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Overcoming the “Dark Side” of Technology—A Scoping Review on Preventing and Coping with Work-Related Technostress

Associated data.

In the course of the digitalisation of work, the phenomenon of technostress is increasingly being examined. While there is a plethora of research on its causes and consequences, a growing body of research on mitigating work-related technostress is emerging. In order to identify opportunities to overcome this “dark side” of technology, this scoping review aims to provide a comprehensive overview of the current state of research on how to prevent and cope with work-related technostress. The databases PubMed, MEDLINE, PsycInfo, PSYNDEX, and Web of Science were searched in the time period between 2008 and 2021. The studies were screened independently by two authors and selected based on predefined inclusion and exclusion criteria. Sixty-two studies were included and their methodological quality was assessed using standardised checklists. Resources were identified at the technical, organisational, social and personal level, including, e.g., leadership, organisational and technical support as well as self-efficacy and IT mindfulness. Problem- and emotion-focused coping strategies were, e.g., seeking support or distancing from IT. None of the included studies investigated prevention measures, emphasising a dearth of research that needs to be addressed in the future. Nevertheless, the identified resources and coping strategies provide starting points to address adverse work- and health-related consequences and reduce work-related technostress.

1. Introduction

In the working context, information and communication technologies (ICT) have become widely adopted over the past few years and have recently experienced a further boost from the coronavirus disease 2019 (COVID-19) pandemic, which necessitated remote, and thus, digital working arrangements [ 1 , 2 ]. While ICT often entail beneficial qualities and facilitate our work, they may also be stressful or even harmful to our health [ 3 , 4 ]. Although technostress, like stress in general, is a process depending on an individual’s experience and appraisal [ 5 ], it has often been referred to as the “dark side” of technology [ 3 , 4 , 6 , 7 ]. Since the term was coined by Craig Brod in 1984, technostress is widely understood as the “inability to adapt or cope with new computer technologies in a healthy manner” [ 8 ] (p. 16). Based on this definition, Ragu-Nathan and colleagues (2008) elaborated five technostress creators which cause this specific type of stress [ 9 , 10 ]. Firstly, techno-overload refers to the technology-related demand to work longer and faster, whereas constant connectivity and, consequently, a diffusion of work into private life are defined as techno-invasion [ 10 ]. Techno-complexity implies an individual’s difficulty to understand certain tasks or conditions [ 10 , 11 ]. Moreover, techno-insecurity can be triggered, e.g., when employees feel threatened with losing their jobs due to their perceived insufficient understanding of technologies or as a consequence of automation. Lastly, techno-uncertainty refers to stressful situations with ambiguous expectations or outcomes [ 10 ]. Ragu-Nathan et al. thus conclude that technostress extends other stress-related theoretical frameworks [ 10 ]. In support of this, a more recent scientometric analysis has found that most of the examined studies on work-related technostress were based on the transactional stress model by Lazarus [ 4 , 12 , 13 ]. In this vein, technostress is associated with health- and work-related outcomes, such as exhaustion [ 12 ], satisfaction and performance [ 13 ].

1.1. Theoretical Framework

As Tarafdar et al. [ 5 ] have described and Bondanini et al. [ 4 ] have recently shown, the technostress literature is predominantly based on Lazarus’ approach to the transactional stress model [ 14 , 15 ]. Although this theoretical framework seems appropriate for the technostress concept, its insufficient consideration of stress-inducing conditions has been criticised in the literature [ 16 , 17 ]. The occupational psychological stress model (German: Arbeitspsychologisches Stressmodell) by Bamberg and colleagues [ 16 , 17 ] offers a suitable extension. This model adapts key elements of the stress-and-strain concept by Rohmert and Rutenfranz [ 18 ] and the transactional stress model by Lazarus and Folkman [ 14 ]. It also combines the transactional approach including appraisal and coping strategies of Lazarus and Folkman [ 14 ] with the key elements job demands, job and personal resources of the job demands–resources model of Bakker and Demerouti [ 19 , 20 ], which has recently been used as a theoretical framework in the technostress literature as well e.g., [ 6 , 21 , 22 , 23 , 24 ]. Therefore, the occupational psychological stress model considers job demands or stressors, person-related risk factors, environmental and personal resources as well as primary and secondary appraisal, and problem- or emotion-focused coping. The consequences of stress are divided into short- and long-term consequences on somatic, cognitive–emotional, and behavioural levels [ 16 , 17 ]. These consequences can affect the individual and social environments and organisations, potentially triggering a spiral of stress [ 17 ], comparable to the job demands–resources model’s gain and loss spirals proposed by Bakker and Demerouti [ 19 , 20 ]. Due to the more comprehensive consideration of external and internal factors and their interaction, the scoping review was based on this occupational psychological stress model [ 16 , 17 ], as depicted in Figure 1 .

Occupational psychological stress model (adapted based on Bamberg et al. [ 17 ]).

1.2. Study Aim

Recently, the COVID-19 pandemic boosted working from home and, thus, private and work-related ICT use [ 2 ]. In this light, counteracting technostress becomes even more vital considering mental health issues [ 4 ]. However, remote work environments during the COVID-19 pandemic represent different circumstances that need to be distinguished from digital work outside the pandemic context [ 25 ]. Recent growing research on work-related technostress outside this specific context has already elaborated the causes and effects of technostress on an extensive scale [ 4 , 9 , 26 , 27 ], mostly focusing on the so-called “dark side”, the negative aspects, or techno-distress as characterised by Tarafdar and colleagues [ 5 ]. Previous literature reviews in this context have focused, e.g., on remote e-work and well-being [ 26 ], the psychological impacts of new ways of working [ 27 ] or the effects of technological developments on work [ 28 ]. With regard to technostress, previous literature reviews have addressed associated symptoms and risks [ 29 ], causes, strains, inhibitors and impacts [ 30 ], or provided a more general overview of technostress in organisations [ 31 ] as well as its relation to mental health and work outcomes [ 32 , 33 , 34 ]. Yet, far less research has investigated how to address these causes or deal with adverse effects resulting from technostress [ 5 , 9 ]. Specifically, there is considerably less scientifically substantiated evidence on adequate coping strategies [ 5 , 22 , 27 , 35 ] or prevention measures [ 36 , 37 ]. Apart from more specific reviews which could not identify strategies to prevent technostress among nurses [ 37 ] or focused on coping with discrepant information technology events [ 35 ], there is no comprehensive systematic overview of research on how to prevent and cope with technostress yet. Due to the previous research focus on causes and consequences technostress and fewer studies on coping and prevention, the scoping review method was chosen to explore and include a broader extent of the current literature and to filter out relevant results. Thus, the diversity of available research from heterogeneous sources and methodological approaches could be addressed [ 38 ]. Thereby, this scoping review aims at gathering existing empirical findings and at providing an overview of the current literature. For this purpose, the findings will be mapped to explore and systematically summarise the current state of research outside the pandemic context of COVID-19 [ 38 , 39 ]. Considering the topicality of the COVID-19 pandemic and related changes in remote work, the review results will be discussed by taking initial pandemic-related studies into account.

2. Materials and Methods

As stated in the first section, a scoping review was conducted to examine the extent and nature of the current state of research as well as to summarise findings and identifying research gaps in the existing literature on preventing and coping with work-related technostress [ 40 , 41 ]. For this purpose, this scoping review followed the methodological framework suggested by Arksey and O’Malley [ 40 ], its extension by Levac et al. [ 42 ] and the recent recommendations by Peters et al. [ 43 ].

2.1. Identifying the Research Questions

Based on the theoretical background described above, this scoping review addresses the following research questions:

• What kind of techno-stressors, job demands, and person-related risk factors have already been identified?
• Which environmental and personal resources (including coping strategies) help employees and managers to cope with work-related technostress?
• Which behavioural and structural prevention measures have already been examined and have proven to be effective in counteracting adverse effects of work-related technostress on employees and managers?
• How do these different resources, coping strategies and prevention measures mitigate adverse health- and work-related effects of technostress among employees and managers?

2.2. Identifying Relevant Studies

A search string with various search terms for the research questions was iteratively formed and tested. Considering the interdisciplinarity of the technostress concept [ 5 , 22 , 44 ] as well as the research questions’ focus on occupational (mental) health, coping and prevention, both medical and psychological as well as interdisciplinary databases were selected. The initial search string was adapted individually for each database. Relevant studies were identified by searching the following five electronic databases in November 2020 and August 2021: PubMed, MEDLINE, PsycInfo, PSYNDEX and Web of Science. In addition, further eligible studies were identified through a manual search. All five search strings are provided as Supplementary Materials (Tables S1–S5 ).

2.3. Study Selection

Predefined eligibility criteria based on the extended scheme considering participants, concept and context (PCC scheme) were used to decide on the inclusion and exclusion of studies [ 43 ]. To be included, the studies had to contain at least one variable that could be assigned to one of the five techno-stressors from the technostress concept of Ragu-Nathan et al. [ 10 ] or examine them in a qualitative approach. Furthermore, the study had to address work-related technostress among employees or managers. Studies among self-employed workers, with non-work-related and student samples, or examining technostress in the private context were excluded. Additionally, studies had to include either an intervention or prevention measure, coping strategy or an environmental or personal resource to mitigate technostress. Moreover, the outcomes examined in the studies had to be health- or work-related, i.e., measures of physical and mental health and well-being (e.g., stress, exhaustion, burnout, work–life balance) or, e.g., satisfaction, commitment and engagement, productivity and performance at work (see Supplementary Materials Table S6 ). Studies conducted during the COVID-19 pandemic were excluded from the analysis unless they explicitly described that the participants’ work remained unaffected by the pandemic. An exclusive examination of personality traits in dealing with technostress also led to exclusion. From a methodological perspective, empirical field studies following a qualitative, quantitative or mixed-methods approach published in scientific journals, conference papers, research reports, theses or dissertations were included. Non-empirical studies, such as conceptual papers, commentaries, editorials, or opinions, as well as reviews, meta-analyses and experimental studies in laboratory settings were excluded. For two studies [ 45 , 46 ], the authors were contacted and partly provided additional information on the methodology of their study [ 45 ]. Considering the authors’ language skills, studies had to be published in English or German and were excluded otherwise due to limitations of further resources. An initial search in the databases and a manual search were performed on 26 November 2020. To represent the most current research possible, an update was carried out on 20 August 2021 and the same search string was re-run in all of the five databases. Additionally, further sources were identified in a manual search on the same date. Table 1 provides an overview of the eligibility criteria.

Eligibility criteria.

1 The cut-off date for the search was 20 August 2021.

The study selection was carried out in two steps: first, one author (E.R.) screened the titles and abstracts of all identified studies for eligibility criteria, then two authors (E.R. and J.-C.F.) independently screened the full texts of the remaining studies for eligibility criteria. The inter-rater reliability was calculated using Cohen’s kappa. Disagreements in screening were discussed among the authors until a consensus was reached.

2.4. Charting the Data

The charting of the data was based on Arksey and O’Malley’s [ 40 ] guidelines for scoping reviews. Accordingly, Table S6 provided in the Supplementary Materials includes the following information: authors(s), year of publication, study location(s), methodology (i.e., publication type, methodological approach, study design(s)), study population and sample size, aim(s) of the study, outcome measures (i.e., main measurements), and important results. Furthermore, the included studies were coded and categorised in a deductive approach based on the theoretical framework (job demands, person-related risk factors, environmental resources, personal resources, coping strategies, health-related outcomes and work-related outcomes) [ 16 , 17 ] with MAXQDA software (version 12, VERBI Software) [ 47 ].

2.5. Collating, Summarising, and Reporting the Results

According to Levac et al.’s [ 42 ] recommendation and addition to Arksey and O’Malley [ 40 ], qualitative data analytical techniques were used to conduct the thematic analysis of the data. To link the results of this scoping review to its aim, purpose and research questions, the results are reported based on the structure of the theoretical framework presented in Section 2.1 . The discussion part provides broader implications stemming from the results for further research and practice, as suggested by Levac et al. [ 42 ].

2.6. Quality Assessment

While methodological quality assessment was challenged by Arksey and O’Malley [ 40 ] as well as Levac et al. [ 42 ], more recent literature recommends conducting a methodological quality assessment in scoping reviews [ 41 ]. As it contributes to the aim of this scoping review to map the literature and identifying gaps in the current state of research, a quality assessment was conducted to evaluate the methodological quality of the current state of research on preventing and coping with work-related technostress. For this purpose, two of the authors (E.R. and J.-C.F.) used the checklists provided by the Joanna Briggs Institute’s critical appraisal tool for analytical cross-sectional studies [ 48 ], cohort studies [ 49 ] and qualitative research [ 50 ]. To assess the methodological quality of mixed-methods studies, a combination of the respective checklists [ 48 , 50 ] was applied for the qualitative and quantitative components of the study.

Searching the databases resulted in an initial total of 591 identified records. A further 25 records were identified through a manual search and based on the references of the included studies. Of these 616 identified records, 531 records were screened based on their titles and abstracts by one author (E.R.) after duplicates were removed. A total of 108 studies were identified as eligible for full-text screening, which was conducted by two authors (E.R. and J.-C.F.). Finally, 52 studies were identified and included. According to Landis and Koch [ 51 ], the inter-rater reliability among the authors was substantial based on Cohen’s kappa (κ = 0.65). During the update in August 2021, 115 further records were identified, of which 85 were excluded after screening titles and abstracts (E.R.). The remaining 30 records were included in the same full-text screening procedure (E.R. and J.-C.F.), resulting in an inclusion of ten additional records for the qualitative synthesis of this review. Cohen’s kappa of κ = 0.65 indicates a substantial inter-rater reliability for the update process as well [ 51 ]. All deviations were discussed individually between the authors until agreement was reached. As a result, a total of 62 studies were included in the qualitative synthesis of this scoping review. A visualisation of the study selection process based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) [ 52 ] is provided in Figure 2 .

PRSIMA flow diagram depicting the study selection process.

3.1. Study Characteristics

The 62 included studies were published between 2008 and 2021, with the majority of studies published in 2020 ( n = 18) and 2019 ( n = 9). The studies were distributed internationally across 20 different countries and five continents. The majority of the studies were conducted in or published by authors from the United States of America ( n = 17). At continental level, most of the studies were conducted in Europe ( n = 24). Table 2 provides further details regarding the international distribution of the included studies.

Distribution of studies according to countries of implementation or first author location.

Note. If no information on the study location was provided, the first author’s location is given. The total amount of n = 66 results from four studies containing either two samples or participants from different countries [ 7 , 45 , 53 ]. 1 Including Taiwan ( n = 4). 2 The exact countries were not explicitly mentioned by the authors.

Among the included studies, there was a large majority of quantitative cross-sectional studies ( n = 48) [ 6 , 9 , 10 , 13 , 21 , 22 , 24 , 46 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 ] as can be seen in Table S6 in the Supplementary Materials . In total, 53 studies followed a quantitative methodological approach [ 6 , 9 , 10 , 13 , 21 , 22 , 23 , 24 , 46 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 ], of which 5 studies in a longitudinal design were included [ 23 , 93 , 94 , 95 , 96 ]. Eight studies were of qualitative nature [ 45 , 97 , 98 , 99 , 100 , 101 , 102 , 103 ] and one mixed-methods study [ 7 ] was included. One paper included a cross-sectional and a longitudinal study [ 23 ]. There were no intervention studies among the included studies.

The included studies examined a total of 40,940 participants. Gender information was provided for 36,949 participants, of whom 17,996 were female and 18,894 were male. For 59 participants, it was explicitly stated that they did not give any information about their gender. It becomes apparent that many studies did not provide (complete) information on the participants’ gender, while in seven studies [ 6 , 7 , 48 , 79 , 94 , 100 , 102 ], no demographic information was provided regarding gender. In the 52 studies reporting the age of participants, it ranged from 17 to 75 years, with a mean of 40.62 years [ 6 , 7 , 9 , 10 , 21 , 22 , 23 , 24 , 46 , 53 , 54 , 55 , 56 , 57 , 58 , 59 , 60 , 61 , 62 , 63 , 64 , 65 , 66 , 67 , 68 , 69 , 70 , 71 , 72 , 73 , 74 , 77 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 , 91 , 92 , 93 , 94 , 95 , 96 , 97 , 101 ]. Ten studies did not provide any information regarding the age of participants [ 13 , 45 , 75 , 76 , 78 , 98 , 99 , 100 , 102 , 103 ].

Many studies lacked more detailed information on the occupational setting, population or investigated sample. However, all included studies were conducted with employees from different industries who use or rely on ICT in their daily work. Thus, most of the included samples were simply categorised as “employees using ICT at work” ( n = 30) [ 7 , 10 , 23 , 46 , 58 , 59 , 60 , 61 , 63 , 65 , 66 , 67 , 68 , 71 , 73 , 74 , 77 , 79 , 81 , 82 , 84 , 85 , 88 , 90 , 92 , 93 , 94 , 95 , 96 , 101 ]. Other larger groups were employees at educational and research institutions ( n = 6) [ 48 , 58 , 59 , 73 , 76 , 91 ], salespeople or sales professionals ( n = 6) [ 6 , 9 , 24 , 56 , 83 , 87 ], knowledge workers ( n = 5) [ 7 , 22 , 66 , 97 , 98 ], and public sector employees ( n = 3) [ 15 , 55 , 64 ]. The mean work experience among those studies reporting it ( n = 28) was 9.15 years [ 6 , 9 , 13 , 21 , 24 , 54 , 56 , 57 , 59 , 60 , 65 , 66 , 67 , 68 , 71 , 72 , 75 , 80 , 83 , 84 , 86 , 87 , 89 , 91 , 93 , 95 , 97 , 103 ]. More study characteristics are provided in the Supplementary Materials in Table S6 .

3.2. Quality Assessment

After assessing the quality of the included studies independently, the inter-rater reliability based on Cohen’s kappa (κ = 0.53 for the initial search, κ = 0.45 for the update) indicated a moderate agreement according to Landis and Koch [ 51 ] between the two authors (E.R. and J.-C.F.). All of the included qualitative studies fulfilled at least 50% (5 out of 10) of the checklist’s quality criteria. Regarding the included longitudinal studies ( n = 5), they all fulfilled at least six out of eleven criteria (55%). Among the included cross-sectional quantitative studies, ten (20%) fulfilled all of the eight criteria. Forty-five cross-sectional studies (92%) fulfilled at least half of the quality criteria. Following the study aim to provide a holistic overview of the existing literature, no studies were excluded based on their quality assessment. Rather, their methodological quality will be reflected upon in the discussion section. The results of the methodological quality assessment of the included studies are provided in detail in the Supplementary Materials in Tables S7–S9 .

3.3. Job Demands and Person-Related Risk Factors

With regard to the first research question, what kind of job demands and techno-stressors as well as person-related risk factors have already been described in terms of work-related technostress in the literature, job demands were identified at the organisational and technical level, including the five technostress creators by Ragu-Nathan et al. [ 10 ].

3.3.1. Job Demands: Organisational Level

Twelve studies examined organisational-level demands [ 48 , 55 , 58 , 63 , 66 , 75 , 86 , 88 , 94 , 97 , 98 , 103 ]. These included response pressure [ 58 , 94 , 103 ], a competitive climate [ 53 ], power centralisation in an organisation [ 88 ] and an organisational climate or culture of innovation [ 63 , 66 , 88 ]. However, the latter was also assumed to be a resource in one study [ 74 ]. Task interdependence [ 86 ] and task complexity [ 74 ], need for redistribution of work and administrative support [ 103 ], social conflicts [ 48 , 66 , 103 ], poor communication [ 94 , 103 ], necessity of emails and confusion of responsibilities [ 98 ] were mentioned as well. Lack of support [ 45 ] and of sense of achievement [ 64 ] were further described. In addition, Andreou found that negative opinions of colleagues concerning technology shaped how new workers thought about it as negative vicarious experiences [ 97 ].

3.3.2. Job Demands: Technical Level

All of the included studies analysed technostress based on the definition or including technostress creators based on Ragu-Nathan et al. [ 10 ]. One study analysed technostress, personal resources and work-related outcomes with an adaption of the original scale [ 10 ], but lacked more detailed information on the included techno-stressors [ 46 ]. The authors did not provide further information upon request either. Besides this study, 52 (83.9%) of all included studies examined techno-overload, 43 (69.4%) techno-invasion, 42 (67.7%) techno-complexity, 34 (54.8%) techno-insecurity and 35 (56.5%) techno-uncertainty. Given that qualitative studies usually do not examine relationships between variables, overlapping codings of the included quantitative studies were cross-referenced to illustrate how often relationships between techno-stressors and outcomes were analysed.

Table 3 provides an overview of how many of the included quantitative studies examined the individual technostress creators in total and with regard to work- and health-related outcomes. It demonstrates that techno-overload, techno-invasion and techno-complexity were also most frequently analysed among the quantitative studies, whereas work- and health-related outcomes were examined almost equally often. The most frequently investigated work-related outcomes with regard to techno-stressors included different measures of satisfaction [ 10 , 15 , 21 , 56 , 63 , 64 , 66 , 72 , 73 , 80 , 83 ] and performance [ 6 , 15 , 26 , 72 , 73 , 76 , 87 , 96 ]. The most commonly examined health-related consequences of technostress creators were various forms of exhaustion [ 9 , 22 , 54 , 55 , 56 , 64 ] as well as strain [ 62 , 80 , 82 , 86 ].

Frequencies of techno-stressors, work- and health-related outcomes in quantitative studies ( n = 54) 1 .

1 Including the quantitative part of the mixed-methods study [ 7 ], excluding one study which did not provide sufficient information, not on request either [ 46 ].

Further technology-related stressors which did not refer to the techno-stressors as defined by Ragu-Nathan et al. [ 10 ] were mentioned in 20 studies [ 21 , 22 , 45 , 54 , 60 , 61 , 64 , 68 , 70 , 76 , 79 , 85 , 86 , 94 , 97 , 98 , 99 , 100 , 102 , 103 ]. Some of them are technology-induced but may be reinforced by organisational conditions, for example role stress [ 70 ], i.e., role ambiguity [ 21 , 86 ], which can be amplified by a high intensity of telework [ 86 ], and role overload [ 21 , 66 , 71 , 80 ], but also invasion of privacy [ 66 , 86 , 99 ]. Similarly, IT presenteeism, i.e., being reachable and able to access others [ 104 ], was examined in connection with invasion of privacy and especially affected employees with a low intensity of teleworking [ 86 ]. Occasionally, specific demands were stated such as terminology misfit between ICT systems and healthcare [ 103 ], challenging nature of data [ 102 ] and lack of control over dealing with emails [ 98 ]. At the same time, a fear of missing out important information was mentioned in qualitative studies [ 98 , 103 ]. Moreover, Kwanya et al. referred to technolust, the continuous desire for brand new technology regardless of whether it is needed and which respondents associated, inter alia, with pressure, frustration and dissatisfaction [ 45 ]. Among salespeople, the use of social media for sales activity was examined as an antecedent of technostress [ 54 ]. Other technology-related demands included, e.g., performance monitoring [ 60 , 64 ], unreliability of technology [ 22 , 48 , 63 , 66 , 103 ] due to different kinds difficulties [ 48 , 62 , 94 , 97 , 103 ] such as interruptions [ 22 , 66 , 100 , 103 ] or not being provided with adequate technology [ 48 , 66 , 69 , 103 ] or being dependent on technology [ 69 , 77 , 85 ]. However, one study could not provide support for significant associations of perceived reliability and technology dependence with technostress [ 76 ].

Several studies indicated that relationships between technostress creators and resources as well as work-related outcomes may not be linear, but rather inverted U-shaped [ 6 , 79 , 96 ]. Accordingly, a moderate level of technostress can contribute to improved performance, whereas low or high levels of technostress degrade performance [ 96 ]. In a similar vein, a curvilinear relationship was found between job design and technostress [ 78 ] as well as between system feature overload and salespeople’s effort to use technology, administrative performance and outcome performance, respectively. This indicates that after an initial decrease in effort and performance when confronted with technology, salespersons’ effort and performance increase when they are able to process and handle incoming information [ 6 ].

3.3.3. Person-Related Risk Factors

Person-related risk factors refer to an individual’s characteristics that may trigger stress, but do not necessarily lead to stress for every individual. Thus, individual differences are taken into account [ 17 ]. Twelve of the included studies identified several person-related risk factors that can potentially increase the risk of experiencing technostress [ 10 , 54 , 62 , 63 , 64 , 70 , 75 , 77 , 95 , 96 , 97 , 100 ]. Among them were many sociodemographic factors such as gender [ 10 , 56 , 76 ] and age [ 10 , 64 , 95 , 96 , 100 ]. For both of them, the included studies showed contradictory results: some found (partly) significant gender differences [ 10 , 54 ] while others did not [ 75 ]. Most of the studies examining age found significant differences in the perception of technostress [ 10 , 64 , 96 , 100 ], except for one [ 95 ]. Findings indicated that advanced age was associated with increased perception of technostress [ 64 , 96 , 100 ]. However, one study found the opposite effect of decreasing technostress with increasing age [ 10 ]. Additionally, Maier et al. found that neuroticism was related to higher technostress perception while other personality traits revealed no significant effect on technostress [ 96 ]. Gaudioso et al. and Hauk et al. examined gender differences in terms of coping strategies. Older employees seemed to engage in coping, but more effectively through the use functional rather than dysfunctional strategies compared to younger ones [ 63 , 95 ]. Examining prevention focus as a regulatory focus did not show that it would amplify the adverse effects of technostress creators [ 70 ]. A higher educational level [ 10 ] and working full-time rather than part-time [ 96 ] was associated with lower technostress perception, whereas literacy facilitation was more strongly associated with techno-overload and techno-complexity among participants with longer work experience [ 75 ]. Moreover, low self-efficacy, negative or too positive states of arousal, individual experiences [ 97 ], attitudes and beliefs [ 84 , 97 ] and intensity of ICT use in the sense of the number and frequency of use [ 64 , 77 ] determine and may increase technostress perception. Especially when employees used many different technologies, but only rarely, they reported higher levels of technostress [ 64 ].

3.4. Environmental and Personal Resources

Following the theoretical framework, environmental and personal resources helping employees and managers to cope with work-related technostress were examined subsequently to answer the second and fourth research question. Many of the included studies examined a wide range of resources. Environmental resources were thus assigned to different levels, i.e., the technical, organisational and social level. Again, overlapping codings were cross-referenced to illustrate how often relationships between techno-stressors and resources at different levels were analysed in quantitative studies. Table 4 displays the distribution of technostress creators analysed in relation to environmental and personal resources. Personal resources were investigated most frequently. Most of the environmental resources examined were located at the organisational level. Resources were particularly less often investigated in combination with techno-uncertainty.

Frequencies of techno-stressors and resources in quantitative studies ( n = 54) 1 .

3.4.1. Environmental Resources: Social Level

In total, 11 studies (6 quantitative [ 9 , 27 , 55 , 57 , 66 , 67 ] and 5 qualitative [ 48 , 97 , 99 , 100 , 103 ] studies) examined resources at the social level. These resources mainly referred to social support and leadership, including, e.g., understanding employee differences [ 100 ], sharing ideas and best practices [ 45 ], being acknowledged as a new worker and benefitting from positive opinions, mindsets, and social persuasion of colleagues [ 97 ]. Friendship opportunities at work improved general health and buffered adverse effects of techno-stressors [ 65 ]. Moreover, when learning to use new technology, receiving a short introduction by an instructor and being encouraged to ask questions and able to easily access social support by co-workers and their digital literacy were mentioned as helpful resources in qualitative studies [ 97 , 103 ]. Several studies specifically examined different styles of leadership as a resource in dealing with technostress. A good relationship of employees with their supervisors [ 64 ] and managerial intervention [ 99 ] helped to reduce technostress. Although supervisors’ influence on ICT usage did not significantly reduce technostress, leadership in general did [ 9 ]. Empowering leadership was found to buffer the relationship between techno-invasion, but not techno-overload or techno-complexity and emotional exhaustion [ 55 ]. Moreover, a positive leadership climate buffered the effect of techno-stressors on job distress [ 53 ] and high leader–member exchange did the same in the relationship between communication, system feature overload (but not information overload) and work–family conflict [ 66 ].

3.4.2. Environmental Resources: Organisational Level

Resources at the organisational level were identified in a total of 29 of the included studies, of which 6 were qualitative studies [ 45 , 98 , 99 , 100 , 101 , 103 ] and 23 were quantitative studies [ 10 , 15 , 21 , 24 , 27 , 59 , 60 , 62 , 64 , 66 , 68 , 69 , 73 , 75 , 76 , 79 , 80 , 86 , 87 , 89 , 91 , 93 , 96 ]. Involvement facilitation [ 10 , 15 , 64 , 73 , 76 , 80 , 87 , 96 ] and literacy facilitation [ 10 , 59 , 64 , 73 , 75 , 76 , 80 , 87 ] were most frequently studied, as already considered by Ragu-Nathan et al. [ 10 ]. However, in a few of these studies, no or only partially significant effects were found [ 60 , 64 , 96 ]. Other types of organisational support were also commonly mentioned and found to be valuable resources to reduce technostress or buffer its adverse effects [ 60 , 62 , 68 , 89 ]. More specifically, they included, e.g., innovation support, which in turn was positively related to involvement facilitation [ 13 ], administrative support [ 103 ], provision of adequate resources [ 45 ] and organisational support for strengths use [ 65 ]. With regard to techno-invasion, high perceived organisational support in work–home boundary management amplified the relationship between daily positive affect and diminished the relationship between daily negative affect and daily partnership satisfaction [ 93 ]. Among employees with a high intensity of telework, techno-invasion was not significantly related to strain, whilst a high intensity of teleworking also buffered the negative effect of strain on job satisfaction [ 86 ].

More generally, health and well-being programmes were perceived to reduce technostress [ 100 ]. Several organisational resources were mentioned by librarians, such as keeping pace with the developments in the market, making prompt decisions and having effective change management plans and considering staff planning while implementing new technologies, realistic time scheduling to avoid multitasking, providing time to implement and learn how to use new technologies and developing and maintaining comprehensive technology standards, effective communication and continuous staff training [ 45 ]. Communication, knowledge sharing and training were also described to reduce technostress by logistic managers [ 100 ]. A quantitative study among salespeople supported that continuous training programmes for technologies reduced technostress and had a positive effect on the participants’ beliefs about technology [ 24 ]. Communication measures were mentioned as valuable resources in several other qualitative studies including different occupational groups [ 98 , 100 , 101 , 103 ]. In particular, email culture was emphasised, such as informal, universally known rules about the use of adequate media depending on the situation (e.g., email or phone call) were perceived as helpful [ 98 ], or meeting in person instead of writing emails as well as communicating about digital communication with co-workers, e.g., discussing ways to reduce the number of emails [ 103 ]. Identifying best practices was further described as a measure to address techno-overload in addition to improved communication with executives and providing ways to find information and support more efficiently. Regulating after-work email traffic and communicating such regulations allow employees to end their working day and thus reduce techno-invasion [ 101 ]. Although internal communication was not found to moderate the relationships between techno-overload, techno-invasion, techno-complexity or techno-insecurity and commitment to change, high internal communication buffered the negative effect of techno-uncertainty on commitment to change [ 91 ].

Moreover, increased scope for action and a hierarchical, i.e., process-oriented organisational culture [ 64 ], human resource management effectiveness [ 67 ], transparency and fairness in the distribution of work and a reduced workload [ 99 ] were identified as organisational-level resources in dealing with work-related technostress. While perceived technostress was significantly negatively associated with customer satisfaction [ 21 ], customer satisfaction was on the other hand perceived to reduce technostress [ 99 ]. Against the authors’ expectations, job design, including job autonomy, skill variety, task identity, task significance and task feedback, was found to increase technostress, which again indicate an inverted U-shaped relationship rather than the assumed linear one [ 78 ]. Job autonomy was found to be able to reduce strain by reducing perceived invasion of privacy [ 86 ]. However, job control, stress management training and individual rewards could neither reduce job stress nor buffer adverse effects of technostress creators on job stress [ 68 ].

3.4.3. Environmental Resources: Technical Level

At the technical level, which was addressed by 13 studies, among them 5 qualitative studies [ 48 , 97 , 98 , 100 , 103 ] and 8 quantitative studies [ 10 , 64 , 73 , 74 , 76 , 77 , 80 , 87 ], different kinds of resources were identified. Some of them are also influenced by organisational circumstances or implementation by the organisation. These included improving the technological infrastructure [ 45 ] or being able to rely on IT experts [ 100 ]. Technical support provision was investigated most frequently [ 10 , 64 , 73 , 74 , 76 , 80 , 87 , 103 ] and the majority of results clearly supported its significant influence as a technostress reducer or mitigator [ 64 , 73 , 74 , 80 , 87 , 103 ]. In addition, usability and benefits of technologies, e.g., enabling flexibility and automation [ 97 ] or facilitating communication and documentation [ 98 ], as well as back-up routines [ 103 ] were mentioned as resources in dealing with technostress. Reliability of technology, in contrast, was not found to significantly reduce technostress [ 76 ].

3.4.4. Personal Resources

Thirty studies covered personal resources of different kinds; most of them were quantitative studies [ 6 , 10 , 21 , 23 , 24 , 46 , 54 , 64 , 70 , 71 , 73 , 74 , 76 , 77 , 80 , 81 , 82 , 83 , 85 , 87 , 89 , 90 , 93 , 94 , 96 ]. Only four of them were qualitative studies [ 97 , 98 , 99 , 103 ] and one was a mixed-methods study [ 7 ]. In particular, different types of self-efficacy [ 6 , 23 , 24 , 26 , 56 , 74 , 75 , 77 , 85 , 87 , 89 , 97 ] were frequently examined in the studies. Andreou differentiated different sources of self-efficacy that mitigated technostress among new knowledge workers. Being new to the organisation was described to come along with eagerness to learn new things and, thus, positive psychological arousal. Although it took more time to understand it completely, learning to use a new technology individually on their own could therefore be helpful. Thus, mastery experiences helped to mitigate techno-complexity, techno-insecurity and techno-invasion (as long as ICT were not overused and created work–home conflicts). In a similar manner, psychological arousal mitigated techno-overload, techno-complexity and techno-uncertainty and positively impacted self-efficacy. However, too much positive arousal and eagerness caused concentration problems and work–home conflicts. While negative experiences of colleagues impacted new workers negatively, positive vicarious experiences reduced techno-complexity. Likewise, social persuasion by other colleagues could raise existing self-efficacy by creating a positive mindset and psychological arousal, and reduced techno-uncertainty, but when lacking self-efficacy, social persuasion could even create techno-uncertainty [ 97 ]. Other studies provided further support that (technology- or job-related) self-efficacy can mitigate negative effects of technostress [ 6 , 23 , 26 , 56 , 74 , 77 , 85 , 89 ]. In addition, continuous techno-training was significantly positively associated with techno-efficacy [ 24 ] and technology self-efficacy was significantly positively related to sales performance [ 87 ].

Moreover, (IT) mindfulness was found to decrease technostress [ 72 , 81 , 96 ] and increase user satisfaction [ 71 ] as well as decrease job burnout, but it did not significantly buffer the relationship between techno-stressors and job burnout [ 80 ]. Job commitment did not buffer the negative relationship between technostress creators and job satisfaction, but the positive association of technostress creators and role stress [ 83 ].

High IT control mostly helped to reduce the adverse moderating effects of emotion-focused coping strategies (i.e., distress venting and distancing from IT) on the techno-stressors–strain relationship [ 81 , 82 ]. Likewise, empowerment through control over reachability reduced technostress [ 99 ]. IT use autonomy reduced the negative effect of techno-stressors on IT-enabled productivity and simultaneously increased productivity [ 7 ]. Having a high degree of control over the boundaries of work and leisure time significantly reduced work–family conflict created by extended availability [ 94 ] and mitigated negative effects of high after-hours availability expectations and frequent work-related smartphone use after work on psychological detachment [ 77 ]. However, work–home integration was also found to significantly reinforce both, the effect of daily positive and negative affect on partnership satisfaction [ 93 ].

Further personal resources reducing or mitigating negative effects of perceived technostress included promotion focus [ 70 ], optimism towards technology [ 21 ], personal innovative in IT (i.e., the willingness to try out new technologies [ 105 ]) [ 96 ] or being interested in technology [ 97 ], trust in people and processes [ 99 ] as well as computer confidence [ 10 ], a confident attitude [ 103 ] or confidence in dealing with ICT [ 64 ]. Moreover, digital literacy was described as a helpful individual competence to deal with technostress in a qualitative study [ 103 ], but, just as information literacy, was not found to significantly reduce adverse effects of technostress in a quantitative study [ 46 ]. A similarly contradictory result was found by Gimpel et al. in whose study increased digital media literacy was associated with lower perceptions of techno-complexity, but with increased perceptions of other technology-related stressors [ 64 ]. Another quantitative study found that technology competence was positively related to technology-enabled innovation and productivity [ 87 ].

3.5. Appraisal

Only five studies [ 58 , 84 , 92 , 93 , 98 ] addressed appraisal, i.e., the process that decides whether potential stressors are actually perceived as threatening or not [ 17 ]. The knowledge of available resources influences the appraisal. Similarly, coping strategies are also dependent on resources [ 16 ]. Several authors followed this approach and examined how employees appraised stress caused by email traffic [ 56 , 98 ] and found that framing technology as an opportunity or a threat shaped the consequences of being exposed to technostress creators [ 84 ]. Challenge appraisals were associated with problem-focused coping strategies and positive outcomes, while hindrance appraisals were associated with emotion-focused coping strategies and negative outcomes [ 92 , 93 ].

3.6. Coping Strategies

In accordance with the theoretical framework, problem- and emotion-focused coping strategies were investigated. Both were examined with comparable frequency. Table 5 displays the frequencies of analysed relationships between coping strategies and techno-stressors for the quantitative studies based on cross-referenced codings.

Frequencies of techno-stressors and coping strategies in quantitative studies ( n = 54) 1 .

3.6.1. Problem-Focused Coping Strategies

Twelve of the included studies examined problem-focused coping strategies, of which six were quantitative studies [ 22 , 65 , 66 , 70 , 92 , 95 ], five were qualitative studies [ 48 , 97 , 100 , 102 , 103 ] and one was a mixed-methods study [ 7 ]. They included proactive behaviours such as confronting stressful situations head-on, which was associated with increased productivity and buffered the negative relationship between communication overload and productivity [ 69 ]. Other proactive behaviours were coming up with a plan [ 64 ] and preparation [ 103 ] as well as active actions and efforts to improve the situation [ 64 , 95 ], of which the latter was only partially supported by the data [ 64 ]. Another common problem-focused coping strategy was seeking support from others, either instrumental support [ 92 ] or support from family and friends [ 64 ] or social support from colleagues [ 95 ]. However, the latter one could not be supported by quantitative data [ 95 ]. These active-functional strategies were also jointly examined (active coping and social support [ 22 ] or active coping, planning and seeking instrumental support [ 63 ]). Both of these combinations significantly reduced exhaustion [ 22 , 63 ] and buffered [ 22 ] or mediated [ 63 ] the technostress-exhaustion relationship. Interestingly, older employees seemed to use these strategies more than younger ones and hours of work per day were significantly negatively related to these coping strategies [ 63 ].

Moreover, using digital solutions to deal with ICT demands were used to cope with technostress [ 102 , 103 ]. Learning and skill development as successful coping strategies included developing IT use skills [ 7 ], learning by doing [ 103 ], persevering and learning from mistakes [ 97 ]. Structuring and organising were described to be helpful, in particular, time management and prioritisation strategies [ 100 ], replying flexibly [ 103 ] or only to necessary emails and keeping a record of passwords [ 45 ]. Additionally, setting aside time for specific tasks and switching off ICT while working on them were mentioned, which also buffered negative effects of technostress and increased productivity [ 7 ]. When dealing with an overwhelming amount of information, looking for summaries and trends, developing dashboards, filtering and being selective about data sources were identified as successful coping strategies [ 102 ]. Apart from establishing routines and structures, improvisation was also highlighted as a coping strategy for technostress [ 103 ]. Lastly, several studies identified separating work and private life by using separate devices [ 7 , 103 ] and even limiting ICT use outside work [ 97 ] as helpful problem-focused coping strategies.

3.6.2. Emotion-Focused Coping Strategies

Emotion-focused coping strategies were addressed by 11 studies [ 7 , 22 , 35 , 61 , 65 , 66 , 70 , 82 , 92 , 95 , 102 ], including only one qualitative study [ 102 ] and one mixed-methods study [ 7 ]. Four of them examined distress venting [ 7 , 35 , 82 ] or distress venting and psychological distancing [ 92 ] and their results mostly supported the hypothesis that blowing off steam would mitigate adverse outcomes caused by techno-stressors [ 7 , 81 ], but also decreased productivity [ 7 , 92 ]. Distancing from IT was also examined separately in quantitative studies [ 81 , 82 ] and a mixed-methods study [ 7 ] and significantly reduced technostress or related adverse outcomes in most of these studies [ 7 , 82 ]. As another type of emotion-focused coping strategies, reframing situations was identified in the included studies, such as looking at the bright side, taking things with humour [ 64 ], being optimistic [ 7 ] and reinterpreting situations positively [ 82 ]. Transforming stressful situations into opportunities, however, was only found to increase productivity, but not to buffer the techno-stressor-productivity relationship [ 69 ]. Moreover, some dysfunctional coping strategies were examined. These included strategies characterised by withdrawal [ 102 ], disengagement or even denial [ 61 , 65 , 95 ], ranging from learning to live with the situation [ 64 ] to alcohol and drug abuse [ 22 ]. However, although dysfunctional coping was associated with increased exhaustion [ 22 , 63 ], it did not reinforce adverse effects of technostress creators on exhaustion [ 22 ], but mediated the relationship [ 63 ]. Similarly, moral disengagement mediated the relationship between techno-stressors and violating information security policies [ 59 ]. Age was significantly negatively correlated with behavioural disengagement, which in turn and together with techno-stressors, mediated the positive correlation between age and technology-related strain [ 95 ].

3.7. Preventing Work-Related Technostress

As mentioned above, at the time of research, no studies which systematically field-tested interventions or scientifically evaluated prevention concepts at the behavioural or structural level could be identified to answer research questions number three and four. The lack of and need for research on techno-training, coping and interventions has also been addressed by other authors of included studies [ 7 , 23 , 24 , 26 , 55 , 94 ]. However, as described in Section 3.4.2 , some studies examined stress management training [ 68 ] or continuous techno-training [ 24 ] and some participants addressed trainings for technology and well-being and health programmes [ 100 ].

4. Discussion

The aim of this scoping review was to provide a comprehensive overview and to gather and map existing empirical findings on preventing and coping with work-related technostress based on the theoretical framework of Bamberg et al. the occupational psychological stress model [ 16 , 17 ]. This review of the current state of research shows that although some findings on resources and coping strategies are available, no preventive measures have been scientifically evaluated yet. Moreover, the global distribution of authorships and studies emphasise the international and cross-cultural relevance of the topic. Likewise, the high amount of studies published particularly in the last two years indicate a rapidly growing interest in the scientific community. Both of these results are consistent with findings from recent scientometric [ 4 ] and bibliometric [ 106 ] analyses on technostress. In line with a recent systematic review on mental health and work outcomes, strain and stress, burnout and exhaustion as well as satisfaction, performance and productivity were identified as the most frequent outcome measures. Moreover, our results support the review’s finding that techno-overload and techno-invasion were the most frequently examined techno-stressors and that many studies did not examine all of the five techno-stressors [ 33 ]. This scoping review further demonstrates that previous research was carried out on a wide variety of occupational groups and thus examined work-related technostress in a wide range of conditions.

4.1. The “Dark Side”: Challenges of Work-Related ICT Use

While this scoping review focused on the concept of technostress and the five technostress creators defined by Ragu-Nathan et al. [ 10 ], the synthesis reveals that techno-overload, techno-invasion and techno-complexity were most frequently studied in the work context. However, many studies also identified further technology-related stressors and some described organisational stressors. Tarafdar et al. refer to them as technology environmental conditions [ 5 ]. Stressful organisational circumstances may interact with techno-stressors and thus reinforce their negative effects [ 53 , 56 ]. However, according to transactional stress theories, stressors are not harmful per se, but only perceived as such depending on an individuals’ appraisal [ 5 , 13 , 17 ]. Depending on an individuals’ person-related risk-factors, appraisal and available resources in a stressful situation, stressors will be perceived as harmful or not [ 16 , 17 ]. As noticed before [ 64 ], person-related risk factors, particularly gender and age, revealed contradictory results. Although women seem to be more prone to report stress, they may have different working conditions that may explain their lower technostress levels in some studies. Another possible explanation could be the distribution of influential factors among the samples, e.g., gender differences in the adoption of technology may not apply to younger employees [ 10 ]. Differences in perceiving technostress in terms of age may be related to work experience or organisational tenure [ 10 , 107 ]. Younger employees, such as millennials, who are used to dealing with different media on a daily basis, may on the one hand be less prone to technostress as digital natives, whereas media literacy might be lower among older employees [ 108 ]. On the other hand, younger employees may be more easily overloaded as they probably consume more media in their free time than older employees who might benefit from more experience and therefore be less susceptible to (techno-)stress [ 109 ]. In the same vein, a systematic review did not identify linear trends between age and technostress perception [ 110 ].

Moreover, the quality and quantity of a stressor may determine the perception of technostress. For example, the intensity of ICT use indicated different levels of technostress perception [ 90 ] or using emails as a means of communication was perceived ambivalently, as facilitating communication but also leading to overload and a lack of control [ 98 ]. Thus, it is important to emphasise that digital technologies also have beneficial properties that may even help to reduce perceived technostress. For example, using different technologies may even reduce techno-overload [ 101 ]. Moreover, strain may not be automatically caused by technostress, but may rather depends on the scope of functions and how they are implemented within organisations [ 99 ]. In this regard, an organisational climate of innovation was also shown to reduce perceived unreliability of technology while positively affecting user satisfaction and job satisfaction. However, competitiveness and perceived uncertainty may downsize this positive effect. The way of implementation in the organisation is therefore crucial [ 61 ].

Similarly to the inverted U-shaped relationship of arousal and performance specified in the Yerkes-Dodson law [ 111 ], several authors of the included assumed the curvilinear relationship between technostress and work-related outcomes [ 6 , 79 , 96 ] which is also supported by Srivastava et al. [ 112 ]. Therefore, in line with the occupational psychological stress model [ 16 , 17 ], techno-stressors should be understood as not fundamentally negative or harmful. Rather, the degree of techno-stressors in combination with an individual’s perceived resources seems to be decisive for the appraisal and extent of experienced technostress [ 16 , 17 , 79 ].

4.2. Overcoming the “Dark Side” of ICT

4.2.1. using resources against work-related technostress.

Resources are not only important with regard to coping [ 16 ] with work-related technostress, but can also be considered as a starting point for measures to reduce technostress at different levels. Our results point out the important role of leadership, which can reduce and buffer negative effects of technostress at work [ 9 , 55 , 57 , 67 ]. Harris et al. explain their unexpected finding of leader-member exchange amplifying the relationship between information overload and work–family conflict by the possibility that supervisors are important in providing employees with information. Therefore, supervisors’ information sharing in combination with increased output expectations could explain their finding [ 66 ]. In contrast, when they are new to the organisation, some employees may not have the courage to ask colleagues for help [ 97 ]. Providing organisational support can therefore be a key resource [ 15 , 27 , 48 , 60 , 62 , 68 , 89 , 93 , 103 ].

At the organisational level, communication measures were identified as another important resource [ 91 , 98 , 100 , 101 , 103 ]. The results provide evidence that availability policies can help employees to mentally switch off from work in their leisure time [ 77 ]. However, universal rules may also restrict employees in their flexibility and, thus, in a valuable resource and communication measures may also entail negative effects. For example, shutting down email servers overnight could help to prevent employees from emailing in the evening during their free time, but may instead cause a flood of emails the next morning. Thus, techno-invasion would only be averted at the price of techno-overload as another techno-stressor [ 101 ]. Comparably, Delpechitre et al. found that some job resources may also entail further job demands and stress [ 6 ]. This highlights the importance of providing resources at the technical level as well, particularly technical support provision [ 64 , 73 , 74 , 80 , 87 , 103 ], e.g., by providing IT experts [ 100 ] or a help desk [ 62 , 113 ]. The importance of choosing the most appropriate means of communication, precise email correspondence, avoiding sending emails outside of working hours, hardware and software equipment was also emphasised in a recent qualitative survey [ 114 ]. Further study results affirm the positive influence of perceived organisational ICT support on ICT demands and psychological well-being [ 115 ], which were also found in the included studies [ 60 , 62 , 68 , 89 ].

At the personal level, particularly many findings on self-efficacy [ 6 , 23 , 24 , 26 , 56 , 74 , 75 , 77 , 85 , 87 , 89 , 97 ], mindfulness [ 72 , 81 , 96 ] and control [ 35 , 82 , 99 ] were identified. Although (IT) mindfulness was not able to buffer the effect of technostress creators on job burnout [ 80 ], several studies found support that it reduces technostress and burnout while increasing user satisfaction [ 72 , 81 , 96 ]. Hence, (IT) mindfulness may not lead to successful coping responses [ 80 ] but could nonetheless be helpful to reduce technostress. Previous research suggests that mindfulness and self-efficacy can be trained [ 116 , 117 , 118 , 119 ], while different sources of self-efficacy may influence each other [ 24 , 120 ]. Moreover, in line with our results, a recent review by Virone et al. has identified, inter alia, autonomy, time pressure, understanding of roles and attitude as relevant factors for coping with technostress among healthcare employees [ 121 ]. Other study results affirm our findings that a promotion focus [ 122 ] and data literacy [ 123 ] can reduce technostress creators.

4.2.2. Coping with Work-Related Technostress

Following the theoretical framework, problem- and emotion-focused coping strategies [ 17 ] were differentiated. Problem-focused and emotion-focused coping strategies were almost equally often examined in the included studies. To reduce work-related technostress, seeking support from others seems to be a promising problem-focused coping strategy [ 22 , 65 , 66 , 92 ]. Commonly investigated emotion-focused coping strategies reducing technostress included distress venting and distancing from IT [ 7 , 35 , 82 , 92 ]. Similar to IT distancing, digital detoxing behaviours can be helpful to reduce overload resulting from work-related ICT use when working remotely, as research from the COVID-19 pandemic shows [ 124 ]. Furthermore, positive reframing of situations was also identified as a coping strategy in a multi-organisational case study [ 125 ]. However, coping strategies can further be distinguished as problem-focused, emotion-focused and dysfunctional coping strategies. Accordingly, dysfunctional strategies include behavioural and mental disengagement, denial, venting, and substance abuse [ 126 , 127 ]. Such coping strategies may provide short-term relief, but are often not functional in the long term and can, therefore, even be harmful to the individual [ 22 ].

Interestingly, employees seem to apply several coping strategies when experiencing increased technostress. Employees, who coped with technostress in different ways, also rated their health and work ability better and reported less difficulties in mentally detaching from work in their free time than those who only used few coping strategies [ 64 ]. Findings from a study among adolescents support the assumption of increasing coping with higher levels of technostress [ 128 ]. While Saxena and Lamest were startled by the absence of team-based coping strategies in their case study [ 102 ], our results demonstrate that coping strategies are usually examined at the individual level. Although some studies identified social support among colleagues as an important resource in coping with technostress, it was inquired at the individual level [ 97 , 103 ]. However, first studies on dyadic coping among colleagues seem to be emerging [ 129 ].

4.2.3. Developing Prevention Measures for Work-Related Technostress

As with a recent scoping review on nurses’ strategies to prevent technostress, no studies on prevention measures or strategies were identified in this scoping review [ 37 ]. Given the lack of studies addressing primary, secondary or tertiary prevention or technostress interventions, merely first approaches based on the findings presented in this review can be suggested. Interventions should aim at altering appraisal and coping processes [ 92 ]. For example, Gaudioso, Turel and Galimberti suggested training employees in adaptive coping strategies and in being aware which coping strategy they use [ 63 ]. Rayburn et al. agree that there is a gap in research on the prevention of technostress through training [ 24 ]. Some researchers have already made use of the first findings on technostress mitigation through gamification in e-learning [ 130 ] and developed a game-based digital training platform [ 131 ], which remains to be scientifically evaluated yet. The gamification approach was also suggested as a prevention measure in a recent research report [ 113 ]. In this report, Gimpel et al. introduced 24 approaches to strengthen resources and reduce demands from different techno-stressors and technology environmental conditions [ 113 ], providing a catalogue of measures to prevent technostress in the workplace.

4.3. Strengths and Limitations

This scoping review followed a systematic approach to summarise and map the current state of research, including the recommended screening and methodological assessment processes carried out independently by several authors [ 43 , 45 , 46 ]. The high quality of this scoping review is further reflected in the inclusion of multidisciplinary databases, study designs and languages. Following the theoretical framework of an extended transactional stress theory [ 16 , 17 ], this review adopts a model used by most of the relevant studies [ 4 ] and is thus in line with the prevailing consensus of leading researchers in this field [ 5 ]. Moreover, this scoping review exclusively focused on work-related technostress. Although technostress may also arise from private ICT use, using ICT at work is rather bound to a purpose instead of entertainment [ 132 ]. Employees may therefore only have limited or no possibilities to influence their exposure or dose of ICT at work, which highlights the importance of researching prevention and coping options in this context. The review was based on the most widely adopted [ 133 ] conceptualisation of technostress and technostress creators [ 10 ]. Aiming to comprehensively present the current state of research, identified records were carefully examined and included based on the technostress creators’ definitions by Ragu-Nathan et al. [ 10 ], including adapted scales or items adhering to these definitions, which were discussed thoroughly among the authors. Overall, this scoping review contributes a comprehensive overview of the current state of research and identifies starting points for further research and practice.

However, some limitations need to be addressed. Not all studies included all of the five technostress creators. Sometimes, only four dimensions seemed to fit the investigated context [ 53 ]. Some studies used the items or adapted them to their specific research questions. Others added or combined them with further technostress creators, which are not included in Ragu-Nathan et al.’s concept [ 10 ]. The underlying conceptualisation of technostress creators was developed more than a decade ago; therefore, some authors have extended this concept more recently by further technology-related stressors [ 64 , 133 ], which can partly be referred to as technology environmental conditions [ 5 ]. Due to their recentness and thus lower prevalence in already published studies, these newly added factors are not primarily considered in this review. Instead, it focused on the most widely established five techno-stressors [ 10 , 106 , 134 ].

Included studies represent a large period of time (2008–2021), which may limit the comparability of studies considering technological progress that may contribute to increased technostress. The included studies also represent many different occupational groups, potentially limiting the comparability. However, this inclusive approach was chosen since many studies did not clearly state their inclusion criteria for participation, included a wide range of occupations in their samples or described their samples broadly as “employees using ICT at work”. In contrast, from a transactional perspective, technostress is considered highly contextual [ 5 , 14 ]. Apart from situational specificity, individual perception and appraisal, it may also differ among occupational groups [ 134 ] or cultures [ 53 , 92 ]. The examination of different occupational groups, including diverse tasks and job demands, may therefore explain divergent results among the included studies [ 55 ]. Moreover, all included studies relied on self-reported data measuring technostress. Despite many of them using reliable and validated scales, using other data measuring technology-induced stress, e.g., bio-physiological or observational data could additionally support and confirm the validity of the data in a mixed-methods approach [ 63 , 135 ]. This, however, is often difficult to realise in terms of feasibility.

While the inter-rater reliability in the screening processes was substantial, indicating well-defined inclusion criteria, it was only moderate in the quality assessment. The partially low degree of criteria fulfilment, especially among the cross-sectional studies, also indicates that the selected checklists may not have been appropriate for the context of the included studies under review. This could be because the checklists of the Joanna Briggs Institute originated from the health and medical sciences context [ 48 , 49 , 50 ]. However, it could also be attributed to the inclusion of conference papers, assuming they are limited in length and, thus, provide less information compared to journal articles. Ultimately, we strived to provide a comprehensive overview, yet knowing that even the combination of search strings, searching different databases and manual search cannot possibly identify all relevant records or map the state of research exhaustively.

4.4. Theoretical and Practical Implications

4.4.1. implications for further research.

As our results strongly point out, there is an urgent need for research on specific prevention approaches or the development and evaluation of interventions [ 53 ]. The distribution of how often the different techno-stressors were examined in the included studies indicates a need for further research, particularly on techno-insecurity and techno-uncertainty. Similarly, according to the frequencies of the cross-referenced techno-stressors, resources and coping strategies, further resources can be explored at the social and technical level. For the development of interventions, further insights on functional coping strategies and their consequences will be particularly useful. However, as several authors already noted, the positive effects of techno-stressors [ 5 , 93 ] and coping strategies [ 7 , 26 , 27 ] also need to be further illuminated. Since many of the coping strategies identified in this review included dysfunctional coping strategies, future research should focus on functional coping strategies to promote a healthy approach to technostress. The dearth of research on work-related technostress prevention or particular interventions is reflected in the framework developed by Tarafdar et al. (2019). Presenting their research agenda, they advocate investigating the positive effects of technostress as well as mitigating its negative effects through appropriate technology design. Information systems design features may be applied to support coping and positive, or to diminish negative, aspects of techno-stressors and outcomes [ 5 ]. Whether technology use may also lead to techno-eustress will need to be further researched in the future [ 5 , 22 ].

As different authors stated before, future research could focus more on organisational mechanisms and approaches to reduce technostress besides the already investigated technostress inhibitors [ 68 , 71 ]. However, as in this scoping review, different levels should therefore be considered [ 22 ]. At the organisational level, further resources and opportunities for interventions need to be identified and their effects further explored to foster structural prevention approaches. Regarding work design, not only the consideration of different factors at the respective levels, but also possible interactions should be taken into account [ 64 ]. Moreover, the impact of organisational culture should be further examined [ 79 ] due to the paucity of research in relation to technostress [ 63 , 66 , 75 , 88 ]. At the individual level, more research is needed on employees’ coping with technostress [ 10 , 81 ] and on possible interdependencies of different coping mechanisms [ 81 ].

Furthermore, Benlian criticised the static concept of technostress, calling for a more dynamic approach that could also account for within-person processes [ 93 ]. Therefore, as supported by the large amount of cross-sectional study designs among the included studies in this scoping review, several authors [ 22 , 24 , 55 , 79 , 93 ], have already called for more longitudinal studies to be conducted in the future. Longitudinal studies could provide insight into the extent to which the use of coping strategies affects individuals’ resources over time [ 95 ] and offer important implications for the design of interventions or could be used for pre–post analyses when introducing new technologies [ 10 ]. Nevertheless, interdisciplinary research remains important to gain a deeper understanding of positive and negative consequences of technostress as well as how to mitigate adverse effects [ 5 , 22 ]. Since technostress can be considered a “cross-domain phenomenon” [ 93 ] (p. 1278), future research should not only examine technostress from multidisciplinary perspectives, but it should also draw on different measurements [ 5 ] and sources, such as supervisors or family members in addition to employees [ 23 , 93 ] or group-level analyses [ 53 ]. Overall, the findings, particularly on techno-invasion and work–family conflict, provide evidence that work-related technostress and its effects reach far beyond the work sphere. They also impact employees’ private life. Therefore, they need to be examined in both spheres and, consequently, be counteracted with holistic approaches. In this vein, emerging research also considers the influence of personality traits in the context of technostress perception [ 112 , 136 , 137 , 138 ].

In the past two years, working conditions have changed significantly due to the COVID-19 pandemic and work with ICT has increased as a result [ 139 ]. The now widespread possibility to work remotely puts workplace health promotion, particularly dealing with techno-invasion, in the spotlight. Preliminary study results indicate higher technostress levels during the COVID-19 pandemic compared to before [ 140 ], but a decrease among employees who were already accustomed to the use of ICT pre-pandemic [ 141 ]. In a study where remote work during the COVID-19 pandemic was negatively related to technostress, remote work was also positively associated with flow at work [ 142 ]. Supporting the notion that leadership can serve both, as a potential stressor or resource [ 53 , 143 ], authoritarian leadership was found to have an either enhancing (when high) or protective (when low) effect on the workaholism–technostress relationship among completely remotely working employees during the pandemic, depending on its degree of expression [ 144 ]. Job crafting and organisational communication could be further protective factors (i.e., environmental resources) when experiencing technostress while working remotely [ 145 , 146 ]. Significant relations were found between working conditions (i.e., technical equipment) and perceived technostress, which also became apparent in blood cortisol levels [ 147 ]. Moreover, while other technology-related stressors such as techno-unreliability gain in importance during remote work in the COVID-19 pandemic [ 148 ], other strain reactions, e.g., techno-fatigue, emerge [ 149 , 150 ] and require newly developed behavioural and structural prevention approaches. Due to this unforeseen and substantial change in working conditions, studies related to preventing and coping with work-related technostress due to remote work during COVID-19 should be addressed in a separate review once a sufficient database is provided.

4.4.2. Implications for Practice

The findings further provide some implications for organisations to prevent and support employees in coping with work-related technostress. For this purpose, prevention measures can be subdivided into behavioural and structural prevention measures.

At the behavioural level, an initial important step for organisations is to support employees to adopt functional coping mechanisms [ 151 ] by educating them about possible coping behaviour based on the results presented in this review, thus providing them with different options for action to engage in. Starting from there, organisations can offer trainings for employees to develop IT competencies [ 113 ] and individual coping behaviours. Regarding personal resources to reduce work-related technostress, trainings could also strengthen mindfulness [ 80 , 116 ], self-efficacy [ 23 , 117 , 118 , 119 ] and IT control [ 35 , 82 , 99 ]. Stress management techniques can help to counteract irritation and stress [ 72 ]. As is evident from our results, although it might not address the root of the problem or be beneficial in the long run, emotional coping such as distress venting can reduce technostress effectively [ 81 ]. Employees should be encouraged to share their coping strategies and experiences among colleagues to increase benefit and be motivated to try out different strategies. However, dysfunctional coping strategies such as alcohol consumption that reduce technostress in the short term can have serious consequences in the medium or long term that may even exceed the consequences of permanently experienced technostress. In this regard, organisations should support the application of functional coping strategies among employees wherever possible [ 22 ].

Assistive technology can also be used to promote healthy behaviours and trainings could support employees to improve their self- and time management, sensitise and promote self-reflection about the causes, effects and consequences of technostress and one’s own way of working and managers to lead digitally [ 113 ]. Given that individual employees within an organisation require individual strategies, flexible IT use policies, e.g., email management strategies, might help employees to adopt various coping strategies rather than generalising measures such as shutting down servers [ 101 ]. Monotasking and taking breaks during the work day or reducing ICT use in leisure time may help to gain distance from digital demands [ 78 , 97 , 113 ]. Furthermore, it might be helpful to use ICT selectively, i.e., to use ICT only when it is functionally sensible and appropriate to do so [ 45 ]. Overall, employees and managers need to discover their personal healthy boundaries of ICT use [ 97 ] and understand that coping with technostress also relates to a sound ICT use in private life [ 7 ]. Especially when it comes to availability expectations, perceived techno-invasion may not merely be encouraged by ICT design but also by peer influence [ 132 ], i.e., managers’ and colleagues’ expectations and behaviours. Moreover, technostress mitigation requires self-regulation and can therefore impede health-promoting behaviour [ 132 ], e.g., resist checking emails after work, knowing colleagues may be doing so. Adjusting expectations regarding email response times, developing and complying with clear corporate guidelines on availability expectations as well as personal rules and guidelines regarding ICT use at and outside of work may help to cope with technostress [ 114 ]. Managers should appeal to employees’ self-responsibility in terms of ICT use and reconciling work and private life [ 114 ] and properly delegate tasks to reduce their own technostress [ 88 ]. In this vein, a balanced combination of autonomy and control based on the individual employee’s needs is required [ 152 ]. Regarding distressing work-related social media use, it might be helpful for managers to draft specific policies [ 58 ], ideally in cooperation with their employees, e.g., establishing team norms and a shared understanding of when, why and how employees are available for work-related communication [ 113 ]. Interventions aiming at improving psychological detachment from work may also be helpful in this context [ 79 ].

Within organisations, multipliers who pass on information on preventing and coping with work-related technostress to employees could be managers. At the same time, with regard to health-oriented leadership, managers should always pursue the two directions of leadership, i.e., self-directed health-oriented leadership (SelfCare) and follower-directed health-oriented leadership (StaffCare) [ 153 ]. In this dual role, while managers seem to be more susceptible to techno-overload and techno-invasion than employees [ 154 , 155 ], they need to act according to their role model function [ 156 ] and as positive social influencers [ 157 ] to protect themselves and their subordinates, e.g., when dealing with technology and availability expectations. Through their own understanding and practice of dealing with ICT, managers could support employees and counteract harmful developments [ 154 ]. However, dealing with availability can be subject to individual preferences of integration or segmentation of work and private life [ 158 ]. Possibilities for availability rules, demarcation and self-organisation should therefore be consciously reflected upon by managers and employees and incorporated into workplace health management [ 154 ].

Interventions aiming at non-directive leadership styles could promote employees’ resources [ 55 ]. Moreover, it should be noted that empowering leadership could also increase the burden of emotional exhaustion in employees. Therefore, managers should be careful not to burden employees when autonomy and responsibility are rather perceived as overburdening [ 55 , 159 ]. This again underlines the individuality of appraisal and coping processes of employees and managers. Managers should provide employees merely with necessary or relevant information to prevent information and communication overload [ 6 ] and state clear role expectations for employees to reduce role ambiguity, especially when new technologies are implemented. In this case, adequate IT infrastructure and sufficient information in case of technology breakdowns for employees should be ensured [ 21 ]. Organisations should also offer ongoing training, managerial and technological support in digital change processes [ 113 ]. Trainings may also serve to develop an understanding and appreciation for (new) technology and to increase the effectiveness of change management processes. In this vein, Kwanya et al. recommend individual trainings [ 45 ]. It is also recommended to regularly foster education on new technologies and to prevent resistance to technological change as an emotional process which might counteract efforts to mitigate technostress [ 24 , 72 ]. Moreover, training may improve confidence in using ICT [ 85 ]. With regard to involvement facilitation and literacy facilitation, managers could support and reward using newly introduced ICT as well as sharing this knowledge among team members [ 62 ]. Organisations should provide training for employees and managers that does not only meet their demands [ 154 ], but also aims at compensating deficits (e.g., in digital media literacy), and focuses on individual strengths [ 55 ]. This strengthening of resources may counteract negative spirals, as suggested in the occupational psychological stress model [ 17 ] or Hobfoll’s conservation of resources theory [ 160 ]. Accordingly, a combination of a stressful work environment and low resources can lead to a self-reinforcing stress spiral of stressors and stress consequences [ 17 ]. Similarly, initial resource loss of lack of resources may cause a loss cycle. Additionally, while resources are needed to recover from or protect against such a loss, resource loss will be disproportionately more salient than a resource gain [ 160 ]. Therefore, the implementation of resource-strengthening interventions should be targeted. In this vein, Goetz and Boehm suggest that teambuilding events could facilitate friendship opportunities among colleagues [ 65 ], thus strengthening the environmental resource of social support at work.

While interventions should be implemented at both the individual and the organisational level [ 79 ], counteracting some techno-stressors may require a more general, organisational-level approach. In a structural approach to prevention, organisations should seek to keep the demands on their employees as manageable as possible since the literature suggests that both too low and too high levels could be damaging [ 22 ]. Moreover, it should be considered that adverse effects of work-related technostress may also negatively impact customer satisfaction and relationships [ 21 , 84 ]. A central approach to avoid technostress in a primary preventive way is the design of the technology [ 5 , 113 , 155 ]. Therefore, to prevent work-related technostress in its genesis, an adequate IT infrastructure needs to be built and maintained [ 113 ]. A technological infrastructure allowing for collaborative teaching and learning could further contribute to technostress reduction within the organisation and among colleagues [ 72 ]. Moreover, allowing employees to choose technologies they assume to fit best for their tasks apart from mandatory ICT could increase their perceived control [ 81 ]. From an organisational and work design perspective, not only do various factors, e.g., work organisation, work environment and work equipment, need to be considered to prevent technostress, but also the interaction of technological and organisational factors [ 64 ]. According to the technology acceptance model, acceptance can be increased through perceived usefulness and ease of use [ 161 ]. These can be achieved, for example, through an exchange between software developers and users [ 103 ] and clarified in training courses to thwart technostress. Recent study results support that usability, i.e., reliability, usefulness and ease of use, can reduce techno-overload and IT-related strain [ 162 ]. Regarding technology acceptance and adoption, task–technology fit [ 163 ] and possible interactions between task and organisational processes, attributes of technology and the individual using it should thus be considered in prevention [ 154 , 164 ]. Organisations can therefore use these theories for assessments before implementing new technologies and for evaluations [ 151 ]. Especially in digital change processes, procedures should be adapted preventively, necessary competences should be developed and software or experts should be provided to prepare relevant information for employees in a comprehensible and user-friendly way. Thereby, a needs-based competence development can reduce employees’ techno-uncertainty. Jager and Thiemann also highlight the importance of quickly available competent experts for technical problems [ 154 ].

When implementing strategies for prevention at the organisational level, the practicability and cost–benefit ratio should be assessed in advance since mitigation strategies may also have adverse effects [ 101 ]. This point also includes the fact that organisational-level mitigation or prevention measures further need to take into account different individual needs of employees. However, changing the job design alone might not be sufficient to mitigate technostress, if other factors such as working conditions and technical aspects are not considered. Hence, a more holistic, sociotechnical approach is advisable when redesigning jobs and tasks [ 78 ]. Holistic approaches may include peer-to-peer or supervisor-to-employee coaching and mentoring to cope with techno-stressors [ 93 ]. Thereby, although mitigation strategies should be implemented techno-stressor-specific [ 165 ], technostress countermeasures do not necessarily be technology-specific [ 60 ]. Organisational measures such as flexible working times and break sequences as well as opportunities for exchange could further contribute to building social bonds and reducing techno-uncertainty [ 65 , 113 ]. Moreover, resources can be strengthened by developing a cooperative corporate culture and a mission statement on communication [ 113 ]. Nevertheless, employees’ private ICT use also needs to be taken into account when taking a holistic approach to technostress prevention. As Pirkkalainen, Salo, Makkonen and Tarafdar [ 81 ] stated, “technostress-creating conditions cannot be fully prevented in workplaces” (p. 13). Therefore, personal development should be encouraged and supported [ 81 ].

Beyond the individual level, organisational and technological (infra-)structures as well as the legal framework need to be adapted to digital working environments [ 166 , 167 ]. Where laws are not (yet) effective, company regulations are needed to protect employees. It is therefore necessary to incorporate into law that mental stress caused by techno-stressors must be avoided in terms of occupational health and safety (OSH) and that the Working Hours Acts also apply in the digital work context. At the same time, ICT can also support OSH activities [ 168 ]. For a comprehensive prevention of technostress, techno-stressors need to be considered in and become an inevitable part of psychosocial risk assessments at the workplace [ 31 , 81 , 155 ]. Therefore, relevant techno-stressors should be identified and individual prevention measures which meet employees’ demands should be derived. After participatory implementation, the prevention measures should be evaluated and, if necessary, adapted to ensure their sustainable effectiveness [ 113 ]. With regard to flexible digital work, raising awareness and involving employees themselves in OSH is becoming more important [ 168 ]. In addition to OSH experts, not only should the individual needs of employees be taken into account, but also their expertise with regard to techno-stressors at their workplaces [ 169 ]. In light of increasing responsibility for their own health, employees’ health literacy needs to be fostered as part of a sustainable prevention culture [ 167 ].

5. Conclusions

Given the need for an interdisciplinary investigation of technostress, this scoping review links information systems and psychological stress research. While most studies on technostress examined its causes and (adverse) consequences, this review focused on approaches for preventing and coping with work-related technostress. The review provides a comprehensive overview of the current state of research by mapping environmental resources as well as personal resources, problem- and emotion-focused coping strategies to reduce work-related technostress and its potential work- and health-related consequences. Despite a growing body of research on mitigation of technostress, there are no targeted interventions or evaluations of prevention measures yet. Many of the examined resources and coping strategies provide starting points for behavioural prevention measures. However, to overcome work-related technostress comprehensively, an interaction of both behavioural and structural prevention measures will be necessary. Particularly, techno-stressors should be incorporated in psychosocial risk assessments to derive appropriate prevention measures at different levels. Employees and managers should be supported in developing functional coping strategies to deal with work-related technostress. Therefore, to overcome the “dark side” of technology, future research still needs to focus more on the “bright side” of preventing and coping with adverse consequences technostress and examining its positive effects.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/ijerph19063625/s1 , Table S1: Search strategy for the medical database PubMed; Table S2: Search strategy for the medical database MEDLINE; Table S3: Search strategy for the psychological database PSYNDEX; Table S4: Search strategy for the psychological database PsycInfo; Table S5: Search strategy for the interdisciplinary database Web of Science; Table S6: Charting the data: information on included studies based on Arksey and O’Malley (2005); Table S7: Critical Appraisal of Qualitative Studies; Table S8: Critical Appraisal of Longitudinal Studies; Table S9: Critical Appraisal of Cross-Sectional Studies.

Author Contributions

Conceptualization, E.R., J.-C.F., V.H. and S.M.; methodology, E.R. and J.-C.F.; Title and Abstract-Screening E.R.; Full-text Screening E.R. and J.-C.F.; writing—original draft preparation, E.R. and J.-C.F.; writing—review and editing, E.R., J.-C.F., V.H. and S.M.; visualization, E.R.; supervision, V.H. and S.M.; project administration, E.R., J.-C.F., V.H. and S.M. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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The Dark Side of Technology

Emeritus Professor of Experimental Physics in Engineering

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Technological progress comes with a dark side where good ideas and intentions produce undesirable results (extreme downsides include atomic and biological weapons). The many and various unexpected outcomes of technology span humorous to bizarre, to situations that threaten human survival. Development can be positive for some, but negative and isolating for others (e.g. older or poorer people). Progress is often transient, as faster electronics and computers dramatically shorten retention time of data, knowledge, and information loss (e.g. even photos may be unreadable within a generation). Progress and globalization are also destroying past languages and cultures. Advances cut across all areas of science and life, and the scope is vast from biology, medicine, agriculture, transport, electronics, computers, long-range communications, to a global economy. Reliance on technology causes unexpected technology-driven vulnerability to natural events (e.g. intense sunspot activity) that could annihilate advanced societies by destroying satellites or power grid distribution. Similarly, progress of electronics and communication has produced a boom industry in cybercrime, and cyberterrorism. Medical technology offers improvements in health, but can include many drug-related side effects and mutagenic changes. Over enthusiasm in creating a global food economy is devastating the environment and causing extinction of species, just to support an excessive human population. A diverse coverage of such consequences is consciously presented at a level designed for an intelligent, but non-scientific, readership. It includes suggestions for positive future progress with essential planning, investment, and political commitment. Failure to respond implies human extinction.

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The Dark Side of Information Technology

In recent years, digital technologies have been transforming workplaces and increasing economic productivity. But could overuse of information technology now be sapping your employees’ — and your organization’s — well-being?

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Information technology has long been viewed as the power behind a new economic revolution — an evolving set of tools that has made workers much more productive than ever before, powering a step change as dramatic as steam or electricity. According to a report by the World Economic Forum, “digitization boosted world economic output by nearly US$200 billion and created 6 million jobs in 2011.” 1 On a company-by-company basis, a number of studies have found that companies that use more IT have higher productivity than their competitors. 2 However, we may be entering an era in which human frailties begin to slow down progress from digital technologies. In a series of studies, we explored the implications of IT-induced technology stress, technology addiction and IT misuse in the workplace. (See “About the Research.”) One implication of our findings is that the very qualities that make IT useful — reliability, portability, user-friendliness and fast processing — may also be undermining employee productivity, innovation and well-being.

After observing a number of organizations, we found that this rapidly emerging “dark side” of IT hurts employees and their organizations and robs companies of some of the productivity gains they expect from their IT investments. In this article, we describe key negative effects of IT use in the workplace, explain the risks they pose, and suggest ways managers can mitigate their impact.

The Effects of “Technostress”

Pervasive and near-continual use of organizational IT systems is now beginning to take a toll on some employees’ health. Individuals experience “IT use-induced stress” or “technostress” for a number of reasons. 3 They feel forced to multitask rapidly on simultaneous streams of information from different devices simply because information feeds come at them in real time; remote work and flextime tether them round the clock to their devices and workplaces; and short technology cycles and pressures from IT vendors mean constantly changing interfaces, screens and functionalities, often without sufficient FAQs and help-desk support. We also found in a survey of about 600 computer-using professionals that 73% worried that refraining from constant connectivity and instantaneous information-feed response would place them at a disadvantage at work. 4

Employees can experience IT-induced stress for a number of reasons. They are bombarded with a flow of information, and remote work and flextime tether them round the clock to their devices and workplaces.

Complex user interfaces that do not naturally fit with task workflows are an additional source of stress, because they create work overload when they are used. In studying the use of a health-care IT application in the context of care delivery processes in acute care facilities at two major hospitals, we found that physicians had to juggle between numerous different screens on their monitors to access patient data feeds, test results, clinical notes and treatment notes. 5 Most of the doctors complained that they had to do far more work using IT than they thought reasonable. Often, we find that the more enthusiastically and relentlessly organizations embrace IT, the more technostress their employees suffer.

Ironically, even as they dream of escaping from IT, many employees also confess to feeling “addicted” 6 to some of these stress-causing technologies. In a study of organizational mobile email users, 7 we found that 46% exhibited medium to high addiction-like symptoms. On the one hand, they take their work home: Employees spent time responding to work emails when at home (23 minutes on average per day), while commuting (12 minutes), each weekend day (42 minutes) and each vacation day (43 minutes). On the other hand, IT also allows employees to take their leisure-time activities to work (for example, using Facebook). Even when an organization blocks certain websites, the sites can still be accessed via personal mobile devices, especially now, as the bring-your-own-device-to-work trend continues to grow.

As with many addictions, the desire for stimulation becomes progressively harder to satisfy, and over time individuals often seek more ways to “up their dosage.” A remarkable example of the effects of nonwork IT on the workplace is the popularity of the Candy Crush Saga online game. One survey found that 30% of its players called themselves “addicted” to the game, and 28% admitted to playing it at work. 8 One person confessed to “going to bed late, as I do ‘just one more,’ over and over. It seems everyone at work is also addicted. We need a Candy Crush anonymous group…” 9

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Employee Misuse of IT

Another aspect of the dark side of IT is the threat of employees misusing organizational IT resources and triggering “attacks” of different kinds. Firewalls and other network defenses can potentially stop attacks from the outside. However, no security technology can stop an employee who has authorized access to a computer system from, for example, obtaining confidential company information and selling it to competitors. A number of studies have found that attacks stemming from internal sources are greater in scope and severity and can result in about 10 times as many compromised records as those from external sources. Even more disturbingly, a sizeable percentage of such attacks turn out to be deliberate. 10 Other kinds of insider IT misuse range from truly malicious user behavior (such as stealing sensitive corporate data) to unsanctioned behavior (such as accessing unauthorized parts of a corporate network or knowingly using unlicensed software) to naive user actions such as opening an unknown email attachment. Unsanctioned and naive user behaviors make up the vast majority of IT misuses.

Perhaps the most common motivation for IT misuse by an employee, ironically, is a desire to be more effective. We presented respondents with the following scenario and asked them if they would engage in the same behavior under similar circumstances:

Jordan is given a personal computer (PC) at work. However, the new PC is missing a piece of software that Jordan believes would make her more efficient and effective on the job. Jordan requests that the company purchase the software, but her request is denied. To solve the problem, Jordan obtains a copy of the software from a friend outside of the company and installs the software on her PC at work.

Of 269 professionals, 45% indicated at least a medium likelihood (those with a score of 4 or greater on a scale of 1 “strongly disagree” to 7 “strongly agree”) that they would engage in the same behavior in their own companies given the same circumstances. The percentage jumped to 50% when respondents were asked how their coworkers would behave. 11

A slightly weaker but still significant motivation for technology misuse is the desire to help others. Our research suggests that even as employees understand that such behavior violates organizational policy, they view it as innocuous. We presented this scenario to respondents:

Alex is an employee in the human resources department at your organization and thus has been authorized to view the salary information of all employees as part of his job functions. Recently, one of Alex’s friends (who does not work for your organization) contacted Alex and asked for the salary information of all managers in your organization. The friend informed Alex that he was applying for a management position in your organization and wanted to use the information to determine what salary to ask for in case he is offered the position. Although Alex believes that providing the salary information is a violation of company policy, he looks it up and gives it to the friend.

Thirty percent of professionals surveyed indicated at least a medium likelihood (those with a score of 4 or greater on a scale of 1 “strongly disagree” to 7 “strongly agree”) that they might engage in similar behavior. The percentage jumped to 40% when respondents were asked whether their coworkers would do it. Unfortunately, such uses are difficult to combat: IT use requirements such as creating hard-to-guess passwords and blocking access to certain websites and cloud-based storage services such as Dropbox are seen as constraining and are themselves a form of stress that encourages IT misuse. In this same study, we found that respondents who felt that security policies in their organizations were complex, burdensome and stressful were significantly more likely to try to justify their misuse of IT. 12

Why Senior Leaders Should Care

Why should senior executives care about these issues? First, they pose serious risks to productivity and innovation. The more time and effort employees spend keeping abreast of ever-changing applications, struggling through information gluts, trying to understand how best to navigate through and use IT, and making mistakes, the less time they have for the job their IT tools are intended to support. More ominously, the rush to respond to incoming information causes employees to process, hastily and ineffectively, only that information which is immediately available , rather than wait for the information they actually need to do the job. Such an approach can stymie innovation, which often requires unhurried and thoughtful processing of relevant, varied and, as far as possible, reasonably complete information. The distraction posed by IT use and its accompanying flow of incoming information also seems to interfere with relationship building — another potentially serious consequence, as many service-oriented jobs include both technology-enabled and relationship-oriented workflows. In a study of senior and middle managers in professional sales roles, we found that the effects of IT-induced stress are far-reaching enough to reduce innovation and productivity in both types of workflows. 13 In other words, the more IT employees used, the less effective they were. We often heard statements like “Those of us who achieve our sales quota use less IT and are less stressed.”

Second, excessive IT use can harm employee well-being. We found instances where employees actually resigned because they found it too stressful to cope with and learn to use constantly changing workflows/applications. In two separate studies, one on stress from common workflow applications such as enterprise systems and email and another on addiction to mobile devices/email, we found that each correlated with higher employee desire to leave the job and reduced organizational commitment. 14 IT use-induced stress from work-related IT use was also associated with lower overall satisfaction with the job.

Third, there are monetary and reputational risks. IT misuse has financial ramifications for organizations. Employee IT misuse may in some cases provide grounds for litigation; an early example is the energy company Chevron Corporation, which was ordered in 1995 to pay female employees $2.2 million to settle a sexual harassment lawsuit stemming in part from inappropriate email circulated by employees. 15 Public disclosure of problems caused by employee or contractor IT use can also cause negative publicity and competitive disadvantage to a company. For example, the retailer Target Corp., based in Minneapolis, Minnesota, experienced a high-profile Thanksgiving season data breach in 2013, which has been attributed to a credentials theft that took place through a phishing email sent to a Target vendor. 16 The data breach led to the resignation of both Target’s chief information officer and chief executive officer. 17

A fourth risk is to the technical integrity and operational viability of the corporate IT system. Certain forms of addiction, such as visiting websites that host online gambling activities or pornography, are magnets for malware infections. Organizationally unsanctioned but seemingly mild IT misbehaviors such as using unlicensed software, downloading email attachments or sharing passwords with coworkers and sensitive information with nonemployees can put the organization’s data and systems at risk. IT misuse also has operational costs. Unlicensed software usage can lead to significant user downtime, and sending or receiving email/phishing viruses can cause system downtime. Password-sharing practices undermine the effectiveness of technical access control measures while compromising audit trails and accountability. Employees who are addicted to social media sites may post sensitive company information that they are not permitted to share with outsiders. Seemingly innocuous actions such as sharing company news (for example, a new product or patent) on a social networking site such as LinkedIn can assist hackers or other unscrupulous individuals in stealing a company’s intellectual property. And even when secrets aren’t spilled, a lot of time is stolen: In our research, we found that employees who find it difficult to control their use of social networking websites such as Facebook may spend time on these sites at the expense of time on task. They may take longer breaks, miss deadlines, be reckless with confidential information and neglect organizational IT use policies.

Finally, there are legal risks. If certain legal conditions are met — such as clear breach of duty by the organization in, for example, not adequately disclosing the potential for IT addiction and the presence of potential damage for the employee — it may be possible for Internet-addicted employees to argue that employers are liable, leaving the organization vulnerable to employee lawsuits. Indeed, there has been some effort to establish Internet addiction as a disability, regardless of its origin and cause. 18 As the CIO of one major enterprise told us: “As society moves in a greater manner to technical intermediation, the impacts [of technology-related addictions] to employees are critical. … [Employers] should be cautious regarding work-life balance. … It may turn to litigious issues.” Organizations may also have to deal with the hassle of possible litigation on privacy violations and sexual harassment from rash and compulsive use of applications such as social media at work, but not for work, by technology-addicted employees.

Resisting the Dark Side

Organizations have traditionally taken primarily technical approaches to helping their employees use IT. These have consisted largely of routine, mostly one-time and one-size-fits-all technical training activities where employees go through material on how and when they can use features of particular systems. We find, however, that the interaction between employees and IT is beginning to increasingly consist of a continual stream of use of different types of IT (for example, applications on both mobile and fixed devices) in addition to specific periods of use of a particular application. This kind of multimodal use demands a multimodal response by senior executives, IT leaders and HR leaders to effectively combat impacts from IT’s dark side. (See “Tackling the Dark Side of IT.”) In particular, we suggest that managers go beyond technology-oriented solutions and encourage employees to step back and examine their personal relationship with IT.

What Senior Executives Can Do

Our research on employee-initiated security breaches shows that top management attitudes are a critical element in promoting pro-security behaviors such as safe computing practices to coworkers. 19 “Top management should be committed to making security a function of business processes” was a recurrent theme in our interviews. As one respondent said, “The senior leadership provides the example of our security culture.”

Second, company leaders should empower employees to be circumspect and mindful about how they use IT and about its potential good and bad impacts. This is perhaps a departure from and certainly a complement to typical current leadership mindsets that focus singularly on the benefits of IT use. Senior leadership should encourage employees to reflect on not only how they use IT from or at work, but for work, from anywhere . More than 65% of employees we asked reported feeling stressed from the work-home blurring and invasive effects of work IT. However, the rather common idea that older employees feel stressed from “technology insecurity” because they cannot keep up with the constant waves of new digital technologies that organizations introduce appears to something of a myth. Indeed, we found older employees to be on average 15% to 20% less stressed than younger employees, possibly because they are able to marshal experience and knowledge of their work and organizational context in order to engage in more informed use of IT — such as, for example, not allowing themselves to be interrupted by their cellphones unless the message concerns a higher priority. Leaders should organize and promote “mindful technology use” events and practices such as “email-free” weekday afternoons. If they don’t, it’s possible that the labor lobby will start demanding such practices. Interestingly, a major labor agreement in France recently moved to outlaw certain employees’ responding to email message outside of normal work hours, and there are some similar efforts being considered in Germany. 20 While that may not be something that all organizations want or should ask for, it does suggest rising concern among union leaders and policy makers about the potentially harmful effects of excessive IT usage on employee well-being.

Third, leaders should create a climate that encourages employees to really understand the IT they use at work. When faced with IT applications having more functionality than is needed, many respondents said they either “switch off” or “use the bare minimum information to look good,” both of which they thought hindered the effectiveness of their IT use. We find that this mastery of IT is often best achieved by giving employees the resources to “mess around” and experiment with the devices and applications. Users need to learn in less formal and more enriching ways, outside the structured and often limited training paradigm. Once they “speak” the language of the application, employees tend to be less overwhelmed by excessive features or distracted by glitzy ones.

What IT Leaders Can Do

Given their natural domains of expertise, IT leaders have a special responsibility to instruct the organization about pertinent aspects of IT systems and applications that could either exacerbate or mitigate their darker effects. One of our studies showed that a simple educational video on the risks of Internet overuse significantly increased the motivation of users to control and reduce Internet use. 21 IT leaders should provide forums, such as brown-bag meetings, for employees to discuss and share their IT-related experiences, challenges and remedies with colleagues. Users of enterprise systems, for instance, in spite of training on how to navigate screens for standard workflows, often find it a stressful experience to extract information and build the custom reports they actually need. Sharing stories with others who have been able to do so reduces some of that stress: Employees whose companies encouraged them to share experiences and learning with new applications reported 20% lower stress levels than those in organizations that did not. In studying the use of electronic health record systems by doctors and medical staff, we found that those who continued to learn about IT features informally from one another were better able to counter the effects of stress. Employees’ propensity to misuse IT resources was about 40% lower in organizations where IT leaders made an effort to deliver ongoing security education and training. 22 Efforts to inform and educate users on the potential risks of IT can likewise reduce the incidence of naive misuse behaviors such as opening an unknown email attachment or accidental data entry. The key is to inject information that alerts employees to the negative effects of IT usage and encourages them to explore these topics on their own and with others if necessary.

Traditional IT training programs, which are mostly one-time exercises (unless employees specifically request otherwise) delivered face-to-face or electronically and focusing largely on how to use common application features, are able to address only part of employees’ training needs. For most applications, employees need to understand how to combine and use features and functions in nonstandard ways to fit their particular tasks and activities. Learning to use the system for nonstandard uses typically entails a process of tinkering to find features, workarounds and shortcuts that work, which may be different for different people. In one of our studies, we found hospital physicians and staff felt stressed when patient charts became accessible on their mobile devices because they felt pressured to respond whenever they heard the device alert from a new or changed chart. The physicians and staff then worked through the application and learned how to create their own work practices; some turned to the IT department to help them turn off the immediate alarm notification and create one at a different time more conducive to their schedules, which helped ease the stress. Rather than keep installing systems and tossing them over to users, IT leaders need to remain engaged with users for significantly longer periods after implementing a system. They especially need to let employees know about features such as email archiving that can potentially counter or offset impacts such as information overload. We found that employees who felt informed and involved reported 10% to 15% lower stress while using IT.

One of our studies showed that a simple educational video on the risks of Internet overuse significantly increased the motivation of users to control and reduce Internet use. IT leaders should provide forums, such as brown-bag meetings, for employees to discuss and share their IT-related experiences.”

Of course, technical guardrails must still play a role in curbing IT misuse. IT leaders can create an environment that keeps the organization vigilant to and, to some extent, safe from the dark-side effects on a day-to-day basis. We found that the more employees know about specific security-related countermeasures, the more they are inclined to abide by security policies and procedures. 23 Such countermeasures include, for instance, blocking technologies to address workplace addiction to IT and technical controls for tackling IT misuse, such as multiple levels of authentication and disabling access to USB ports where necessary. Engaging employees in the design and implementation of such countermeasures can help increase compliance and reduce misuse. Another way to improve productivity is to streamline systems to mirror workflows of key groups of IT users. We asked 169 physicians in one of our studies to use a simpler interface with fewer screens over a period of 40 weeks that covered about 1,300 patients. By the end of the trial, all of them reported less time spent using the IT application. 24

IT leaders can also develop what are called “persuasive” systems 25 that “nudge” users towards more effective IT use. Giving users different prebuilt options regarding how they should process interruptions from, for instance, work-related email or social media information could guide them in addressing interruptions meaningfully. We found in further studies that designing simple prioritizing heuristics into IT applications based on importance and source of interruption messages led to a 39% reduction in the time that physicians spent in handling simultaneous information from multiple sources. We find that such real-time feedback also helps employees to reduce errors from IT-related overload, 26 as the persuasion can be directed towards corrective suggestions in the moment of use — provided, of course, that the feedback itself is not intrusive and does not cause information overload. 27 IT leaders could also commission the design and rollout of simple dashboard applications that track the timing, extent and type of IT-use activities during and after working hours. Employees can use this sort of information to help decide for themselves when their IT use is becoming a problem.

Finally, IT leaders can develop and implement IT-use policies that clearly define appropriate use and forbid the misuse of the company’s IT resources. It is important, however, that these policies be understandable and not a significant hindrance to employees’ normal job duties, or else they risk becoming an additional source of stress. For example, such policies should clearly explain technical requirements of IT use for nontechnical employees. Most technology-use policies are limited to specifying how employees may use specific IT resources such as shared printing and scanning equipment and services. These use policies should also alert employees to — and deter them from — the risks posed by technology addiction. Such policies also provide legal protection for organizations, because by developing and implementing them, organizations can demonstrate that they did not breach their duty to their employees.

What HR Leaders Can Do

HR leaders have perhaps the most significant role to play in combating the dark-side effects of IT. The first issue that HR leaders face is assessing the extent of the dark-side effects of technostress or technology addiction that their organization may be facing. The difficulty here is that these sorts of phenomena are not easily quantified. For instance, a number of our study respondents mentioned that they were “stressed” by or “fed up” with constant upgrades to software in their workplaces, but they were not able to pinpoint what made them stressed and how their performance or well-being at work suffered as a result. HR leaders should work together with IT leaders to create programs and audit exercises to regularly measure and monitor the extent to which employees are plagued by these effects and are less productive, innovative or effective as a result. (See “Signs of IT’s Dark Side.”)

A second issue for HR executives is improving employees’ sense of well-being at work. Stress and addiction diminish indicators of well-being such as job satisfaction, clarity of work expectations and work-life balance. Our findings imply that internal company policies such as limiting IT use after working hours, monitoring use and providing warning signs when improper/excessive use is detected, and training employees regarding the various risks associated with the improper/excessive use of technologies at the workplace and beyond can help reduce stress and addiction. Circulating information on external help and resources can also have a positive impact. At the same time, our research indicates that being unhappy at work leads to more improper security-related behaviors. Research on organizational behavior also suggests that general moods on a particular day influence whether or not employees comply with security policies, as does a higher level of job satisfaction. 28 HR initiatives such as job enrichment programs that contribute to job satisfaction should positively influence security behavior. Mood-management strategies, such as fostering a positive work environment, helping employees find meaning in their work and offering a pleasant physical work environment, among others, should also help reduce IT misuse.

Third, HR leaders need to consider tailoring organizational policies regarding IT use to individual-specific traits. Why does A take so long to answer email while B does not? was an oft-repeated sentiment we came across. We found in a study with 129 professionals 29 that those more naturally inclined toward multitasking 30 respond relatively closer to real time to interruptions from different devices they carry. HR leaders should make employees aware of these sorts of differences and their implications for how they might expect colleagues to use IT, such as how quickly they answer email. Such actions can help employees develop a healthy understanding and tolerance for the different kinds of IT use their colleagues might engage in. We also found that certain personality traits such as neuroticism make employees more prone toward deviant IT misuse behaviors. 31 Although these characteristics are not readily observable, they can become apparent over time, and HR leaders might need to consider whether specific kinds of individual characteristics should be taken into account when matching employees to positions that involve sensitive data.

Lastly, in extreme cases, HR leaders will increasingly face the prospect of having to decide on alternate and complex courses of action (organizational, legal and medical) to deal with deviant IT use, stress and addiction. They may need to weigh the cost of terminating employees who are repeatedly caught in misuse or addictive use of their company’s IT systems versus possibly helping them with rehabilitation. There are a number of treatment centers for Internet addiction, and given the potential legal issues that might arise from a dismissal, companies in some cases may be better off funding rehabilitation than pursuing organizational and/or legal sanctions.

Organizations invest in IT because they expect to boost their efficiency. The dark-side phenomena that we address in this article instead diminish productivity and innovation. Fortunately, a holistic and integrated approach by senior executives, IT leaders and HR leaders can help mitigate the most damaging consequences.

About the Authors

Monideepa Tarafdar is a professor of information systems and a codirector of the HighWire Doctoral Training Centre at Lancaster University in Lancaster, United Kingdom. John D’Arcy is an assistant professor of management information systems at the Alfred Lerner College of Business and Economics at the University of Delaware in Newark, Delaware. Ofir Turel is a professor of information systems and decision sciences at the College of Business and Economics at California State University, Fullerton as well as a scholar in residence in the department of psychology at the University of Southern California in Los Angeles. Ashish Gupta is an associate professor of analytics and information systems in the College of Business and director of the Big Data and Analytics Research Center at the University of Tennessee Chattanooga.

1. B. Bilbao-Osorio, S. Dutta and B. Lanvin, eds., “The Global Information Technology Report 2013” (Geneva, Switzerland: World Economic Forum, 2013), vii.

2. E. Brynjolfsson and L.M. Hitt, “Computing Productivity: Firm-Level Evidence,” Review of Economics and Statistics 85, no. 4 (November 2003): 793-808; and E. Brynjolfsson and A. McAfee, “The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies” (New York: W.W. Norton & Co., 2014), 98-106.

3. While stress due to IT use in the workplace is an emerging phenomenon, workplace stress due to organizational roles and tasks is well-known; see, for example, R. Kahn and P. Byosiere, “Stress in Organizations” in “Handbook of Industrial and Organizational Psychology,” vol. 3, 2nd ed., ed. M.D. Dunnette and L.M. Hough (Palo Alto, California: Consulting Psychologists Press, 1992), 571-650.

4. T.S. Ragu-Nathan, M. Tarafdar, B.S. Ragu-Nathan and Q. Tu, “The Consequences of Technostress for End Users in Organizations: Conceptual Development and Empirical Validation,” Information Systems Research 19, no. 4 (December 2008): 417-433.

5. A. Gupta, R. Sharda, Y. Dong, R. Sharda, D. Asamoah and B. Pickering, “Improving Rounding in Critical Care Environments Through Management of Interruptions,” Decision Support Systems 55, no. 2 (May 2013): 516-527.

6. Addiction to “thrill-producing” technologies such as social networking sites and mobile email is a growing problem for which the workplace implications are just beginning to be understood. It is different from substance addiction, about which, for example, see T.E. Robinson and K.C. Berridge, “Addiction,” Annual Review of Psychology 54, no. 1 (2003): 25-53.

7. O. Turel, A. Serenko and N. Bontis, “Family and Work-Related Consequences of Addiction to Organizational Pervasive Technologies,” Information & Management 48, no. 2-3 (March 2011): 88-95.

8. E. Dockterman, “Candy Crush Saga: The Science Behind Our Addiction,” Nov. 15, 2013, www.time.com.

9. Saint, comment on L.J. Williamson, “‘Candy Crush Saga’ Gives Addicted Mobile-Game Players a Sugar Rush,” May 25, 2013, http://herocomplex.latimes.com.

10. For example, see Verizon, “2008 Data Breach Investigations Report,” 2008, http://verizonenterprise.com.

11. J. D’Arcy, A. Hovav and D. Galletta, “User Awareness of Security Countermeasures and Its Impact on Information Systems Misuse: A Deterrence Approach,” Information Systems Research 20, no. 1 (March 2009): 79-98.

12. J. D’Arcy, T. Herath and M.K. Shoss, “Understanding Employee Responses to Stressful Information Security Requirements: A Coping Perspective,” Journal of Management Information Systems 31, no. 2 (fall 2014): 285-318.

13. M. Tarafdar, E.B. Pullins and T.S. Ragu-Nathan, “Techno-stress: Negative Effect on Performance and Possible Mitigations,” Information Systems Journal, in press, published electronically July 24, 2014.

14. Ragu-Nathan et al., “The Consequences of Techno-stress”; and Turel, Serenko and Bontis, “Family and Work-Related Consequences.”

15. T. Lewin, “Chevron Settles Sexual Harassment Charges,” New York Times, Feb. 22, 1995.

16. G. Laasby, “Target Data Breach Started With Phishing Malware Email at Contractor,” Milwaukee Wisconsin Journal Sentinel, Feb. 12, 2014, www.jsonline.com.

17. M. Riley and D. Lawrence, “As Data Breach Woes Continue, Target’s CEO Resigns,” May 5, 2014, www.businessweek.com.

18. See N. Kakabadse, G. Porter and D. Vance, “Addicted to Technology,” Business Strategy Review 18, no. 4 (winter 2007): 81-85; and J. Fitzgerald, “Lawsuit Claims Addiction to Internet Is a Disability,” Seattle Times, Feb. 19, 2007.

19. J. D’Arcy and G. Greene, “Security Culture and the Employment Relationship as Drivers of Employees’ Security Compliance,” Information Management & Computer Security 22, no. 5 (2014): 474-489.

20. K. Stuart, “German Minister Calls for Anti-Stress Law Ban on Emails Out of Office Hours,” August 29, 2014, www.theguardian.com; P. Oltermann, “Germany Ponders Ground-Breaking Law to Combat Work-Related Stress,” September 18, 2014, www.theguardian.com; T. de Castella, “Could Work Emails Be Banned After 6 p.m.?,” April 10, 2014, www.bbc.com; and “Volkswagen Turns Off Blackberry Emails After Work Hours,” December 23, 2011, www.bbc.com.

21. Turel, Serenko and Bontis, “Family and Work-Related Consequences.”

22. D’Arcy, Hovav and Galletta, “User Awareness of Security Countermeasures.”

24. Gupta et al., “Improving Rounding in Critical Care Environments.”

25. B.J. Fogg, “Persuasive Technology: Using Computers to Change What We Think and Do” (San Francisco, California: Morgan Kaufmann, 2002).

26. Gupta et al., “Improving Rounding in Critical Care Environments.”

27. P. Khanal, A, Vankipuram, A. Ashby, M. Vankipuram, A. Gupta, D. Drumm-Gurnee, K. Josey, L. Tinker and M. Smith, “Collaborative Virtual Reality Based Advanced Cardiac Life Support Training Simulator Using Virtual Reality Principles,” Journal of Biomedical Informatics 51 (October 2014): 49-59.

28. See, for example, D’Arcy and Greene, “Security Culture and the Employment Relationship.”

29. H. Li, A. Gupta, X. Lou and M. Warkentin, “Exploring the Impact of Instant Messaging on Subjective Task Complexity and User Satisfaction,” European Journal of Information Systems 20, no. 2 (March 2011): 139-155.

30. Such individuals have “polychronous” personalities; see J.M. Conte and J.N. Gintoft, “Polychronicity, Big Five Personality Dimensions and Sales Performance,” Human Performance 18, no. 4 (2005): 427-444.

31. M. Kajzer, J. D’Arcy, C. Crowell, A. Striegel and D. Van Bruggen, “An Exploratory Investigation of Message-Person Congruence in Information Security Awareness Campaigns,” Computers & Security 43, (June 2014): 64-76.

i. The 14 studies are: Ragu-Nathan et al., “The Consequences of Technostress”; M. Tarafdar, Q. Tu, B.S. Ragu-Nathan and T.S. Ragu-Nathan, “The Impact of Technostress on Role Stress and Productivity,” Journal of Management Information Systems 24, no. 1 (summer 2007): 301-328; M. Tarafdar, Q. Tu and T.S. Ragu-Nathan, “Impact of Technostress on End-User Satisfaction and Performance,” Journal of Management Information Systems 27, no. 3 (winter 2010-11): 303-334; Tarafdar, Pullins and Ragu-Nathan, “Technostress: Negative Effect on Performance and Possible Mitigations”; O. Turel, M. Mouttapa and E. Donato, “Preventing Problematic Internet Use Through Video-Based Interventions: A Theoretical Model and Empirical Test,” Behaviour & Information Technology, in press, published electronically July 7, 2014; Turel, Serenko and Bontis, “Family and Work-Related Consequences”; O. Turel, A. Serenko and P. Giles, “Integrating Technology Addiction and Use: An Empirical Investigation of Online Auction Users,” MIS Quarterly 35, no. 4 (December 2011): 1043-1061; D’Arcy, Hovav and Galletta, “User Awareness of Security Countermeasures”; D’Arcy, Herath and Shoss, “Understanding Employee Responses to Stressful Information Security Requirements”; D’Arcy and Greene, “Security Culture and the Employment Relationship”; Kajzer et al., “An Exploratory Investigation of Message-Person Congruence”; Gupta et al., “Improving Rounding in Critical Care Environments”; Li et al., “Exploring the Impact of Instant Messaging”; and Khanal et al., “Collaborative Virtual Reality Based Advanced Cardiac Life Support Training Simulator Using Virtual Reality Principles.”

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Tales from the Dark Side of Technology Acceptance: The Dark Triad and the Technology Acceptance Model

• Published: 28 April 2023

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• Matthew J. Aplin-Houtz   ORCID: orcid.org/0000-0001-5793-9789 1 ,
• Sean Leahy   ORCID: orcid.org/0000-0003-2308-3980 1 ,
• Sarah Willey   ORCID: orcid.org/0000-0003-2384-0603 1 ,
• Emily K. Lane   ORCID: orcid.org/0000-0002-3493-7817 1 ,
• Sachin Sharma   ORCID: orcid.org/0000-0002-3785-8276 1 &
• John Meriac   ORCID: orcid.org/0000-0002-6040-9890 1  

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With the dramatic shifts in the workforce that have emerged in the post–COVID-19 world, workers’ emotions have often presented very negatively, causing people to overtly display the dark aspects of their personality while at work. At the same time, organizations have been forced to adopt new technologies to fill the gaps in their desired outcomes and cope with changes in market demand. The ensuing clash between negative emotions and technological implementation may lead to intense conflicts and adverse work outcomes. This study hypothesizes a direct relationship between dark personality traits (narcissism, Machiavellianism, and psychopathy) and technology acceptance. Using a multiple regression model with a sample of general workers from various industries ( n  = 396), the study found that narcissism and psychopathy significantly impacted technology acceptance, while Machiavellianism did not. The findings offer valuable theoretical, practical, and managerial insights.

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Introduction

The loss of 4.4 million American workers in the Great Resignation of 2021 paints a dark picture in terms of the challenges faced by managers in the post-pandemic world (Richter, 2021 ). Moreover, the lack of “new blood”—that is, new human capital—being pumped into these organizations has left the surviving employees in the organization burned out and often resentful of management and those workers who have left the workforce (Kapoor et al., 2021 ; Mejia et al., 2021 ; Slaughter et al., 2021 ). Beyond resentment, many of the remaining employees appear fearful about the future in regard to their jobs. Some scholarship supports the contention that this fear evokes dark personality traits such as Machiavellianism, narcissism, and psychopathy in response to the perceived stimuli; the resulting consequence is a workforce filled with people exhibiting latent dark traits of their personality (Coney, 2017 ; Harper et al., 2020 ). With the current workforce appearing less agreeable, lacking compassion, displaying low empathy, having low life satisfaction, and experiencing difficulty in seeing the good in themselves and others (Kaufman et al., 2019 ), and given a growing literature that suggests dark personality traits may actually encourage positive workplace outcomes (Grijalva et al., 2015 ; Higgs, 2009 ; Paunonen et al., 2006 ; Rosenthal & Pittinsky, 2006 ), future managers may be able to harness employee negativity and turn it into positive benefits for their organizations.

In the post–COVID-19 pandemic world, significant changes and challenges await the workforce, as business technology is increasingly being applied to fill gaps in employees’ skills and talents. In a study by Davis ( 2021 ), nearly 44% of businesses reported that they were devising ways to advance their digital transformation processes, and 30% of the same organizations planned to develop additional training resources for remote workers to address the coming changes. Additionally, nearly 32% of firms planned to implement new technology to connect with employees soon (Davis, 2021 ). However, employees often struggle with accepting or adapting to the latest software or hardware when their organizations implement new technology, and their struggle can cost their employers millions of dollars (Venkatesh & Davis, 1996 ). We argue that managers could take advantage of the dark personality traits of their workforce to facilitate the transition to new technologies.

Even though the ultimate adoption of these technologies carries a high level of value for practitioners and scholars alike, we argue that exploring the potential acceptance of a technology is a necessary step in determining if dark personality traits play any part in the ultimate adoption of a technology. Considering that dark personality traits are known to affect individuals' behavior and decision-making, including their technology acceptance and adoption behaviors,, understanding the potential influence of these traits on key components of technology acceptance can help researchers develop appropriate measures and interventions to promote positive outcomes even though there is potential for negative consequences of having high presentations of dark traits in the the workforce. Given the potential negative consequences of dark personality traits on technology acceptance and ultimate adoption, it is important for researchers to consider the impact of these traits at all phases of evaluation of a technology to provide insights into the underlying psychological mechanisms that may drive technology-related behaviors and inform the development of targeted interventions to promote positive outcomes. As such, we seek to address the following research question: Do negative personality traits impact perceptions associated with general technology acceptance for a potential new technology?

In the current literature associated with technology acceptance and dark personality traits, few empirical studies exist as yet exploring the direct link between dark traits and perceptions associated with technology acceptance. Notably, Harper et al. ( 2020 ) suggested that scholars explore the utility of these dark traits in various settings. We respond to that call with this paper. Additionally, this study addresses the gap in empirical studies covering how dark personality traits by exploring not specific groups or specific technologies.

Using the variables from Davis’s ( 1989 ) technology acceptance model (TAM) and the Dark Triad (DT), we sampled general workers employed in a diverse set of industries, using a two-wave survey to evaluate the direct effects of the traditionally viewed negative personality traits found in the DT on perceptions associated with the perceived usefulness in general technology acceptance. In this manuscript, we first explore the literature associated with TAM to establish a theoretical framework for perceptions that lead to general technology acceptance. After defining this framework, we then review the literature related to each element of the DT while detailing common behaviors of people who exhibit these traits. We logically connect each trait and the associated behaviors with established scholarship associated with similar behaviors and traits tested against variables in TAM. Based on this connection to the literature, we formulate testable hypotheses to be applied to perceptions of perceived usefulness in TAM and present our model. We then test our hypotheses using a multiple regression model and present our findings to answer our research question. Finally, using our results from our sample, we discuss our findings and present managerial implications and suggestions for implementing changes in practice along with further directions for research.

Literature Review

Technology acceptance model.

The technology acceptance model (TAM) permeates the literature as the quintessential theory for studying how and why people accept technology in their work and personal lives. TAM theoretically explains what influences the decisions of potential users to accept newly presented technology and how well that technology will be adopted (Davis, 1989 ). As decades of influential scholarly applications of the model (Ma & Liu, 2004 ; Pavlou, 2003 ; Van der Heijden, 2004 ) demonstrate, TAM serves as a cornerstone of the literature seeking to understand how people perceive and interface with newer technology. This model has been applied to a wealth of industries, including healthcare (Beglaryan et al., 2017 ), entrepreneurship (Do et al., 2020 ), information technology (Khan et al., 2014 ), and retail (Gefen & Straub, 1997 ). Additionally, the literature abounds with explorations of TAM in conjunction with various demographic elements, such as age (Chen & Chan, 2011 , 2014 ; Chung et al., 2010 ; Tarhini et al., 2014 ), gender (Gefen & Straub, 1997 ; Tarhini et al., 2014 ), and race (Porter & Donthu, 2006 ). Despite multiple attempts to expand or reframe the structure of this theoretical model (Fathema et al., 2014 , 2015 ; Jaradat & Al-Mashaqba, 2014 ; Lowry et al., 2012 ; Scherer, 2005 ; Venkatesh & Davis, 2000 ), the core of technology acceptance remains perceived usefulness (PU), perceived ease of use (PEOU), and the relationships involving other external variables (e.g., perceived self-efficacy, motivation, and autonomy) found in Davis’s ( 1989 ) original model.

For this study, we purposely chose to use the TAM compared to other models of technology acceptance/adoption because it most appropriately aids in answering our research question by exploring what influences the fundamental perceptions surrounding acceptance of a potential technology through evaluating direct effects on PU. Other models such as the unified theory of acceptance and use of technology (UTAUT: Venkatesh et al., 2003 ) were considered but we argue that the original TAM is a more suited model than UTAUT for several reasons. First, the TAM is simpler and more parsimonious than UTAUT. As Davis ( 1989 ) noted, the model is "elegant in its simplicity" (p. 320) because the model proposes that only two factors – PU and PEOU – are necessary to explain users' intention to use a technology. In contrast, UTAUT proposes four factors that are more complex and less parsimonious. Additionally, some scholars argue that the complexity of UTAUT makes it difficult to apply in practice (LeGris et al., 2003 ; Taylor & Todd, 1995 ). However, our major rationale for using TAM compared to UTAUT centers on the exploratory nature of our research question and the requirements to use UTAUT. Fundamentally, UTAUT requires a specific technology to be evaluated in a population to determine what are the antecedents and determinants of adoption of the technology (Venkatesh et al., 2003 ). Considering that our research question focuses on the initial perceptions associated with personality traits on general technology acceptance and not on specific technologies, UTAUT is inappropriate for use in modeling to aid in answering the research question. Furthermore, we argue that this study's use of the TAM is a more novel approach to ultimately understanding technology adoption because TAM is a precursor to UTAUT. In this study, we further argue that by understanding the potential antecedents of TAM, we address the needed first step to establish that dark personality traits do indeed have influence, which may then lead to future studies using more complex technology adoption models.

In Davis’s ( 1989 ) original construction of the model, PU explained how an individual would determine the value of technology by considering how use of a particular system would alter their job performance and how the technology could help the individual accomplish their goals. Although critics of TAM postulate that PU has little overall importance for understanding the nuances of technology acceptance as compared to hedonic and other motivational forces (Benbasat & Barki, 2007 ; Chuttur, 2009 ; Saadé, 2007 ), broad support exists for the assertion that this factor explains a significant portion of the individual’s acceptance of a technology and represents the focal variable in relation to the likelihood of an individual accepting a technology (Ma & Liu, 2004 ; Pavlou, 2003 ; Svendsen et al., 2013 ; Van der Heijden, 2004 ).

PEOU, as explained in the initial Davis ( 1989 ) model, describes the adoption of a specific technology in terms of its relative ease of use and the individual’s belief that the technology can be easily implemented in their environment. The literature concedes that even multimillion-dollar projects may fail to win acceptance among employees if those projects have poor user interfaces (Venkatesh & Davis, 1996 ). However, PEOU often embodies a set of inherent antecedents and determinants. In particular, elements of internal and external control, intrinsic motivation, and emotional stress (e.g., anxiety when approaching the technology) may negatively affect PEOU and impede technology acceptance (Venkatesh, 2000 ). If a technology is perceived as easy to use, individuals are more likely to have a positive attitude towards it and to ultimately adopt it. In contrast, if a technology is seen as difficult to use, individuals may have a negative attitude towards it and be less likely to adopt it.

Throughout the literature supporting TAM, scholars universally present the idea that the PEOU of technology directly leads to an individual’s assessment of the PU of that technology. We expect to find the same relationship in our study, as expressed in the following hypothesis:

Hypothesis 1: High PEOU for a potential technology will positively impact PU for the same technology.

Beyond the two primary elements of TAM (PU and PEOU), a third element often explains either the direct or indirect effects to or from PU (Davis, 1989 ). As evidenced by the various meta-analyses cataloging relationships associated with PU (King & He, 2006 ; Schepers & Wetzels, 2007 ; Yousafzai et al., 2007a , b ), the literature teems with examples of elements that explain, at least partially, why people do or do not accept a technology.

The quality of leadership for one who is determining if a technology will be accepted presents as a factor in the literature (Hwang et al., 2016 , 2020 , 2021 ; Schepers et al., 2005 ). For example, the stream of research by Hwang et al. ( 2016 , 2020 , 2021 ) strongly supports that the impact of Leader-Member exchange (LMX) influences technology acceptance conceptually and empirically. The LMX construct measures and captures the quality of the employee's relationship with their direct supervisor. According to studies examining the relationship between LMX and change, those who have better relationships with their supervisors have the strongest perceptions of the change climate (Tierney, 1999 ). Also, LMX has been found to influence the relationship between supervisors' influence tactics and the effectiveness of those tactics in addressing change resistance (Furst & Cable, 2008 ). Generally, higher quality exchanges are less resistant to change (e.g., Van Dam et al., 2008 ). In the Hwang et al.’s manuscripts ( 2020 , 2021 ), the authors found that LMX functioned as a moderator to positively impact technology acceptance. However, leadership also impacts technology acceptance in direct effects. For example, Schepers et al. ( 2005 ) evaluated transformative leadership’s impact on the perceived usefulness of a technology and found a positive relationship. However, the authors did note that the dimension of intellectual stimulation fully accounted for how the transformational leadership influenced the acceptance. Even though the component of intellectual stimulation was the primary explanatory factor in their modeling, we argue that intellectual stimulation is an important aspect of LMX (Keskes et al., 2018 ; Zacher et al., 2014 ) that would be captured by exploring LMX generally. Owing to the above scholarship, we expect to also observe LMX as a positive component in our modeling for this study. Through the use of the variable of LMX, we will attempt to extract part of the variance associated with technology acceptance as a control.

The original TAM study looked at individuals’ use of technology and their behavior but did not explore how their personality affected any part of technology acceptance (Davis, 1989 ). Later scholars have agreed that PU involves an emotional state that impacts technology acceptance (Benbasat & Barki, 2007 ; Chuttur, 2009 ; Devaraj et al., 2008 ; Saadé, 2007 ; Svendsen et al., 2013 ). Whereas there is broad support for positive personality traits affecting technology acceptance (Devaraj et al., 2008 ; Rosen & Kluemper, 2008 ; Svendsen et al., 2013 ), to date few studies have delved into how adverse personality traits influence such acceptance. In our study, we explore negative personality traits to answer our research question and help understand how these traits impact perceptions as precursors to the TAM. In the following subsections, we describe the personality traits associated with the DT in the extant literature, and use established connections for similar personality traits to understand better how the DT and its elements connect to TAM.

The Dark Triad

When some elements of the DT were initially presented in the psychology literature, this subject matter was considered highly controversial because scholars in academic circles did not openly discuss the darker sides of personality outside of the pathological realm (McHoskey et al., 1998 ). However, the scholarly contributions of Paulhus and Williams ( 2002 ) presented significant evidence of behavioral, personality, and cognitive differences between the traits and inspired a growing stream of literature dealing with these topics (Jakobwitz & Egan, 2006 ; Kaufman et al., 2019 ; O’Boyle et al., 2012 ).

The Dark Triad is a nonpathological personality construct composed of three elements: narcissism, Machiavellianism, and psychopathy (Paulhus & Williams, 2002 ). In their study, Paulhus and Williams ( 2002 ) suggested that even though an individual may not directly have nefarious intentions, some aspects of those traits are always present in one’s personality. When people exhibit low levels of one or more DT elements, they tend to be indistinguishable from others in the general public (Kaufman et al., 2019 ; Paulhus & Williams, 2002 ). Conversely, people scoring high on these traits are often more prone to commit crimes, create social disturbances, and cause difficulties for an organization; moreover, the severity of these traits is compounded when the individual is in a leadership position (O’Boyle et al., 2012 ). Additionally, higher scorers tend to be less agreeable, lack compassion, display lower levels of empathy, find lower satisfaction with their overall lives, and experience difficulty seeing the good in themselves and others (Kaufman et al., 2019 ).

In the following subsections, we review the literature for each element in the DT individually to explain the nuances of the traits and rationalize their connections to TAM.

Psychopathy

Contemporary culture has often depicted psychopathy as a dominant and defining personality trait that drives the individual to interact with the world in an emotionless and self-serving way, often typified by serial killers (Hannibal Lecter: DeLisi et al., 2010 ; Dexter Morgan: DePaulo, 2010 ). In fact, mental health practitioners do not have a clinical diagnosis that matches these popular views (American Psychiatric Association, 2013 ). Instead, the business and psychology literature characterizes psychopathy into two overall constructs (primary and secondary) based on associated traits and behaviors (Ali et al., 2009 ; Newman et al., 2005 ).

Individuals with primary psychopathy often exhibit callousness, have lower emotional responses to stimuli, are manipulative, and use their superficial charm to circumvent social situations (Newman et al., 2005 ). Additionally, such individuals may manifest defensive dysfunctions, such as having less fear, exhibiting cunning, and lacking empathy or remorse without being overtly harmful to themselves, others, or the organizations for which they work (Ali et al., 2009 ; Newman et al., 2005 ). Despite their generally negligent actions, the literature contends that such individuals have multiple adverse effects on their organizations, themselves, and others (Carre et al., 2018 ; Neo et al., 2018 ; Reio & Sanders-Reio, 2006 ). In summary, primary psychopathy is an aspect of one’s overall personality that is associated with the display of some degree of antisocial behavior, impulsivity, callousness, and lack of empathy for others (Frick & White, 2008 ). In contrast, secondary psychopathy is linked to impulsivity, highly neurotic and hostile behavior, and direct nefarious acts (Benning et al., 2005 ). When exploring the general topic of psychopathy, scholars often have not distinguished between the primary and secondary variants in establishing variables for studies (Newman et al., 2005 ). The vast majority of the business literature uses primary psychopathy in evaluating the overall trait exhibited in the workplace (Ali et al., 2009 ; Benning et al., 2005 ; Newman et al., 2005 ).

Many subtraits associated with psychopathy have been connected to TAM in the established personality literature. Most notably, the subtrait of impulsivity and coercion has been linked through self-regulation to technology acceptance (Bagozzi, 2007 ; Soror et al., 2015 ; Tseng & Kuo, 2010 ). Bagozzi ( 2007 ) has argued that the traditional relationships in the TAM literature derive from the expectation of self-regulation of cognitive processing (including freedom from impulsivity or coercion). Considering that psychopathy as a general construct includes elements of both impulsive and intended coercion, we logically argue that the antecedent relationship of a variable that contains these elements would not result in the same positive relationship that a trait that does not include these elements would. Rather, we argue that an individual who has high primary psychopathy scores would be focused rather on impulsively attempting to circumvent attempts to accept a technology through the use of coercion or other tactics to maintain a self-perceived control of the circumstance through self-regulation where a technology required efforts to adopt. Therefore, we expect a negative relationship with TAM elements and propose the following hypotheses:

Hypothesis 2: Higher levels of self-reported psychopathy will negatively affect perceptions of PU as a precursor to the acceptance of a new technology.

Machiavellianism

When one considers Machiavellianism, the image of a puppeteer deviously manipulating human puppets from the shadows readily comes to mind. Machiavellianism comprises efforts to manipulate and exploit others via an amoral, emotionless, callous approach to social interactions that ultimately serve oneself (Jakobwitz & Egan, 2006 ). Individuals who demonstrate this trait possess superior intelligence at high levels, as evidenced by their adeptness at interpersonal manipulation, facilitated by their high emotional intelligence, and their ability to influence other people in social situations (Kowalski et al., 2018 ). Moreover, Machiavellianism is highly correlated with the Big Five’s agreeableness scale (Jakobwitz & Egan, 2006 ); that is, Machiavellians are able to easily manipulate people high in agreeableness, who tend to be more liked and prone to assume leadership roles (Kichuk & Wiesner, 1997 ). In the workplace, the literature supports an association between Machiavellianism and counterproductive behaviors such as deceit, coercion, cheating, and abusive supervision (Mitchell et al., 2018 ; Shu et al., 2011 ).

Machiavellianism connects to TAM in multiple ways. First, we argue that Machiavellianism can be theoretical substitute for components of the Big Five Inventory (BFI), and the BFI has been extensively associated with TAM (Devaraj et al., 2008 ; Rosen & Kluemper, 2008 ; Svendsen et al., 2013 .). When comparing the traits of the BFI, Hodson et al. ( 2009 ) found that Openness linked Machiavellianism via Social Dominance orientation (SDO: Pratto et al., 1994 ). Owing to the construct of SDO directly connects to prejudice views of an experience (Carnahan & McFarland, 2007 ) and because Machiavellians often engage in deceit, coercion, and workplace cheating behaviors (Mitchell et al., 2018 ; Shu et al., 2011 ), they are able to avoid new technology by manipulating others to adopt and use the latest technology, while not using the technology themselves. Therefore, we expect Machiavellianism to have a negative relationship with TAM elements and propose the following hypotheses:

Hypothesis 3: Higher levels of Machiavellianism will negatively affect perceptions of PU as a precursor to the acceptance of a new technology.

Although we have proposed arguments for the connection of TAM with direct effects for psychopathy and Machiavellianism individually, some scholars contend that psychopathy and Machiavellianism are roughly the same construct. Thus, they assert the two should be evaluated as a single variable when studied (Ali et al., 2009 ; Carter et al., 2015 ; Miller et al., 2017 ; Rogoza & Cieciuch, 2020 ). Therefore, we must discuss how the two constructs potentially interact.

Psychopathy and Machiavellianism are, indeed, closely correlated when examined empirically (Carter et al., 2015 ; Glenn & Sellbom, 2015 ; Miller et al., 2017 ; Vize et al., 2018 ). Both traits encompass similar affective qualities (i.e., emotional detachment, a lack of remorse) and similar behavioral characteristics (i.e., exploitation/manipulation, an inclination toward malicious and antisocial behaviors). Nevertheless, each trait can be differentiated at specific levels—for example, in terms of how psychopathic and Machiavellian individuals differentially manipulate others to achieve their goals (Hare & Neumann, 2008 ; Levenson et al., 1995 ; Rauthmann, 2012 ; Rauthmann & Will, 2011 ). Both Machiavellianism and psychopathy are associated with similar cognitive characteristics via individuals being self-centered and having a negative view of self and others; their self-serving nature and agentic orientations are also aligned (Hare & Neumann, 2008 ; Levenson et al., 1995 ; Neal & Sellbom, 2012 ; Patrick et al., 2009 ; Rauthmann, 2012 ; Rauthmann & Will, 2011 ). However, they ultimately differ in the elements used to control their impulses (Glenn & Sellbom, 2015 ; Jones & Paulhus, 2011 ; Rogoza & Cieciuch, 2020 ). Considering that impulse control likely impacts technology acceptance, we argue that combining Machiavellianism and psychopathy would likely result in skewed findings. For these reasons, we model and test each construct separately in this study.

Narcissism has appeared in popular culture via fictional exemplars such as Miranda Priestly in the film The Devil Wears Prada (Thawait, 2021 ), the group of teenagers known as the “Plastics” in Mean Girls (Karlyn, 2021 ), and Charles Foster Kane in Citizen Kane (Germain, 2018 ). Unlike psychopathy, narcissism is associated with a clinical diagnosis, and extreme displays of this personality trait require management via medication and other psychiatric interventions (American Psychiatric Association, 2013 ). Clinical narcissistic personality disorder (NPD) is described as an augmented sense of self-importance combined with entitlement, exaggeration of one’s talents and accomplishments, preoccupation with cognitive perceptions of self-involving acquisition of power, feelings of superiority, monopolization of conversations, extreme arrogance, unwillingness to consider other emotions, and pathological show-up-manship (American Psychiatric Association, 2013 ; Ronningstam, 2011 ). However, clinical presentation of NPD is relatively rare, accounting for approximately 6% of 1% of the total clinical diagnosed psychiatric cases appearing in the general population (Stinson et al., 2008 ). In the business literature, narcissism presents much differently than the clinical diagnosis, as the vast majority of the literature explores nonclinical cases that do not require medical intervention for management (Jakobwitz & Egan, 2006 ; Kaufman et al., 2019 ; Miller et al., 2017 , 2021 ; O’Boyle et al., 2012 ; Paulhus & Williams, 2002 ).

The trait of narcissism manifests as self-absorption, entitlement, dominance, grandiosity, and a feeling of overall superiority (Corry et al., 2008 ). Even though narcissism significantly correlates with psychopathy (Vernon et al., 2008 ), it also relates to some other personality traits: extraversion, openness to experience, and a lack of agreeableness (Paulhus & Williams, 2002 ; Vernon, 2008 ). When one evaluates the overall effect of narcissism as a personality trait via openness to experience, a logical connection to acceptance of new technology can be made, as openness to experience exemplifies an objective attempt at viewing a new technology. Support for this claim comes from Svendsen et al.’s ( 2013 ) study, which found that an individual’s openness is connected to PU of a new technology.

Narcissistic individuals tend to prioritize their personal goals and desires over others (Carpenter, 2012 ; Rose & Campbell, 2014 ), which can lead to a distorted perception of the usefulness of a technology (Buffardi & Campbell, 2008 ; Twenge & Campbell, 2009 ). As such, they tend to focus on how a technology can benefit them personally rather than considering how it can be useful to society as a whole (Amichai-Hamburger & Vinitzky, 2010 ). As a result, they may be more likely to perceive the usefulness of technologies that serve their own interests rather than those that benefit society. Furthermore, research has shown that narcissistic individuals tend to be more attracted to technologies that allow them to present themselves in a positive light to others, such as social media platforms (Buffardi & Campbell, 2008 ). They may also be more likely perceive the usefulness of a technology for self-promotion and self-enhancement rather than for genuine social connection or communication (Amichai-Hamburger & Vinitzky, 2010 ). Additionally, narcissistic individuals tend to seek out activities that provide them with a sense of control and power (Campbell et al., 2004 ). Therefore, they may be more attracted to technologies that allow them to exercise control and power over others, such as online gaming or social media (Buffardi & Campbell, 2008 ). Moreover, narcissistic individuals tend to be more interested in immediate gratification and rewards (Miller et al., 2017 ). Therefore, they may be more likely to adopt technologies that offer instant gratification, such as mobile gaming or social media, rather than those that require delayed gratification, such as online learning platforms. Finally, research has shown that narcissistic individuals tend to be more risk-tolerant and less risk-averse than non-narcissistic individuals (Campbell et al., 2004 ). This may make them more willing to adopt new technologies, even if they are not fully tested or proven, as they may view the adoption of such technologies as a way to gain social recognition or status.

The research suggests that individuals with higher levels of narcissism may view technology as a means of achieving personal gain and validation, including admiration from others. While this may be particularly relevant for technologies such as social media, which offer opportunities for individuals to showcase their achievements and garner praise and attention from others, it is possible that this perspective could be applied to a wide range of technologies.

Research by Cisek et al. ( 2014 ) found that individuals with higher levels of narcissism tended to view their possessions as extensions of themselves, and to use them to enhance their self-image and reputation. They argued that this perspective could be applied to a wide range of possessions, including technologies such as smartphones and laptops. Similarly, other research has suggested that individuals with higher levels of narcissism may be more likely to adopt technologies that offer immediate gratification and rewards, as well as those that offer opportunities for self-promotion and self-enhancement (Vogel et al., 2015 ). This suggests that individuals with higher levels of narcissism may be more likely to view a wide range of technologies as useful in achieving their personal goals and desires. While it is possible that some technologies may be more relevant to individuals with higher levels of narcissism, it is likely that this perspective could be applied to a wide range of technologies. Therefore, we propose the following hypotheses:

Hypothesis 4: Higher levels of narcissism will positively affect perceptions of PU as a precursor to the acceptance of a new technology.

Hypothesized Model

​To aid in answering the research question “Do negative personality traits impact perceptions associated with general technology acceptance for a potential new technology?”, we will explore the relationships in our proposed model, shown in Fig.  1 .

Proposed model

Our model consists of two overarching areas: (1) the DT segmented into three factors (psychopathy, Machiavellianism and narcissism) and (2) perceived usefulness (PU) and perceived ease of use (PEOU) as TAM’s core elements. Hypotheses 2–4 lead to PU in the extended technology acceptance model (TAM2), and not to PEOU as in the original TAM. When developing TAM2, Venkatesh and Davis ( 1996 , 2000 ) also evaluated the antecedent relationships for PU and PEOU in separate papers to achieve a similar focus. In a similar vein, we purposely chose not to explore the relationship of dark personality traits with PEOU directly to clearly define the antecedent roles of dark personality elements on PU. However, the potential interactions of our proposed antecedents of PU (i.e., PEOU, narcissism, psychopathy, and Machiavellianism) have the potential to explain variance of one’s ultimate PU for a new technology. Therefore, we argue that our model explains at a macro level the traditional direct effects found in the relationship between PEOU and PU, thereby explaining the perceptions of potential usefulness of a technology in relationship of DT elements in general technology acceptance.

Participants and Procedures

We used the following inclusion criterion for our sample: (a) currently employed in the United States, (b) at least 18 years old, (c) have at least 5 years of work experience, and (d) signed up to participate in a Qualtrics Panel. After receiving institutional review board (IRB) approval, we began data collection attempts. Given that sampling using Internet vendor–based sources often results in more consistent sample composition, respondent integrity, data quality, data structures, and substantive results when compared to non–Internet vendor–based sample collection (Smith et al., 2016 ), we chose to gather data via the third-party company Qualtrics. From October 24 to November 24, 2021, we surveyed our participants two times (1-week gap minimum between each sampling), with each scale/questionnaire being answered by the participants once during the data collection wave. Data on variables other than those included in our model were also collected. The average time of the first sampling was 22.57 min, while the second time was 31.65 min. The combined and averaged total time of participants’ survey responses was 54.08 min. Once participants completed the measures, they were debriefed and thanked for their participation.

Our raw sample consisted of 396 independent responses from people (231 male, 164 female, one nonbinary) ages 30–87 ( M  = 59.01, SD  = 10.722) with varying education levels and between 5 and 70 years of work experience ( M  = 35.60, SD  = 11.89) who were currently employed in the United States. After data screening (detailed later in this paper), our sample for analysis consisted of people (231 male, 164 female, one non-binary) ages 30–81 ( M  = 59.01, SD  = 10.722) with varying education levels and between 5 and 70 years of work experience ( M  = 35.600 SD  = 11.8900) who were currently employed in the United States. The race/ethnicity of the sample was highly skewed toward homogeneity, with 91.162% of the participants self-identifying as White/Caucasian. See Table 1 for more details.

Perceived Usefulness (PU)

Using PU components in the TAM (Davis, 1989 ), we evaluated participants’ perception of their PU for new technology (6 items on a 7-point Likert-type scale unified as a single variable). Multiple meta-analyses in the literature have provided support for the validity of this measure as a representation of a participant’s self-reported perception of the PU of new technology (Chuttur, 2009 ; King & He, 2006 ; Marangunić & Granić, 2015 ).

Perceived Ease of Use (PEUO)

Using the components for PEUO in the TAM (Davis, 1989 ), we evaluated participants’ perception of the PEUO of new technology (6 items on a 7-point Likert-type scale unified as a single variable). Again, we drew from meta-analyses in the literature (Chuttur, 2009 ; King & He, 2006 ; Marangunić & Granić, 2015 ) for external support that this measure represents a participant’s self-reported perception of the PEOU of new technology.

Psychopathy (PYS)

Using the components for PYS in the Dark Triad (Dirty Dozen: Jonason & Webster, 2010 ), we evaluated participants’ perception of PYS (4 items on a 7-point Likert-type scale unified as a single variable).

Machiavellianism (MACH)

Using the components for MACH in the Dark Triad (Dirty Dozen: Jonason & Webster, 2010 ), we evaluated participants’ perception of MACH (4 items on a 7-point Likert-type scale unified as a single variable).

Narcissism (NARC)

Using the components for NARC in the Dark Triad (Dirty Dozen; Jonason & Webster, 2010 ), we evaluated participants’ perception of NARC (4 items on a 7-point Likert-type scale unified as a single variable).

Multiple scholars have supported the validity of the DT measures (NARC, PSY, and MACH) as a representation of a participant’s self-reported levels for each construct (Carter et al., 2015 ; Dinić et al., 2018 ; Jonason & Webster, 2010 ).

Control Variables

To eliminate alternative explanations for the hypothesized relationships in this study, we followed Bernerth and Aguinis ( 2016 ) and included control variables. Initially, we controlled for our participants’ job satisfaction to account for the impact of varying levels of job satisfaction on technology acceptance (Benbasat & Barki, 2007 ; Davis, 1989 ; Venkatesh et al., 2003 , 2007 ). We used Brayfield and Rothe’s ( 1951 ) measure for job satisfaction (JSat)(six items on a 5- point Likert-type scale unified as a single variable.) Considering that change uncertainty is a foundational a factor in technology acceptance (Furst & Cable, 2008 ; Van Dam et al., 2008 ), we next controlled for the change uncertainty (CU). This variable also addresses the need to control for the likelihood that our participants were experiencing perceptions of stress from uncertainty as an effect of living and working through the Covid-19 Pandemic. Using the questions associated with the factor for change uncertainty in Rafferty and Griffin’s ( 2006 ) multi-item measure for job-based change perceptions, we evaluated our participants’ self-reported perceptions of uncertainty surrounding aspects of their job (four items on a 5- point Likert-type scale unified as a single variable.) Owing to the literature supporting that employees who have high turnover intention likely have lower technology acceptance (e.g., Shahreki et al., 2019 ; Thatcher et al., 2002 ; Tomer et al., 2022 ), we used O'Driscoll and Beehr’s ( 1994 ) measure for turnover intentions (TOI)(three items on a 5- point Likert-type scale unified as a single variable.) To account for the variance that could be explained by leader-member exchange (LMX) that the literature supports impacts technology acceptance (Hwang et al., 2016 , 2020 , 2021 ; Schepers et al., 2005 ), we used Graen and Ulh-Bien’s ( 1995 ) measure of LMX (seven items on a 5- point Likert-type scale unified as a single variable.) Additionally, we controlled for age, gender, and organizational tenure because these demographic elements are the most commonly used demographic control variables in the motivation and performance literature (Bernerth & Aguinis, 2016 ).

Data Screening

Missing data.

In our sampling of the 396 participants, we captured data associated with the items in variables during two waves of sampling. During the first time of sampling, we captured data associated with demographics (age, gender, race, organization tenure, and education level). Additionally, we gathered scale items associated with our focal variables for the dark triad (psychopathy, Machiavellianism, and narcissism). Also in the first sampling, we sampled responses associated with our intended controls (change uncertainty and leader-member exchange). The missing values with the first sampling were minimal. Demographic missing values included zero cases for gender, race, education level, and organization tenure. Only one case contained missing values for age (0.300%). Our focal dark triad variable items each had one case missing (0.300%). The control scale items for change uncertainty had zero missing cases and the leader-member exchange items had one missing case for each of the scale items (0.300). In the first sampling, the missing case was the same participant among the items.

The second sampling contained no demographics measures, the focal technology acceptance items (perceived ease of use and perceived usefulness), and items for two additional control measures (turnover intentions and job satisfaction). Our focal technology acceptance measures each had 146 cases with missing values accounting for 36.900% of missingness. For our control measures, we found that all items for turnover intentions and five of the six items for the job satisfaction measures contained 146 missing values (36.900%). The sixth item for job satisfaction had 147 missing cases (37.100%). In a similar vein as the first sampling, all cases containing missing values overlapped in the second sampling.

To determine if the items were Missing Completely at Random (MCAR), we analyzed the data with SPSS via Little’s MCAR test X 2 (150) = 157.323, p  = 0.325. Considering that we found a non-significant p-value, we determined that the values were missing at random. Accordingly, we used the expectation maximization (EM) method to impute the missing values for the 147 cases of missing values (Shortreed & Forbes, 2010 ). For the variables that contained 36.900% and 37.100% of missing values, there was less than 0.19 value shift of difference between the means of items when compared to cases with non-imputed values. Therefore, we determined that our imputation method appropriately imputed values for further analysis efforts.

We screened the data for our focal variables (PU, PEOU, MACH, PSYC, and NARC) and our control variables (CU, JobSat, LMX, and TOI) for both outliers and normality. As just noted, our initial sample size was n  = 396. None of the variables exhibited high skewness or kurtosis exceeding the cutoffs of ± 2.0 for skewness and ± 7.0 for kurtosis (Hair et al., 2010 ). The skewness values for these variables ranged from –0.733 to 1.372, and the kurtosis values ranged from -0.815 to 1.569. Univariate outliers were examined using z -scores, based on Raykov and Marcoulides’s ( 2008 ) recommended cutoff of ± 3.0 for extreme cases. We identified six cases for PSYC, seven cases for MACH, three cases for NARC, and six cases JSat. The largest Z-score were -3.109 (JSat) and 3.649 (MACH). When we removed the outliers, the Q-Q plot appeared largely unchanged. In addition, multivariate outliers were examined using Mahalanobis distances, with a cutoff of 26.13 based on 9 df at p  < 0.001. Multiple cases exceeded this value—namely, the same extreme cases identified earlier. We reviewed each question in the outlier cases and determined that the answers were consistent and free of potential manipulation. Accordingly, we did not remove any additional cases, and the resulting sample size was n  = 396.

Analysis and Results

Measurement model analysis.

Utilizing Wasko and Faraj’s ( 2005 ) guideline of 0.700 as a cutoff value for composite reliability (CR), we tested the reliability of our constructs. Additionally, we assessed Cronbach’s alpha scores using a cutoff value of 0.70 to determine if the measure was appropriate for analysis. It All measures met these criteria; thus, we determined that the individual constructs adequately represented the respective variables. We also assessed the acceptability of convergent validity using a minimum score of 0.50 for average variance extracted (AVE). All measures were above this threshold. For more details, see Table 2 . This table also shows the factor loadings for the individual items that loaded into the constructs. We tested discriminant validity with the Fornell–Larcker criterion to determine if the square root of AVE for each construct was greater than the interconstruct correlation for the other constructs to be tested. Further, we confirmed discriminant validity with the heterotrait–monotrait ratio of correlations (Henseler et al., 2015 ). Considering that all values in question were less than Henseler et al.’s threshold (0.90), we determined that discriminant validity was established (see Table 3 for more details).

Direct Effects

Using the G*Power statistical tool (Faul et al., 2007 , 2009 ), we evaluated our sample and potential effect size for this study. With a proposed 0.80 as a convention for 'general use' when performing this power analysis (Cohen, 1988 , 1992 ), we determined that a modest sample size of n  = 118 or more with 80% power will be of statistical significance given the number of proposed variables in our study. Considering that our sample size was n  = 396, we determined that our sample size was adequate for our intended analysis method.

Regression Analysis

A multiple regression was conducted with SPSS to examine the relationship between the elements of the dark triad, PEOU, and PU. The predictors together explained a significant proportion of variance in PU (F(11, 395) = 32.025, p  < 0.000; R 2  = 0.478). Three of the predictors were statistically significant, PEUO (β = 0.572, p  < 0.000), PSYC (β = -0.198, p  < 0.000) and NARC (β = 0.145, p  < 0.002). Thus, MACH (β = 0.031, p  = 0.605) was not significant a predictor in the model. Additionally, we assessed the controls in our modeling and found both significant and non-significant relationships. Age (β = -0.140, p  = 0.002), OrgTen (β = 0.136, p  < 0.000), and CU (β = 0.149, p  = 0.002) were significant contractors to the model. Conversely, Gender (β = -0.005, p  = 0.853), JSat (β = 0.051, p  = 0.382), LMX (β = -0.073, p  = 0.161), and TOI (β = 0.010, p  = 0.895) were non-significant.

We supplemented the multiple regression results using relative weight analysis (Johnson, 2000 ). Of the total 47.8% of the variance explained by the model, 69.2% was explained by PEOU. The rest of the dimensions present substantially less variance, with PSYC explaining 4.2%, MACH explaining 1.6%, and NARC explaining 4.6% of the variance. The controls relative weight explain variance in the following way: Age (8.9%), Gender (0.3%), OrgTen (3.3%), JStat (2.6%), CU (3.6%), LMX (0.9%), and TOI (0.8%).

Considering that PEOU carried a substantial amount of the variance in our modeling and the construct of technology acceptance includes both PEOU and PU, we evaluated collapsing PEOU and PU into a single construct (TAM) as the dependent variable for an alternative model to assess relationships of variance. The predictors together explained a significant proportion of variance in TAM (F(10, 395) = 10.490, p  < 0.000; R 2  = 0.221). Two of the predictors were statistically significant: PSYC (β = -0.288, p  < 0.000) and NARC (β = 0.118, p  = 0.011). Thus, MACH (β = 0.115, p  = 0.107) was not significant a predictor in the model. Additionally, we assessed the controls in our modeling and found both significant and non-significant relationships. Age (β = -0.035, p  < 0.000), Gender (β = -0.269, p  = 0.015), OrgTen (β = 0.011, p  = 0.022), and JSat (β = 0.351, p  = 0.001) were significant contributors to the model. Conversely, CU (β = 0.029, p  = 0.524), LMX (β = 0.007, p  = 0.928), and TOI (β = 0.096, p  = 0.111) were non-significant.

We supplemented the multiple regression results using relative weight analysis. Of the total 22.1% of the variance explained by the model, PSYC explaining 12.9%, MACH explaining 5.8%, and NARC explaining 9.2% of the variance. The controls relative weight explain variance in the following way: Age (39.1%), Gender (3.2%), OrgTen (3.7%), JStat (15.8%), CU (1.7%), LMX (4.7%), and TOI (3.9%).

Please see Table 4 for more details of our path analysis and Fig.  2 for a visual description of our significant and non-significant hypotheses in our model.

Supported and unsupported hypotheses for conceptual model

Hypothesis Testing

Overall, we found support for hypotheses 1, 2 and 4 in both of our models. Despite finding a positive relationship between MACH and our dependent variables in both models, we did not have support for hypothesis 3 due to the non-significance of the p-value. Please see Table 5 for more detail.

Like opening Pandora’s box, our study unleashes the malevolent aspects of personality, allowing them to be incorporated into the TAM-related literature. Given that the dark traits of personality impact technology acceptance, we must now explore the deeper meaning of their inclusion in this context. We do not recommend using torches and pitchforks when seeking out monsters to eliminate the threat of their existence as frightened villagers in an old black-and-white movie might, but instead suggest shining an inward-focused scholarly light to understand how the monstrous aspects of our personalities play into our technology acceptance. Overall, we argue that our findings demonstrate how, on a macro level, the three dark elements—psychopathy, Machiavellianism, and narcissism—connect to technology acceptance in a straightforward, black-and-white way. However, exploring the micro levels of each of these dark traits allows one to observe nuances of color in how each subelement of personality traits affects technology acceptance. In the following subsections, we dissect the DT elements and connect them to the existing literature to better understand our findings.

Despite our significant finding that narcissism positively impacts PU of new technology, our model addresses only a part of the process by which narcissism ultimately influences technology acceptance. Considering narcissism as a single construct appears by far to be the most common approach taken in the literature (Jakobwitz & Egan, 2006 ; Kaufman et al., 2019 ; Miller et al., 2017 , 2021 ; O’Boyle et al., 2012 ; Paulhus & Williams, 2002 ), and there is a clear reason why most scholars use narcissism as a single variable in their studies. Often, narcissism as a single variable within the DT is studied to determine its effects on other variables in an effort to understand whether narcissism (in conjunction with psychopathy and Machiavellianism) plays a role in the proposed model without needlessly exploring the finer nuances of narcissism if it is found to be nonsignificant in the initial studies (Miller et al., 2021 ). Considering our significant findings, we must now break narcissism down further.

In their recent meta-analysis, Miller et al. ( 2021 ) presented hierarchical breakdowns of narcissism, including one to three factors for understanding how the presentation of the personality trait varies among individuals. The one-factor model measures narcissism as a single construct in this study. In the two-factor model, narcissism is segmented into grandiose narcissism and vulnerable narcissism, while the three-factor model contains three elements: agentic extraversion, antagonism, and narcissistic neuroticism.

The image of a grandiose narcissist fits the popular lexicon, in which this type of narcissism embodies an extroversive and overt expression of superiority and entitlement (Besser & Priel, 2010 ; Miller & Maples, 2011 ; Miller et al., 2011 ). Though some scholars have argued that this trait has a subclinical presentation (Miller & Maples, 2011 ), the egomaniacal nature of grandiose narcissists creates a belief that they are entitled to special treatment because they are innately better than others (Besser & Priel, 2010 ; Miller & Maples, 2011 ; Miller et al., 2011 ). Whereas narcissism as a single construct might be defined simply as demonstrating an openness to experience (Paulhus & Williams, 2002 ; Vernon, 2008 ), a grandiose narcissist would be open to an experience only if it was their idea. Therefore, our hypothesized relationship with PU would not likely occur if one categorized the data collected based on grandiose narcissism and directly tested for this condition. Additionally, grandiose narcissism presents with low neuroticism (Besser & Priel, 2010 ; Miller & Maples, 2011 ; Miller et al., 2011 ). Studies on the BFI and TAM have found that neuroticism negatively affects PU and PEOU (Barnett et al., 2015 ; Punnoose, 2012 ), and grandiose narcissism is associated with only a mild trait presentation; thus, one would expect conflicting connections for this condition with TAM.

In dramatic contrast, vulnerable narcissism manifests as introversive self-absorbedness and acute extreme sensitivity to any level of criticism, in tandem with high neuroticism (Besser & Priel, 2010 ; Miller & Maples, 2011 ; Miller et al., 2011 ). Although vulnerable narcissists do not overtly express their feelings of superiority over others, they “are just as convinced that they’re better than others as any other narcissist, but they fear criticism so viscerally that they shy away from, and even seem panicked by, people and attention” (Malkin, 2015 , p. 34). The high level of neuroticism associated with this version of narcissism is likely negatively associated with PU and PEOU, per the studies on BFI and TAM (Barnett et al., 2015 ; Punnoose, 2012 ).

In the three-factor model of narcissism, the constructs of grandiose and vulnerable narcissism are further broken down: Grandiose narcissism is split into agentic and antagonistic narcissism, and vulnerable narcissism is linked to neurotic narcissism (Back et al., 2013 ; Crowe et al., 2019 ; Krizan & Herlache, 2018 ). Agentic narcissism manifests as the traits of assertiveness, self-promotion, striving for social attention, and desire for admiration (Hater et al., 2021 ). We argue that owing to the individual’s desire for admiration and seeking for social situations, this aspect of narcissism will likely lead to better technology acceptance because the narcissist will push through perceived obstacles to accept the new technology and self-publicize themselves as adopting the latest technology when their peers struggle with it. Conversely, antagonistic narcissism is associated with traits such as frequent devaluation and exploitation of others, striving for supremacy, and arrogance (Hater et al., 2021 ). It will likely result in a negative relationship with TAM elements because the narcissist will probably manipulate opportunities so as to circumvent assimilating aspects of technology adoption, such as by having others do the tasks a new technology would be used for or finding ways to avoid the use of new technology altogether. Notably, the literature associated with Machiavellianism appears to at least partly parallel this line of thinking regarding antagonistic narcissism. Therefore, one can rationally expect to see potential correlations and relative fuzziness when exploring narcissism and Machiavellianism concurrently in modeling. When considering neurotic narcissism, the linked traits of insecurity, shame, and hypersensitivity to and avoidance of criticism likely share the same relationships and negative effects on TAM found in conjunction with vulnerable narcissism.

We argue that despite our significant finding that psychopathy negatively impacts PU of new technology, this relationship provides only part of the larger picture of how the vast construct of psychopathy ultimately plays into technology acceptance. Just as with narcissism, the majority of scholars approach psychopathy as a single construct in conjunction with the DT to determine if a significant relationship exists before exploring the individual aspects of the trait in detail (Jakobwitz & Egan, 2006 ; Kaufman et al., 2019 ; Miller et al., 2017 , 2021 ; O’Boyle et al., 2012 ; Paulhus & Williams, 2002 ). Scholars dissect general psychopathy via the triarchic model (Patrick et al., 2009 ) into the three archetypes: disinhibition, meanness, and boldness.

When examining boldness, scholars often associate decreased fear of social situations with higher stress tolerance levels, along with exhibiting high self-confidence and social assertiveness (Patrick et al., 2009 ; Skeem et al., 2011 ). In the contemporary work environment literature, boldness typically appears as a negative construct (Carre et al., 2018 ; Neo et al., 2018 ). However, this trait is perceived positively in some types of roles, such as leadership (Lilienfeld et al., 2012 ), and can support adaptive behaviors that lead toward stronger organizational citizenship (Preston et al., 2021 ). When comparing Venkatesh’s ( 2000 ) finding that anxiety related to computers can impact one’s PEOU and PU with the higher levels of stress-tolerance demonstrated by individuals who exhibit bold psychopathic traits (Patrick et al., 2009 ; Skeem et al., 2011 ), one could logically deduce that higher levels of bold psychopathy would have a positive relationship with technology acceptance—specifically, that individuals with this trait would possess the characteristics needed to overcome the anxiety that might otherwise limit PU.

Disinhibition is associated with low impulse control via cognitive difficulties in planning and the need for immediate gratification (Patrick et al., 2009 ; Robertson et al., 2002 ). Like boldness, disinhibition also trends to be viewed negatively in the literature in terms of its impact on the workplace (Carre et al., 2018 ; Neo et al., 2018 ; Reio & Sanders-Reio, 2006 ). Most notably, Reio and Sanders-Reio ( 2006 ) found that disinhibition hinders workplace learning, diminishes office socialization, and lowers job performance. Considering that the acceptance of a new technology ultimately comes from learning how to use the technology, we propose a logical connection—namely, that the dimension of disinhibition in psychopathy will negatively affect PU because disinhibition decreases workplace learning. Our findings in this study also provide support for this claim.

The meanness component encompasses a lack of empathy, a dislike for forming close attachments to others, overall resistance to elements of authority, and use of exploitive means to accomplish tasks (Patrick et al., 2009 ; Skeem et al., 2011 ). Scholars contend mean individuals often exploit others so as to obtain power through cruelty (Skeem et al., 2011 ). Many of these individuals are arrogant, aggressively competitive, prone to verbal abuse/bullying, defiant, lacking in close personal relationships, and prone to seeking stimulation through their destructiveness (Osumi & Ohira, 2017 ; Skeem et al., 2011 ).

Though our findings indicate that Machiavellianism does not significantly impact technology acceptance, we argue that further analysis to understand how it impacts technology acceptance is warranted. While we might desire to obtain a larger sample to retest the relationships, gathering more data would not likely yield a different result (Wood et al., 2014 ). Instead, we argue that the nuances of Machiavellianism probably would deliver variable results if analyzed in this context. Therefore, in this subsection, we explore the multi-factor elements of Machiavellianism to obtain a deeper understanding of how each likely impacts TAM elements.

Unlike for narcissism and psychopathy, traditional scholars do not segment Machiavellianism into categorically and overtly descriptive named constructs, but instead distinguish between high (High Mach) and low (Low Mach) levels of this construct (Fehr & Samsom, 2013 ; Rauthmann, 2013 ). Individuals considered to be High Machs exhibit extremes of manipulative behavior to accomplish self-serving goals and produce adverse workplace outcomes and counterproductive workplace behaviors such as deceit, coercion, cheating, and abusive supervision (Mitchell et al., 2018 ; Shu et al., 2011 ). Conversely, Low Machs express less desire for playing the role of puppet master in search of power, money, and status (Mitchell et al., 2018 ; Shu et al., 2011 ). In our study, we did not use an adequate measure that would enables us to determine if our sample was composed of Low or High Machs; thus, we cannot state with any certainty how the differences between these groups affected technology acceptance. As per our literature review and the preceding discussion of antagonistic narcissism, we expect this trait’s manipulative nature to affect TAM elements negatively in (future) studies in which the construct is evaluated separately.

The Machiavellian Personality Scale (MPS; Dahling et al., 2009 ) distinguishes between four associated traits (distrust of others, desire for status, desire for control, and amoral manipulation) commonly found in Machiavellianism, allowing for a more nuanced understanding of the overall trait (Dahling et al., 2009 ; Grijalva & Newman, 2015 ; Kiazad et al., 2010 ). These associated traits likely impact technology acceptance differently than Machiavellianism as a single construct does. Most notably, the desire for status and the desire for control could potentially motivate an individual to circumvent obstacles to technology acceptance, resulting in a positive effect on PU; in contrast, distrust of others and amoral manipulation would have a negative relationship because the Machiavellian would create obstacles to PU.

It is worthy of note that the collinearity analysis revealed that psychopathy and Machiavellianism were closely correlated. We were not surprised by this result because our literature review also pointed to the stream of scholarship finding that these constructs often appear heavily correlated empirical (Carter et al., 2015 ; Glenn & Sellbom, 2015 ; Miller et al., 2017 ; Vize et al., 2018 ). As described in detail above for each of the dark traits, we argue that parsing out the constructs with more robust measures that include more factors and sub-traits will help eliminate multi-collinearity between analysis of the constructs.

Overall, our significant findings crack open the door, by suggesting that dark personality traits may contribute to technology acceptance. However, as our discussion has clarified, subtraits associated with the DT would likely introduce variance into our significant findings and require further study.

Trust Relating to Technology Acceptance and Dark Traits

Evaluating trust is an important aspect of technology acceptance that must be considered in conjunction with this study, as it can influence users' perceptions of the technology's reliability and usefulness. The works of Cenfetelli have been particularly influential in highlighting the role of trust in technology acceptance, and in providing a framework for understanding how trust influences users' attitudes and behaviors towards technology. Cenfetelli's research has shown that trust is a key factor in technology acceptance, and that it can impact users' willingness to use and recommend technology (Cenfetelli, 2004 ). According to Cenfetelli, trust is built through a combination of factors, including perceived competence, perceived integrity, and perceived benevolence. When users perceive a technology as competent, trustworthy, and beneficial, they are more likely to accept and adopt it (Cenfetelli & Bassellier, 2009 ). Other researchers have also emphasized the importance of trust in technology acceptance. For example, a study by Gefen et al. ( 2003 ) found that trust was a significant predictor of online purchase intentions. Similarly, a study by Kim et al. ( 2008 ) found that trust was a key factor in the adoption of mobile banking services. Furthermore, Cenfetelli's work has been cited in various studies that highlight the importance of trust in different contexts of technology adoption.

When considering how psychopathy and narcissism impact trust perceptions in association with the acceptance and adoption of general technology, we will now discuss how these traits likely impact perceptions. Trust is a crucial factor in the adoption and acceptance of technology, as it affects the perceived risks and benefits of technology use. Research has shown that psychopathy is negatively associated with trust in technology. A study by Holtz and Appel ( 2011 ) found that individuals with high levels of psychopathy were less likely to trust mobile banking technology. This lack of trust was attributed to the fact that psychopaths are less likely to consider the consequences of their actions, leading to a perception that the technology may be used for unethical purposes. Another study by Bélanger and Crossler ( 2011 ) found that individuals with high levels of psychopathy were less likely to trust e-commerce websites. This lack of trust was attributed to a perception that the website may be fraudulent or used for malicious purposes.

However, it is important to note that not all studies have found a negative relationship between psychopathy/narcissism and trust in technology. For example, a study by Hsu and Lu ( 2007 ) found that individuals with high levels of psychopathy were more likely to trust and use mobile commerce services. This trust was attributed to the fact that psychopaths are more likely to take risks, which may lead to a perception that the technology is innovative and exciting. As such, narcissism may lead to a lack of trust in technology, which may in turn impact adoption.

Considerations of Dark Traits on Technology Adoption

Individuals with dark personality traits, such as narcissism, may perceive technology as useful due to their grandiose self-image and desire for admiration. However, these traits may also hinder technology adoption due to the personality characteristics associated with them. The personality traits may make the individual less willing to seek help, less willing to follow rules, and less willing to cooperate (Tangney et al., 2007 ), making technology adoption more challenging. Our study has shown that narcissism is positively related to perceived usefulness of technology, indicating that individuals with this trait may see technology as a means of achieving their grandiose goals (Baumeister & Bushman, 2014 ). However, this positive relationship is not always reflected in actual technology adoption. This is likely due to the fact that narcissism is associated with a sense of entitlement, which may make it difficult for these individuals to follow rules (Tangney et al., 2007 ) and learn new skills required for technology adoption.

In conclusion, although dark personality traits such as narcissism may lead individuals to perceive technology as useful, their characteristics may hinder the ultimate adoption of technology. The entitlement, lack of empathy, and disregard for rules associated with these traits may make technology adoption more challenging for individuals with these personalities. Therefore, it is important to consider the role of dark personality traits when examining technology adoption and to design technology interventions that take into account the specific characteristics of these individuals.

Limitations and Future Research

Like any study, our research has some limitations that future researchers can exploit—namely, by using a part of the Machiavellian aspect of their personality to understand and drive theory in this topic. Like most researchers evaluating the DT, we utilized general constructs obtained from the Dirty Dozen questionnaire (Jakobwitz & Egan, 2006 ; Jonason & Webster, 2010 ; Kaufman et al., 2019 ; Miller et al., 2017 ; O’Boyle et al., 2012 ; Paulhus & Williams, 2002 ). This measure identifies overall relationships of three variables—psychopathy, Machiavellianism, and narcissism—with TAM elements; however, the nuances of these general variables do not adequately explain the variance in each personality trait’s effects on the aspects of TAM. Future researchers should evaluate psychopathy’s effects on TAM by utilizing Levenson et al.’s ( 1995 ) questionnaire to measure primary and secondary psychopathy and the Psychopathic Personality Inventory–Revised (PPI-R; Lilienfeld et al., 2005 ) to measure the other factors associated with psychopathy. In the same vein, researchers should consider gathering data on the multi-factor nature of Machiavellianism’s effects on TAM elements by administering the MACH-IV (Christie & Geis, 1970 ). However, scholars should use caution if they choose to utilize the MACH-V questionnaire (Christie & Geis, 1970 ), as Shea and Beatty ( 1983 ) found the measures to be negligible in effect for hypothesis testing. Researchers can also apply the Machiavellian Personality Scale (MPS; Dahling et al., 2009 ) to gain more nuanced perspectives on how each factor of Machiavellianism affects TAM elements. Additionally, scholars should evaluate the multifaceted aspects of narcissism on TAM elements with the Five-Factor Narcissism Inventory (FFNI; Glover et al., 2012 ).

Our measurement tool was modified to not test a specific technology in favor of a general technology in order to more accurately ascertain the antecedents of technology acceptance. However, now that there is support that a general technology can be perceived easy to use, future research should consider testing specific specific technologies to determine if narcissism influences universally. Particularly, we suggest technologies that do not give immediate feedback or may not directly appear useful (i.e. communication, gaming, and social media technologies.)

Outside of the dark traits, our discussion also suggests that future studies should include trust as a variable when exploring technology acceptance and adoption. Even though there appears to be no studies combining trust and data traits in this vein, we argue that future studies should study direct, indirect, moderating effects to determine how trust impacts the relationships found in our study.

In our investigation, we studied the general population and the general concept of accepting a technology. Future research should consider studying specific industries/job types and technologies given that there is a vast difference between, for example, getting a new feature on a smartphone and implementing a customer relationship management (CRM) system.

Another limitation of our study is the highly skewed sample in regards to race and age. Owing to the significant p values for age and gender in our controls, the sample limits the generalization of our findings. Our findings may only be salient in people over the age of 30 and those who are white. Therefore, future researchers should consider sampling both younger workers and focus on races other than white to determine if our findings can appropriately translate to these important demographics. With the heavily skewed demographic of age being between the age of 56 to 70 years old (245 participants representing 61.869% of the sample), our findings can keenly provide a theoretical understanding of the “greying workforce” in contemporary settings. According to United States Department of Labor. (n.d.), people over the age of 55 make up over 25% of the total workforce and this percentage is raising because people in the United States are working beyond retirement to meet the cost of living. Our findings provide evidence that older workers are impacted by their dark personality traits when considering a new technology. The literature already supports that age (Chen & Chan, 2011 , 2014 ; Chung et al., 2010 ; Tarhini et al., 2014 ) and gender (Gefen & Straub, 1997 ; Tarhini et al., 2014 ) impact technology acceptance. Our findings indicate that there exists relationships between the above variables for this demographic group. Considering that our study is at heart an exploratory study, we were not able to ascertain or evaluate the “why” behind findings. Future studies need to explore how the “greying workforce” experiences our hypothesized relationship with qualitative richness to truly understand the nuances required for honoring this demographic.

Managerial Implications

Our findings have several implications for managers. Specifically, we can foresee several costly issues arising when organizations undergo significant software and hardware upgrades (Venkatesh & Davis, 1996 ). Given that narcissistic leaders often drive organizational change (Gerstner et al., 2013 ) and that early adopters of technology often exhibit the trait of narcissism (Baumgarten, 1975 ; Gerstner et al., 2013 ), managers can use employees with narcissistic traits as change agents when implementing new technological platforms. To identify these individuals, managers should seek out people who overtly display social-climbing behaviors in the organization. Those individuals are likely to manifest traits such as assertiveness, self-promotion, striving for social attention, and desire for admiration (Hater et al., 2021 ) and are likely to apply these traits to push through perceived obstacles to not only accept a new technology but also self-publicize themselves as adopting the new technology when their peers are struggling. We argue that other employees will, in turn, be motivated to embrace the new technology by the successes of these narcissistic change agents in the organization. Thus, the overall acceptance of a newly implemented technology by other employees will likely come from their striving to achieve similar mastery of the technology that the leader-narcissist displays.

However, managers should employ caution when seeking out narcissists to be change agents. Narcissism in leadership presents both positive and negative elements that occur concurrently and result in a curvilinear total effect size (Grijalva et al., 2015 ; Higgs, 2009 ; Paunonen et al., 2006 ; Rosenthal & Pittinsky, 2006 ). Higgs ( 2009 ) has argued that the literature has primarily associated the positive elements with leadership. Such positive elements include the emergence of leadership through extroversion (Grijalva et al., 2015 ) and high levels of charisma and grand vision that are vital to effective leadership (Rosenthal & Pittinsky, 2006 ). Conversely, negative elements include followers’ malicious and benign envy and supervisor-targeted counterproductive work behaviors (Braun et al., 2018 ). Recruiting a narcissistic change agent could backfire on management if the narcissist decides to subvert their newly obtained status and influence so as to damage the relationships between other employees and management. Additionally, the followers of a narcissistic change agent could display envy, decrease their productivity, and potentially have more difficulty accepting the new technology. Nonetheless, there is potential for utilizing the narcissistic traits of employees to accept new technology even in the face of these potential drawbacks.

Our research explores how the dark trait of narcissism positively affects general technology acceptance. In contrast, psychopathy negatively affects the potential acceptance of a new technology. Though we did not find support for a direct relationship between Machiavellianism and technology acceptance, we suggested ways that future research could account for this dark trait’s potentially negative relationship with technology acceptance. Our results indicate that managers should consider exploiting elements of dark personality traits (especially narcissism) when implementing new technology. Beyond the fundamental relationships, we suggested future research dig deeper, thereby enhancing our understanding of how dark personality traits impact technology acceptance.

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Aplin-Houtz, M.J., Leahy, S., Willey, S. et al. Tales from the Dark Side of Technology Acceptance: The Dark Triad and the Technology Acceptance Model. Employ Respons Rights J (2023). https://doi.org/10.1007/s10672-023-09453-6

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Negative Effects of Technology

Negative effects of technology by: erin santiago how much is too much can you go one week without video games can you go one week without your myspace or facebook – powerpoint ppt presentation.

• By Erin Santiago
• Can you go one week without video games?
• Can you go one week without your MySpace or Facebook?
• Can you go one week without internet?
• Lately I have been reflecting on the fact that we as a society sometimes depend upon technology a little too much. Technology has made our lives so much easier and more simple, but I fear that it has also paralyzed the way our generation thinks and behaves.
• Our generation has come out with all of these new technologies such as the Wii, Playstation, and Xbox.
• These technologies may be fun but they have caused young kids to start depending on technology at an early age.
• Instead of doing their homework, chores, spending time with their family, or playing sports most choose video games as their main priority.
• The following clips contain foul language and is not acceptable for children under the age of 13.
• This clip is a story of a boy who is addicted to MySpace, to the point that he seems to be mentally unstable.
• http//www.youtube.com/watch?vg7wWC0tfJZY
• This is a clip of a kid playing halo, he is ranked 100.
• http//www.youtube.com/watch?v93Af4zxwrvM
• Lastly, this is a clip of a teenager whose mom canceled his World of Warcraft account. Is this behavior normal?
• http//www.youtube.com/watch?vYersIyzsOpc
• Even though you might not realize it, technology has taken away from relationships and the business world.
• People used to always call meetings, or have business lunches, sometimes a quick e-mail is all youll receive from a superior, (Heather).
• Our generation is now more likely to send an e-mail or a text to family members and friends rather than picking up the phone and actually talking to them.
• MySpace and Facebook are social networking sites that can help you build stronger relationships with friends but, it can also break them.
• Specifically romantic relationships.
• It can increase jealousy and trust issues.
• Maybe because your significant other isnt always sending you comments but rather is talking to their friends or people of the opposite sex more.
• Maybe they dont put up a lot of pictures of the two of you together like you do or gush about your relationship all over their profile.
• Some people become obsessed with constantly viewing their partners profiles in order to see what they are doing.
• Accessibility of information Increased info about the interactions of significant others lead to increased monitoring and jealousy for 19.1 of participants
• Relationship jealousy 16.2 of respondents were explicitly linked to Facebook use contributing to jealousy
• Facebook as an addiction 10.3 of participants had major difficulty limiting the amount of time he or she looked at his or her partners Facebook profile.
• Lack of context 7.4 of respondents referenced how Facebook can be ambiguous and that, without context, jealousy can be spurred over misunderstandings.
• Ben Alexander, 19, is a former student at the University of Iowa but
• spent every waking minute of his time playing World of Warcraft. As
• a result, he flunked out. In order to break his addiction that he says, is just
• as addictive as alcohol and drugs, he needed to seek professional help.
• Alexander was able to find reSTART, which is the first residential treatment center for
• Internet addiction in the United States. It opened in July and for 14,000 offers a 45-
• day program intended to help people wean themselves from pathological computer use, which
• can include obsessive use of video games, texting, Facebook, eBay, Twitter and any other time-
• killers brought courtesy of technology, (Geranios).
• Technology has made life a lot easier on us and I am not trying to say technology is bad because, I personally would have a hard time functioning without it. However, there comes a point when we need to set boundaries on how much technology intake we consume because, not all good things stay good. There are negative side effects to technology as you can see and we need to be aware of them in order to control it.
• Geranious, Nicholas K. "Addicted to the Internet? Theres rehab for that - Tech and gadgets-
• msnbc.com." Breaking News, Weather, Business, Health, Entertainment, Sports, Politics, Travel,
• Science, Technology, Local, US World News- msnbc.com. 3 Sept. 2009. Web. 07 Dec. 2009.
• lthttp//www.msnbc.msn.com/id/32679167/ns/technolo gy_and_science-tech_and_gadgets/gt.
• Heather. "Sometimes We Depend on Technology Too
• Much." 26 Mar. 2008. Web. 5 Dec. 2009.
• Live Science Staff. "Some Children Really Are Addicted to Video
• Games LiveScience." LiveScience Science, Technology, Health
• Environmental News. 20 Apr. 2009. Web. 06 Dec. 2009.
• lthttp//www.livescience.com/health/090420-childre n-video-games-addicted.htmlgt.

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The Dark Side of the Internet

Published by Jonas Martinsson Modified over 4 years ago

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Presentation on theme: "The Dark Side of the Internet"— Presentation transcript:

Threats To A Computer Network

Don’t Lose Your Identity – Protect Yourself from Spyware Dan Frommer Sherry Minton.

Definition : Computer Virus A computer program with the characteristic feature of being able to generate copies of itself, and thereby spread. Additionally.

S EC (4.5): S ECURITY 1. F ORMS OF ATTACK There are numerous way that a computer system and its contents can be attacked via network connections. Many.

1 The Information School of the University of Washington Nov 17fit spyware © 2006 University of Washington The Dark Side of the Internet INFO/CSE.

 Proxy Servers are software that act as intermediaries between client and servers on the Internet.  They help users on private networks get information.

Title: The Internet LO: Security risks. Security risks Types of risks: 1.Phishing 2.Pharming 3.Spamming 4.Spyware 5.Cookies 6.Virus.

Hierarchical file system Hierarchical file system - A hierarchical file system is how drives, folders, and files are displayed on an operating system.

Internet safety By Lydia Snowden.

Internet Safety Basics Being responsible -- and safer -- online Visit age-appropriate sites Minimize chatting with strangers. Think critically about.

Over 3,000 computers are affected monthly by Malware and Phishing.

First Community Bank Prevx Safe Online Rollout & Best Practice Presentation.

Spyware! Tia. What Is Spyware? With so many types of popular software being spread around the Internet, it is important to be aware of what spyware is.

© 2007 Cisco Systems, Inc. All rights reserved.Cisco Public ITE PC v4.0 Chapter 1 1 Basic Security Networking for Home and Small Businesses – Chapter 8.

Networks and Security. Types of Attacks/Security Issues  Malware  Viruses  Worms  Trojan Horse  Rootkit  Phishing  Spyware  Denial of Service.

PHISHING AND SPAM INTRODUCTION There’s a good chance that in the past week you have received at least one that pretends to be from your bank,

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The Dark Side of AI_

Jun 09, 2023

30 likes | 381 Views

The complexity of AI systems poses another challenge, as they constantly learn and evolve, demanding ongoing conservation to keep up with the ultimate advancements.<br>

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The Dark Side of AI: How it Can Harm Your Mobile App. Artificial intelligence( AI) is revolutionising the world, and its impact on mobile app development can not be overlooked. With AI, developers can produce more immersive and customised mobile apps. Still, it's important to consider the possible disadvantages that come with it. One significant disadvantage is the cost associated with AI-powered mobile app development. applying AI requires technical experience and proficiency, which can increase the development charges. The complexity of AI systems poses another challenge, as they constantly learn and evolve, demanding ongoing conservation to keep up with the ultimate advancements. Read also: iOS app development companies in UAE

Security is a major concern when it comes to AI-powered mobile apps. Due to their competence to honour patterns and make prognoses, they come more susceptible to hacking and fraud. The threat of biassed issues is also current since AI systems are trained on datasets that may contain essential impulses, which can impact the app's production. Despite these challenges, AI remains a premium tool in mobile app development. By administering the following tips, you can assuage the associated disadvantages and harness the benefits of AI effectively: ● Choose the right AI technology: Understand the colourful AI technologies available and select the one that aligns with your app's requirements. Some AI technologies are better suited for specific types of mobile apps than others. ● Seek the right proficiency: Developing AI-powered mobile apps demand technical skills. However, consider partnering with a mobile app development company, similar to Brill Mindz Technology, If you need in-house proficiency. Their educated AI inventors can guide you through the process. ● Thoroughly test and watch your app: After developing your AI-powered mobile app, conduct comprehensive testing to identify any implicit issues. Cover the app's performance to ensure it remains secure and dependable, making necessary accommodations as needed. In conclusion, imprecisely weigh the pros and cons before incorporating AI into your mobile app development process. By following the suggested tips, you can minimise threats and maximise the advantages of using AI. However, Brill Mindz Technology is a commanding choice, If you are looking for a dependable mobile app development company in UAE. Their crew of educated AI contrivers can help you in creating engaging, substantiated, and secure AI-powered mobile apps. Contact Brill Mindz Technology today to discover how AI can enhance your mobile app development programs.

Get in touch with us at, [email protected]

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The Dark Side of GenAI: Safeguarding Against Digital Fraud

Generative artificial intelligence (GenAI) has the capacity to create new opportunities, disrupt how we work, and change how we think about AI regulation . Some predict it will be as disruptive, if not more so, than the widespread adoption of the internet. But with new opportunities come new challenges and threats. While GenAI continues to dominate the attention of businesses, the media, and regulators, it’s also caught the attention of fraudsters.

Recent technological advances mean it’s never been cheaper or easier to be a fraudster. In this brave new digital-first world, fraudsters have more tools at their fingertips than ever before. And it’s set to cost. Online payment fraud losses are predicted to increase from $38 billion in 2023 to $91 billion in 2028.

The rise of the GenAI fraudster

Fraudsters generally fall into two groups: 1. the lone amateur and 2. organized criminal enterprises. Traditionally the latter, with more resources at their disposal, has posed a higher threat to businesses. But GenAI offers even the most amateur fraudsters easy access to more scalable and increasingly sophisticated types of fraud.

The evidence is in the data. Over the last few years, less sophisticated or “easy” fraud dominated. Proprietary data from Onfido, an Entrust company , found that between 2022 and 2023, 80.3% of fraud caught fell into this category. The remainder was classed as “medium” (19.6%) or “hard” (0.1%). But recently there’s been an increase in more sophisticated fraud. Comparing these figures to data from the last six months finds a jump in both medium fraud (accounting for 36.4%) and hard fraud (accounting for 1.4%).

How fraudsters are using generative AI

GenAI programs have made it easy for anyone to create realistic, fabricated content including audio, photos, and videos. Deepfake videos in particular, sophisticated synthetic media where a person’s likeness is replaced with someone else’s, are becoming increasingly common and convincing. Fraudsters have started using deepfakes to try and bypass biometric verification and authentication methods. These videos can be pre-recorded or generated in real time with a GPU and fake webcam, and typically involve superimposing one person’s face onto another’s.

This type of attack has surged in recent years. Comparing 2023 with 2022, there’s been a 3,000% increase in deepfake attempts. This is particularly concerning in the realm of digital onboarding and identity verification, where the integrity of personal identification is paramount.

Currently, a few fraudsters are responsible for creating deepfakes at scale. But the growing popularity of “fraud-as-a-service” offerings (where experienced fraudsters offer their services to others), combined with improvements in deepfake software, suggests their volume and sophistication will increase in 2024.

Document forgeries

Many customer due diligence processes involve the authentication of identity documents. But image manipulation software, and the emergence of websites such as OnlyFakes — an online service that sells the ability to create images of identity documents it claims are generated using AI — have made it easier for fraudsters to fake documents.

There are four different ways for fraudsters to create fake documents:

• Physical counterfeit: A fake physical document created from scratch
• Digital counterfeit: A fake digital representation of a document created from scratch (i.e., in Photoshop)
• Physical forgery: An existing document that is altered or edited
• Digital forgery: An existing document that is altered or edited using digital tools

Historically, most fake documents were physical counterfeits (fake documents fraudsters created entirely from scratch). In 2023, Onfido identified that 73.2% of all document fraud caught was from physical counterfeits. In the last six months, that’s dropped to 59.56%, with digital forgeries accounting for a larger proportion of document fraud than prior years (34.8%).

This increase in digital forgeries can be attributed to the emergence of websites such as OnlyFakes. Fraudsters have wised up to the fact it’s a faster, cheaper, and more scalable way to create fake documents.

Synthetic identity fraud

Synthetic identity fraud is a type of fraud where criminals combine fake and real personal information, such as Social Security Numbers (SSNs) and names, to create a new identity. This new, fake identity is then used to open fake accounts, access credit, or make fraudulent purchases.

Generative AI tools offer a way for fraudsters to generate fake information for synthetic identities at scale. Fraudsters can use AI bots to scrape personal information from online sources, including online databases and social platforms, before using this information to create synthetic identities.

With synthetic identity fraud projected to generate $23 billion USD in losses by 2030, businesses are adopting advanced fraud detection and prevention technologies to root out synthetic fraud. Keeping fraudsters from entering in the first place with a reliable identity verification solution at onboarding is the foundational element in this detection framework.

During phishing attacks, fraudsters reach out to individuals via email or other forms of communication requesting they provide sensitive data or click a link to a malicious website, which may contain malware.

Generative AI tools offer fraudsters an easy way to create more sophisticated and personal social engineering scams at scale. For example, using AI tools to write convincing phishing emails or for card cracking. Research has found that the top tools used by bad actors in 2023 include the dark web, fraud as a service, and generative AI. This includes the tool wormGPT, which provides a fast method for generating phishing attacks and malicious code.

Combatting GenAI fraud with… AI

The advancement in GenAI means we’re entering a new phase of fraud and cyberattacks. But the good news is that any technology fraudsters can access is accessible to those building fraud detection solutions. The best cyber defense systems of tomorrow will need AI to power them to combat the speed and scale of attacks. Think of it as an “AI versus AI showdown.”

With the right training, AI algorithms can recognize the subtle differences between authentic and synthetic images or videos, which are often imperceptible to the human eye. Machine learning, a subset of AI, plays a crucial role in identifying irregularities in digital content. By training on vast datasets of both real and fake media, machine learning models can learn to differentiate between the two with high accuracy.

One of the strengths of using AI to fight deepfakes and other GenAI fraud is its ability to continuously learn and adapt. As deepfake technology evolves, so too do the AI algorithms designed to detect them.

Securing digital identities against fraud

With AI-driven attacks from phishing, deepfakes, and synthetic identities on the rise, Entrust’s AI-powered, identity-centric solutions  are critical in ensuring the integrity and authenticity of digital identities.

By innovating and integrating Onfido capabilities across the Entrust portfolio, we’re committed to helping:

• Fight phishing and credential misuse with enhanced authentication leveraging biometrics and digital certificates
• Neutralize deepfakes while creating secure digital experiences with AI/ML-driven identity verification
• Enable trusted digital onboarding, authenticating customers or employees, and issue credentials in a matter of minutes while reducing fraud exposure and staying compliant with regulations and standards
• Secure data and cryptographic assets with cutting-edge encryption, key management, and compliance solutions

To learn more, download the full report here: https://go.entrust.com/identity-fraud-report-2024

• Cybersecurity Institute

Tony Ball is President of the Payments & Identity portfolio at Entrust. Mr. Ball joined the company in 2016 to provide leadership, global strategy and innovation for the access control and authentication solution segments.

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Hades 2: All Selene Boons

Selene is the Moon Goddess who makes her graceful appearance in Hades 2 to assist the player by bestowing boons on them that will help them in their encounters.

Hades 2: Eris Boss Guide

Eris AKA Strife Incarnate is one of the final bosses you'll be facing in Hades 2's Early Access, and she's not easy. Here's how to easily beat her.

Selene’s boons can either be very good, or be decent at best. All of it depends on the player’s luck when they encounter her to see what she has to offer. Here’s a guide on which of her boons are worth getting and how to make use of the Path of Stars.

Best Selene Boons

Dark Side sounds like a great ability in theory, but it isn’t all that good. This boon from Selene allows the player to be turned into what is described as an “Impervious Living Nightmare,” which grants the player invincibility. However, this all lasts only 5 seconds, which isn’t long enough to justify choosing this boon over other way better options. It also needs 90 Magick to be used in order to be used again.

Wolf Howl is, perhaps, the weakest ability granted by Selene. Simply put, it allows the player to use their hex to rise up and then crash down on a targeted area. This deals 200 damage, which isn’t great compared to other abilities. This boon also leaves the player vulnerable to enemy attacks . The only upside is that it doesn’t require that much Magick to be able to be used again (only 80), and this is relatively low compared to others.

Twilight Curse

Twilight Curse is an ability that can give the player an advantage when faced with a barrage of enemies. Indeed, this boon allows the player to launch projectiles that, when connected, inflict morph. This can be done on up to 10 enemies. The downside of this ability is that it can be resisted by some strong enemies, which means that, as an ability, it might be pretty useless against bosses . It also doesn’t help the player as it uses up 140 Magick before being available again.

Phase Shift

Phase Shift is a great ability when facing a large group of enemies. This boon allows the player’s hex to make everything move 80% slower for 5 seconds. While the time isn’t a lot, it can sometimes be enough to take down enemies as they’re slowed down. This ability can also be quite useful against bosses, as it gives the player a good opening to attack them when they’re more vulnerable. This boon requires the player to dish out 150 Magick before being available once again.

Night Bloom

Night Bloom is a very interesting ability. With this power, the player is able to resurrect one of the enemies they have slain during that specific encounter and have them fight on their side. The enemy that is raised back is random, so it is a gamble, in that sense that Night Bloom might raise a borderline useless enemy. This ability also lasts 12 seconds, which can be quite useful, especially in boss fights, as they will help to chip down at the boss’s Health Points.

The player will be able to raise an enemy from the dead one time per 100 Magick, which isn’t bad at all. Overall, Night Bloom can be very useful.

Total Eclipse

Total Eclipse is quite a powerful boon from Selene. Making use of this boon, the player will be able to use their hex to blast an area and deal 1000 damage after 4 seconds. While this ability is very good, it can be quite a pain to use it as the player needs to first make use of 200 Magick, which is a significant amount, making it the most demanding of all Selene’s boons.

Lunar Ray is, perhaps, the most destructive of the bunch when it comes to what Selene offers. Using this boon, the player can fire up a beam that deals 1200 damage over 3 seconds using their hex. The player can control and move the beam while it’s active. This can be very useful against both powerful enemies, as well as a group. However, the player will have to direct the laser well or risk wasting it, as moving enemies can be a bit annoying to keep the beam aimed at.

Hades 2: How to Get Zodiac Sand (Z Sand)

Venture deep into the Underworld to find this ultra-rareHades 2 crafting material.

This ability can be used once every 120 Magick, which shouldn’t be hard to achieve.

While most of Selene’s Boons can be incredibly useful, Moon Water might take the crown as the very best boon that she offers.

This incredible boon allows the player to heal up to three times, every time using their hex. This is reset every time the player reaches and uses a fountain. The player will be able to use this boon after using 70 Magick. Each healing grants the player +25 HP. This is especially useful against bosses , as quite a few of them hit very hard.

Selenes Path of Stars

Now that the player has chosen their boon to unlock with Selene, the next time they meet this moon goddess, they won't be able to choose another boon. Instead, the player will get the option of upgrading their already-picked boon, which is known as the Path of Stars.

Each boon has its own skill tree. Whether it’s worth upgrading or not depends on which one the player has. For example, one boon that should definitely be upgraded is the Moon Water boon, for reasons discussed already. Indeed, as a starter, the player can only use it three times until they reach the next fountain. However, using Selene’s upgrades, depending on how many points they have to spend, the player can make it 5 instead of 3 times.

This is definitely a game-changer, especially when encounters and boss fights get harder. Using the Path of the Stars, the player can also upgrade how much the Moon Water heals the player. It starts off at 25+ Health Points whenever used, and can go up a lot. With a few points, the player could make it up to 55+ HP if not more. Being able to heal 5 times, with each heal offering 55+ HP, instead of 3 times with 25+ HP is a game changer.

On the other hand, the player shouldn’t bother upgrading a boon such as Wolf Howl, as it isn't that great of an ability. They can try getting other boons instead.

Tips on How To Make the Best Use Of Selenes Boons

Selene’s boons are quite unique in the sense that the player has to use a certain amount of Magick to be able to use them. The amount depends on which boon they choose. Because of that, the player should employ an omega-based fighting style to use up a lot of Magick.

It’s also useful to try and drink as many Soul Tonics as possible to increase the max Magick.

Different Gods’ boons that use up a lot of Magick are also recommended, such as Breaker Sprint from Poseidon.

Platform(s) PC

Released May 6, 2024

Publisher(s) Supergiant Games

Genre(s) Roguelike, Action

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Solar Storm Intensifies, Filling Skies With Northern Lights

Officials warned of potential blackouts or interference with navigation and communication systems this weekend, as well as auroras as far south as Southern California or Texas.

By Katrina Miller and Judson Jones

Katrina Miller reports on space and astronomy and Judson Jones is a meteorologist.

A dramatic blast from the sun set off the highest-level geomagnetic storm in Earth’s atmosphere on Friday that is expected to make the northern lights visible as far south as Florida and Southern California and could interfere with power grids, communications and navigations system.

It is the strongest such storm to reach Earth since Halloween of 2003. That one was strong enough to create power outages in Sweden and damage transformers in South Africa.

The effects could continue through the weekend as a steady stream of emissions from the sun continues to bombard the planet’s magnetic field.

The solar activity is so powerful that the National Oceanic and Atmospheric Administration, which monitors space weather, issued an unusual storm watch for the first time in 19 years, which was then upgraded to a warning. The agency began observing outbursts on the sun’s surface on Wednesday, with at least five heading in the direction of Earth.

“What we’re expecting over the next couple of days should be more significant than what we’ve seen certainly so far,” Mike Bettwy, the operations chief at NOAA’s Space Weather Prediction Center, said at a news conference on Friday morning.

For people in many places, the most visible part of the storm will be the northern lights, known also as auroras. But authorities and companies will also be on the lookout for the event’s effects on infrastructure, like global positioning systems, radio communications and even electrical power.

While the northern lights are most often seen in higher latitudes closer to the North Pole, people in many more parts of the world are already getting a show this weekend that could last through the early part of next week.

As Friday turned to Saturday in Europe, people across the continent described skies hued in a mottling of colors.

Alfredo Carpineti , an astrophysicist, journalist and author in North London, saw them with his husband from the rooftop of their apartment building.

“It is incredible to be able to see the aurora directly from one’s own backyard,” he said. “I was hoping to maybe catch a glimpse of green on the horizon, but it was all across the sky in both green and purple.”

Here’s what you need to know about this weekend’s solar event.

How will the storm affect people on Earth?

A geomagnetic storm watch or warning indicates that space weather may affect critical infrastructure on or orbiting near Earth. It may introduce additional current into systems, which could damage pipelines, railroad tracks and power lines.

According to Joe Llama, an astronomer at Lowell Observatory, communications that rely on high frequency radio waves, such as ham radio and commercial aviation , are most likely to suffer. That means it is unlikely that your cellphone or car radio, which depend on much higher frequency radio waves, will conk out.

Still, it is possible for blackouts to occur. As with any power outage, you can prepare by keeping your devices charged and having access to backup batteries, generators and radio.

The most notable solar storm recorded in history occurred in 1859. Known as the Carrington Event, it lasted for nearly a week, creating aurora that stretched down to Hawaii and Central America and impacting hundreds of thousands of miles of telegraph lines.

But that was technology of the 19th century, used before scientists fully understood how solar activity disrupted Earth’s atmosphere and communication systems.

“That was an extreme level event,” said Shawn Dahl, a forecaster at NOAA’s Space Weather Prediction Center. “We are not anticipating that.”

Unlike tornado watches and warnings, the target audience for NOAA’s announcements is not the public.

“For most people here on planet Earth, they won’t have to do anything,” said Rob Steenburgh, a space scientist at NOAA’s Space Weather Prediction Center.

The goal of the announcements is to give agencies and companies that operate this infrastructure time to put protection measures in place to mitigate any effects.

“If everything is working like it should, the grid will be stable and they’ll be able to go about their daily lives,” Mr. Steenburgh said.

Will I be able to see the northern lights?

It is possible that the northern lights may grace the skies this week over places that don’t usually see them. The best visibility is outside the bright lights of cities.

Clouds or stormy weather could pose a problem in some places. But if the skies are clear, even well south of where the aurora is forecast to take place, snap a picture or record a video with your cellphone. The sensor on the camera is more sensitive to the wavelengths produced by the aurora and may produce an image you can’t see with the naked eye.

Another opportunity could be viewing sunspots during the daytime, if your skies are clear. As always, do not look directly at the sun without protection. But if you still have your eclipse glasses lying around from the April 8 event, you may try to use them to try to spot the cluster of sunspots causing the activity.

How strong is the current geomagnetic storm?

Giant explosions on the surface of the sun, known as coronal mass ejections, send streams of energetic particles into space. But the sun is large, and such outbursts may not cross our planet as it travels around the star. But when these particles create a disturbance in Earth’s magnetic field, it is known as a geomagnetic storm.

NOAA classifies these storms on a “G” scale of 1 to 5, with G1 being minor and G5 being extreme. The most extreme storms can cause widespread blackouts and damage to infrastructure on Earth. Satellites may also have trouble orienting themselves or sending or receiving information during these events.

The current storm is classified as G5, or “extreme.” It is caused by a cluster of sunspots — dark, cool regions on the solar surface — that is about 16 times the diameter of Earth. The cluster is flaring and ejecting material every six to 12 hours.

“We anticipate that we’re going to get one shock after another through the weekend,” said Brent Gordon, chief of the space weather services branch at NOAA’s Space Weather Prediction Center.

Why is this happening now?

The sun’s activity ebbs and flows on an 11-year cycle, and right now, it is approaching a solar maximum. Three other severe geomagnetic storms have been observed so far in the current activity cycle, which began in December 2019, but none were predicted to cause effects strong enough on Earth to warrant a watch or warning announcement.

The cluster of sunspots generating the current storm is the largest seen in this solar cycle, NOAA officials said. They added that the activity in this cycle has outperformed initial predictions .

More flares and expulsions from this cluster are expected, but because of the sun’s rotation the cluster will be oriented in a position less likely to affect Earth. In the coming weeks, the sunspots may appear again on the left side of the sun, but it is difficult for scientists to predict whether this will cause another bout of activity.

“Usually, these don’t come around packing as much of a punch as they did originally,” Mr. Dahl said. “But time will tell on that.”

Jonathan O’Callaghan contributed reporting from London.

An earlier version of this article misstated the radio frequencies used by cellphones and car radios. They are higher frequencies, not low.

How we handle corrections

Katrina Miller is a science reporting fellow for The Times. She recently earned her Ph.D. in particle physics from the University of Chicago. More about Katrina Miller

Judson Jones is a meteorologist and reporter for The Times who forecasts and covers extreme weather. More about Judson Jones

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