Technostress Research: A Nurturing Ground for Measurement Pluralism?

Abstract

Because technostress research is multidisciplinary in nature and therefore benefits from insights gained from various research disciplines, we expected a high degree of measurement pluralism in technostress studies published in the Information Systems (IS) literature. However, because IS research, in general, mostly relies on self-report measures, there is also reason to assume that multi-method research designs have been largely neglected in technostress research. To assess the status quo of technostress research with respect to the application of multi-method approaches, we analyzed 103 empirical studies. Specifically, we analyzed the types of data collection methods used and the investigated components of the technostress process (person, environment, stressors, strains, and coping). The results indicate that multi-method research is more prevalent in the IS technostress literature (approximately 37% of reviewed studies) than in the general IS literature (approximately 20% as reported in previous reviews). However, our findings also show that IS technostress studies significantly rely on self-report measures. We argue that technostress research constitutes a nurturing ground for the application of multi-method approaches and multidisciplinary collaboration.

Full text

Communications of the Association for Information Systems
.+3,% 02)#+%

Technostress Research: A Nurturing Ground for
Measurement Pluralism?
omas Fischer
University of Applied Sciences Upper Austria2(.,!1;1#(%0:12%60!2
René Riedl
University of Applied Sciences Upper Austria and University of Linz
.++.52()1!-$!$$)2).-!+5.0*1!2 (</!)1%+!)1-%2.0'#!)1
9)1,!2%0)!+)1"0.3'(22.6.3"62(%.30-!+1!2+%#20.-)#)"0!06%2(!1"%%-!##%/2%$&.0)-#+31).-)-.,,3-)#!2).-1.&2(%
11.#)!2).-&.0-&.0,!2).-612%,1"6!-!32(.0)7%$!$,)-)120!2.0.&+%#20.-)#)"0!06%.0,.0%)-&.0,!2).-/+%!1%#.-2!#2
%+)"0!06!)1-%2.0'
%#.,,%-$%$)2!2).-
)1#(%09.,!1!-$)%$+%-8%#(-.120%11%1%!0#(30230)-'0.3-$&.0%!130%,%-2+30!+)1, Communications
of the Association for Information Systems .+02)#+%
4!)+!"+%!2 (</!)1%+!)1-%2.0'#!)14.+)11

C
ommunications of the
A
I
S
ssociation for nformation
ystems
Research Paper ISSN: 1529-3181
Volume 40 Paper 17 pp. 375 – 401 May 2017
Technostress Research: A Nurturing Ground for
Measurement Pluralism?
Thomas Fischer
University of Applied Sciences Upper Austria
thomas.fischer@fh-steyr.at
René Riedl
University of Applied Sciences Upper Austria and
University of Linz
Abstract:
Because technostress research is multidisciplinary in nature and, therefore, benefits from insights gained from various
research disciplines, we expected a high degree of measurement pluralism in technostress studies published in the
information systems (IS) literature. However, because IS research mostly relies on self-report measures in general,
reasons exist to also assume that technostress research has largely neglected multi-method research designs. To
assess the status quo of technostress research with respect to the application of multi-method approaches, we
analyzed 103 empirical studies. Specifically, we analyzed the types of data-collection methods used and the
investigated components of the technostress process (person, environment, stressors, strains, and coping). The
results indicate that multi-method research is more prevalent in the IS technostress literature (approximately 37% o
f
reviewed studies) than in the general IS literature (approximately 20% as reported in previous reviews). However, ou
r
findings also show that IS technostress studies significantly rely on self-report measures. We argue that technostress
research constitutes a nurturing ground for the application of multi-method approaches and multidisciplinar
y
collaboration.
Keywords: Literature Review, Technostress, Measurement Pluralism, Multi-Method, Stress, Stressors, Strains.
This manuscript underwent editorial review. It was received 06/01/2016 and was with the authors for 3 months for 2 revisions. Eric
Walden served as Associate Editor.

376 Technostress Research: A Nurturing Ground for Measurement Pluralism?
Volume 40 Paper 17
1 Introduction
Technostress (Brod, 1982) refers to stress that results from both the use of information and
communication technologies (ICTs) (Ragu-Nathan, Tarafdar, Ragu-Nathan, & Tu, 2008) and the
pervasiveness and expectations of ICT use in society in general (Riedl, 2013). Technostress is an
increasingly important subject in information systems (IS) research because it negatively impacts many IS
outcome variables such as usage intention (Fuglseth & Sørebø, 2014; Maier, Laumer, Eckhardt, &
Weitzel, 2014), end-user satisfaction (Fuglseth & Sørebø, 2014; Maier et al., 2014), or technology-
supported performance (Tams, Hill, Ortiz de Guinea, Thatcher, & Grover, 2014; Tarafdar, Pullins, & Ragu-
Nathan, 2015). Thus, most major IS publication outlets now unsurprisingly publish technostress studies1.
Because ICTs are likely to become more pervasive in the future and because IS scholars’ work involves
researching relevant IS phenomena, it is worthwhile to examine the state of the art in technostress
research. Specifically, in this work, we examine the types of constructs that technostress research
examines and the types of methods it uses to examine them. By doing so, we help technostress
researchers to identify the constructs and methods that are underrepresented in our understanding of the
technostress phenomena.
An important aspect of stress as a phenomenon and, hence, of technostress is the inherent need for
multidisciplinary investigation. Cummings and Cooper (1998) highlight that investigations of stress
phenomena (e.g., in the context of organizational stress research) require the combined efforts of
researchers from medicine, psychology, management, and sociology to advance our understanding of
stress in this domain. Hence, researchers have unsurprisingly advocated for multi-method designs, which
combine the research traditions of various disciplines, in related disciplines, including technostress
research (e.g., Riedl, Davis, & Hevner, 2014; Riedl, 2013).
Tams et al. (2014) demonstrate the strengths of a multi-method approach in technostress research. In the
authors’ experiment, participants performed a computer-based task (a memory game) while instant
messages frequently interrupted them. Measuring the resulting stress on both a psychological level (using
self-report measures) and a physiological level (using measures of stress hormone excretion), the authors
explained a higher degree of the variance in task performance than with each method alone.
Mingers (2001a) argues that IS researchers can draw on a wide range of related disciplines (e.g.,
psychology, sociology, economics, and mathematics) and should accordingly also embrace their
respective research traditions. By adopting different measurement approaches, researchers in a
multidisciplinary discipline, such as IS in general or technostress in particular, can often overcome the
limitations of each single approach and its inherent, limited world view (Mingers & Brocklesby, 1997). To
bring these differing views together and create a more holistic representation of reality, combining data-
collection methods is advantageous (Mingers, 2001a; Mingers & Brocklesby, 1997; Pinsonneault &
Kraemer, 1993), as demonstrated by Tams et al. (2014) who combined self-report and physiological
measures to explain more variance in individual performance.
However, despite their inherent strengths, multi-method studies remain rare in the IS discipline. For
example, Mingers (2001a) shows that IS research published since the 1990s has used few methods
(mainly self-report based methods such as surveys and interviews) as its data-collection instrument
(sample: empirical papers in MIS Quarterly, Information Systems Research, Communications of the ACM,
Information Systems Journal, European Journal of Information Systems, and Information and
Organization over the 1993 to 1998 period). Specifically, Mingers shows that only 13 percent of the
investigated studies applied more than one type of measurement. In a similar study, based on a larger
sample, the same author found a multi-method research rate of approximately 20 percent (Mingers,
2001b). Still, although these rates seem low, they are high compared to mixed-methods research in IS
(i.e., a subtype of multi-method research that requires one to not only apply multiple data-collection
methods but also mix different paradigms, such as qualitative and quantitative). Orlikowski and Baroudi
(1991) found that approximately 3 percent of IS studies had applied such a paradigm-spanning approach
to collect data, which has not changed much over time as Venkatesh, Brown, and Bala (2013) show in
their more recent review (they still found a mixed-methods research rate of less than 5 percent).
1 Examples: EJIS: Maier et al. (2014); ISJ: Maier, Laumer, Weinert, and Weitzel (2015b); Srivastava, Chandra, and Shirish (2015);
Tarafdar, Pullins, and Ragu-Nathan (2015); ISR: Ragu-Nathan et al. (2008); JAIS: Galluch, Grover, and Thatcher (2015); Tams et al.
(2014); JMIS: D'Arcy, Herath, and Shoss (2014); Moody and Galletta (2015); Tarafdar, Tu, Ragu-Nathan, and Ragu-Nathan (2007);
MISQ: Ayyagari, Grover, and Purvis (2011).

Communications of the Association for Information Systems 377
Volume 40 Paper 17
Against the background of these findings, we have reason to assume that multi-method designs are also
rare in IS technostress research. Hence, to assess the status quo of multi-method research in the context
of technostress, we reviewed technostress research (N = 103) and first analyzed the used measurement
methods. Additionally, given that we hope to foster future multi-method technostress research, we also
examined the technostress components (e.g., stressors or strains) that technostress research investigated
with these methods. We based this investigation on a subsample of the reviewed studies (N = 93). Finally,
we discuss major motivations that have driven multi-method technostress research.
In Section 2, we present the methodology of our literature review and provide details on the corresponding
analyses. In Section 3, we present our main results about 1) the share of multi-method research in the IS
technostress literature and its development over time, 2) the data-collection methods that the IS
technostress literature has applied, and 3) the components in the technostress process that the IS
technostress has investigated. In Section 4, we close this paper with reflections on our findings and the
potential future of multi-method research in the technostress discipline.
2 Literature Review and Analysis
We used Google Scholar2 and SCImago3 to identify relevant studies. We used “technostress” as a
keyword in Google Scholar (3 February to 7 February, 2016) while excluding citations and patents from
our results; this search led to an initial 3,300 hits, which constituted the basis for further analyses. Next,
we selected only journal and conference publications and also introduced a quality criterion (namely, that
a paper must have at least five citations; see, e.g., Riedl, 2013, for a comparable procedure). To minimize
the possibility of missing recent high-quality IS publications (which do not yet have five citations), we
additionally searched for recent technostress publications in major AIS journals (i.e., journals in the Senior
Scholars’ basket4) and in the proceedings of its flagship conference (i.e., ICIS) 5. In the search process,
we identified many studies that cited the pioneering publications by Brod (1982, 1984) but did not actually
focus on technostress (but on related topics). Hence, we also excluded the following types of publications:
1. Studies that focused on individual traits, such as computer attitudes or dependence tendencies,
that may be predictors of technology-related stress but that do not actually involve stress and its
effects (e.g., we excluded Brock and Sulsky (1994) due to this criterion).
2. Studies that focus on the adoption of technology in organizations and related outcomes on the
individual and/or organizational level (e.g., resistance to change) but that do not relate directly to
individual-level stress perceptions (e.g., we excluded Helliwell and Fowler (1994) due to this
criterion).
3. Studies that focus on phenomena related to modern technology but not necessarily focus on
ICTs, such as examinations of perceived electronic hypersensitivity or effects of magnetic fields
in general (e.g., we excluded Oftedal, Vistnes, and Rygge (1995) due to this criterion).
The described search and elimination routines resulted in 121 publications plus an additional 10 studies
from our search in AIS outlets. To further check that the criterion of five citations was not too restrictive,
we chose high-impact journals in other research disciplines that had published at least one of the
publications we had selected thus far and searched for recent technostress publications. This step
resulted in our identifying no additional publications (see Appendix A for a list of included journals). Next,
we excluded another 25 papers because they were not empirical in nature, and we excluded three other
studies due to their low quality of presentation6. Thus, 103 technostress publications comprised the
empirical basis of our review (see Appendix B for a full list).
2.1 Categories for Classification
To analyze the 103 studies, we used two dimensions: 1) applied data-collection methods and 2)
investigated components in the technostress process.
2 https://scholar.google.com
3 https://www.scimagojr.com
4 http://aisnet.org/?SeniorScholarBasket
5 10 February to 14 February, 2016: “Technostress” and “Techno*Stress” in the journal archives of the outlets included in the Senior
Scholars’ basket with the exception of Journal of MIS where we selected “online stress” and “technostress” from available keywords.
15 February to 16 February, 2016: Selection of papers from the ICIS proceedings of the last ten years based on title and abstract.
6 Lee, Jin, and Choi (2012), Popoola and Olalude (2013), Yu et al. (2009).

378 Technostress Research: A Nurturing Ground for Measurement Pluralism?
Volume 40 Paper 17
2.1.1 Data-collection Methods
Based on initially reviewing the identified 103 publications, we devised several categories to classify these
studies according to their applied data-collection methods. In total, we developed seven categories: self-
reports, interviews, logs, observations, objective performance measures, biological measures, and
miscellaneous measures (see Table 1).
Table 1. Types of Data-collection Methods in Technostress Research
Category Description
Self-reports In this category, we included all questionnaire-based measures independent of their medium
(e.g., paper-based or electronic), structure (e.g., select choice or open-ended questions), or
format (e.g., types of rating scales).
Interviews In this category, we included all types of direct self-reports by individuals although again
independent of the interview’s structure (e.g., structured, semi-structured, or completely
open-ended).
Logs
We used the term “log” to classify all types of self-report measures where individuals
themselves took the initiative to write down impressions or take notes on certain events,
such as critical incidents (e.g., Salo, Makkonen, & Hekkala, 2015) or communication
activities (e.g., Schellhammer, Haines, & Klein, 2013).
Observations
In this category, we included researchers’ observations and other forms of monitoring
activities that subjects did not initiate. For example, Brooks (2015) recorded the use of a
variety of Internet technologies while subjects were supposed to watch an informational
video on which they would later have to answer questions.
Objective performance
measures
In this category, we included data on the performance of individuals that researchers
collected independently of subjective perceptions—predominantly performance in
experimental tasks (e.g., time taken to complete tasks and/or error rates; Moody & Galletta,
2015; Tams et al., 2014).
Biological measures
In this category, we included measures that reflect the many biological systems involved in
the stress process (e.g., Joels & Baram, 2009). The main types of measures we included
concerned hormone excretion (e.g., cortisol, Dickerson & Kemeny, 2004) or
psychophysiological processes (e.g., increases in cardiovascular activity, Hjortskov et al.,
2004).
Miscellaneous
measures
In the case one or at most two studies exclusively used a data-collection method, we
combined them in this overall category. For example, Arnetz (1996) employed several
environmental sensors to capture contextual aspects inside a building such as office lighting.
2.1.2 Components of Technostress
To frame our classification, we used Lazarus’s (1966) and Lazarus and Folkman’s (1984) transactional
model of stress. This theory constitutes the conceptual foundation of many studies in technostress
research, especially in recent years (e.g., Galluch et al., 2015; Neben & Schneider, 2015; Srivastava et
al., 2015; Tarafdar et al., 2015). This transactional model posits that stress is not one component of a
process (e.g., a stimulus or a response) but the process itself that can lead to detrimental effects in
individuals.
This process involves environmental conditions (e.g., demands of the job, such as a certain workload) that
the individuals perceive, and, if situational circumstances do not correspond to internal conditions (e.g.,
task demands that exceed an individual’s resources, such as skills and abilities, or desires regarding the
situation in general, such as the wish for a lower workload), then they appraise them as a threat to their
wellbeing (i.e., a result of so-called primary appraisal). To cope with the detrimental effects of such
demands (i.e., strains, such as reduced physical and mental wellbeing), the individuals evaluate the
alternatives that could help attenuate the negative effects of these demands based on their available
resources and then enact the most promising stress-reducing behaviors (i.e., coping, a result of so-called
secondary appraisal).
Based on this basic description of the transactional model (for detailed descriptions, please see Folkman
& Lazarus, 1985; Folkman, Lazarus, Dunkel-Schetter, DeLongis, & Gruen, 1986), we focus on five main
components: person, environment, stressors, strains, and coping (see Table 2). For stressors and coping
measures, note that, at least theoretically, most, if not all, elements in the environment could enact
demands on the individual or be resources for coping. For example, Nastjuk and Kolbe (2015)

Communications of the Association for Information Systems 379
Volume 40 Paper 17
demonstrated that the same IS artifact (in their case an in-car navigation system) could even be a source
of stress and a coping measure at the same time depending on the given context (e.g., while the
navigation system lowered the threat of not arriving at a certain destination at a specific time based on the
remaining fuel, individuals perceived constant interaction with the navigation system as a technological
stressor). We classified a construct as a stressor if its original study described it as increasing strain;
likewise, we classified a construct as a coping measure if its study described it as decreasing strain.
To adapt the classification scheme to the context of technostress, we further subdivided “environment”
into task environment, organizational environment, technological environment, and social environment.
Researchers have frequently used task, organizational, and social environment in the context of stress
research (Cooper & Marshall, 1976; McGrath, 1976; Sonnentag & Frese, 2013), and we added the
technological environment as another domain due to its relevance in the technostress context.
Table 2. Components in the Technostress Process
Component Description and Exemplary Constructs
Person
In the presented stress process, the individual perceives and appraises external demands before
enacting regulatory behaviors. Hence, in the “person” component, we include all the constructs
that can influence the perception and appraisal processes.
Exemplary constructs include individual characteristics, such as personality variables (e.g.,
Ayyagari et al., 2011; D'Arcy et al., 2014; Emurian, 1993; Maier et al., 2015b; Srivastava et al.,
2015; Yan, Guo, Lee, & Vogel, 2013) or attitudes toward technology or one’s own ability to handle
technology (e.g., Ragu-Nathan et al., 2008; Shu, Tu, & Wang, 2011; Tarafdar, Pullins, & Ragu-
Nathan, 2014, 2015).
Environment
Task
The “task environment” includes demands on the individual that originate from the individual’s
formal and informal roles in an organization (e.g., tasks that are part of one’s formal job
description but also those tasks that arise from other roles in an organization, such as being a
source of support for less experienced colleagues), which previous technostress research has
frequently found interest in (e.g., Barley et al., 2011; D'Arcy et al., 2014; Galluch et al., 2015;
Sellberg & Susi, 2014; Srivastava et al., 2015).
Additionally, task environment includes constructs that represent characteristics of these roles,
such as traits of the job, including job autonomy or dependence on technology (e.g., Bailey &
Konstan, 2006; Galluch et al., 2015; Shu et al., 2011; Wang, Ye, & Teo, 2014).
Organization
The “organizational environment” is mostly an encompassing unit for task-related variables and
forms that result from the social interactions of an organization’s current or former members.
Hence, organization environment includes constructs such as organizational culture (e.g., Barley
et al., 2011; Wang, Shu, & Tu, 2008) or availability of organizational resources, including the
provision of technical support (e.g., Fuglseth & Sørebø, 2014; Ragu-Nathan et al., 2008; Tarafdar
et al., 2015). It also includes physical characteristics of the organizational environment, such as
office ergonomics or lighting conditions, because they can be potential stressors (e.g., Arnetz,
1997; Berg & Arnetz, 1996).
Social
The “social environment” encompasses stressors and coping resources that arise from
interpersonal relationships. Although one could include organizational roles here (e.g., as in
McGrath (1976) who, in his classification, depicted roles as a combination of tasks that arise from
social interaction), we apply a more narrow distinction: we focus mostly on social interaction that
is not related to the work environment but rather to the private domain.
Exemplary constructs in social environment include perceived non-work demands (e.g., Chen &
Karahanna, 2011; Voakes, Beam, & Ogan, 2003) or social support from family or friends (e.g., Al-
Fudail & Mellar, 2008; Thomée, Härenstam, & Hagberg, 2012; Yan et al., 2013).

380 Technostress Research: A Nurturing Ground for Measurement Pluralism?
Volume 40 Paper 17
Table 2. Components in the Technostress Process
Technology
The “technological environment” comprises the technologies and their characteristics that the
individual uses throughout the day not only in the organizational environment but also in the
private domain (e.g., mobile devices, which can easily cross these domains). In particular, we
focus on the potential of technology to directly influence individual stress perceptions (as a
stressor or coping resource) but do not include mediator effects. For example, we would classify
perceived “invasion” of the private life of an individual through technology (e.g., continuous work
demands in the form of emails) as pertaining to the “task environment” rather than the
“technological environment” because technology (email in this case) is merely the carrier of the
demand that is actually causing stress (work tasks).
Exemplary constructs relate to technology acceptance, such as usefulness (e.g., Ayyagari et al.,
2011; Maier, Laumer, Weinert et al., 2015) and ease of use (e.g., Al-Fudail & Mellar, 2008;
Ayyagari et al., 2011; Maier, Laumer, Eckhardt, & Weitzel, 2012; Moody & Galletta, 2015) or to
indicators of system performance, such as system reliability (e.g., Al-Fudail & Mellar, 2008;
Ayyagari et al., 2011; Moody & Galletta, 2015; Riedl, Kindermann, Auinger, & Javor, 2012, 2013).
Stressors
“Stressors” are demands (or a force in general) that force a variable outside of its range of
stability (Cummings & Cooper, 1979, 1998). For example, unusual task demands might force an
individual to handle a workload with which the individual is not comfortable, or system
malfunctions might create interruptions in an individual’s usual workflow. The individual must
perceive these demands first and then appraise them as detrimental to their wellbeing (e.g., a
higher workload could also be perceived as beneficial if the individual needs higher levels of
stimulation) to be stressors. Accordingly, we classified those constructs that the reviewed studies
included as antecedents to detrimental effects (i.e., strains) as stressors. In the context of
technostress, such constructs include the “technostress creators” (overload, invasion, complexity,
insecurity, and uncertainty) that Ragu-Nathan et al. (2008) introduced.
Strains
“Strains” are the detrimental effects of stressors on an individual’s wellbeing pertaining to the
psychological, physiological, and/or behavioral levels (e.g., Carayon, Smith, & Haims, 1999;
Sonnentag & Frese, 2013). Exemplary constructs include exhaustion (e.g., Ayyagari et al., 2011;
Galluch et al., 2015; Maier, Laumer, & Eckhardt, 2015), increased stress hormone excretion (e.g.,
Galluch et al., 2015; Riedl et al., 2012; Tams et al., 2014), or reduced performance (e.g., Brooks,
2015; Moody & Galletta, 2015; Tams et al., 2014).
Coping
Individuals primarily enact “coping” behaviors to reduce the detrimental impact that stressors can
have on their wellbeing, although there can also be organizational-level interventions that help
one reduce stress (e.g., technical support). These individual behaviors or organizational
interventions can focus on diminishing the stressor itself (problem-focused coping, such as
resolving a software malfunction) or just the resulting strains (emotion-focused coping, such as
taking a break in the case of a malfunction).
In the context of technostress, interventions that have received repeated attention include breaks
and break schedules (e.g., Boucsein & Thum, 1997; Ye et al., 2007), relaxation (e.g., Arnetz,
1996), and technology literacy facilitation (e.g., Ragu-Nathan et al., 2008).
2.1.3 Classification Procedure
For this literature review, we needed two classification phases. First, we had to select suitable publications
for the review, which required our developing several exclusion criteria (see the beginning of Section 2.1)
to narrow down our selection to publications that focus on the negative effects of technology from a stress
perspective. We jointly developed these criteria in the process of reviewing the abstracts of the initially
identified publications, while the first author performed the classification (inclusion or exclusion) based on
abstract and full text, which the second author then reviewed. We discussed borderline cases until we
reached agreement. This process led to our selecting 103 publications.
Second, the main classification for this review involved our identifying 1) applied data-collection methods
and 2) the purpose for which the methods were applied (i.e., measurement of which technostress
components). The first author performed the first part of this (straightforward) classification, and the
second author reviewed the classification results. The main challenge in this stage involved classifying the
measured constructs, which required reviewing each individual publication in depth. As such, this stage
involved frequent discussion between the two authors.

Communications of the Association for Information Systems 381
Volume 40 Paper 17
In general, we tried to assign measured constructs to as few components as possible based on their role
in different research models. For example, consider individual use of technology. In the case of
technology-mediated tasks, we assigned technology usage to the task environment because the involved
technologies were mainly tools for handling tasks related to one’s assigned role (e.g., Schellhammer et
al., 2013). In comparison, if technology use was independent of a given task (e.g., Brooks, 2015) or if a
global measure of the general exposure to technology was used (e.g., Riedl et al. 2012), then we
assigned technology use to the “person” component as a characteristic of individual behaviors. Finally, if
technologies were used as a way to cope with given stressors (e.g., Maier et al., 2015), then technology
use was classified as a form of coping.
Furthermore, in the case of self-report measures, we analyzed the specific items that the studies used to
decide to which component of the stress process we should assign a variable. As an example, consider
the “technostress creators” that Ragu-Nathan et al. (2008) introduce. This self-report instrument
comprises 25 items arranged along five main factors. Tarafdar, Tu, Ragu-Nathan, and Ragu-Nathan
(2011, p. 117) briefly define these factors as:
1. Techno-overload: “IS users face information overload and IS-enabled multitasking”.
2. Techno-invasion: “IS users never feel ‘free’ of IS”.
3. Techno-complexity: “IS users find it intimidating to learn and use IS”.
4. Techno-insecurity: “IS users feel insecure about their jobs in the face of new IS and others who
might know more about these technologies”.
5. Techno-uncertainty: “IS users feel unsettled by continual upgrades and accompanying software
and hardware changes”.
One of these factors, “techno-overload”, includes the following five items (Ragu-Nathan et al., 2008, p.
426):
 I am forced by this technology to work much faster.
 I am forced by this technology to do more work than I can handle.
 I am forced by this technology to work with very tight time schedules.
 I am forced to change my work habits to adapt to new technologies.
 I have a higher workload because of increased technology complexity.
While Ragu-Nathan et al. (2008) intended these items to measure stress appraisal regarding work
overload caused by technology, based on some of the involved items, we would also assign this construct
to the task environment. While working much faster, having a higher workload, or adapting to new
technologies may seem stressful to some individuals, those individuals’ “hell” may be another individual’s
“heaven” (Cummings & Cooper, 1998, p. 106). In this case, only the second item actually asks for an
individual’s perception in the context of the individual’s own capabilities (i.e., “more than I can handle”
would actually indicate the result of an appraisal process). Analogously, we assigned not only techno-
overload to both the task environment and the list of potential stressors but also the other four factors to
components outside of potential stressors (i.e., techno-invasion to the task and social environment,
techno-complexity to the technological environment and personal characteristics, techno-insecurity to the
organizational environment, and techno-uncertainty to the technological environment).
Even in the case that a specific study used established inventories, such as the described “technostress
creators” for data collection, the classification required an in-depth analysis because some studies did not
use the full inventory of items (e.g., some studies only used four of the presented factors, such as Sellberg
and Susi (2014) and Tarafdar et al. (2015), or only three of them, such as Brooks (2015), D'Arcy et al.
(2014), and Maier et al. (2012)) or made adaptations to items for their use in a specific study context. For
example, Maier et al. (2012), Maier et al. (2014), and Maier et al. (2015b) adapted the technostress
creators for the context of online social networks. Due to the characteristics of involved technologies (ICT
in an enterprise context versus ICT in a social networking context), their items focus more intensely on the
social environment than on the task environment. For example, Maier et al. (2015b, p. 293) used the
construct “social overload” (e.g., “I address my friends’ problems too much on Facebook”) instead of the
original construct “work overload”.
Due to the challenges related to recognizing such nuanced differences, we opted to not classify measured
components in the technostress process if a study was solely based on qualitative data-collection

382 Technostress Research: A Nurturing Ground for Measurement Pluralism?
Volume 40 Paper 17
methods (e.g., only interviews or observations). However, when used as an addition to quantitative
methods (e.g., self-reports), we classified them as contributing to the measurement of the same
components as their quantitative counterparts. Hence, while we classified the overall data-collection
methods applied for each of the 103 publications, we assessed only 93 studies regarding the contributions
they have made to the measurement of specific technostress components.
3 Data-collection Methods in Technostress Research
A look at the various components involved in the technostress process (person, environment, stressors,
strains, and coping) strongly implies that measuring the ongoing interaction between person and
environment necessitates measurement pluralism. Hence, our ex ante expectation before starting the
analyses was that technostress research would be a good exemplary domain that embraces multi-method
approaches to data collection. More specifically, we expected a rate of multi-method research that was
greater than the rate in IS research in general that Mingers has reported (2001a: ~13%; 2001b: ~20%).
The results confirmed our expectations. We found a multi-method research rate of 37 percent in
technostress research (i.e., 38 of 103 studies). In other words, a bit more than one third of technostress
studies applied more than one data-collection method.
To visualize the development of technostress research over time, we looked at the chronological
distribution of the 103 reviewed studies. In Figure 1, we indicate the number of publications (y-axis) per
year (x-axis) for mono-method studies (one method of data collection) and multi-method studies (more
than one method of data collection). Overall, the number of technostress publications has increased
substantially over the years; however, we were also interested in potential differences in development for
both types of studies. Hence, we also included regression lines (calculated in SPSS 24) for both mono-
method studies (F (1,36) = 55.643, p = .000, with an R² of .607) and multi-method studies (F (1,36) =
4.681, p = .037, with an R² of .115), which indicate that, although both types of studies became more
popular over time (see the positive slopes of both functions; mono-method: .1303, multi-method: .0497), a
gap has opened up between both functions. The difference between mono-method and multi-method
research over time was statistically significant (mono-method: average of 1.71 publications per year; multi-
method studies: average of 1.00 publications per year; U = 523.5; p = .029).
Figure 1. Overview of Reviewed Technostress Studies Based on Publication Yea
r
We surmise that one major reason for this finding is that recent technostress research has not attracted
many researchers outside of the IS discipline who typically have sound knowledge on methods that are
less established in the IS discipline (e.g., physicians’ knowledge on biological measures). Note that non-IS
scholars (predominantly psychologists (e.g., Brod, 1982) and physicians (e.g., Arnetz, 1996)) conducted
most of the early technostress research that appeared in the 1980s and 1990s.
The change in outlets in which technostress research has appeared further supports this impression. For
example, if we cluster the reviewed studies based on their publication year into three blocks of roughly ten
years (i.e., 1995 and before, 1996-2005, and 2006-2015), we find that IS outlets now publish most

Communications of the Association for Information Systems 383
Volume 40 Paper 17
technostress research7, while non-IS outlets initially published most of it. Specifically, of the 18 studies
published before 1995, non-IS outlets published 16 of them, IS and non-IS outlets published an equal
number in the 1996-2005 period (14 each), and IS outlets published the large majority of papers (49 of 57)
in the most recent period.
As such, this basic overview of technostress research and the distribution of multi-method studies over the
years warranted further analysis of the technostress components and corresponding methods. For this
purpose, we further analyzed 93 of the original 103 studies. Table 3 presents the results.
Of the 93 studies, 92 applied self-reports to measure various components of the technostress process
(note that self-reports may refer to measurements of theoretical constructs and control variables), and, in
58 studies, self-reports were the only means of data collection (of 65 studies that used only one method of
data collection). This finding shows that self-report measures are not only the dominant data-collection
method but are also frequently used without application of further measurement methods. It is also
noteworthy that many studies do not report on survey measurement items in detail. Of the 92 studies that
used self-report instruments, 54 did not fully cover the involved items. As we highlight in Section 2.1.3,
without knowing the actual items used, it is not possible to classify even established self-report
inventories. Therefore, we only classified self-report inventories if all the items were actually available
even if they were reported in a different publication due to the possibility of item adaptations for a specific
research context.
Table 3. Data-collection Methods along the Technostress Process
Data-collection methods (N = 103) Technostress components (N = 93)
Mono-method (N = 65)
Self-reports
Interviews
Logs
Observations
Objective performance
Biological measures
Miscellaneous
Person
Task
Organization
Social
Technology
Stressors
Strains
Coping
Environment
Totals - 92 16 7 10 14 22 4 85 50 32 24 35 40 50 14
Self-reports 58 - .07 .04 .10 .15 .23 .04 85
(37)
45
(24)
26
(16)
22
(19)
31
(21)
37
(26)
37
(24)
10
(8)
Interviews 6 .38 - .31 .13 .06 .19 .06 2 2 3 2 3 2 1 3
Logs 0 .57 .71 - .14 .00 .14 .00 0 2 1 0 2 1 0 2
Observations 0 .90 .20 .10 - .30 .50 .20 3 5 0 1 3 2 2 1
Objective performance 0 1 .07 .00 .21 - .64 .00 1
Not applicable (N/A)
13
N/A
Biological measures 1 .95 .14 .05 .23 .41 - .09 1 22
Miscellaneous 0 1 .25 .00 .50 .00 .50 - 0 2 3 0 0 1 3 0
In Table 3, we present the result of this classification (right side) for self-report measures in two ways:
outside of parentheses, we list the number of studies that reported all the items for at least one construct
involved in the measurement of the specific component, and in parentheses, we include the number of
studies that reported all items used, with at least one variable being related to the specific component. For
example, in the case of “person” variables, studies often presented sample demographics in detail,
including the used items and rating scales, whereas most did not fully report items involved in measuring
7 To classify outlets as “IS” or “non-IS”, we looked up the “subject areas” of a publication outlet in SCImago. Because “computer
science” was the only subject area that all the journals in the Senior Scholars’ basket shared, we classified a venue as “IS” if it was
categorized as a “computer science” outlet. As an alternative, we considered but rejected the subject category “information systems”
because, for example, it did not include the Journal of MIS. In the case of venues’ not being listed (e.g., in the case of conferences),
we classified them according to the main topics that their respective outlines presented.

384 Technostress Research: A Nurturing Ground for Measurement Pluralism?
Volume 40 Paper 17
self-perceptions (e.g., attitudes or personality characteristics), which led to a large discrepancy between
the numbers reported for this component.
In total, 56 potential combinations of methods and components are principally possible (seven methods ×
eight components; note that we divided environment into four subcomponents), although the nature of the
involved methods restricted their potential in some ways. We indicate this restriction for biological
measures and objective performance measures in Table 3, where an application independent of the
individual is not possible. This fact limits their application potential to current individual states and strains
(e.g., current academic performance via GPA; Galluch et al., 2015; or chronically high blood pressure;
Korunka, Huemer, Litschauer, Karetta, & Kafka-Lützow, 1996).
In addition to self-report measures, many studies in the reviewed sample frequently applied biological
measures (22 of 103 studies). This finding is, at least from an IS perspective, surprising due to the
practical challenges related to collecting and analyzing the involved measures (e.g., high data-collection
costs or required expertise to analyze data; Dimoka et al., 2012; Riedl et al., 2010; Riedl et al., 2014). Ten
of these studies collected biological samples from blood (4 studies), urine (4 studies), and/or saliva (4
studies) to detect the excretion of stress hormones, such as cortisol (e.g., Arnetz, 1996; Riedl et al., 2013)
or alpha-amylase (e.g., Galluch et al., 2015; Tams et al., 2014). Another 10 studies measured data related
to cardiovascular activity of participants (e.g., heart rate or blood pressure), eight measured electro-
dermal activity, four measured muscular tension (e.g., activity of jaw or neck muscles), and two used other
means of data collection (i.e., respiration and bodily motion; Boucsein & Thum, 1997; eye tracking;
Eckhardt, Maier, & Buettner, 2012).
Studies used interviews, logs, and observations throughout the entire spectrum of technostress
components and, due to the small number of applications, we did not find any specific trends regarding
their application. In “miscellaneous measures”, we included more exotic data-collection methods, such as
a dermatological investigation that Berg, Arnetz, Lidén, Eneroth, and Kallner (1992) employed, expert
ratings of office ergonomics that Arnetz, Berg, and Arnetz (1997) employed, or the use of environmental
sensors to measure variables such as office lighting that Berg and Arnetz (1996) employed.
We also indicate the relative frequency of combinations of measures in Table 3 on the left side. For
example, of the 92 studies that used self-report measures, 23 percent also used biological measures.
Other interesting findings are that studies often combined interviews and logs (31% of the 16 studies that
used interviews or even 71% of 7 studies that used logs), combined observations in addition to biological
measures (50% of 10 studies that used observations), and combined biological measures with objective
performance measures (64% of 14 studies that used objective performance measures also used
biological measures or 41% of 22 studies that used biological measures also used objective performance
measures), mostly in laboratory experiments. Furthermore, in each study that used objective performance
measures, the study also used self-report measures. 8
Another finding of our study is that studies most frequently investigated strains based on multiple
measures. The multi-faceted nature of strains, including a variety of psychological, physiological, and
behavioral outcomes, can explain this finding (e.g., Carayon et al., 1999; Sonnentag & Frese, 2013). Of
93 studies, 50 measured some type of strain (again adjusted for those studies that did not fully report the
items of their self-report measures). Of those 50, 11 used more than one type of data-collection method to
measure this specific component of the stress process alone.
At this point, however, the question arises whether measurement pluralism (in our case, the use of multi-
method designs) actually has an impact on technostress research. As previous studies have shown (e.g.,
Korunka et al., 1996; Tams et al., 2014), one needs to apply multiple methods in the context of strains
because single measures (e.g., self-reports) alone do not explain as much variance in a dependent
variable as multiple methods typically do. Tams et al. (2014) even used different types of measures for
each main type of strain (i.e., self-reports for psychological strains, biological measures for physiological
strains, and objective performance measures for behavioral strains). Hence, achieving better explanatory
power is a major motivation for researchers to apply multi-method designs.
8 It is not surprising that authors never combined objective performance measures and logs because they mainly used objective
performance measures in the context of laboratory studies based on a cross-sectional design, whereas authors mainly used logs to
gather information on individual perceptions outside of controlled research situations.

Communications of the Association for Information Systems 385
Volume 40 Paper 17
However, when reviewing the 38 studies that applied multiple methods, we found only a few in which the
authors attributed higher levels of explained variance to the variety of applied data-collection methods. For
example, Birdi and Zapf (1997) used observations in addition to self-reports to collect data on user
behavior and the effects of age in the context of computer trouble (i.e., errors). They found converging
evidence from self-reported and observed data that age is an important predictor of negative emotions in
response to computer errors. In several further studies, data collected without using self-reports also
helped explain additional variance. For example, in Wiholm and Arnetz (1997), hormone levels helped to
account for the occurrence of musculoskeletal disorders; in Eckhardt et al. (2012), objective performance
in computer-based tasks helped to illuminate user satisfaction; and, in Moody and Galletta (2015),
indicators of physiological stress (e.g., galvanic skin response, heat flux, near body temperature, skin
temperature) helped to explain the variance in individual task performance.
Although Tams et al. (2014) have clearly demonstrated that multi-method designs can be useful to
achieve complementary insight, motivations other than the potential complementarity of methods have
often guided previous technostress research. According to Venkatesh et al. (2013), major arguments for
mixing methods include achieving completeness and, thus, creating a more holistic representation of the
technostress phenomenon (Tams et al., 2014) and compensating for the weaknesses of other methods
(e.g., common method bias in the context of self-report measures; Podsakoff, MacKenzie, Lee, &
Podsakoff, 2003).
For example, Wastell and Newman’s (1996a, 1996b) studies used self-report measures, observations,
physiological measures (cardiovascular indicators), and measures of objective performance to investigate
the influence of a new system on an ambulance service’s performance and working environment. As a
strength of their multi-method approach, they highlighted that “[i]t is important to emphasize the holistic
nature of this approach…[with which one can gain] a much richer understanding of complex psychosocial
processes...[than with] a one-dimensional analysis" (Wastell & Newman, 1996b, p. 285). In a similar vein,
Gallivan (2003) combined self-reports and interviews to investigate the implementation of a new
technology and highlighted that “[w]hile multi-method studies pose special challenges to researchers, they
may also provide unique insights that are not revealed by qualitative or quantitative methods alone” (p.
14). Finally, Barley et al. (2011) examined the stress potential of common means of communication in
organizations (e.g., meetings, encounters, e-mail) using a combination of self-reports, interviews, and
logs. They justified their using a multi-method approach by stating that (p. 891):
Because we sought to deepen our understanding of the mechanisms that might underlie [the]
relationship, we collected both quantitative and qualitative data…. Combining both types of data
is valuable because it not only allows one to confirm common findings across methods…, but
just as importantly, one can identify dynamics obscured by one data source or another.
Another common reason why studies used more than one method to collect data was to overcome the
weaknesses of single methods. For example, Galluch et al. (2015) investigated the influence of ICT-
enabled interruptions on individual stress perceptions and physiological strain indicated through hormone
excretion. With respect to their choice of data-collection methods, they highlighted that “Our design was
particularly effective because, in each hypothesis, we captured the two constructs being tested with a
unique technique…. [Doing so] significantly reduced method bias” (p. 27). Moody and Galletta (2015),
who investigated the effect of information scent, time constraints, and physiological stress on performance
and website attitude, made a similar argument; they state that “[u]sing objective measures such as GSR,
latent semantic analysis, and task performance helps to avoid common method bias” (p. 214).
We also checked whether publications of both groups (mono-method and multi-method studies) received
different average numbers of citations per year. At first glance, mono-method studies (N = 65) received
distinctively more citations on average per year (M = 7.31; SD = 10.63) than multi-method studies (M =
5.92; SD = 8.47). Hence, we statistically tested for a significant difference in citations per year between
both groups. Due to a number of remarkable outliers, we first tested for normality of the distribution using
the Shapiro-Wilk test (SPSS 24) and found that neither of the samples was normally distributed (p = .000
for both groups). Hence, we applied the Mann-Whitney test (SPSS 24) for non-parametric values and
found no significant difference between the groups (U = 1,125; p = .452). These findings indicate that the
number of applied data-collection methods has no impact on the number of citations that technostress
publications receive over time.

386 Technostress Research: A Nurturing Ground for Measurement Pluralism?
Volume 40 Paper 17
4 Conclusion, Limitations, Future Research
Initially, we expected that we would find technostress research to constitute a nurturing ground for multi-
method research. To some extent, our findings met our expectations because the overall rate of multi-
method research was higher in the technostress domain than in IS in general (if one uses Mingers (2001a,
2001b) as a benchmark). We also found a positive development in the overall number of technostress
studies published per year, which is a good indicator of the growing research interest in this domain.
Considering the developmental pattern of multi-method research (see Figure 1), one could argue that an
increasing number of studies have applied multi-method designs each year; however, these types of study
designs still seem to evolve into a niche practice compared to the number of mono-method studies.
In particular, we found that, much as in IS research generally (e.g., Riedl & Rueckel, 2011), many
technostress studies have used self-report measures as their sole method to collect data. For example,
Podsakoff (1986) highlighted that self-reports can offer a variety of applications that are also relevant to
technostress research (e.g., to collect demographic data, personality data, or data on psychological states
and perceptions). However, Podsakoff et al. (2003) later argued that using the same measurement
methods to collect data on related variables could lead to a tremendous share of explained variance being
attributable to inflation or deflation caused by common method variance (with up to 40% in some of the
exemplary studies on attitudes they mentioned). While the general threat of common method variance for
single-method studies has been challenged by, for example, Spector (2006), Spector also highlighted that
common method variance could pose a threat especially for such cases where the involved variables are
actually related to each other as is definitely the case in the stress process.
Although researchers have proposed statistical methods to attenuate the effects of common method
variance (e.g., Podsakoff et al., 2003), some have also suggested that the most straightforward way to
minimize this threat is to use more than one source of data (e.g., Donaldson & Grant-Vallone, 2002;
Podsakoff et al., 2003). Accordingly, in recent technostress research (e.g., Galluch et al., 2015; Moody &
Galletta, 2015), overcoming weaknesses of single-method studies was a legitimate motivation for applying
multi-method designs. Still, researchers such as Ahmed and Sil (2012) have contested this justification for
multi-method research. They argue that overcoming the weaknesses of single methods should not be the
main argument for multi-method research because this argument would require researchers in
multidisciplinary academic fields such as IS to assimilate a variety of research traditions. In turn, doing so
would lead to a loss in actual knowledge that researchers could create because the specific perspectives
and world views that are unique to each research domain would be weakened due to researchers’ no
longer being able to focus on their own discipline and having to consider many others at the same time.
Hence, instead, they suggest that one should see multi-method research as a means of uniting
researchers from different disciplines and enabling “cross-cultural communication” (p. 948) and, ultimately,
collaboration among researchers who specialize in their respective disciplines.
In addition to the potential practical uses of multi-method designs (e.g., convergent validation and more
holistic representation), multi-method research could, therefore, support IS researchers in their
collaborative efforts and help them fulfill their role “as builders and creators that piece together many
pieces of a complex puzzle into a coherent whole” (Tams et al., 2014, p. 739). In this paper, we further
emphasize this notion and call for more frequent collaboration of IS researchers with researchers from
other, related disciplines. For this purpose, we use technostress as an exemplary, multidisciplinary topic,
which is already a domain for frequent collaboration among researchers from varying disciplines but could
also still benefit from additional efforts in this regard. In favor of such collaborations, we also highlight that
they can lead to the establishment of new, thriving domains, such as neuroIS (Dimoka, Pavlou, & Davis,
2007), a research discipline that applies methods and knowledge from neuroscience in IS research (e.g.,
Dimoka et al., 2012; Riedl et al., 2010; Riedl & Léger, 2016).
Notably, although we focus here on applying multiple data-collection methods because previous
technostress research has frequently called for more diversity in this context (e.g., Ayyagari et al., 2011;
Moody & Galletta, 2015; Srivastava et al., 2015; Tarafdar et al., 2015), other interesting methodological
avenues for future technostress research exist. Researchers have highlighted that stress research in
general and organizational stress research in particular still need more studies that apply longitudinal
designs (e.g., Kahn & Byosiere, 1992; Kasl, 1978; Sonnentag & Frese, 2013), a call that researchers have
also made for technostress research specifically (e.g., Fischer & Riedl, 2015; Ragu-nathan et al., 2008;
Tarafdar et al., 2015). Additionally, researchers have called for a greater variety in the samples and
contexts that such research uses, to reduce the reliance on student samples (e.g., Galluch et al., 2015;

Communications of the Association for Information Systems 387
Volume 40 Paper 17
Srivastava et al., 2015; Tarafdar et al., 2015), and to increase the use of field studies (e.g., Moody &
Galletta 2015; Tarafdar et al., 2015).
For our own research, we seek to analyze the measurement instruments (particularly the survey
instruments) that previous technostress research have used in detail. Doing so will help us identify those
related disciplines and their theories and methods that have informed technostress research the most and
might offer still more directions for future research in this regard. We plan to base this analysis on the
details of the used measurement instruments, which remained absent in many publications (e.g., the
concrete survey items) of our present sample. This analysis can also serve as the basis for the
construction of future survey-based measurement tools in the technostress domain. Notably, a limitation
of our review is the focus on previous technostress research alone to guide the development of future
measurement approaches. However, by studying approaches in related domains (e.g., stress research in
general rather than technostress), the measurement toolset from which IS scholars could benefit would
become even larger. Despite this current limitation, we hope that the present paper helps advance
technostress research in particular and informs IS researchers interested in multi-method research in
general.
Acknowledgments
This research was funded by the Upper Austrian Government as part of the PhD program “Digital
Business International”, a joint initiative between the University of Applied Sciences Upper Austria and the
University of Linz, and as part of the project “Digitaler Stress in Unternehmen” (Basisfinanzierungsprojekt)
at the University of Applied Sciences Upper Austria.

388 Technostress Research: A Nurturing Ground for Measurement Pluralism?
Volume 40 Paper 17
References
Al-Fudail, M., & Mellar, H. (2008). Investigating teacher stress when using technology. Computers &
Education, 51(3), 1103-1110.
Ahmed, A., & Sil, R. (2012). When multi-method research subverts methodological pluralism—or, why we
still need single-method research. Perspectives on Politics, 10(04), 935-953.
Arnetz, B. B. (1996). Techno-stress: A prospective psychophysiological study of the impact of a controlled
stress-reduction program in advanced telecommunication systems design work. Journal of
Occupational & Environmental Medicine, 38(1), 53-65.
Arnetz, B. B. (1997). Technological stress: Psychophysiological aspects of working with modern
information technology. Scandinavian Journal of Work, Environment & Health, 97(3), 97-103.
Arnetz, B. B., Berg, M., & Arnetz, J. (1997). Mental strain and physical symptoms among employees in
modern offices. Archives of Environmental Health, 52(1), 63-67.
Ayyagari, R., Grover, V., & Purvis, R. (2011). Technostress: Technological antecedents and implications.
MIS Quarterly, 35(4), 831-858.
Bailey, B. P., & Konstan, J. A. (2006). On the need for attention-aware systems: Measuring effects of
interruption on task performance, error rate, and affective state. Computers in Human Behavior,
22(4), 685-708.
Barley, S. R., Meyerson, D. E., & Grodal, S. (2011). E-mail as a source and symbol of stress. Organization
Science, 22(4), 887-906.
Berg, M., & Arnetz, B. B. (1996). An occupational study of employees with VDU-associated symptoms:
The importance of stress. Stress Medicine, 12(1), 51-54.
Berg, M., Arnetz, B. B., Lidén, S., Eneroth, P., & Kallner, A. (1992). Techno-stress: A psychophysiological
study of employees with VDU-associated skin complaints. Journal of Occupational Medicine, 34(7),
698-701.
Birdi, K. S., & Zapf, D. (1997). Age differences in reactions to errors in computer-based work. Behaviour &
Information Technology, 16(6), 309-319.
Boucsein, W., & Thum, M. (1997). Design of work / rest schedules for computer work based on
psychophysiological recovery measures. International Journal of Industrial Ergonomics, 20, 51-57.
Brock, D. B., & Sulsky, L. M. (1994). Attitudes toward computers: Construct validation and relations to
computer use. Journal of Organizational Behavior, 15(1), 17-35.
Brod, C. (1982). Managing technostress: Optimizing the use of computer technology. Personnel Journal,
61(10), 753-757.
Brod, C. (1984). Technostress: The human cost of the computer revolution. Reading, MA: Addison-
Wesley.
Brooks, S. (2015). Does personal social media usage affect efficiency and well-being? Computers in
Human Behavior, 46, 26-37.
Carayon, P., Smith, M. J., & Haims, M. C. (1999). Work organization, job stress, and work-related
musculoskeletal disorders. Human Factors: The Journal of the Human Factors and Ergonomics
Society, 41(4), 644-663.
Chen, A. J., & Karahanna, E. (2011). Personal life interrupted: Understanding the effects of technology-
mediated interruptions from work to personal life. In Proceedings of the International Conference on
Information Systems.
Cooper, C. L., & Marshall, J. (1976). Occupational sources of stress: A review of the literature relating to
coronary heart disease and mental ill health. Journal of Occupational Psychology, 49, 11-28.
Cummings, T. G., & Cooper, C. L. (1979). A cybernetic framework for studying occupational stress.
Human Relations, 32(5), 395-418.

Communications of the Association for Information Systems 389
Volume 40 Paper 17
Cummings, T. G., & Cooper, C. L. (1998). A cybernetic theory of organizational stress. In C. L. Cooper
(Ed.), Theories of organizational stress (pp. 101-121). Oxford, New York: Oxford University Press.
D’Arcy, J., Herath, T., & Shoss, M. K. (2014). Understanding employee responses to stressful information
security requirements: A coping perspective. Journal of Management Information Systems, 31(2),
285-318.
Dickerson, S. S., & Kemeny, M. E. (2004). Acute stressors and cortisol responses: A theoretical
integration and synthesis of laboratory research. Psychological Bulletin, 130(3), 355-391.
Dimoka, A., Banker, R. D., Benbasat, I., Davis, F. D., Dennis, A. R., Gefen, D., Gupta, A., Ischebeck, A.,
Kenning, P. H., Pavlou, P. A., Muller-Putz, G., Riedl, R., vom Brocke, J., & Weber, B. (2012). On
the use of neurophysiological tools in IS research: Developing a research agenda for neuroIS. MIS
Quarterly, 36(3), 679-702.
Dimoka, A., Pavlou, P. A., & Davis, F. D. (2007). Neuro-IS: The potential of cognitive neuroscience for
information systems research. In Proceedings of the International Conference on Information
Systems.
Donaldson, S. I., & Grant‐Vallone, E. J. (2002). Understanding self-report bias in organizational behavior
research. Journal of Business and Psychology, 17(2), 245-260.
Eckhardt, A., Maier, C., & Buettner, R. (2012). The influence of pressure to perform and experience on
changing perceptions and user performance: A multi-method experimental analysis. In Proceedings
of the International Conference on Information Systems.
Emurian, H. H. (1993). Cardiovascular and electromyograph effects of low and high density work on an
interactive information system. Computers in Human Behavior, 9(4), 353-370.
Fischer, T., & Riedl, R. (2015). Theorizing technostress in organizations: A cybernetic approach. In O.
Thomas & F. Teuteberg (Eds.), Proceedings of the 12th International Conference on
Wirtschaftsinformatik (pp. 1453-1467).
Folkman, S., & Lazarus, R. S. (1985). If it changes it must be a process: Study of emotion and coping
during three stages of a college examination. Journal of Personality and Social Psychology, 48(1),
150-170.
Folkman, S., Lazarus, R. S., Dunkel-Schetter, C., DeLongis, A., & Gruen, R. J. (1986). Dynamics of a
stressful encounter: Cognitive appraisal, coping, and encounter outcomes. Journal of Personality
and Social Psychology, 50(5), 992-1003.
Fuglseth, A. M., & Sørebø, Ø. (2014). The effects of technostress within the context of employee use of
ICT. Computers in Human Behavior, 40, 161-170.
Gallivan, M. (2003). Examining gender differences in IT professionals’ perceptions of job stress in
response to technological change. In M. Mandviwalla & E. Trauth (Eds.), Proceedings of the
SIGMIS Conference on Computer Personnel Research (pp. 10-23).
Galluch, P., Grover, V., & Thatcher, J. B. (2015). Interrupting the workplace: Examining stressors in an
information technology context. Journal of the Association for Information Systems, 16(1), 1-47.
Helliwell, J., & Fowler, A. (1994). Introducing IT into a mature production related work environment: The
human resource factor. Journal of Information Technology, 9(1), 39-50.
Hjortskov, N., Rissén, D., Blangsted, A. K., Fallentin, N., Lundberg, U., & Søgaard, K. (2004). The effect of
mental stress on heart rate variability and blood pressure during computer work. European Journal
of Applied Physiology, 92(1-2), 84-89.
Joels, M., & Baram, T. Z. (2009). The neuro-symphony of stress. Nature Reviews Neuroscience, 10, 459-
466.
Kahn, R. L., & Byosiere, P. (1992). Stress in organizations. In M. D. Dunnette & L. M. Hough (Eds.),
Handbook of industrial and organizational psychology (2nd ed., pp. 571-650). Palo Alto, CA:
Consulting Psychologists Press.
Kasl, S. V. (1978). Epidemiological contributions to the study of work stress. In C. L. Cooper & R. Payne
(Eds.), Stress at work (pp. 3-48). Chichester: Wiley.

390 Technostress Research: A Nurturing Ground for Measurement Pluralism?
Volume 40 Paper 17
Korunka, C., Huemer, K., Litschauer, B., Karetta, B., & Kafka-Lützow, A. (1996). Working with new
technologies: Hormone excretion as an indicator for sustained arousal. A Pilot Study. Biological
Psychology, 42(3), 439–452.
Lazarus, R. S. (1966). Psychological stress and the coping process. New York: McGraw Hill.
Lazarus, R. S., & Folkman, S. (1984). Stress, appraisal, and coping. New York: Springer.
Lee, S.-J., Jin, S.-H., & Choi, B.-J. (2012). The influence of technostress and antismart on continuous use
of smartphones. In S. I. Ao (Ed.), Proceedings of the World Congress on Engineering and
Computer Science. Hong Kong: IAENG International Association of Engineers.
Maier, C., Laumer, S., & Eckhardt, A. (2015a). Information technology as daily stressor: Pinning down the
Causes of burnout. Journal of Business Economics, 85(4), 349-387
Maier, C., Laumer, S., Eckhardt, A., & Weitzel, T. (2012). When social networking turns to social overload:
Explaining the stress, emotional exhaustion, and quitting behavior from social network sites’ Users.
In Proceedings of the International Conference on Information Systems.
Maier, C., Laumer, S., Eckhardt, A., & Weitzel, T. (2014). Giving too much social support: Social overload
on social networking sites. European Journal of Information Systems, 24(5), 447-464
Maier, C., Laumer, S., Weinert, C., & Weitzel, T. (2015b). The effects of technostress and switching stress
on discontinued use of social networking services: A study of Facebook use. Information Systems
Journal, 25(3), 275-308.
McGrath, J. E. (1976). Stress and behavior in organizations. In M. D. Dunnette (Ed.), Handbook of
industrial and organizational psychology (pp. 1351-1395). Chicago: Rand McNally College Pub. Co.
Mingers, J. (2001a). Combining IS research methods: Towards a pluralist methodology. Information
Systems Research, 12(3), 240-259.
Mingers, J. (2001b). Combining IS research methods: Variety is (not) the spice of life. In Proceedings of
the Americas Conference on Information Systems.
Mingers, J., & Brocklesby, J. (1997). Multimethodology: Towards a framework for mixing methodologies.
Omega, 25(5), 489-509.
Moody, G. D., & Galletta, D. F. (2015). Lost in cyberspace: The impact of information scent and time
constraints on stress, performance, and attitudes online. Journal of Management Information
Systems, 32(1), 192-224.
Nastjuk, I., & Kolbe, L. (2015). On the duality of stress in information systems research—the case of
electric vehicles. In Proceedings of the International Conference on Information Systems.
Neben, T., & Schneider, C. (2015). Ad intrusiveness, loss of control, and stress: A psychophysiological
study. In Proceedings of the International Conference on Information Systems.
Oftedal, G., Vistnes, A. I., & Rygge, K. (1995). A double blind study of skin symptoms after reduction of
electric fields from VDUs. Scandinavian Journal of Work, Environment & Health, 21(5), 335-344.
Orlikowski, W. J., & Baroudi, J. J. (1991). Studying information technology in organizations: Research
approaches and assumptions. Information Systems Research, 2(1), 1-28.
Pinsonneault, A., & Kraemer, K. (1993). Survey research methodology in management information
systems: An assessment. Journal of Management Information Systems, 10(2), 75-105.Podsakoff,
P. M. (1986). Self-reports in organizational research: Problems and prospects. Journal of
Management, 12(4), 531-544.
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003). Common method biases in
behavioral research: A critical review of the literature and recommended remedies. Journal of
Applied Psychology, 88(5), 879-903.
Popoola, S. O., & Olalude, F. O. (2013). Work values, achievement motivation and technostress as
determinants of job burnout among library personnel in automated federal university libraries in
Nigeria. Library Philosophy & Practice.

Communications of the Association for Information Systems 391
Volume 40 Paper 17
Ragu-Nathan, T. S., Tarafdar, M., Ragu-Nathan, B. S., & Tu, Q. (2008). The consequences of
technostress for end users in organizations: Conceptual development and empirical validation.
Information Systems Research, 19(4), 417-433.
Riedl, R. (2013). On the biology of technostress: Literature review and research agenda. DATA BASE for
Advances in Information Systems, 44(1), 18-55.
Riedl, R., Banker, R. D., Benbasat, I., Davis, F. D., Dennis, A. R., Dimoka, A., Gefen, D., Gupta, A.,
Ishebeck, A., Kenning, p. Muller-Putz, G., Pavlou, P. A., Straub, D. W., vom Brocke, J., & Weber, B.
(2010). On the foundations of neurois: Reflections on the Gmunden Retreat 2009. Communications
of the Association for Information Systems, 27, 243-264.
Riedl, R., Davis, F. D., & Hevner, A. R. (2014). Towards a neuroIS research methodology: Intensifying the
discussion on methods, tools, and measurement. Journal of the Association for Information
Systems, 15(10), i-xxxv.
Riedl, R., Kindermann, H., Auinger, A., & Javor, A. (2012). Technostress from a neurobiological
perspective—system breakdown increases the stress hormone cortisol in computer users. Business
& Information Systems Engineering, 4(2), 61-69.
Riedl, R., Kindermann, H., Auinger, A., & Javor, A. (2013). Computer breakdown as a stress factor during
task completion under time pressure: Identifying gender differences based on skin conductance.
Advances in Human-Computer Interaction, 2013(1), 1-8.
Riedl, R., & Léger, P.-M. (2016). Fundamentals of neuroIS: Information systems and the brain. Berlin,
Heidelberg: Springer.
Riedl, R., & Rueckel, D. (2011). Historical development of research methods in the information systems
discipline. In Proceedings of the Americas Conference on Information Systems.
Salo, M., Makkonen, M., & Hekkala, R. (2015). “I just cursed and opened a beer”: Explaining mobile users’
non-complaining behavior through coping. In Proceedings of the International Conference on
Information Systems.
Schellhammer, S., Haines, R., & Klein, S. (2013). Investigating technostress in situ: Understanding the
day and the life of a knowledge worker using heart rate variability. In Proceedings of the Hawaii
International Conference on System Sciences (pp. 430-439).
Sellberg, C., & Susi, T. (2014). Technostress in the office: A distributed cognition perspective on human–
technology interaction. Cognition, Technology & Work, 16(2), 187-201.
Shu, Q., Tu, Q., & Wang, K. (2011). The impact of computer self-efficacy and technology dependence on
computer-related technostress: A social cognitive theory perspective. International Journal of
Human-Computer Interaction, 27(10), 923-939.
Sonnentag, S., & Frese, M. (2013). Stress in organizations. In I. B. Weiner, N. W. Schmitt, & S. Highhouse
(Eds.), Handbook of psychology (Vol. 12, pp. 560-592). Hoboken, NJ: John Wiley & Sons.
Spector, P. E. (2006). Method variance in organizational research: truth or urban legend? Organizational
Research Methods, 9(2), 221-232.
Srivastava, S. C., Chandra, S., & Shirish, A. (2015). Technostress creators and job outcomes: Theorising
the moderating influence of personality traits. Information Systems Journal, 25(4), 355-401.
Tams, S., Hill, K., Ortiz de Guinea, A., Thatcher, J., & Grover, V. (2014). NeuroIS—alternative or
complement to existing methods? Illustrating the holistic effects of neuroscience and self-reported
data in the context of technostress research. Journal of the Association for Information Systems,
15(10), 723-753.
Tarafdar, M., Pullins, E. B., & Ragu-Nathan, T. S. (2014). Examining impacts of technostress on the
professional salesperson’s behavioural performance. Journal of Personal Selling & Sales
Management, 34(1), 51-69.
Tarafdar, M., Pullins, E. B., & Ragu-Nathan, T. S. (2015). Technostress: Negative effect on performance
and possible mitigations. Information Systems Journal, 25(2), 103-132.

392 Technostress Research: A Nurturing Ground for Measurement Pluralism?
Volume 40 Paper 17
Tarafdar, M., Tu, Q., Ragu-Nathan, B., & Ragu-Nathan, T. (2007). The impact of technostress on role
stress and productivity. Journal of Management Information Systems, 24(1), 301-328.
Thomée, S., Härenstam, A., & Hagberg, M. (2012). Computer use and stress, sleep disturbances, and
symptoms of depression among young adults—a prospective cohort study. BMC Psychiatry, 12.
Venkatesh, V., Brown, S. A., & Bala, H. (2013). Bridging the qualitative-quantitative divide: Guidelines for
conducting mixed methods research in information systems. MIS Quarterly, 37(1), 21-54.
Voakes, P. S., Beam, R. A., & Ogan, C. (2003). The impact of technological change on journalism
education: A survey of faculty and administrators. Journalism and Mass Communication Educator,
57(4), 318-334.
Wang, K., Shu, Q., & Tu, Q. (2008). Technostress under different organizational environments: An
empirical investigation. Computers in Human Behavior, 24(6), 3002-3013.
Wang, X., Ye, S., & Teo, H.-H. (2014). Effects of interruptions on creative thinking. In Proceedings of the
International Conference on Information Systems.
Wastell, D., & Newman, M. (1996a). Information system design, stress and organisational change in the
ambulance services: A tale of two cities. Accounting, Management and Information Technologies,
6(4), 283-300.
Wastell, D., & Newman, M. (1996b). Stress, control and computer system design: A psychophysiological
field study. Behaviour & Information Technology, 15(3), 183-192.
Wiholm, C., & Arnetz, B. B. (1997). Musculoskeletal symptoms and headaches in VDU users—a
psychophysiological study. Work & Stress, 11(3), 239-250.
Yan, Z., Guo, X., Lee, M. K., & Vogel, D. R. (2013). A conceptual model of technology features and
technostress in telemedicine communication. Information Technology & People, 26(3), 283-297.
Ye, Z., Abe, Y., Kusano, Y., Takamura, N., Eida, K., Takemoto, T.-i., & Aoyagi, K. (2007). The Influence of
visual display terminal use on the physical and mental conditions of administrative staff in Japan.
Journal of Physiological Anthropology, 26(2), 69–73.
Yu, J.-C., Kuo, L.-H., Chen, L.-M., Yang, H.-J., Yang, H.-H., & Hu, W.-C. (2009). Assessing and managing
mobile technostress. WSEAS Transactions on Communications, 8(4), 416-425.

Communications of the Association for Information Systems 393
Volume 40 Paper 17
Appendix A: List of Additional Journals Checked
We searched the following list of journals (Table A1 below) for additional publications on 15 and 16 April
2016 using the term “technostress”.
Table A1. Overview of Journals Checked for Additional Publications
(ISSN) Journal title Journal website
(1554-3528) Behavior Research Methods http://www.springer.com/psychology/cognitive+psychology/
journal/13428
(1362-3001) Behaviour and Information Technology http://www.tandfonline.com/toc/tbit20/current
(0301-0511) Biological Psychology http://www.journals.elsevier.com/biological-psychology
(1471-244X) BMC Psychiatry http://bmcpsychiatry.biomedcentral.com/
(1471-2458) BMC Public Health https://bmcpublichealth.biomedcentral.com/
(1867-0202) Business and Information Systems
Engineering http://www.bise-journal.com/
(1435-5566) Cognition, Technology and Work http://www.springer.com/computer/hci/journal/10111
(1479-5759) Communication Education http://www.tandfonline.com/toc/rced20/current
(1552-3810) Communication Research http://crx.sagepub.com/
(1557-7317) Communications of the ACM http://cacm.acm.org/
(1529-3181) Communications of the AIS http://aisel.aisnet.org/cais/
(0360-1315) Computers and Education http://www.journals.elsevier.com/computers-and-education
(0747-5632) Computers in Human Behavior http://www.journals.elsevier.com/computers-in-human-
behavior
(1600-0536) Contact Dermatitis http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1600-
0536
(2152-2715) Cyberpsychology, Behavior, and Social
Networking
http://www.liebertpub.com/overview/cyberpsychology-
behavior-brand-social-networking/10/
(0095-0033) DATA BASE for Advances in Information
Systems http://sigmis.org/the-data-base/
(0040-0912) Education and Training http://www.emeraldgrouppublishing.com/products/journals/
journals.htm?id=et
(0091-6765) Environmental Health Perspectives http://ehp.niehs.nih.gov/
(1366-5847) Ergonomics http://www.tandfonline.com/toc/terg20/current
(1748-8583) Human Resource Management http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1748-
8583
(0378-7206) Information and Management http://www.journals.elsevier.com/information-and-
management/
(1471-7727) Information and Organization http://www.journals.elsevier.com/information-and-
organization/
(0020-0255) Information Sciences http://www.journals.elsevier.com/information-sciences
(0959-3845) Information Technology and People http://www.emeraldgrouppublishing.com/products/journals/
journals.htm?id=itp
(2329-4884) International Journal of Business
Communication http://job.sagepub.com/
(0167-8760) International Journal of Psychophysiology http://www.journals.elsevier.com/international-journal-of-
psychophysiology
(1573-3424) International Journal of Stress
Management http://www.apa.org/pubs/journals/str/

394 Technostress Research: A Nurturing Ground for Measurement Pluralism?
Volume 40 Paper 17
Table A1. Overview of Journals Checked for Additional Publications
(1741-5276) International Journal of Technology
Management http://www.inderscience.com/jhome.php?jcode=ijtm
(1365-2729) Journal of Computer Assisted Learning http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1365-
2729
(1541-4140) Journal of Educational Computing
Research
https://uk.sagepub.com/en-gb/eur/journal-of-educational-
computing-research/journal202399
(1552-6550) Journal of Marketing Education http://jmd.sagepub.com/
(1536-5948) Journal of Occupational and
Environmental Medicine http://journals.lww.com/joem/pages/default.aspx
(1099-1379) Journal of Organizational Behavior http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1099-
1379
(0022-3999) Journal of Psychosomatic Research http://www.journals.elsevier.com/journal-of-psychosomatic-
research/
(1741-2978) Journal of Sociology http://jos.sagepub.com/
(0092-0703) Journal of the Academy of Marketing
Science
http://www.springer.com/business+%26+management/journal/
11747
(1533-4406) New England Journal of Medicine http://www.nejm.org/
(1526-5455) Organization Science http://pubsonline.informs.org/journal/orsc
(0018-9219) Proceedings of the IEEE http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=5
(1094-9054) Reference and User Services Quarterly https://journals.ala.org/rusq
(0090-7324) Reference Services Review http://www.emeraldgrouppublishing.com/rsr.htm
(1795-990X) Scandinavian Journal of Work,
Environment and Health https://www.jstor.org/journal/scanjworkenvihea
(1533-8525) Sociological Quarterly http://onlinelibrary.wiley.com/journal/10.1111/(ISSN)1533-
8525
(1532-2998) Stress and Health http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)1532-
2998
(0040-1625) Technological Forecasting and Social
Change
http://www.journals.elsevier.com/technological-forecasting-
and-social-change
(0736-5853) Telematics and Informatics http://www.journals.elsevier.com/telematics-and-informatics
(1464-5335) Work and Stress http://www.tandfonline.com/toc/twst20/current

Communications of the Association for Information Systems 395
Volume 40 Paper 17
Appendix B: List of Studies in Review (Chronological Order)
Below, we list of all 103 publications that constituted the empirical basis of this review (in chronological
order). We highlight publications that were not part of the in-depth analysis (10) with an asterisk (*) before
the authors’ names:
Johansson, G., Aronsson, G., & Lindstrom, B. O. (1978). Social psychological and neuroendocrine stress
reactions in highly mechanised work. Ergonomics, 21(8), 583-599.
Johansson, G., & Aronsson, G. (1984). Stress reactions in computerized administrative work. Journal of
Organizational Behavior, 5(3), 159-181.
*Bichteler, J. (1986). Human aspects of high tech in special libraries. Special Libraries, 77(3), 121-128.
Hudiburg, R. A. (1989). Psychology of computer use. XVII. The computer technology hassles scale:
Revision, reliability, and some correlates. Psychological Reports, 65(3f), 1387-1394.
Hudiburg, R. A. (1989). Psychology of Computer Use: VII. Measuring Technostress: Computer-related
Stress. Psychological Reports, 64(3), 767–772.
Hudiburg, R. A. (1990). Relating computer-associated stress to computerphobia. Psychological Reports,
67(1), 311-314.
Ballance, C. T., & Rogers, S. U. (1991). Psychology of computer use: XXIV. Computer-related stress
among technical college students. Psychological Reports, 69(2), 539-542.
*Compton, D. C., White, K., & DeWine, S. (1991). Techno-sense: Making sense of computer-mediated
communication systems. Journal of Business Communication, 28(1), 23-43.
Emurian, H. H. (1991). Physiological responses during data retrieval: Comparison of constant and variable
system response times. Computers in Human Behavior, 7(4), 291-310.
Hudiburg, R. A. (1991). Relationship of computer hassles, somatic complaints, and daily hassles.
Psychological Reports, 69(3 Pt 2), 1119-1122.
Hudiburg, R. A., & Jones, T. M. (1991). Psychology of computer use. XXIII. Validating a measure of
computer-related stress. Psychological Reports, 69(1), 179-182.
Ballance, C. T., & Ballance, V. V. (1992). Psychology of computer use: XXVI. Computer-related stress and
in-class computer usage. Psychological Reports, 71(1), 172-174.
Berg, M., Arnetz, B. B., Lidén, S., Eneroth, P., & Kallner, A. (1992). Techno-stress: A psychophysiological
study of employees with VDU-associated skin complaints. Journal of Occupational Medicine, 34(7),
698-701.
Hudiburg, R. A. (1992). Factor analysis of the computer technology hassles scale. Psychological Reports,
71(3), 739-744.
Emurian, H. H. (1993). Cardiovascular and electromyograph effects of low and high density work on an
interactive information system. Computers in Human Behavior, 9(4), 353-370.
Hudiburg, R. A., Brown, S. R., & Jones, T. M. (1993). Psychology of computer use: XXIX. Measuring
computer users' stress: The computer hassles scale. Psychological Reports, 73(3 Pt 1), 923-929.
Fujigaki, Y., Asakura, T., & Haratani, T. (1994). Work stress and depressive symptoms among Japanese
information systems managers. Industrial Health, 32(4), 231-238.
Hudiburg, R. A. (1995). Psychology of computer use. XXXIV. The computer hassles scale: Subscales,
norms, and reliability. Psychological Reports, 77(3), 779-782.
Arnetz, B. B. (1996). Techno-stress: A prospective psychophysiological study of the impact of a controlled
stress-reduction program in advanced telecommunication systems design work. Journal of
Occupational & Environmental Medicine, 38(1), 53-65.
Arnetz, B. B., & Berg, M. (1996). Melatonin and adrenocorticotropic hormone levels in video display unit
workers during work and leisure. Journal of Occupational & Environmental Medicine, 38(11), 1108-
1110.

396 Technostress Research: A Nurturing Ground for Measurement Pluralism?
Volume 40 Paper 17
Ballance, C. T., & Ballance, V. V. (1996). Psychology of computer use: XXXVII. Computer-related stress
and amount of computer experience. Psychological Reports, 78(3 Pt 1), 968-970.
Berg, M., & Arnetz, B. B. (1996). An occupational study of employees with VDU-associated symptoms:
The importance of stress. Stress Medicine, 12(1), 51-54.
Hudiburg, R. A., & Necessary, J. R. (1996). Psychology of computer use: XXXV. Differences in computer
users' stress and self-concept in college personnel and students. Psychological Reports, 78(3 Pt 1),
931-937.
Korunka, C., Huemer, K., Litschauer, B., Karetta, B., & Kafka-Lützow, A. (1996). Working with new
technologies: Hormone excretion as an indicator for sustained arousal. A pilot study. Biological
Psychology, 42(3), 439-452.
Wastell, D., & Newman, M. (1996). Information system design, stress and organisational change in the
ambulance services: A tale of two cities. Accounting, Management and Information Technologies,
6(4), 283-300.
Wastell, D. G., & Newman, M. (1996). Stress, control and computer system design: A psychophysiological
field study. Behaviour & Information Technology, 15(3), 183-192.
Arnetz, B. B., Berg, M., & Arnetz, J. (1997). Mental strain and physical symptoms among employees in
modern offices. Archives of Environmental Health, 52(1), 63-67.
*Benamati, J., & Lederer, A. L., & Singh, M. (1997). Changing information technology and information
technology management. Information & Management, 31(5), 275–288.
Birdi, K. S., & Zapf, D. (1997). Age differences in reactions to errors in computer-based work. Behaviour &
Information Technology, 16(6), 309-319.
Boucsein, W., & Thum, M. (1997). Design of work/rest schedules for computer work based on
psychophysiological recovery measures. International Journal of Industrial Ergonomics, 20, 51-57.
Wiholm, C., & Arnetz, B. B. (1997). Musculoskeletal symptoms and headaches in VDU users—a
psychophysiological study. Work & Stress, 11(3), 239-250.
*Rose, P. M., Stoklosa, K., & Gray, S. A. (1998). A focus group approach to assessing technostress at the
reference desk. Reference & User Services Quarterly, 37(4), 311-317.
Joshi, K., & Rai, A. (2000). Impact of the quality of information products on information system users' job
satisfaction: An empirical investigation. Information Systems Journal, 10(4), 323–345.
Kaluzniacky, E. (2000). Work stress among information systems professionals in Manitoba. In
Proceedings of the Information Resources Management Association International Conference.
Rozell, E., & Gardner, W. I. (2000). Cognitive, motivation, and affective processes associated with
computer-related performance: A path analysis. Computers in Human Behavior, 16(2), 199–222.
Salanova, M., Grau, R., Cifre, E., & Llorens, S. (2000). Computer training, frequency of usage and
burnout: The moderating role of computer self-efficacy. Computers in Human Behavior, 16(6), 575–
590.
Benamati, J., & Lederer, A. L. (2001). Coping with rapid changes in IT. Communications of the ACM,
44(8), 83-88.
Poole, C. E., & Denny, E. (2001). Technological change in the workplace: A statewide survey of
community college library and learning resources personnel. College & Research Libraries, 62(6),
503-515.
Towell, E. R., & Lauer, J. (2001). Personality differences and computer related stress in business
students. American Journal of Business, 16(1), 69-76.
Ogan, C., & Chung, D. (2002). Stressed out! A national study of women and men journalism and mass
communication faculty, their uses of technology, and levels of professional and personal stress.
Journalism & Mass Communication Educator, 57(4), 352-369.
*Allan, J., & Lawless, N. (2003). Stress caused by on-line collaboration in e-learning: A developing model.
Education and Training, 45(8/9), 564-572.

Communications of the Association for Information Systems 397
Volume 40 Paper 17
Gallivan, M. (2003). Examining gender differences in IT professionals' perceptions of job stress in
response to technological change. In M. Mandviwalla & E. Trauth (Eds.), Proceedings of the
SIGMIS Conference on Computer Personnel Research (pp. 10-23).
Voakes, P. S., Beam, R. A., & Ogan, C. (2003). The impact of technological change on journalism
education: A survey of faculty and administrators. Journalism and Mass Communication Educator,
57(4), 318-334.
Shepherd, Sonya S. Gaither (2004). Relationships between computer skills and technostress: How does
this affect me? In Proceedings of the ASCUE Conference (pp. 225-231).
Iwanaga, K., Liu, X. X., Shimomura, Y., & Katsuura, T. (2005). Approach to human adaptability to stresses
of city life. Journal of Physiological Anthropology and Applied Human Science, 24(4), 357-361.
Tu, Q., Wang, K., & Shu, Q. (2005). Computer-related technostress in China. Communications of the
ACM, 48(4), 77-81.
Bailey, B. P., & Konstan, J. A. (2006). On the need for attention-aware systems: Measuring effects of
interruption on task performance, error rate, and affective state. Computers in Human Behavior,
22(4), 685-708.
*Porter, G., & Kakabadse, N. K. (2006). HRM perspectives on addiction to technology and work. Journal
of Management Development, 25(6), 535-560.
Ibrahim, R. Z. A. R., Bakar, A. A., & Nor, S. B. M. (2007). Techno stress: A study among academic and
non academic staff. In Dainoff, M. J. (Ed.) Ergonomics and health aspect of work with computers.
Berlin: Springer (pp. 118-124). Berlin, Heidelberg: Springer.
Tarafdar, M., Tu, Q., Ragu-Nathan, B., & Ragu-Nathan, T. (2007). The impact of technostress on role
stress and productivity. Journal of Management Information Systems, 24(1), 301-328.
Thomée, S., Eklöf, M., Gustafsson, E., Nilsson, R., & Hagberg, M. (2007). Prevalence of perceived stress,
symptoms of depression and sleep disturbances in relation to information and communication
technology (ICT) use among young adults—an explorative prospective study. Computers in Human
Behavior, 23(3), 1300–1321.
Ye, Z., Abe, Y., Kusano, Y., Takamura, N., Eida, K., Takemoto, T.-I., & Aoyagi, K. (2007). The influence of
visual display terminal use on the physical and mental conditions of administrative staff in Japan.
Journal of Physiological Anthropology, 26(2), 69-73.
Al-Fudail, M., & Mellar, H. (2008). Investigating teacher stress when using technology. Computers &
Education, 51(3), 1103-1110.
Moody, G., & Galletta, D. (2008). I'm losing patience with your site: The impact of information scent and
time constraints on effort, performance, and attitudes. In Proceedings of the International
Conference on Information Systems.
Ragu-Nathan, T. S., Tarafdar, M., Ragu-Nathan, B. S., & Tu, Q. (2008). The consequences of
technostress for end users in organizations: Conceptual development and empirical validation.
Information Systems Research, 19(4), 417-433.
Tu, Q., Tarafdar, M., Ragu-Nathan, T. S., & Ragu-Nathan, B. S. (2008). Improving end-user satisfaction
through techno-stress prevention: Some empirical evidences. In Proceedings of the Americas
Conference on Information Systems.
Wang, K., & Shu, Q. (2008). The moderating impact of perceived organizational support on the
relationship between technostress and role stress. In E. Ferro, E. Paolucci, & M. Cantamessa
(Eds.), Proceedings of the 19th International Conference on Database and Expert Systems
Applications (pp. 420-424).
Wang, K., Shu, Q., & Tu, Q. (2008). Technostress under different organizational environments: An
empirical investigation. Computers in Human Behavior, 24(6), 3002-3013.
Ahmad, U. N. U., Amin, S. M., & Ismail, W. K. W. (2009). The impact of technostress on organisational
commitment among Malaysian academic librarians. Singapore Journal of Library & Information
Management, 38, 103-123.

398 Technostress Research: A Nurturing Ground for Measurement Pluralism?
Volume 40 Paper 17
Burke, M. S. (2009). The incidence of technological stress among baccalaureate nurse educators using
technology during course preparation and delivery. Nurse Education Today, 29(1), 57-64.
Sahin, Y. L., & Coklar, A. N. (2009). Social networking users’ views on technology and the determination
of technostress levels. Procedia—Social and Behavioral Sciences, 1(1), 1437-1442.
Tarafdar, M., Tu, Q., & Ragu-Nathan, T. S. (2010). Impact of technostress on end-user satisfaction and
performance. Journal of Management Information Systems, 27(3), 303–334.
*Thomée, S., Dellve, L., Härenstam, A., & Hagberg, M. (2010). Perceived connections between
information and communication technology use and mental symptoms among young adults—a
qualitative study. BMC Public Health, 10, 1-14.
Ayyagari, R., Grover, V., & Purvis, R. (2011). Technostress: Technological antecedents and implications.
MIS Quarterly, 35(4), 831-858.
Barley, S. R., Meyerson, D. E., & Grodal, S. (2011). E-mail as a source and symbol of stress. Organization
Science, 22(4), 887-906.
Chen, A. J., & Karahanna, E. (2011). Personal life interrupted: Understanding the effects of technology-
mediated interruptions from work to personal life. In Proceedings of the International Conference on
Information Systems.
Hung, W.-H., Chang, L.-M., & Lin, C.-H. (2011). Managing the risk of overusing mobile phones in the
working environment: A study of ubiquitous technostress. In Proceedings of the Pacific-Asia
Conference on Information Systems.
Koo, C., & Wati, Y. (2011). What factors do really influence the level of technostress in organizations? An
empirical study. In J. Kacprzyk, N. T. Nguyen, B. Trawiński, & J. J. Jung (Eds.), Studies in
computational intelligence: New challenges for intelligent information and database systems (Vol.
351, pp. 339-348). Berlin, Heidelberg: Springer.
Shu, Q., Tu, Q., & Wang, K. (2011). The impact of computer self-efficacy and technology dependence on
computer-related technostress: A social cognitive theory perspective. International Journal of
Human-Computer Interaction, 27(10), 923-939.
Tarafdar, M., Pullins, E. B., & Ragu-Nathan, T. S. (2011). Examining impacts of technostress on the
professional salesperson’s performance. In Proceedings of the Americas Conference on
Information Systems.
Tarafdar, M., Tu, Q., Ragu-Nathan, T. S., & Ragu-Nathan, B. S. (2011). Crossing to the dark side:
Examining creators, outcomes, and inhibitors of technostress. Communications of the ACM, 54(9),
113-120.
Ayyagari, R. (2012). Impact of information overload and task-technology fit on technostress. In
Proceedings of the Southern Association for Information Systems Conference (pp. 18-22).
Eckhardt, A., Maier, C., & Buettner, R. (2012). The influence of pressure to perform and experience on
changing perceptions and user performance: A multi-method experimental analysis. In Proceedings
of the International Conference on Information Systems.
Maier, C., Laumer, S., Eckhardt, A., & Weitzel, T. (2012). Online social networks as a source and symbol
of stress: An empirical analysis. In Proceedings of the International Conference on Information
Systems.
Maier, C., Laumer, S., Eckhardt, A., & Weitzel, T. (2012). When social networking turns to social overload:
Explaining the stress, emotional exhaustion, and quitting behavior from social network sites' users.
In Proceedings of the European Conference on Information Systems.
Riedl, R., Kindermann, H., Auinger, A., & Javor, A. (2012). Technostress from a neurobiological
perspective—system breakdown increases the stress hormone cortisol in computer users. Business
& Information Systems Engineering, 4(2), 61-69.
Thomée, S., Härenstam, A., & Hagberg, M. (2012). Computer use and stress, sleep disturbances, and
symptoms of depression among young adults—a prospective cohort study. BMC Psychiatry, 12(1).

Communications of the Association for Information Systems 399
Volume 40 Paper 17
Maier, C., Laumer, S., & Weitzel, T. (2013). Although I am stressed, I still use IT! Theorizing the decisive
impact of strain and addiction of social network site users in post-acceptance theory. In
Proceedings of the International Conference on Information Systems.
*Niehaves, B., Köffer, S., & Ortbach, K. (2013). The effect of private IT use on work performance—
towards an it consumerization theory. In R. Alt & B. Franczyk (Eds.), Proceedings of the 11th
International Conference on Wirtschaftsinformatik.
*Niehaves, B., Köffer, S., Ortbach, K., & Reimler, S. (2013). Boon and bane of IT consumerization: The
burnout-engagement-continuum. In J. P. Shim, Y. Hwang, & S. Petter (Eds.), Proceedings of the
Americas Conference on Information Systems.
Riedl, R., Kindermann, H., Auinger, A., & Javor, A. (2013). Computer breakdown as a stress factor during
task completion under time pressure: Identifying gender differences based on skin conductance.
Advances in Human-Computer Interaction, 2013(1), 1-8.
Salanova, M., Llorens, S., & Cifre, E. (2013). The dark side of technologies: Technostress among users of
information and communication technologies. International Journal of Psychology, 48(3), 422-436.
Schellhammer, S., & Haines, R. (2013). Towards contextualizing stressors in technostress research. In
Proceedings of the International Conference on Information Systems.
Self, R. J., & Aquilina, C. (2013). TechnoStress in the 21st century; does it still exist and how does it affect
knowledge management and other information systems initiatives. In L. Uden, F. Herrera, J. Bajo
Pérez, & J. M. Corchado Rodríguez (Eds.), Proceedings of the 7th International Conference on
Knowledge Management in Organizations (Vol. 172, pp. 117-127). Berlin, Heidelberg: Springer.
Yan, Z., Guo, X., Lee, M. K., & Vogel, D. R. (2013). A conceptual model of technology features and
technostress in telemedicine communication. Information Technology & People, 26(3), 283-297.
D'Arcy, J., Herath, T., & Shoss, M. K. (2014). Understanding employee responses to stressful information
security requirements: A coping perspective. Journal of Management Information Systems, 31(2),
285-318.
Fuglseth, A. M., & Sørebø, Ø. (2014). The effects of technostress within the context of employee use of
ICT. Computers in Human Behavior, 40, 161-170.
Lee, Y.-K., Chang, C.-T., Lin, Y., & Cheng, Z.-H. (2014). The dark side of smartphone usage:
Psychological traits, compulsive behavior and technostress. Computers in Human Behavior, 31,
373-383.
Maier, C., Laumer, S., Eckhardt, A., & Weitzel, T. (2014). Giving too much social support: Social overload
on social networking sites. European Journal of Information Systems, 24(5), 447-464
Sellberg, C., & Susi, T. (2014). Technostress in the office: A distributed cognition perspective on human–
technology interaction. Cognition, Technology & Work, 16(2), 187-201.
Tams, S., Hill, K., Ortiz de Guinea, Ana, Thatcher, J., & Grover, V. (2014). NeuroIS—alternative or
complement to existing methods? Illustrating the holistic effects of neuroscience and self-reported
data in the context of technostress research. Journal of the Association for Information Systems,
15(10), 723-753.
Tarafdar, M., Pullins, E. B., & Ragu-Nathan, T. S. (2014). Examining impacts of technostress on the
professional salesperson's behavioural performance. Journal of Personal Selling & Sales
Management, 34(1), 51-69.
Wang, X., Ye, S., & Teo, H.-H. (2014). Effects of interruptions on creative thinking. In Proceedings of the
International Conference on Information Systems.
Brooks, S. (2015). Does personal social media usage affect efficiency and well-being? Computers in
Human Behavior, 46, 26–37.
Califf, C., Sarker, S., Sarker, S., & Fitzgerald, C. (2015). The bright and dark sides of technostress: An
empirical study of healthcare workers. In Proceedings of the International Conference on
Information Systems.

400 Technostress Research: A Nurturing Ground for Measurement Pluralism?
Volume 40 Paper 17
Galluch, P., Grover, V., & Thatcher, J. B. (2015). Interrupting the workplace: Examining stressors in an
information technology context. Journal of the Association for Information Systems, 16(1), 1-47.
Maier, C., Laumer, S., Weinert, C., & Weitzel, T. (2015). The effects of technostress and switching stress
on discontinued use of social networking services: A study of Facebook use. Information Systems
Journal, 25(3), 275-308.
Moody, G. D., & Galletta, D. F. (2015). Lost in cyberspace: The impact of information scent and time
constraints on stress, performance, and attitudes online. Journal of Management Information
Systems, 32(1), 192-224.
Nastjuk, I., & Kolbe, L. (2015). On the duality of stress in information systems research—the case of
electric vehicles. In Proceedings of the International Conference on Information Systems.
Neben, T., & Schneider, C. (2015). Ad intrusiveness, loss of control, and stress: A psychophysiological
study. In Proceedings of the International Conference on Information Systems.
*Salo, M., Makkonen, M., & Hekkala, R. (2015). “I just cursed and opened a beer”: Explaining mobile
users’ non-complaining behavior through coping. In Proceedings of the International Conference on
Information Systems.
Srivastava, S. C., Chandra, S., & Shirish, A. (2015). Technostress creators and job outcomes: Theorising
the moderating influence of personality traits. Information Systems Journal, 25(4), 355-401.
Tarafdar, M., Pullins, E. B., & Ragu-Nathan, T. S. (2015). Technostress: Negative effect on performance
and possible mitigations. Information Systems Journal, 25(2), 103-132.

Communications of the Association for Information Systems 401
Volume 40 Paper 17
About the Authors
Thomas Fischer is a PhD candidate at the Department of Digital Business at the University of Applied
Sciences Upper Austria. He holds a BA in Electronic Business and a MSc in Digital Business
Management (both with honours) from the University of Applied Sciences Upper Austria (BA, MSc) and
the University of Linz (MSc). In his PhD thesis, he addresses the topic of technostress in an organizational
context, applying a multi-method approach to the collection of empirical data.
René Riedl is a professor of Digital Business and Innovation at the University of Applied Sciences Upper
Austria and an associate professor of Business Informatics at the University of Linz. Moreover, he serves
on the executive board of the Institute of Human Resources and Organizational Development in
Management (IPO) at the University of Linz. He has published in the following outlets, among others:
Advances in Human-Computer Interaction, Behavior Research Methods, BMC Neurology, Business &
Information Systems Engineering, Communications of the AIS, DATA BASE for Advances in Information
Systems, Industrial Management & Data Systems, Journal of Computer Information Systems, Journal of
Information Technology, Journal of Management Information Systems, Journal of Neuroscience,
Psychology, and Economics, Journal of the Association for Information Systems, PLoS ONE, and MIS
Quarterly. He holds or has held various editorial positions (AIS Transactions on Human-Computer
Interaction, Business & Information Systems Engineering, DATA BASE for Advances in Information
Systems, Information Systems Journal, Information Systems Research, Journal of Information Technology
Theory and Application, Journal of Management Information Systems, MIS Quarterly, and Journal of the
AIS).
Copyright © 2017 by the Association for Information Systems. Permission to make digital or hard copies of
all or part of this work for personal or classroom use is granted without fee provided that copies are not
made or distributed for profit or commercial advantage and that copies bear this notice and full citation on
the first page. Copyright for components of this work owned by others than the Association for Information
Systems must be honored. Abstracting with credit is permitted. To copy otherwise, to republish, to post on
servers, or to redistribute to lists requires prior specific permission and/or fee. Request permission to
publish from: AIS Administrative Office, P.O. Box 2712 Atlanta, GA, 30301-2712 Attn: Reprints or via e-
mail from publications@aisnet.org.