, I conducted a
literature search in four major databases: EBSCO, ProQuest, ScienceDirect and
Emerald. Following Eppler and Mengis (2004), I searched for the keywords
‘information overload’’, ‘‘information load’’, ‘‘cognitive load’’, and ‘‘cognitive
overload’’ with the following conditions: written in English, published after 2004,
research articles/papers, peer-reviewed, published in journals. After removing
duplicates, 1042 papers were collected in the literature search with the four major
databases. Thus, comprehensive data triangulation was achieved by snowball
sampling and database query, resulting in a robust bibliographic database with the
following characteristics: 1537 research articles/papers in peer-reviewed journals,
written by 818 authors, published in 383 academic peer-reviewed journals.
To focus on business administration, I used the VHB-JOURQUAL3 (a ranking of
journals relevant to business research based on evaluations by members of the
German Academic Association for Business Research) to identify relevant
journals.
4
I excluded papers published in journals that are either not listed in the
VHB-JOURQUAL3, are listed in category ‘‘D’’, or are ranked as ‘‘k.w.Z.’’ (= ‘no
academic journal’’). This procedure resulted in 171 articles ranked in the VHB-
JOURQUAL3.
To ensure that I did not miss business research-relevant papers, I performed a
snowball sampling (Biernacki and Waldorf 1981) with the 171 articles. I found that
39 of these articles are cited in articles published in a peer-reviewed academics
journal that do not appear in the VHB-JOURQUAL3: The Elsevier Journal named
‘Computers in Human Behavior’’ [CiteScore: 4.54, Impact Factor: 3.435, 5-Year
Impact Factor: 4.252, SNIP: 2.137, SCImago Journal Rank (SJR): 1.595].
5
Regarding the journal’s metrics, this journal can be seen as comparable to other
IS journals in the VHB-JOURQUAL3 in category ‘‘B’’ (= important and renowned
business research journals). Within the journal ‘‘Computers in Human Behavior’’, I
repeated the literature search with the four major databases with the same
parameters. I found 138 articles on information overload, but only 18 of these
articles are business research-relevant, while the majority of the other articles focus
on information overload in pedagogy or general information processing without any
business context. I included the 18 articles in the business research-relevant sub-
sample with the 171 articles ranked in the VHB-JOURQUAL3, resulting in 189
articles on information overload.
4
In this literature review, I address a limitation noted by Eppler and Mengis (2004), namely that research
focusing on information overload from other perspectives (e.g., psychology, health care, and mass
communication) is not addressed adequately. As interdisciplinary journals are ranked in the VHB-
JOURQUAL3, the database includes management-related articles from psychology or health economics
and management. Particularly in health economics and management, physicians and patients face a
substantial information load in time-critical decision situations. Due to the high relevance of time as a
decisive success (or stress) factor in information overload-related situations for managers (e.g., Bawden
and Robinson 2009; Pennington and Tuttle 2007; Schick et al. 1990; Tushman and Nadler 1978), the
results of information overload studies including a strong reference to time are interesting for all
disciplines of business administration.
5
Values as of April 30, 2018 (https://www.journals.elsevier.com/computers-in-human-behavior).
Business Research (2019) 12:479–522 485
123
4 Descriptive results of the bibliometric analysis
Prior literature reviews on information overload stated that information (over-)load
research reached a peak in the 1980s and 1990s, but interest in this topic declined in
the early 2000s (Eppler and Mengis 2004; Ding and Beaulieu 2011; Lewis 1996;
Edmunds and Morris 2000; Feather 1988). Considering the development of
information overload research after Eppler and Mengis (2004)’s review, two trends
could be revealed: First, the number of peer-reviewed journal publications on
information overload per year across all areas of research significantly increase per
year, b= 12.374, t(11) = 25.194, p\0.001 (see Fig. 2). Second, publications in
business research-relevant journals are somewhat left behind, and their increase per
year is slightly unstable when compared to total publications, b= 0.742,
t(11) = 2.417, p\0.05. Since 2005, the development has been characterized by
strong outlays with lows particularly in 2006, 2009 and 2015 and with peaks in 2007
and 2012 (see Fig. 3). Although a chart depicting the number of publications on
information overload cannot show a cause and effect relationship, the significant
OLS-regressions regarding the increase in publications could be a first hint.
Moreover, I find a significant correlation (q= 0.740, p\0.01) between the number
of publications in business research-relevant journals and the total number of peer-
reviewed journal publications on information overload per year across all areas of
research.
4.1 Publications in business research-relevant journals
Regarding the publications in business research-relevant journals, a comparison
between disciplines of business research shows that the top five disciplines driving
research forward on information overload in business administration are informa-
tion systems and computer science, marketing, general management, logistics, and
accounting (see Table 1). Particularly in information systems and computer science,
0
20
40
60
80
100
120
140
160
180
200
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Peer-reviewed papers published per year
Year
All Publicaons Published in Selected Business Journals
Fig. 2 Historical development of the number of information overload publications
486 Business Research (2019) 12:479–522
123
two journals publish 74.68% of all research articles in this field: decision support
systems and computers in human behavior. The strong IS-related research can be
found in the other top disciplines, e.g., in accounting, and the top publishing journal
is the International Journal of Accounting Information Systems. The role of
computer- or IS-based decision-making is often the starting point or a
mediator/moderator.
0
5
10
15
20
25
30
2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Peer-reviewed papers published per year
Year
Published in Selected Business Journals
Fig. 3 Historical development of the number of information overload publications in business research-
relevant journals
Table 1 Publications in business research-relevant journals
Discipline No. of journals Publications Share (%)
Information systems and computer science 14 83 42.13
Marketing 10 28 14.21
General management 14 27 13.71
Logistics 2 19 9.64
Accounting 5 10 5.08
Human resources 4 8 4.06
Finance 3 7 3.55
Operations research 3 6 3.05
Innovation and entrepreneurship 3 3 1.52
Organization 3 3 1.52
Environmental management 1 1 0.51
Health economics 1 1 0.51
Public administration 1 1 0.51
Total (multiple assignment possible) 66 197 100
Business Research (2019) 12:479–522 487
123
Table 2shows the distribution of publications regarding the VHB-JOURQUAL3
categories. The majority of publications belong to ‘‘B’’-rated journals. A Kol-
mogorov–Smirnov test with the Lilliefors significance correction indicates a normal
distribution (p[0.05). Only eight papers are published in ‘‘A?’-rated journals.
Here, a temporary peak occurs in 2006/07 when four of the eight papers are
published. The results do not reveal any correlation between time and journal rating.
4.2 Authorship
Regarding authorship, Schaltegger et al. (2013) draw on the ‘‘Ortega hypothesis’
(Cole and Cole 1972), which implies that scientific progress is based on the work of
a small percentage and number of researchers and authors in each field. While prior
work questions this hypothesis (e.g., Sza
´va-Kova
´ts 2004), I analyzed whether any
authors were dominant in the field of information overload. In the business research-
relevant sample of 189 papers from 462 different authors, no authors published
more than 3 papers. In the total sample of 1537 papers from 818 different authors,
one author has ten publications (Fred Paas), and 7 authors have five publications.
Thus, I find no indication for the ‘‘Ortega hypothesis’’ in business-relevant
information overload research.
4.3 Methodological approaches and underlying theories
Different methodological approaches exist for analyzing the literature on informa-
tion overload. Between 2005 and 2017, 21.16% of the published papers are non-
empirical (e.g., conceptual), whereas 78.84% draw on empirical methods. This
review shows the heterogeneity of methods typically used to detect information
overload.
In information overload research, the two dominating research methods are lab
experiments and surveys (Table 3). Drawing on Hair et al. (2007), documenting
effects in field research (e.g., by surveys) is important to test the external validity of
experimental research (e.g., lab experiments). While lab research is very well suited
to test theories, it is limited in its external validity. Thus, findings may not occur in
practice, and laboratory research must be taken to the field to test its relevance. Next
to lab experiments, researchers use online experiments to get more information on
Table 2 Publications in business research-relevant journals for different rating categories
VHB rating Number of publications (%)
A?8 4.23
A 34 17.99
B 96 50.79
C 31 16.40
Listed but not ranked 2 1.06
Not listed 18 9.52
Total 189 100.00
488 Business Research (2019) 12:479–522
123
user behavior (e.g., in social networks). Field experiments or mixed methods,
however, can be considered ‘‘rare orchid’’ methods, playing a negligible niche role
in information overload research, although these methods might have the strength of
a field approach that shows effects occurring in the workplace (Hair et al. 2007;
Wang et al. 2014). While prior research has found many single leverage points that
affect information search, information processing, and decision-making behavior,
comprehensive and overarching studies are missing. Due to the limitations of lab
experiments and surveys (Birnberg et al. 2008; Luft and Shields 2003; Sprinkle and
Williamson 2008), new empirical methods such as action research or a combination
of field experiments, surveys and archival data within larger companies might
provide deeper insights into information overload.
5 New conceptual model for information overload-related research
The described lack of a common definition of information overload might root in
the distinct heterogeneity of theoretical backgrounds used (see Table 4). The most-
used theory is the human information processing approach by Schroder et al. (1967),
on which the working definition of information overload has been based. Similar to
the second most-used theory—information processing approach by Miller (1956)—
the human information processing approach by Schroder et al. (1967) makes general
assumptions about how decision-makers process information but do not limit the
range of possible applications in research. Surprisingly, 35.45% of the papers do not
use a theory but argue logically.
Table 3 Publications in business research-relevant journals for methods
Research method No. of publications Share (%)
Lab experiment 57 30.16
Survey 54 28.57
Conceptual 22 11.64
Archival data 16 8.47
Simulation 9 4.76
Case study 8 4.23
Online experiment 6 3.17
Review articles 5 2.65
Qualitative interviews 4 2.12
Combinatorial optimization 4 2.12
Meta-analysis 3 1.59
Field experiment 1 0.53
Total 189 100.00
Business Research (2019) 12:479–522 489
123
Table 4 Publications in business research-relevant journals regarding theoretical backgrounds
Theoretical background No. of
publications
Share
(%)
Human information processing approach by Schroder et al. (1967) 27 14.29
Information processing approach by Miller (1956) 19 10.05
Cognitive load theory 11 5.82
Information overload approach by Malhotra et al. (1982) 10 5.29
Information theory 4 2.12
Principal agent theory synthesized with assumptions of bounded rationality 4 2.12
Wilson’s (1999) model of information behavior 3 1.59
Attention deficit disorder/attention deficit trait 2 1.06
Bounded rationality 2 1.06
News communication approach by Rogers and Agarwala-Rogers (1975) 2 1.06
Additive information approach by Butters (1977) 1 0.53
Affect and social behavior approach by Moore and Isen (1990) 1 0.53
Affect infusion model by Forgas (1995) 1 0.53
Classical choice theory 1 0.53
Cognitive load theory synthesized with social capital theory 1 0.53
Communication theory 1 0.53
Constructive processing perspective by Payne et al. (1992) 1 0.53
Cultural management approach by Hofstede and Hofstede (2005) 1 0.53
Distraction conflict theory 1 0.53
Dual-process theory 1 0.53
Filter model of attention by Broadbent (1958) 1 0.53
Hierarchy of effects model synthesized with information overload approach
by Malhotra et al. (1982)
1 0.53
Human information processing approach by Schroder et al. (1967) synthesized
with prospect theory
1 0.53
Information diffusion theory based on epidemiology susceptible-infected-
recovered-susceptible (SIRS) models by Bailey (1975)
1 0.53
Information overload approach by Malhotra et al. (1982) synthesized with
paralysis by analysis approach by Lewis (1996)
1 0.53
Information overload concept by O’Reilly (1980) 1 0.53
Information processing approach by Miller (1956) synthesized with filter
model of attention by Broadbent (1958)
1 0.53
Information processing approach by Miller (1956) synthesized with news
communication approach by Rogers and Agarwala-Rogers (1975)
1 0.53
Information processing approach by Miller (1956) synthesized with SMCR
model by Berlo (1960)
1 0.53
Information processing approach by Miller (1956) synthesized with mood
congruency approach by Forgas and George (2001)
1 0.53
Information use approach by Stigler (1961) 1 0.53
Information weighting approach by Wedell and Senter (1997) 1 0.53
Input-processing-output (IPO) model 1 0.53
Job burn-out approach by Maslach and Jackson (1981) 1 0.53
Knowledge-based view 1 0.53
490 Business Research (2019) 12:479–522
123
The occurrence of such a range of theories and approaches reveals a need to
conceptualize research areas and survey and synthesize prior research using a
framework. Following the recommendations of Webster and Watson (2002), I
structured prior research in a thorough framework.
Eppler and Mengis (2004) used a conceptual framework, structuring research on
information overload as a cycle (causes ?symptoms ?countermea-
sures ?causes). By contrast, I arrange these elements as a functional chain to
structure the research (see Fig. 4); this structure allows me to follow the
psychological understanding of the prior management literature. Mental processes
and states are interpreted as mediators between a stimuli and a behavior (Birnberg
et al. 2008). By focusing on the individual instead of organizations or societies,
management psychology explains ‘‘subjective phenomena’’ (Birnberg et al. 2008,
p. 115).
Furthermore, a thorough framework is missing that spans from the starting
situation to the information search and selection via information processing to
decision-making and to the ex post consequences of the decision. Prior approaches
can capture the steps between, such as Wilson’s (1999) model of information
behavior, which focuses on information seeking and information satisfaction in a
documentation/library setting, or the Factor-Based Model of Jackson and Farzaneh
Table 4 continued
Theoretical background No. of
publications
Share
(%)
Knowledge-based view synthesized with organizational learning approach by
Huber (1991)
1 0.53
Need for cognition approach 1 0.53
Organization of decentralized information processing approach by Radner
(1993)
1 0.53
Organizational learning approach by Shrivastava (1983) synthesized with
Lindblom’s (1959) concept of incrementalism
1 0.53
Passive bounded rationality model by DeShazo and Fermo (2002) 1 0.53
Personal construct theory by Kelly (1955) 1 0.53
Prospect theory 1 0.53
Relational complexity model by Halford et al. (1998) 1 0.53
Sensemaking approach by Weick (1995) 1 0.53
Social cognition theory 1 0.53
Strength of weak ties approach by Granovetter (1983), theory of affordances 1 0.53
Theory of semantic internalization 1 0.53
Transactional theory of stress 1 0.53
No explicit theory addressed/argumentation with empirical evidence only 67 35.4
Total 189 100
Business Research (2019) 12:479–522 491
123
(2012), which approaches information overload as a scale of information underload
and overload.
Thus, a framework is developed that categorizes the important elements relevant
to information overload research as derived from the analyzed articles and shows
the relationships between these elements. Considering the big picture, the empirical
literature on information load is characterized by concrete, highly detailed empirical
research with very specific studies using a wide range of theoretical backgrounds.
These studies primarily focus on the following broad topic areas:
1. Which ex ante starting situation leads to changes in information processing
behavior and/or decision-making?
2. What role does the source of information (e.g., information system, commu-
nication, database, social media, websites, online communities) play in
information search, information processing, and decision-making? How does
the type of information source affect an individual’s behavior in information
search, processing and decision-making?
3. Which biases occur during information search and processing (e.g., evaluation,
editing)? How do information processing characteristics and conditions (e.g.,
limited time, stress) affect information search and processing?
Tas k El eme nts
(e.g., Goals, Difficulty,
Complexity)
Environmental Factors
(e.g., Content,
Situaonal Demands)
Incenves
Personal Characteriscs
(e.g., Experience, Knowledge,
Ability, Movaon)
Informaon
Systems
Face-2-Face
Communicaon
Websi tes, So cial
& Online
Communies
Other
Informaon
Sources
Management
Reporng
Informaon Search &
Informaon Processing
Informaon Processing
Characteriscs & Condions (e.g.,
Time, Stress)
Valuable, Task
relevant
Informaon
(Informaon
sought)
Addional
Informaon with
limited Value
(Informaon found
accidentally)
Redundant
Informaon
Contradictory,
Inconsistent
Informaon
Informaon Search & Processing
Biases
Informaon Sources
Subjecve Informaon Stand in
Decision Situaon
Decision-Making & Choice
Judgement &
Decision-Making
Tas k
Performan ce
Social
Parcipaon
Communicaon
Behavior
Emoonal &
Physical Effects
Knowledge,
Learning, &
Habits
Starng Situaon
Behavior & Emoons aer Decision-Making & Choice
Decision-Making Biases
Coping Strategies
Cognive or Condional Biases
Fig. 4 Framework
492 Business Research (2019) 12:479–522
123
4. How does the processing of information itself influence the subjective
informational stance of the decision-maker in the decision situation?
5. What effects do these identified influences and biases have on information
overload and how does information overload affect the situation after the
decision has been made (e.g., judgement, task performance, behavior,
emotions)?
These broad topic areas can be clustered into five categories relevant to decision-
making in information overload situations: starting situation, information sources,
information search and information processing, subjective information stance in
decision-making situations, decision-making and choice, and behavior and emotions
ex post.
The arrows drawn in Fig. 4represent the major steps in the functional chain. For
reasons of clarity and comprehensibility, I do not map the overarching arrows (e.g.,
personal characteristics ?information search and processing biases, personal
characteristics ?motives or personal characteristics ?judgement and decision-
making). Furthermore, relationships might run in the opposite direction (e.g.,
judgement and decision-making ?information sources or communication behav-
ior ?source preferences).
Information overload is seen as a decisive issue across all disciplines within
business administration and economics, but aside from a range of case applications
(see Tables 5,6,7,8,9), new fundamental theory-building research is missing. The
reason for this lack is that human cognitive processes are most often seen as a black
box—except in recent studies in neuroeconomics (Denham 2015; Oizumi et al.
2014). While empirical studies draw on theoretical models of the most-cited
theoretical literature (e.g., the human information processing approach by Schroder
et al. (1967), the information processing approach by Miller (1956), or information
theory (Shannon and Weaver 1959)), comprehensive empirical testing is lacking for
recent theoretical findings in neuroeconomics (Denham 2015; Oizumi et al. 2014).
First, the category starting situation includes the ex ante-relevant factors
influencing the information search, information processing and decision-making
process. It comprises the characteristics of the task or the decision to be made (e.g.,
level of difficulty, complexity, goals), the environmental factors that affect the
situation in which the decision-making process begins (e.g., context, situational
demand), the personal characteristics of the decision-maker (e.g., experience,
knowledge, ability, motivation) and the incentives present (e.g., decision perfor-
mance links to variable payment). This category contains the elements that might
lead to biased behaviors on the next steps and that might be the starting point of
information overload. The known effects of starting situation aspects on information
processing, decision-making and the occurrence of information overload are shown
in Table 5.
The next category is information sources, which plays a decisive role in overload
situations. The selection of information sources and the decision-makers’ source
preferences are fundamental to determining what he or she will consider in his or
her information search and information processing. The subjective perception of the
characteristics of the source (e.g., trust, reputation) and the characteristics of
Business Research (2019) 12:479–522 493
123
Table 5 Known effects of starting situation aspects on information processing, decision-making and the
occurrence of information overload
Aspect of
starting situation
Factor influencing information overload Discipline References
Task element Task complexity and task
interdependency
ACC Ding and Beaulieu (2011), Simnet (1996)
IS Gupta et al. (2013), Speier et al.
(1999,2003), Wang et al. (2014)
MAR Hunter and Goebel (2013)
MS Kock (2000), Tushman and Nadler (1978)
Task novelty, task too innovative MS Tushman and Nadler (1978), Herbig and
Kramer (1994)
Task interdisciplinarity IM/LS Bawden (2001), Foster (2004)
Varying task priorities IS Sharma et al. (2014)
Goal specificity IS Tam and Ho (2006)
Information collection and availability
is a company goal
IS Farhoomand and Drury (2002)
Decision-maker’s attention ECS Anderson and de Palma (2012)
IS Hargittai et al. (2012), Tam and Ho (2006)
Overall diversity of the provided
information in task
ACC Iselin (1988)
Number of alternatives/attributes/
options
MAR Greifeneder et al. (2010), Scheibehenne
et al. (2010)
Multitasking IS Tarafdar et al. (2010)
Environment Heterogenous groups IS Grise and Gallupe (1999/2000), Wilson
(1996)
SCM Hult et al. (2004)
Virtual collaboration IS Bawden (2001), Grise and Gallupe (1999/
2000), Jones et al. (2004), Paul and
Nazareth (2010), Speier et al. (1999)
MAR Schultze and Vandenbosch (1998)
Use of information control instruments MAR Ariely (2000), Wu and Lin (2006)
Personalized information
services/portals/interfaces
IS Tam and Ho (2006), Wang et al. (2014)
MS Sherer et al. (2003)
Technology adaption too high ECS Cukrowski and Baniak (1999)
Choice-rich environment ECS Hensher (2006)
Herd behavior of others IS Hu and Lai (2013)
Frequency/occurrence of interruptions IS Gupta et al. (2013), McCoy et al. (2007),
Speier et al. (1999), Speier et al. (2003)
Technostress at workplace IS D’Arcy et al. (2014)
Cultural background MS Borkovich and Morris (2012), Klausegger
et al. (2007)
Project overload IS, MS Canie
¨ls and Bakens (2012)
Number of network contacts IS Sasaki et al. (2015)
MS Sherer et al. (2003)
Overly high organizational use of
information and communications
technologies (ICT)/IT-driven
environment
IS Allen and Shoard (2005), Bucher et al.
(2013), Moore (2000), Soucek and
Moser (2010), Tarafdar et al. (2010)
Role overload MAR Hunter and Goebel (2013)
Perceived fearful corporate culture PSY Hallowell (2005)
High technology dependency IS Karr-Wisniewski and Lu (2010)
494 Business Research (2019) 12:479–522
123
Table 5 continued
Aspect of
starting situation
Factor influencing information overload Discipline References
Incentives Performance-based monetary incentives ACC Awasthi and Pratt (1990), Tuttle and
Burton (1999)
Mood congruency bias ACC Ding and Beaulieu (2011)
Personal
characteristics
Limited information processing
ability/capacity
ACC Chewning and Harrell (1990), Greiling and
Spraul (2010), Pennington and Tuttle
(2007), Shields (1980,1983), Simnet
(1996)
FI Bouwman et al. (1993), Lev and
Thiagarajan (1993), Rogers and Grant
(1997)
IS Davis and Ganeshan (2009), Hiltz and
Turoff (1985), Shrivastav and Hiltz
(2013)
MAR Herbig and Kramer (1994), Lee and Lee
(2004), Lurie (2004)
MS O’Reilly (1980)
Prior knowledge and experience FI Agnew and Szykman (2005)
IS Saunders et al. (2017)
MAR Bettman and Park (1980), Chen et al.
(2009), Owen (1992), Wu and Lin
(2006)
General personal characteristics/
demographics (e.g., age, gender)
IS Holton and Chyi (2012), Sasaki et al.
(2015)
PSY, IM Benselin and Ragsdell 2016
Polychronic attitude MAR Hunter and Goebel (2013)
Work stress MS Klausegger et al. (2007)
Epistemic motivation PSY Amit and Sagiv (2013)
Awareness IS Saparova et al. (2013)
Information avoidance tendency HE Case et al. (2005)
Health status HE Chan and Huang (2013)
PSY Hallowell (2005)
User’s allegiance IS Hsu and Liao (2014)
Fairness attitude IS Roetzel and Lohmann (2014)
Risk attitude ACC Pennington and Tuttle (2007)
IS Davis and Ganeshan (2009)
Star employee status HR Oldroyd and Morris (2012)
Mood ACC Ding and Beaulieu (2011)
MS Braun-LaTour et al. (2007)
Psychological ill-being HE, IS Swar et al. (2017)
Personal skills ORG, IS Whelan and Teigland (2013)
ACC accounting, ECS economics, FI finance, HE health economics/management, HR human resources, IM/LS infor-
mation management/library science, IN innovation management, INTM international management, IS information
systems, MAR consumer research/marketing, MF manufacturing, MS management science/general management, ORG
organizational science, PSY psychology
Business Research (2019) 12:479–522 495
123
Table 6 Known effects of information source aspects on information processing, decision-making and
the occurrence of information overload
Aspect of
information
sources
Factor influencing
information overload
Discipline References
Databases Relationship internal/
external databases
MS Klausegger et al. (2007)
External knowledge
sources
IS Dong and Netten (2017)
Market knowledge
acquisition
IN Zhou and Li (2012)
Amount and complexity of
user reviews
IS Fink et al. (2018)
Website complexity IS Chen (2018), Lin (2006), Rodrı
´guez-Molina et al. (2015),
Wang et al. (2014)
Social
networks
Participation in social
networks
IS Koroleva and Bolufe-Ro
¨hler (2012), Li and Sun (2014),
Sasaki et al. (2015)
HR Oldroyd and Morris (2012)
Strength of ties to other
network users
IS Koroleva and Kane (2016)
Herd behavior IS Hu and Lai (2013)
Number of friends/
network ties
IS Koroleva and Kane (2016), Sasaki et al. (2015)
Social media news speed IS Lee et al. (2017)
Information
source
design
Suboptimal management
information system
design
IS Ackoff (1967)
Suboptimal information
source presentation
mode
IS Wheeler and Arunachalam (2009)
Suboptimal platform
design
MAR Chen et al. (2009), Holton and Chyi (2012), Li (2016)
HE Cartwright et al. (2002)
System feature overload IS Lee et al. (2016)
System feature use IS Sasaki et al. (2015)
Content recommendation/
personalization
IS Aljukhadar et al. (2012), Chen et al. (2016), Liang et al.
(2007), Xiao and Benbasat (2007), Zhang et al. (2018)
Additional, unwanted
information provided
MAR Wu and Lin (2006)
IS McCoy et al. (2007)
Provided information
filtering tools
MAR Chen et al. (2009)
Provision of search agents IS Yen et al. (2006)
MAR Alba et al. (1997)
Media (over-)richness IS Wheeler and Arunachalam (2009)
Cyber-based information
search only
PSY Misra and Stokols (2012)
Use of push/pull
information systems
HE Wilson (2001)
IM/LS Edmunds and Morris (2000), Herther (1998)
MS Klausegger et al. (2007)
496 Business Research (2019) 12:479–522
123
engaging with the source (e.g., convenience of information collection, ease of
operation, information provided per query) contribute essentially to the effective-
ness of information search and information processing—or to information overload.
This category also includes the availability, clarity and comprehensibility of
information gathered by external sources. Note that this category addresses external
information sources from the decision-maker’s viewpoint. Internal information
sources (e.g., memory) are not included in this category but are part of the next
category [note that even internal sources of information might lead to biases, e.g.,
the availability bias studied by Kahneman (2011)]. The known effects of
Information Sources aspects on information processing, decision-making and the
occurrence of information overload are shown in Table 6.
The third category is information search and information processing (including
aspects of information processing characteristics and conditions), which represents
the actual process through which the decision-maker searches for and processes
information. This category includes the search, evaluation, editing, and weighting of
information. A range of business administration and economic theories focus on
these steps, e.g., prospect theory (Tversky and Kahneman 1974). Information search
and information processing is related triangularly to subjective information stance in
decision-making situations and decision-making and choice. This triangle reflects
the insight of cognitive management psychology that decision-making and choice
might precede search and evaluation (e.g., in confirmation bias situations (Tversky
and Kahneman 1974)). The present framework allows consideration of these
situations (decision-making and choice before information search and information
processing) but allows alternative directions (information search and information
processing before decision-making and choice) as well. The known effects of
information search and information processing aspects on information processing,
decision-making and the occurrence of information overload are shown in Table 7.
Subjective information stand in decision situation is the fourth category. This
category captures what information decision-makers have actually processed and
what information value they have gained as a result. This category must be
established because from the decision-making process view, when the decision-
maker completes information search and information processing, the subjective
information stance is the essential starting point for decision-making. This category
differentiates between four different types of information:
Table 6 continued
Aspect of
information
sources
Factor influencing
information overload
Discipline References
Source
preferences
Trust IS Kim and Benbasat (2009), Koroleva and Kane (2016),
Xiao and Benbasat (2007)
Business Research (2019) 12:479–522 497
123
Table 7 Known effects of information search, information processing and its characteristics and con-
ditions on further information search and processing, decision-making and the occurrence of information
overload
Aspect Factor influencing
information
overload
Discipline References
Information
characteristics
Information
complexity
ACC, FI Plumlee (2003)
ECS Hensher (2006)
IS Paul and Nazareth (2010)
MAR Lee and Lee (2004), Li (2016), Lurie (2004),
Reutskaja and Hogarth (2009)
MS Amit and Sagiv (2013), Driver and Streufert
(1969), Schneider (1987)
Amount of
information
ACC Casey (1980), Chewning and Harrell (1990),
Roetzel (2014), Roetzel et al. (2015), Shields
(1980,1983), Simnet (1996)
MAR Herbig and Kramer (1994), Jacoby et al. (1974),
Jacoby (1977,1984), Malhotra et al. (1982),
Malhotra (1984), Schultze and Vandenbosch
(1998), Wang et al. (2007)
HE Swar et al. (2017)
IS Borkovich and Morris (2012), Gao et al. (2018),
Hiltz and Turoff (1985), Davis and Ganeshan
(2009), Shrivastav and Hiltz (2013)
Novelty of
information
MS Schneider (1987)
Search depth IS Lin (2006)
Ambiguity/diversity
of information
MS Schneider (1987), Schroder et al. (1967)
ACC Iselin (1988)
HE Slawson et al. (1994)
MAR Li (2016), Lurie (2004)
Information
accessibility
IS Hsu and Liao (2014), Roetzel and Lohmann
(2014)
MAR Schultze and Vandenbosch (1998)
Information
equivocality
IS Lee and Lee (2004)
Information
structure
MAR Lurie (2004)
Threat of
information
unavailability
IS Davis and Ganeshan (2009), Tushman and
Nadler (1978)
Share of redundant
information
IS Lee et al. (2016)
Use of incremental
analysis methods
MS Bettis-Outland (2012)
498 Business Research (2019) 12:479–522
123
Valuable task-relevant information: The value of information is determined by
its utility for decision-making. Information that increases the decision-maker’s
insight and understanding of a decision situation obtains a higher value, whereas
information that is useless to the decision-maker in the decision situation obtains
a lower value. The valuable task-relevant information is the share of information
for which the decision-maker actually searched.
Additional information with limited value: The share of information that the
decision-maker found accidentally but can use to some extent for decision-
making.
Redundant information: The share of information whose value depends on the
decision maker’s intention and on the sequence of information search and
information processing, and decision-making and choice. If information search
and information processing precedes decision-making and choice, then redun-
dant information has a limited-to-negative value for decision makers because it
does not increase his or her understanding of the decision situation but ties up
cognitive resources (i.e., information processing capacity). Otherwise (i.e.,
decision-making and choice before information search and information
processing), if the decision maker wants to justify an already made decision
(e.g., in confirmation bias or self-justification situations), the redundant
information might have a positive marginal utility because it underpins the
already-made decision. The latter is a subjective value from the decision-
maker’s viewpoint.
Contradictory, inconsistent information: The share of information that contra-
dicts the decision-maker’s evaluation so far. On the one hand, the decision-
maker might tend to ignore or discard such information [e.g., to avoid cognitive
dissonance (Festinger 1954)]. On the other hand, such information might urge
the decision-maker to search for further information to obtain a clearer
evaluation.
Table 7 continued
Aspect Factor influencing
information
overload
Discipline References
Conditions Time pressure/
restrictions
ACC Pennington and Tuttle (2007), Schick et al.
(1990)
IS Hiltz and Turoff (1985), Paul and Nazareth
(2010)
MAR Scheibehenne et al. (2010)
MS Kock (2000)
PSY Hahn et al. (1992), Misuraca and Teuscher
(2013)
Unconscious
decision-making
IS Gao et al. (2012), Messner and Wa
¨nke (2011)
Business Research (2019) 12:479–522 499
123
Table 8 Known effects of subjective information in decision situation on decision-making and the
occurrence of information overload
Aspect/bias Effect Discipline References
Attractive
stimulus
overload
Increasing number of information and choices in
decision situations lead to intrapersonal conflicts
PSY Lipowski (1970)
Information
search
Decrease in the proportion of information searched ACC Anderson (1988),
Swain and Haka
(2000)
MS Payne (1976)
Increase of variability in information search ACC Anderson (1988),
Swain and Haka
(2000)
IS Cook (1993)
MS Payne (1976)
Less systematic search strategy ACC Swain and Haka
(2000)
Increase of noncompensatory search patterns ACC Pennington and
Tuttle (2007)
IS Cook (1993)
Discard/ignore search results PSY Case et al. 2005
Use of search agents MAR Alba et al. (1997)
IS Lau et al. (2001),
Yen et al. (2006)
Personal interest while searching MAR Alba et al. (1997)
Information
processing
Highly selective information selection and
processing
MAR Herbig and Kramer
(1994)
IS Hiltz and Turoff
(1985), Osburg
et al. (2016)
IM/LS Bawden (2001),
Edmunds and
Morris (2000)
IN Sparrow (1999)
Incongruent information response MAR Braun-LaTour et al.
(2007)
Attention of decision-maker MAR Sicilia and Ruiz
(2010)
ECS Anderson and de
Palma (2012)
Affordance of decision-maker IS Koroleva and Kane
(2016)
500 Business Research (2019) 12:479–522
123
Table 9 Known effects of information overload on decision-maker’s behavior and emotions after
decision-making and choice ex post
Aspect of behavior and
emotions after decision-
making and choice
Result of information
overload/reaction due to
information overload
Discipline References
Task performance Decreasing decision-
making performance
ACC Abdel-Khalik (1973), Chewning and
Harrell (1990), Schick et al.
(1990), Shields (1980)
FI Agnew and Szykman (2005),
Spindler (2011), Ward and
Ramachandran (2010)
IS Gupta et al. (2013), Okike and
Fernandes (2012), Speier et al.
(1999), Scott (2005), Speier et al.
(2003), Ward and Ramachandran
(2010)
MAR Chen et al. (2009), Hunter and
Goebel (2013), Jacoby et al.
(1974), Jacoby (1984), Keller and
Staelin (1987), Korhonen et al.
(2018), Malhotra (1984), Malhotra
et al. (1982), Meyer (1998)
IM/LS Bawden (2001), Bawden and
Robinson (2009), Hwang and Lin
(1999), Edmunds and Morris
(2000)
PSY Hallowell (2005), Misra and Stokols
(2012)
Confusion regarding the
decision
MAR Jacoby et al. (1974), Malhotra et al.
(1982)
Decision delayed or
canceled
MAR Sicilia and Ruiz (2010)
Decreasing decision
satisfaction
MAR Jacoby (1984), Messner and Wa
¨nke
(2011), Reutskaja and Hogarth
(2009)
IS Davis and Ganeshan (2009)
IM/LS Bawden and Robinson (2009)
Increase in decision
satisfaction
ACC O’Reilly (1980)
Decrease/lack/reduction
of attention level
MAR Sicilia and Ruiz (2010)
IS Li and Sun (2014)
Radical innovation
generation
IN Zhou and Li (2012)
Business Research (2019) 12:479–522 501
123
Table 9 continued
Aspect of behavior and
emotions after decision-
making and choice
Result of information
overload/reaction due to
information overload
Discipline References
Judgement Decreasing judgement
accuracy/efficiency/
performance
ACC Pennington and Kelton (2016),
Pennington and Tuttle (2007),
Shields (1983), Simnet (1996)
FI Agnew and Szykman (2005), Hilary
and Menzly (2006), Hilton (2010),
Spindler (2011)
IS Lankton et al. (2012)
MAR Ketron et al. (2016), Sicilia and Ruiz
(2010), Summers (1974)
Decreasing prediction
performance
ACC Snowball (1979,1980)
Greater tolerance of
error
MS Sparrow (1999)
Communication
behavior
Increasing
communication
intensity
HR Oldroyd and Morris (2012)
IS Chen and Lee (2013), Li and Sun
(2014)
Reduction of
communication
intensity
MS Schneider (1987)
Simplification of
communication
IS Jones et al. (2004)
Word-of-mouth
activities
MAR Gottschalk and Mafael (2017), Hutter
et al. (2013)
Reduction of technology
acceptance
IS Swar et al. (2017)
Social network service
fatigue
IS Lee et al. (2016)
Social participation Reduction of active
participation in social
communities
IS Jones et al. (2004), Zha et al. (2018),
Zhang et al. (2016)
Unfriend/unfollow
behavior
IS Sasaki et al. (2015)
502 Business Research (2019) 12:479–522
123
Table 9 continued
Aspect of behavior and
emotions after decision-
making and choice
Result of information
overload/reaction due to
information overload
Discipline References
Knowledge, learning,
and habits
Learning to handle
overload over time
MAR Ariely 2000
Project management
information system
quality decreases
IS, MS Canie
¨ls and Bakens (2012)
E-mail-free workdays IM/LS Bawden and Robinson (2009)
Change of coping
strategy
IS Scott (2005), Zeldes et al. (2007)
MS Ledzin
´ska and Postek (2017),
Luedicke et al. (2017), Savolainen
(2007)
Slower adaption of IT/
ICT
IS Maes (1994)
Growing into
specialized filtering
habits/roles
MAR Wu and Lin (2006)
IS Schuff et al. (2006)
ORG Whelan and Teigland (2013)
Information distribution
behavior
MF, IS Okike and Fernandes (2012), Scott
(2005)
SCM Hult et al. (2004)
Disruption of
established cognitive
processes
HE, PSY Cartwright et al. (2002), Sweller et al.
(1983), Sweller (1988)
Change of user
preference
IS McCoy et al. (2007)
Acceleration of
decision-making
behavior
ACC Pennington and Tuttle (2007)
Increase of overtime MS Klausegger et al. (2007)
Change in
organizational
learning
MS Wei and Ram (2016)
Knowledge acquisition
and retrieval
IS Lankton et al. (2012)
Use of push/pull
information systems
MS Klausegger et al. (2007)
IM/LS Edmunds and Morris (2000), Herther
(1998)
HE Wilson (2001)
Business Research (2019) 12:479–522 503
123
The known effects of subjective information stance in decision situation aspects
on information processing, decision-making and the occurrence of information
overload are shown in Table 8.
The fifth category is decision-making and Choice. This step is the third part of the
triangular relationship with information search and information processing and
subjective information stance in decision-making situations. This category consists
of the decision step of the decision-making process: the selection of one of the
existing alternatives. While the process of information search and information
processing is affected by a variety of possible biases, it is prone to such biases as
well [e.g., bounded rationality (Simon 1955), overconfidence (Tversky and
Kahneman 1974), and emotionally driven decision-making on impulse (Forgas
1995; Moore and Isen 1990)].
Last, the category behavior and emotions after decision-making and choice
describe the results of the decision-making process, including the effects on the
Table 9 continued
Aspect of behavior and
emotions after decision-
making and choice
Result of information
overload/reaction due to
information overload
Discipline References
Emotions and personal
state
Lower job satisfaction MAR Hunter and Goebel (2013)
MS O’Reilly (1980)
User satisfaction IS Liang et al. (2007)
Lower satisfaction with
the organizational
communication
MS O’Reilly (1980)
Overconfidence MAR Jacoby (1984), Meyer (1998)
MR O’Reilly (1980)
PSY Hallowell (2005)
Increased distractibility/
impatience
IM/LS Bawden and Robinson (2009)
Demotivation MS Baldacchino et al. (2002)
Tendency for job
turnover
IS Moore (2000)
Stress/technostress ACC Schick et al. (1990)
MAR Malhotra (1984)
IS D’Arcy et al. (2014), Lee et al.
(2016), Plotnick et al. (2009)
MS Klausegger et al. (2007), Ledzin
´ska
and Postek (2017)
PSY Misra and Stokols (2012)
Poorer health status PSY Hallowell (2005), Misra and Stokols
(2012)
Increase of negative
emotions (anger,
depression)
IS Swar et al. (2017)
504 Business Research (2019) 12:479–522
123
individual decision-maker (e.g., emotions, choice satisfaction, communication
behavior, knowledge, habits), the relevant task or purpose of the decision-making
process (e.g., task performance, judgement, decision-making performance), and the
consequences for the organization (e.g., social participation, corporate performance
as an outcome of individual performance). The known effects of behavior and
emotions after decision-making and choice aspects on information processing,
decision-making and the occurrence of information overload are shown in Table 9.
6 Recent trends and add-ons in information overload literature
2005–2017
Comparing the business administration literature with the literature overview by
Eppler and Mengis (2004), this situation clearly remains unchanged. The level of
citations is very low for the business administration literature and neuroeconomics
in the area of information overload (Eppler and Mengis 2004).
Most studies analyzed in this literature review consider up to three of the five
possible categories of my framework. Due to the limitations of empirical research
(Birnberg et al. 2008; Luft and Shields 2003), and experimental research in
particular, no study depicts the entire framework shown in Fig. 4. Hence, the
empirical research on information overload is quite fragmented. This situation is
compounded by the fact that each discipline within business administration and
economics applies its own focus and tool kit to analyze information overload. While
management accounting research identifies information overload as a negative
mediator affecting the impact of a stimuli (e.g., management control system) on
behavior (Birnberg et al. 2008), IS research focuses on IS design and user
preferences (Borkovich and Morris 2012; Johansson et al. 2014). More recently,
marketing research has treated information overload as a proxy for choice overload,
which in turn reduces the likelihood of triggering a purchase decision
(Scheibehenne et al. 2010).
Furthermore, a range of conceptual papers identify information overload issues in
a variety of disciplines in business administration, in particular in accounting (e.g.,
Greiling and Spraul 2010; Oluwadare and Samy 2015), information systems
research (e.g., Cartwright et al. 2002; D’Arcy et al. 2014; Li and Sun 2014),
international management (e.g., Borkovich and Morris 2012), marketing (e.g.,
Anderson and de Palma 2012), organizational science (e.g., Bettis-Outland 2012),
and economics (e.g., Cukrowski and Baniak 1999). In the following, I describe the
tendencies of the recent research identified in the framework’s five categories (see
Fig. 4).
Furthermore, and following the methodology of Ramnath et al. (2008), I
differentiate by discipline to indicate the different analytical lenses and subjects of
these disciplines.
To ensure compatibility to prior literature reviews, in particular to the
interdisciplinary review of Eppler and Mengis (2004), I provide tables with known
effects on and of information overload. Here, I combine the recently analyzed
Business Research (2019) 12:479–522 505
123
effects and the effects reported in prior literature reviews to facilitate a big picture of
each category.
Furthermore, I draw on my framework to identify ‘‘hotspots’’ of information
overload research as well as areas ‘‘off the beaten track’’ which would significantly
add to the big picture of information overload. Table 10 shows that the majority of
information overload studies focus on topic along the major steps in the functional
chain [146 of 189 (77.25%)]. The remaining papers conduct research within the five
categories. The paths ‘‘information sources ?behavior and emotions after
decision-making and choice’’, ‘cognitive or conditional biases ?behavior and
emotions after decision-making and choice’’, and ‘‘starting situation ?behavior
and emotions after decision-making and choice’’ are intensively investigated
(59.58%).
From a bird’s eye perspective, I see three larger trends in research on information
overload. On the one hand, one may argue that subsuming the heterogeneous field of
information overload literature is an exaggeration towards simplification. On the
other hand, a practical alternative might be to use the range of paths in Table 5to
define the relevant trends. In the latter, one would identify 18 different trends
instead of four. In the light of this trade-off, I decided to take four trends—to avoid
that the reader runs into the danger of suffering from information overload.
6.1 Trend I: ‘‘Information overload as a design issue—caused by the (mis-
)use of computers and information systems’
This major trend draws on a long stream of research rooting in the seminal paper
named ‘‘Management Misinformation Systems’’ by Ackoff (1967). While the core
issue of providing a too high amount of information or too complex information
when using management information systems, databases, etc., may confuse its
users, it may also affect their ability to prioritize or complicate the retrieval of
information (Farhoomand and Drury 2002; Hiltz and Turoff 1985). Retrospectively,
the digitalization and virtualization of the decision-making environment dominate
the literature, which is primarily driven by IS research. Reducing information
overload is one of the major challenges of IS research in the information age (Dean
and Webb 2011). While an information system may facilitate greater information
flow (potentially leading to overload), it also has the potential to help decision-
makers organize, store, and process information. Nevertheless, MISs are seen as one
of the major causes of information overload in information and communication
technology (ICT)-related tasks (Levitin 2014; Shapiro and Varian 2013). Here,
information overload has been shown to lead to decreases in decision performance
in virtual communication (e.g., Jones et al. 2004; D’Arcy et al. 2014), to less
systematic and less thorough search strategies (e.g., Paul and Nazareth 2010; Hiltz
and Turoff 1985).
The issue causing information overload is the same as described by Ackoff
(1967): while the system is getting more efficient, the user adapts in a vastly slower
way. The user’s personal characteristics seem to play a very important role
regarding individual thought patterns, which affect information search, information
506 Business Research (2019) 12:479–522
123
processing, and decision-making behavior (e.g., Allen and Shoard 2005; Benselin
and Ragsdell 2016; Hunter and Goebel 2013).
The prior research regarding system or user adaption is characterized by a strong
orientation toward ‘‘hard’’ technical characteristics such as algorithm efficiency,
availability, compatibility, system feature design, and visualization (see Table 6). In
the last decade, there have been few approaches to the ‘‘soft’’ characteristics (e.g.,
subjective user experience and trust) that shift the focus from a more technical
viewpoint to a psychological viewpoint (e.g., Koroleva and Bolufe-Ro
¨hler 2012;
Wu and Lin 2006).
One major aspect of information processing is not in the focus of researchers yet:
how information can be processed and evaluated by ‘‘intelligent’’ information
systems. While decision support systems or decision aid are widely investigated, the
wide field of machine learning, deep learning and artificial intelligence, which is
one of the most important drivers of digitalization, is not linked with information
overload literature yet.
Table 10 Recent research within the framework
Beginning atNo of
publications
Ending atNo of
publications
Starting situation 41 Information sources 3
Information search and processing 6
Decision-making and choice 6
Behavior and emotions after decision-
making and choice
26
Information sources 43 Information search and processing 7
Decision-making and choice 4
Behavior and emotions after decision-
making and choice
32
Information search and
processing
12 Subjective information stand in
decision situation
1
Decision-making and choice 8
Behavior and emotions after decision-
making and choice
3
Subjective information stand in
decision situation
12 Information search and processing 1
Decision-making and choice 5
Behavior and emotions after decision-
making and choice
6
Cognitive or conditional biases 38 Information search and processing 1
Cognitive or conditional biases 1
Subjective information stand in
decision situation
2
Decision-making and choice 5
Behavior and emotions after decision-
making and choice
29
Business Research (2019) 12:479–522 507
123
6.2 Trend II: ‘‘Information overload as a virus—spreading through (social)
media and news networks’
People do consume more information via the internet than ever before (Levitin
2014). Not surprisingly, the dominant discipline in research on information sources
and its effects on information search, information processing, and decision-making
behavior is IS research. The consideration of information processing and decision-
making on the cloud applications and over social media environments is readily
observable (e.g., Jones et al. 2004; Sasaki et al. 2015; Tarafdar et al. 2010).
Essential topics of research studies in the last decade draw on the rapid developm ent
of the web and the vast amount of information provided on different channels and
portals such as online news streams (e.g., Holton and Chyi 2012), online shopping
(e.g., Li 2016; Wu and Lin 2006) and social network sites (e.g., Koroleva and Bolufe-
Ro
¨hler 2012; Koroleva and Kane 2016 Lee et al. 2016). Another relevant topic,
particularly in marketing research, is the effect and efficiency of (pop-up) ads and
other channels for unwanted advertising (e.g., McCoy et al. 2007).
The common finding of these research studies is that information overload does not
scare users to use these channels or platforms. Users seem to ignore possible side
effects of information overload up to a very high level before retreating from these
channels or platforms. From a bird’s eye perspective, this situation might be compared
with the spread of a disease. Thus, people often act irrationally by infecting others (i.e.,
sending more messages, likes, news to other members of their network) instead of
sparing themselves (i.e., making a rest/recovery from their overloaded status).
Moreover, while prior research in other disciplines finds that trust affects the
information weighting behavior of decision-makers (e.g., in risk management (Earle
2010; Slovic 1993) or innovation management (Bstieler 2006; Staples and Webster
2008)), the role of trust in information selection in potential information overload
situations is widely unclear—except for the study of Koroleva and Kane (2016),
which focuses on Facebook users and trust issues but is not applicable to most
business situations.
The intensive use of social media and the steady exposition to information
overload might cause emotional, mental and physical effects. In the last decade,
there are studies which focus on mental (e.g., Braun-LaTour et al. 2007; Hallowell
2005) and physical health parameters (e.g., Chan and Huang 2013), showing
information overload’s negative effects on emotions (e.g., Swar et al. 2017) and on
perceived health (e.g., Hallowell 2005; Misra and Stokols 2012). Information
overload does not only affect working behavior, but also leads to less time devoted
to contemplative activities (Misra and Stokols 2012).
6.3 Trend III: ‘‘Information overload as an ‘‘search obstacle’’—new ways
to circuit and adaptions in information search and processing’’
Based on the trinity of the three articles of Miller (1956), Newell and Simon (1972)
and Schroder et al. (1967), the inverted U-shaped relationship is replicated and
confirmed in the last decade (e.g., Davis and Ganeshan 2009; Roetzel 2014; Sicilia
and Ruiz 2010). There is a shift in research from the focus on the amount of
508 Business Research (2019) 12:479–522
123
information towards the focus on the complexity (Lee and Lee 2004;Li2016; Lurie
2004; Reutskaja and Hogarth 2009) and interdependence of information (e.g., Amit
and Sagiv 2013; Lankton et al. 2012; Wang et al. 2014).
Moreover, recent research shifts the spotlight on typical work situations affected
by the information age such as production (e.g., Okike and Fernandes 2012),
innovation (e.g., Zhou and Li 2012) or consumer-relevant decisions in households
(e.g., Hensher 2006), risk judgements (Pennington and Tuttle 2007) or virtual work
(Paul and Nazareth 2010). Furthermore, negative effects of the information age and
user-centered aspects in virtual environments are investigated by research (e.g., user
attention (e.g., Anderson and de Palma 2012; Sicilia and Ruiz 2010) or affordance
(e.g., Koroleva and Kane 2016)). These studies show that the fundamental issue of
biased information search is replicable in new work environments.
The main driver of information search and processing issues is a usual suspect
known since the 1960s: limited time (Schroder et al. 1967). Time pressure and time
restrictions often lead to information overload (Misuraca and Teuscher 2013;
Scheibehenne et al. 2010). Thus, human information search and processing biases
are still used to getting their way in digitalized environments.
However, there is a lack of research investigating sources of stress other than
time regarding information search and processing. Other stress factors such as self-
induced stress (e.g., aspiration level) are still under-researched. These stress factors
do not need to be linked to the task; often, employees face stress factors that are not
considered or measurable by the organization (Levitin 2014).
In the information age, there is a need for research to clarify how an oversupply
of information might affect known cognitive biases. While prior research on
information search suggests that information search and information processing are
affected by cognitive biases such as self-justification and that decision-makers react
to unpleasant situations or information stands by acquiring even more information
(e.g., Schultze et al. 2012), studies on information overload are missing.
Moreover, psychological research shows that decision-makers react differently
when information is retrospective or prospective (e.g., Conlon and Parks 1987;
Schultze et al. 2012). This might open interesting avenues for further research
because decision-makers often receive retrospective and/or prospective information
(e.g., corporate planning, budgeting). Further research should analyze whether
decision-makers react differently when facing an overly high level of retrospective
versus prospective information.
In strategies on coping these information search and processing biases, three
major directions are visible in recent research: the technology-centered view
including the use of technical countermeasures (e.g., filter agents, search protocols,
visualization), which has slightly increased (e.g., Koroleva and Bolufe-Ro
¨hler
2012); the human-centered view to consider the decision-maker’s behavior or
emotional or physical effects (e.g., stress reduction), which is essentially driven by
IS research (e.g., D’Arcy et al. 2014; Lee et al. 2016; Plotnick et al. 2009); and the
information process-centered view, which draws on countermeasures to address the
complexity and mass of information (e.g., Lee and Lee 2004; Paul and Nazareth
2010; Sumecki et al. 2011). The three approaches tackle different categories of the
framework. In Table 11, the different categories are assigned to the three views.
Business Research (2019) 12:479–522 509
123
Table 11 Coping strategies
View Leverage point Strategy Discipline References
Human-
centered
view
Decision-maker’s
emotional and
physical effects
Reduction of
stress
ACC Schick et al. (1990)
IS D’Arcy et al. (2014, Lee et al.
(2016), Plotnick et al. (2009)
MAR Malhotra (1984)
MS Klausegger et al. (2007)
PSY Misra and Stokols (2012)
Improvement of
mood
ACC Ding and Beaulieu (2011)
Starting situation
(personal
characteristics)
ICT-related
method
training (e.g.,
prioritization)
ACC Schick et al. (1990)
IM/LS Bawden (2001)
IS Sumecki et al. (2011)
Time-
management
training
IM/LS Bawden (2001)
Withdrawal
strategy
MS Savolainen (2007)
Discarding
information
strategy
IS Holton and Chyi (2012)
Information
processing-
centered
view
Starting situation
(information
characteristics)
Complexity
reduction
ACC Greiling and Spraul (2010), Iselin
(1988)
IS Ackoff (1967), Grise and Gallupe
(1999/2000), Hiltz and Turoff
(1985), Lee and Lee (2004),
Paul and Nazareth (2010),
Sumecki et al. (2011)
MAR Lurie (2004)
Reduction of the
amount of
information
IS Davis and Ganeshan (2009)
Starting situation
(task
characteristics)
Improvement of
goal
specificity/link
to incentives
ACC Tuttle and Burton (1999)
IS Tam and Ho (2006)
MS Baldacchino et al. (2002)
Information search
and information
processing
(conditions)
Relaxation of
time pressure
ACC Pennington and Tuttle (2007),
Schick et al. (1990)
MAR Scheibehenne et al. (2010)
Technology-
centered
view
Decision-maker’s
behavior
Focus on filtering
information/
use of filter
algorithms
IS Koroleva and Bolufe-Ro
¨hler
(2012)
MS Savolainen (2007)
Information source Enhancement of
visualization
IM/LS Chan (2001)
MAR Meyer (1998)
Improvement of
(search) agents
IS Berghel (1997), Edmunds and
Morris (2000), Maes (1994)
MAR Alba et al. (1997)
510 Business Research (2019) 12:479–522
123
7 Conclusions
Discovering the effects of information search, selection, processing, and evaluation
in the decision-making process and the occurring biases and limitations is key for
out understanding of the decision-making process itself. This study incorporates a
wide range of effects from the starting situation ex ante to the decision
consequences ex post.
In conclusion, this review has some limitations to address. First, I include
business-related research only and exclude other research fields (e.g., pedagogy).
There might be insights into these areas which are relevant for business
administration research as well. Further research might address this limitation.
Second, I searched for the keywords ‘‘information overload’’, ‘‘information load’’,
‘cognitive load’’, and ‘‘cognitive overload’’. There might be relevant studies on
information overload or related topics which do not use these keywords in their
titles or abstracts. While the snowball sampling (Biernacki and Waldorf 1981)
might be a valid strategy to reduce such errors, there might be further articles which
are not cited in this review.
In this paper, I have provided some perspective on possible avenues of research
regarding information overload following the three major trends. The avenues for
future research that seem the most promising to me include the following. First, the
interdisciplinary research regarding the link between digitalization, virtual organi-
zations, and business psychology is a decisive uprising research direction, following
the call for research from Eppler and Mengis (2004). Second, there is little research
done to enhance our understanding of the interlinks between all five categories.
Prior research merely focused on one to three of the categories. I look forward to
research clarifying the interdependencies between the influence factors of the
categories. More research is needed to understand the interaction between decision-
maker’s emotions, his or her decision-making-related information processing, and
the virtualness of the environment. I expect this research to have implications for
emerging concepts and theories regarding virtual collaboration in organizations.
Third, I encourage researchers to continue exploring the factors that make some
decision-makers better information processors than others in different tasks and
environments.
Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, dis-
tribution, and reproduction in any medium, provided you give appropriate credit to the original
author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were
made.
References
Abdel-Khalik, A.R. 1973. The effect of aggregating accounting reports on the quality of the lending
decision: An empirical investigation. Journal of Accounting Research 11 (Suppl): 104–138.
Ackoff, Russell L. 1967. Management misinformation systems. Management Science 14 (4): 147–156.
https://doi.org/10.1287/mnsc.14.4.B147.
Business Research (2019) 12:479–522 511
123
Agnew, Julie R., and Lisa R. Szykman. 2005. Asset allocation and information overload: The influence of
information display, asset choice, and investor experience. Journal of Behavioral Finance 6 (2):
57–70. https://doi.org/10.1207/s15427579jpfm0602_2.
Alba, Joseph, John Lynch, Barton Weitz, Chris Janiszewski, Richard Lutz, Alan Sawyer, and Stacy
Wood. 1997. Interactive home shopping: Consumer, retailer, and manufacturer incentives to
participate in electronic marketplaces. Journal of Marketing 61 (3): 38–53. https://doi.org/10.2307/
1251788.
Aljukhadar, Muhammad, Sylvain Senecal, and Charles-Etienne Daoust. 2012. Using recommendation
agents to cope with information overload. International Journal of Electronic Commerce 17 (2):
41–70. https://doi.org/10.2753/JEC1086-4415170202.
Allen, D.K., and M. Shoard. 2005. Spreading the load: Mobile information and communications
technologies and their effect on information overload. Information Research 10 (2): 1–13.
Amit, Adi, and Lilach Sagiv. 2013. The role of epistemic motivation in individuals’ response to decision
complexity. Organizational Behavior and Human Decision Processes 121 (1): 104–117. https://doi.
org/10.1016/j.obhdp.2013.01.003.
Anderson, Matthew J. 1988. A comparative analysis of information search and evaluation behavior of
professional and non-professional financial analysts. Accounting, Organizations and Society 13 (5):
431–446. https://doi.org/10.1016/0361-3682(88)90015-3.
Anderson, Simon P., and Andre
´de Palma. 2012. Competition for attention in the information (overload)
age. The Rand Journal of Economics 43 (1): 1–25. https://doi.org/10.1111/j.1756-2171.2011.00155.
x.
Ariely, Dan. 2000. Controlling the information flow: Effects on consumers’ decision making and
preferences. Journal of Consumer Research 27 (2): 233–248. https://doi.org/10.1086/314322.
Awasthi, Vidya, and Jamie Pratt. 1990. The effects of monetary incentives on effort and decision
performance: The role of cognitive characteristics. The Accounting Review 65 (4): 797–811.
Bailey, N. 1975. The mathematical theory of infectious diseases and its applications. London: Griffin.
Baldacchino, C., C. Armistead, and D. Parker. 2002. Information overload: It’s time to face the problem.
Management Services 46 (1): 18–19.
Bawden, David. 2001. Information overload. Library and Information Briefings 92 (1): 1–15.
Bawden, David, and Lyn Robinson. 2009. The dark side of information: Overload, anxiety and other
paradoxes and pathologies. Journal of Information Science 35 (2): 180–191.
Benselin, J.C., and G. Ragsdell. 2016. Information overload: The differences that age makes. Journal of
Librarianship and Information Science 48 (3): 284–297. https://doi.org/10.1177/
0961000614566341.
Berghel, Hal. 1997. Cyberspace 2000: Dealing with information overload. Communications of the ACM
40 (2): 19–24. https://doi.org/10.1145/253671.253680.
Berlo, D. K. 1960. The process of communication. New York: Rinehart & Winston.
Bettis-Outland, Harriette. 2012. Decision-making’s impact on organizational learning and information
overload. Journal of Business Research 65 (6): 814–820. https://doi.org/10.1016/j.jbusres.2010.12.
021.
Bettman, James R., and C.W. Park. 1980. Effects of prior knowledge and experience and phase of the
choice process on consumer decision processes: A protocol analysis. Journal of Consumer Research
7 (3): 234–248. https://doi.org/10.1086/208812.
Biernacki, P., and D. Waldorf. 1981. Snowball sampling: Problems and techniques of chain referral
sampling. Sociological Methods and Research 10 (2): 141–163.
Birnberg, Jacob G., Joan Luft, and Michael D. Shields. 2008. Psychology theory in management
accounting research. In Handbook of management accounting research, vol. 1, ed. Christopher S.
Chapman, 113–135. Amsterdam: Elsevier.
Blair, Ann. 2012. Information overload’s 2,300-year-old history: Harvard business review online
resources. http://blogs.hbr.org/cs/2011/03/information_overloads_2300-yea.html. Accessed 28 Feb
2017.
Borkovich, D.J., and R. Morris. 2012. When corporations collide: Information overload. Issues in
Information Systems 13 (2): 269–284.
Bouwman, M., P. Frishkoff, and P. Frishkoff. 1993. The relevance of GAAP-based information: A case
study exploring some uses and limitations. Accounting Horizons 9 (1): 22–47.
Braun-LaTour, Kathryn A., Nancy M. Puccinelli, and Fred W. Mast. 2007. Mood, information
congruency, and overload. Journal of Business Research 60 (11): 1109–1116. https://doi.org/10.
1016/j.jbusres.2007.04.003.
512 Business Research (2019) 12:479–522
123
Broadbent, D. 1958. Perception and communication. London: Pergamon Press.
Bstieler, L. 2006. Trust formation in collaborative new product development. Journal of Product
Innovation Management 23 (1): 56–72.
Bucher, Eliane, Christian Fieseler, and Anne Suphan. 2013. The stress potential of social media in the
workplace. Information, Communication and Society 16 (10): 1639–1667. https://doi.org/10.1080/
1369118X.2012.710245.
Burton-Jones, Andrew, and Detmar W. Straub. 2006. Reconceptualizing system usage: An approach and
empirical test. Information Systems Research 17 (3): 228–246. https://doi.org/10.1287/isre.1060.
0096.
Butters, Gerard R. 1977. Equilibrium distributions of sales and advertising prices. The Review of
Economic Studies 44 (3): 465.
Canie
¨ls, Marjolein C.J., and Ralph J.J.M. Bakens. 2012. The effects of project management information
systems on decision making in a multi project environment. International Journal of Project
Management 30 (2): 162–175. https://doi.org/10.1016/j.ijproman.2011.05.005.
Cartwright, Jennifer, Sanji de Sylva, Matt Glasgow, Renee Rivard, and Jeremy Whiting. 2002.
Inaccessible information is useless information: addressing the knowledge gap. The Journal of
medical practice management MPM 18 (1): 36–41.
Case, Donald O., James E. Andrews, J.D. Johnson, and Suzanne L. Allard. 2005. Avoiding versus
seeking: The relationship of information seeking to avoidance, blunting, coping, dissonance, and
related concepts. Journal of the Medical Library Association 93 (3): 353–362.
Casey, C.J. 1980. Variation in accounting information load: The effect on loan officers’ predictions of
bankruptcy. The Accounting Review 55 (1): 36–49.
Chan, S.Y. 2001. The use of graphs as decision aids in relation to information overload and managerial
decision quality. Journal of Information Science 27 (6): 417–425. https://doi.org/10.1177/
016555150102700607.
Chan, Yiu M., and Hong Huang. 2013. Weight management information overload challenges in 2007
HINTS: Socioeconomic, health status and behaviors correlates. Journal of Consumer Health On the
Internet 17 (2): 151–167. https://doi.org/10.1080/15398285.2013.780540.
Chen, Chien C., Shun-Yuan Shih, and Meng Lee. 2016. Who should you follow? Combining learning to
rank with social influence for informative friend recommendation. Decision Support Systems 90:
33–45. https://doi.org/10.1016/j.dss.2016.06.017.
Chen, Min. 2018. Improving website structure through reducing information overload. Decision Support
Systems.https://doi.org/10.1016/j.dss.2018.03.009.
Chen, Wenhong, and Kye-Hyoung Lee. 2013. Sharing, liking, commenting, and distressed? The pathway
between Facebook interaction and psychological distress. Cyberpsychology, behavior and social
networking 16 (10): 728–734. https://doi.org/10.1089/cyber.2012.0272.
Chen, Yu-Chen, Rong-An Shang, and Chen-Yu. Kao. 2009. The effects of information overload on
consumers’ subjective state towards buying decision in the internet shopping environment.
Electronic Commerce Research and Applications 8 (1): 48–58. https://doi.org/10.1016/j.elerap.
2008.09.001.
Chewning, Eugene G., and Adrian Harrell. 1990. The effect of information load on decision makers’ cue
utilization levels and decision quality in a financial distress decision task. Accounting, Organizations
and Society 15 (6): 527–542.
Cole, J.R., and S. Cole. 1972. The Ortega hypothesis: Citation analysis suggests that only a few scientists
contribute to scientific progress. Science (New York, N.Y.) 178 (4059): 368–375. https://doi.org/10.
1126/science.178.4059.368.
Conlon, E.J., and J.M. Parks. 1987. Information requests in the context of escalation. Journal of Applied
Psychology 72 (3): 344–350.
Cook, G.J. 1993. An empirical investigation of information search strategies with implications for
decision support system design. Decision Sciences 24 (4): 683–698.
Cukrowski, Jacek, and Andrzej Baniak. 1999. Organizational restructuring in response to changes in
information-processing technology. Review of Economic Design 4 (4): 295–305. https://doi.org/10.
1007/s100580050039.
D’Arcy, John, Ashish Gupta, Monideepa Tarafdar, and Ofir Turel. 2014. Reflecting on the ‘dark side’’ of
information technology use. Communications of the ACM 35 (5): 109–118.
Davis, J.G., and S. Ganeshan. 2009. Aversion to loss and information overload—an experimental
investigation. ICIS Proceedings 30 (11): 1–14.
Business Research (2019) 12:479–522 513
123
Dean, Derek, and Caroline Webb. 2011. Recovering from information overload. McKinsey Quarterly 11
(1): 80–88.
Denham, S. 2015. Auditory sensing systems: Overview. In Encyclopedia of computational neuroscience,
ed. Dieter Jaeger and Ranu Jung, 1–3. New York: Springer New York.
DeShazo, J.R., and German Fermo. 2002. Designing choice sets for stated preference methods: the effects
of complexity on choice consistency. Journal of Environmental Economics and Management 44
(1):123–143.
Ding, S., and P. Beaulieu. 2011. The role of financial incentives in balanced scorecard based performance
evaluations: Correcting mood congruency biases. Journal of Accounting Research 49: 1223–1247.
Doll, William J., and Gholamreza Torkzadeh. 1988. The measurement of end-user computing
satisfaction. MIS Quarterly 12 (2): 258–274. https://doi.org/10.2307/248851.
Dong, John Q., and Jork Netten. 2017. Information technology and external search in the open innovation
age: New findings from Germany. Technological Forecasting and Social Change 120: 223–231.
https://doi.org/10.1016/j.techfore.2016.12.021.
Driver, M.J., and S. Streufert. 1969. Integrative complexity: An approach to individuals and groups as
information processing systems. Administrative Science Quarterly 14 (2): 272–285.
Driver, Michael J., Kenneth R. Brousseau, and Phillip L. Hunsaker. 1990. The dynamic decision maker,
1st ed. Lincoln: iUniverse.
Earle, Timothy C. 2010. Trust in risk management: A model-based review of empirical research. Risk
analysis an official publication of the Society for Risk Analysis 30 (4): 541–574. https://doi.org/10.
1111/j.1539-6924.2010.01398.x.
Edmunds, A., and A. Morris. 2000. The problem of information overload in business organizations: A
review on the literature. International Journal of Information Management 20 (1): 17–28.
Eppler, M.J., and J. Mengis. 2004. The concept of information overload: A review of literature from
organization science, accounting, marketing, MIS and related disciplines. The Information Society
20 (5): 325–344.
Farhoomand, Ali F., and Don H. Drury. 2002. Managerial information overload. Communications of the
ACM 45 (10): 127–131. https://doi.org/10.1145/570907.570909.
Feather, J. 1988. The information society: A study of continuity and change. London: Library Association.
Festinger, L. 1954. A theory of social comparison. Human Relations 7 (1): 117–140.
Fink, Lior, Liron Rosenfeld, and Gilad Ravid. 2018. Longer online reviews are not necessarily better.
International Journal of Information Management 39: 30–37. https://doi.org/10.1016/j.ijinfomgt.
2017.11.002.
Forgas, Joseph P. 1995. Mood and judgment: The affect infusion model (AIM). Psychological Bulletin
117 (1): 39–66. https://doi.org/10.1037/0033-2909.117.1.39.
Forgas, Joseph P., and Jennifer M. George. 2001. Affective influences on judgments and behavior in
organizations: an information processing perspective. Organizational Behavior and Human
Decision Processes 86 (1): 3–34.
Foster, Allen. 2004. A nonlinear model of information-seeking behavior. Journal of the American Society
for Information Science and Technology 55 (3): 228–237. https://doi.org/10.1002/asi.10359.
Gao, Jie, Cheng Zhang, Ke Wang, and Sulin Ba. 2012. Understanding online purchase decision making:
The effects of unconscious thought, information quality, and information quantity. Decision Support
Systems 53 (4): 772–781. https://doi.org/10.1016/j.dss.2012.05.011.
Gao, Wei, Zhaopeng Liu, Qingqing Guo, and Xue Li. 2018. The dark side of ubiquitous connectivity in
smartphone-based SNS: An integrated model from information perspective. Computers in Human
Behavior 84: 185–193. https://doi.org/10.1016/j.chb.2018.02.023.
Gottschalk, Sabrina A., and Alexander Mafael. 2017. Cutting through the online review jungle—
investigating selective eWOM processing. Journal of Interactive Marketing 37: 89–104. https://doi.
org/10.1016/j.intmar.2016.06.001.
Granovetter, Mark. 1983. The strength of weak ties: a network theory revisited. Sociological Theory 1:
201–233. https://doi.org/10.2307/202051.
Greifeneder, Rainer, Benjamin Scheibehenne, and Nina Kleber. 2010. Less may be more when choosing
is difficult: choice complexity and too much choice. Acta Psychologica 133 (1): 45–50. https://doi.
org/10.1016/j.actpsy.2009.08.005.
Greiling, D., and K. Spraul. 2010. Accountability and the challenges of information disclosure. Public
Administration Quarterly 34 (3): 338–377.
Grise, M. and R. B. Gallupe. 1999/2000. Information overload: Addressing the productivity paradox in
face-to-face electronic meetings. Journal of Management Information Systems 16(3):157–185.
514 Business Research (2019) 12:479–522
123