Integrating and synthesising technostress research: a meta-analysis on technostress creators, outcomes, and IS usage contexts

Figures & data

Figure 1. Research Model of Technostress.

* Given its inherent link to job security, techno-insecurity pertains only to the organisational context.

Table 1. Inclusion criteria.

Criterion	Excluded studies	Examples of excluded studies
Study must report the relationship between at least one technostress creator and one psychological or behavioural outcome.	26	Schmidt et al. (2021)
Study must conceptualise and measure technostress creators and outcomes based on their definitions as given in Tables 2 and 3.	20	Ninaus et al. (2021)
Study must report sufficient information to derive correlations between the technostress creators and psychological or behavioural outcomes.	24	Fieseler et al. (2014)
Study provides an independent data set.	4	The conference paper by Pflügner & Maier (2019) was excluded from our analysis because it was an earlier version of Pflügner’s et al. (2021) paper.

Table 2. Definition of technostress creators.

Independent variable
Technostress creator	Criteria for measures	Literature support
Techno-complexity (“too difficult”): Complexity associated with IS makes users feel their technology skills are insufficient and forces them to put effort into learning and understanding IS.	Insufficient knowledge about IS to handle a task efficiently Requires significant time investment to understand and use IS Requires constantly updating technological skills Lack of time to update technological skills Difficulty comprehending IS or IS policies	Ragu-Nathan et al. (2008); D’Arcy et al. (2014); Galluch et al. (2015); Tarafdar et al. (2007, 2011, 2019)
Techno-invasion (“always connected”): Users’ personal space and privacy is invaded through permanent connectivity and monitoring and the surveillance capabilities of technology.	Being permanently connected through technology Invasion of personal time by technology Sacrifice time with family due to technology Sacrifice personal time such as weekends or vacation to keep current on technologies Invasion of personal space and privacy through monitoring and surveillance	Ragu-Nathan et al. (2008); Day et al. (2012); Tarafdar et al. (2007, 2011); Barber & Santuzzi (2015)
Techno-insecurity (“being replaced”): Users worry about being replaced by technology or individuals with better technological skills.	Feeling constant threat to job security due to new technologies Pressure to permanently update IS skills for fear of being replaced Feeling threatened by technologies or others with better technological skills Reduced IS knowledge sharing among co-workers	Ragu-Nathan et al. (2008); Tarafdar et al. (2007, 2011); Califf et al. (2020)
Techno-overload (“too much”): Users work more or faster to meet requirements caused by technology or IS policies, handle excessive amounts of information or technology features, and meet expectations of others when using, for example, SNS applications.	Pressure to work faster or more due to technology Forced to work within tight time schedules due to technology Adjustment of work habits due to technology Exposed to vast amounts of information due to technology Perceive too many technology-related interruptions	Ragu-Nathan et al. (2008); Tarafdar et al. (2010, 2011); D’Arcy et al. (2014); Maier et al. (2014)
Techno-uncertainty (“too many changes”): Users do not have the chance to familiarise themselves with the technology due to constant updates and changes and as a result must update their skills.	New technological developments that create new demands and are perceived as “unsettling” Constant changes in software, hardware, and networks that require persistent upskilling Existing knowledge and experience with technologies become rapidly obsolete Perceived lack of effective communication on technology upgrades and changes	Ragu-Nathan et al. (2008); Tarafdar et al. (2011); Day et al. (2012); D’Arcy et al. (2014); Califf et al. (2020)

Table 3. Definition of technostress-related outcomes and contexts.

Dependent variables
Outcome	Examples	Literature support
Psychological: Cognitive and affective reactions to technostress creators	Burnout, exhaustion, role overload, role conflict	Tarafdar et al. (2007); Maier, Laumer, Weinert, et al. (2015); Srivastava et al. (2015)
Behavioural: Actions or intended responses to technostress creators	Technology usage, productivity, task performance	Tarafdar et al. (2007); Maier et al. (2014); Maier, Laumer, Weinert et al., (2015); Tarafdar, Pullins, et al. (2015)
Contextual moderators
Context	Examples	Literature support
Organisational: Users have limited (if any) control over the decision to use technology and it serves a predominantly utilitarian purpose.	Healthcare IT, telework, professional sales	Tarafdar, Pullins, et al. (2015); Suh & Lee (2017); Califf et al. (2020)
Private: Users have extended control over the decision to use technology and it serves a predominantly hedonic purpose.	Automotive, smartphones, SNS	Lee et al. (2014); Maier, Laumer, Weinert, et al. (2015); Nastjuk & Kolbe (2015)

Table 4. Meta-analysis results on technostress creators as an aggregate construct in the organisational and private contexts.

Outcome	Predictor	Context	$\hat{\rho }$	Magnitude	k	N	SDρ	SDr	80% CV	95% CI	PVA	FS N
Psychological	Aggregated technostress creators	Org	−.33	Medium	69	25,143	.25	.23	[−.66, .01]	[−.40, −.27]	4.7	45
		Pri	−.51	Large	22	11,848	.23	.21	[−.81, −.22]	[−.61, −.42]	3.5	35
Behavioural		Org	−.18	Small	46	18,927	.18	.17	[−.41, .06]	[−.23, −.12]	8.1	5
		Pri	−.33	Medium	24	14,359	.14	.13	[−.51, −.16]	[−.39, −.27]	9.5	16

$\hat{\rho }$ = mean true score correlation; Magnitude = magnitude of the effect size according to Cohen (1988): small (<.30), medium (.30–.50), large (>.50); k = number of studies contributing to meta-analysis; N = total sample size; SD_ρ = residual standard deviation of ρ; SD_r = observed standard deviation of r; CV = credibility interval around true score correlation ρ; CI = confidence interval around true mean score correlation ρ; PVA = percentage of variance accounted for by sampling and measurement error; FS N = Orwin’s fail-safe N, computed with a criterion correlation of 0.2, Org = organisational, Pri = private.

Table 5. Meta-analysis results on individual technostress creators in the organisational and private contexts.

Outcome	Predictor	Context	$\hat{\rho }$	Magnitude	k	N	SDρ	SDr	80% CV	95% CI	PVA	FS N
Psychological	Techno-complexity	Org	−.43	Medium	26	12,381	.23	.21	[−.73, −.14]	[−.52, −.34]	4.2	30
		Pri	−.40	Medium	5	1,555	.14	.14	[−.58, −.21]	[−.54, −.26]	14.1	5
	Techno-invasion	Org	−.35	Medium	43	16,833	.22	.20	[−.63, −.08]	[−.42, −.29]	5.8	33
		Pri	−.43	Medium	8	3,637	.27	.24	[−.77, −.09]	[−.62, −.24]	3.3	10
	Techno-insecurity	Org	−.35	Medium	26	12,768	.23	.21	[−.64, −.05]	[−.44, −.25]	4.2	20
	Techno-overload	Org	−.41	Medium	56	20,928	.23	.21	[−.70, −.11]	[−.47, −.35]	5.2	59
		Pri	−.49	Medium	22	11,848	.28	.25	[−.86, −.14]	[−.62, −.38]	2.5	32
	Techno-uncertainty	Org	−.14	Small	21	9,626	.17	.16	[−.37, .08]	[−.22, −.06]	8.2	7
		Pri	−.08	Small	5	2,337	.05	.06	[−.15, −.02]	[−.15, −.02]	52.1	3
Behavioural	Techno-complexity	Org	−.25	Small	20	10,309	.18	.16	[−.47, −.02]	[−.33, −.17]	7.1	5
		Pri	−.28	Small	9	6,307	.11	.10	[−.41, −.15]	[−.35, −.21]	13.7	4
	Techno-invasion	Org	−.12	Small	30	13,646	.18	.16	[−.35, .11]	[−.18, −.05]	8.0	12
		Pri	−.33	Medium	11	6,349	.20	.18	[−.59, −.08]	[−.45, −.21]	4.9	8
	Techno-insecurity	Org	−.13	Small	16	8,568	.12	.12	[−.28, .03]	[−.19, −.06]	13.9	6
	Techno-overload	Org	−.19	Small	41	17,355	.21	.20	[−.46, .09]	[−.26, −.12]	6.0	3
		Pri	−.35	Medium	22	13,553	.15	.14	[−.55, −.15]	[−.42, −.28]	7.4	17
	Techno-uncertainty	Org	.01	Small	18	8,594	.21	.19	[−.25, .28]	[−.09, .11]	5.9	19
		Pri	−.21	Small	7	5,171	.09	.09	[−.33, −.09]	[−.29, −.14]	16.6	1

$\hat{\rho }$ = mean true score correlation; Magnitude = magnitude of the effect size according to Cohen (1988): small (<.30), medium (.30–.50), large (>.50); k = number of studies contributing to meta-analysis; N = total sample size; SD_ρ = residual standard deviation of ρ; SD_r = observed standard deviation of r; CV = credibility interval around true score correlation ρ;| CI = confidence interval around true score correlation ρ; PVA = percentage of variance accounted for by sampling and measurement error; FS N = Orwin’s fail-safe N, computed with a criterion correlation of 0.2, Org = organisational, Pri = private.

Schmidt, M., Frank, L., & Gimpel, H. (2021). How adolescents cope with technostress: A mixed-methods approach. International Journal of Electronic Commerce, 25(2), 154–180. https://doi.org/10.1080/10864415.2021.1887696

 Web of Science ®Google Scholar

Ninaus, K., Diehl, S., & Terlutter, R. (2021). Employee perceptions of information and communication technologies in work life, perceived burnout, job satisfaction and the role of work-family balance. Journal of Business Research, 136, 652–666. https://doi.org/10.1016/j.jbusres.2021.08.007

 Web of Science ®Google Scholar

Fieseler, C., Grubenmann, S., Meckel, M., & Müller, S. (2014). The leadership dimension of coping with technostress. Proceedings of the Hawaii International Conference on System Sciences, 530–539. Waikoloa, HI, USA .

 Google Scholar

Pflügner, K., & Maier, C. (2019). Mitigating technostress: An empirical study of mindfulness and techno-stressors. Proceedings of the Americas Conference on Information Systems, 1–10. Cancún, Mexico.

 Google Scholar

Pflügner, K., Maier, C., & Weitzel, T. (2021). The direct and indirect influence of mindfulness on techno-stressors and job burnout: A quantitative study of white-collar workers. Computers in Human Behavior, 115, 1–12. https://doi.org/10.1016/j.chb.2020.106566

 Web of Science ®Google Scholar

Ragu-Nathan, T. S., Tarafdar, M., Ragu-Nathan, B. S., & Tu, Q. (2008). The consequences of technostress for end users in organizations: Conceptual development and empirical validation. Information Systems Research, 19(4), 417–433. https://doi.org/10.1287/isre.1070.0165

 Web of Science ®Google Scholar

D’Arcy, J., Herath, T., & Shoss, M. K. (2014). Understanding employee responses to stressful information security requirements: A coping perspective. Journal of Management Information Systems, 31(2), 285–318. https://doi.org/10.2753/MIS0742-1222310210

 Web of Science ®Google Scholar

Galluch, P. S., Grover, V., & Thatcher, J. B. (2015). Interrupting the workplace: Examining stressors in an information technology context. Journal of the Association for Information Systems, 16(1), 1–47. https://doi.org/10.17705/1jais.00387

 Web of Science ®Google Scholar

Tarafdar, M., Tu, Q., Ragu-Nathan, B. S., & Ragu-Nathan, T. S. (2007). The impact of technostress on role stress and productivity. Journal of Management Information Systems, 24(1), 301–328. https://doi.org/10.2753/MIS0742-1222240109

 Web of Science ®Google Scholar

Tarafdar, M., Tu, Q., Ragu-Nathan, T. S., & Ragu-Nathan, B. S. (2011). Crossing to the dark side: Examining creators, outcomes, and inhibitors of technostress. Communications of the ACM, 54(9), 113–120. https://doi.org/10.1145/1995376.1995403

 Web of Science ®Google Scholar

Tarafdar, M., Cooper, C. L., & Stich, J. F. (2019). The technostress trifecta - techno eustress, techno distress and design: Theoretical directions and an agenda for research. Information Systems Journal, 29(6), 6–42. https://doi.org/10.1111/isj.12169

 Google Scholar

Day, A., Paquet, S., Scott, N., & Hambley, L. (2012). Perceived information and communication technology (ICT) demands on employee outcomes: The moderating effect of organizational ICT support. Journal of Occupational Health Psychology, 17(4), 473–491. https://doi.org/10.1037/a0029837

 PubMed Web of Science ®Google Scholar

Barber, L. K., & Santuzzi, A. M. (2015). Please respond ASAP: Workplace telepressure and employee recovery. Journal of Occupational Health Psychology, 20(2), 172–189.

 PubMed Web of Science ®Google Scholar

Califf, C. B., Sarker, S., & Sarker, S. (2020). The bright and dark sides of technostress: A mixed-methods study involving healthcare IT. MIS Quarterly, 44(2), 809–856. https://doi.org/10.25300/MISQ/2020/14818

 Web of Science ®Google Scholar

Tarafdar, M., Tu, Q., & Ragu-Nathan, T. S. (2010). Impact of technostress on end-user satisfaction and performance. Journal of Management Information Systems, 27(3), 303–334. https://doi.org/10.2753/MIS0742-1222270311

 Web of Science ®Google Scholar

Maier, C., Laumer, S., Eckhardt, A., & Weitzel, T. (2014). Giving too much social support: Social overload on social networking sites. European Journal of Information Systems, 24(5), 447–464. https://doi.org/10.1057/ejis.2014.3

 Web of Science ®Google Scholar

Maier, C., Laumer, S., Weinert, C., & Weitzel, T. (2015). The effects of technostress and switching stress on discontinued use of social networking services: A study of Facebook use. Information Systems Journal, 25(3), 275–308. https://doi.org/10.1111/isj.12068

 Web of Science ®Google Scholar

Srivastava, S. C., Chandra, S., & Shirish, A. (2015). Technostress creators and job outcomes: Theorising the moderating influence of personality traits. Information Systems Journal, 25(4), 355–401. https://doi.org/10.1111/isj.12067

 Web of Science ®Google Scholar

Tarafdar, M., Pullins, E. B., & Ragu-Nathan, T. S. (2015). Technostress: Negative effect on performance and possible mitigations. Information Systems Journal, 25(2), 103–132. https://doi.org/10.1111/isj.12042

 Web of Science ®Google Scholar

Suh, A., & Lee, J. (2017). Understanding teleworkers’ technostress and its influence on job satisfaction. Internet Research, 27(1), 140–159. https://doi.org/10.1108/IntR-06-2015-0181

 Web of Science ®Google Scholar

Lee, Y., Chang, C., Lin, Y., & Cheng, Z. (2014). The dark side of smartphone usage: Psychological traits, compulsive behavior and technostress. Computers in Human Behavior, 31, 373–383. https://doi.org/10.1016/j.chb.2013.10.047

 Web of Science ®Google Scholar

Nastjuk, I., & Kolbe, L. M. (2015). On the duality of stress in information systems research: The case of electric vehicles. Proceedings of the International Conference on Information Systems, 1–22. Fort Worth, Texas, USA.

 Google Scholar

Cohen, J. (1988). Statistical power analysis for the behavioral sciences. Routledge.

 Google Scholar