Next Article in Journal
The Relationship between Depressive Symptoms, Rumination, and Suicide Ideation in Patients with Depression
Next Article in Special Issue
Internet Addiction among Young Adult University Students during the COVID-19 Pandemic: The Role of Peritraumatic Distress, Attachment, and Alexithymia
Previous Article in Journal
Preliminary Research on the Sustainable Determinants of Taiwanese Ecotourism with the International Standards
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
IJERPH
Volume 19
Issue 21
10.3390/ijerph192114493
ijerph-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Validation of a Measurement Scale on Technostress for University Students in Chile

by
Alejandro Vega-Muñoz
1,2,
Carla Estrada-Muñoz
3,
Paola Andreucci-Annunziata
1,
Nicolas Contreras-Barraza
4,* and
Heidi Bilbao-Cotal
5
1
Instituto de Investigación y Postgrado, Facultad de Ciencias de la Salud, Universidad Central de Chile, Santiago 8330507, Chile
2
Public Policy Observatory, Universidad Autónoma de Chile, Santiago 7500912, Chile
3
Departamento de Ergonomía, Facultad de Ciencias Biológicas, Universidad de Concepción, Concepción 4070386, Chile
4
Facultad de Economía y Negocios, Universidad Andres Bello, Viña Del Mar 2531015, Chile
5
Independent Researcher, Santiago 7500885, Chile
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2022, 19(21), 14493; https://doi.org/10.3390/ijerph192114493
Submission received: 11 October 2022 / Revised: 27 October 2022 / Accepted: 28 October 2022 / Published: 4 November 2022
(This article belongs to the Special Issue The Impact of COVID-19 on University Professors’ and Students’ Mental Health: Risk and Protective Processes)
Browse Figure
Versions Notes

Abstract

:
The main aim in this research was to validate a scale for measuring technostress in Chilean university students under the context of hybrid education. There were 212 university students as participants from the central-south zone of Chile. For measuring technostress manifestations, a technostress questionnaire for Chinese university professors and its adaptation for Spanish university students was used as a base instrument to adapt the scale. The exploratory and confirmatory factor analysis generated an adequacy of the psychometric scale by eliminating three items from the original scales but generated important changes by reordering the other 19 items into only three factors, establishing an important local difference with previous versions that contemplated five factors, but retaining as a central axis the stress produced by a misfit between the person and his or her environment. The resulting scale was based on factors such as Abilities-Demands Techno-Educational, Needs-Supplies Resources, and Person-People Factor. It also has a good internal consistency with a scale that allows for the continuation of technostress measurements in the local context; adding to studies on this topic which have already been carried out on diverse actors of the Chilean educational system; proposing a reliable and valid psychometric scale of technostress in Chilean university students; and giving researchers and academic managers the ability to know the adverse effects of the use of technologies and propose mitigation actions.

1. Introduction

The incorporation of information and communication technologies (ICT) in education has changed the nature, methods, and processes of learning [1,2]. Nowadays, education has been restructured due to the increasing ICT usage rate by school and university students [3]. According to Talebian et al. [4], ICT enriches existing educational models and provides new technology-based training and learning schemes. At the higher educational level, technological development has facilitated the student exchange between universities and cooperation instances between international students [3]. Likewise, ICTs have challenged and accelerated technological skills development, stimulating “learning by doing” and contributing to sustainable development through the fourth sustainable development goal on quality education [5,6,7].
ICT use, however, can cause stress, which corresponds to a physical and emotional response to distress caused by an individual’s imbalance between perceived demands and perceived resources, and their abilities to cope with those demands [8]. When stress is associated with ICT use, it is called technostress, a concept first coined by the American psychiatrist Craig Brod [9], who defined it as a “modern adaptative illness caused by the inability to cope with new computer technologies in a healthy manner” [10] (p. 242). In the last two decades, it has become a topic of growing interest for studies [11,12].
Salanova defines technostress as “a negative psychological state related to the ICT use or threat of its use in the future, conditioned by the mismatch perception between the demands and resources related to the ICT use that leads to a high unpleasant psychophysiological activation level and to the negative attitude development towards ICT” [13] (p. 423). For its part, according to Tarafdar et al. [14], technostress is a product of the inability to adapt or cope with new technologies, and constitutes a process characterized by the presence of technological environmental conditions that are evaluated as demands or techno-stressors by the individual, which set in motion coping responses leading to psychological, physical, and behavioral manifestations.
Regarding technostress study fields, at the educational level, research predominates, above all, in primary and secondary education teachers [15,16,17,18,19], in university teachers [20,21,22,23], administrative workers [24,25], and librarians [26]. Even though they are scarce, studies on university students are also mentioned, which highlight that technostress significantly predicts burnout and negatively impacts academic productivity [27,28,29,30].
The new challenges resulting from the COVID-19 pandemic for higher education through ICT [31,32,33,34,35] and given the eruption of hybrid higher education [36,37], an interest in learning about its psychological and mental health effects on students is evident. Given this scarcity of studies on technostress in university students, it is necessary to have psychometric measurement scales adapted and validated to various local contexts to facilitate further study. In this regard, this article aimed to adapt and validate measurement of technostress scale for university students (TS4US) based on previous work by Wang et al. [21] in China, and Penado-Abilleira et al. [38] in Spain. In theoretical terms, this local validation contributes to strengthen the global empirical case set that allows to specify the theoretical constructs incorporated in a psychometric instrument capable of measuring technostress in university students.
In the Chilean case, technostress studies in education have been published in mainstream journals focusing on other members of educational communities, such as teachers [18,19] and managers [25]. In practical terms, this work will allow access to an instrument with structural validity to advance the study in university students.

2. Materials and Methods

The adaptation for Chilean students of the Wang and Li [21] and Penado-Abilleira et al. [23] technostress questionnaires began with a re-translation of the items that compose the original Wang and Li [21] scale to the Spanish of current use in Chile by a specialized linguist who was part of the research team, and adapting the items for a university student population as well as establishing local semantic differences in comparison with Penado-Abilleira et al. [23]. The result was pre-tested with a group of 20 students to evaluate their comprehension. The resulting scale is presented in Appendix A (in Chilean Spanish), keeping a 5-point Likert scale: Strongly Disagree = 1, Disagree = 2, Neither Disagree nor Agree = 3, Agree = 4, and Strongly Agree = 5. The questionnaire has been self-administered online, after giving informed consent response, collecting the survey without respondent identification, and only presenting non-potentially identifiable human data.
Then, SPSS 23 software was used (IBM, New York, NY, USA) to analyze the 22-item TS questionnaire [21,38], for establishing psychometric validity by means of structural evidence [39]. As a first step, a univariate descriptive statistical analysis was applied, with emphasis on variance (>0), skewness and kurtosis (|≤1|, both). To measure confidence levels, the authors applied the Kaiser-Meyer-Olkin measure of sampling adequacy (KMO). Moreover, the authors used Bartlett’s test of sphericity to identify items belonging to the factors within the scale as a form of exploratory factor analysis (EFA) with the extraction method, unweighted least squares (ULS), rotation method, and Oblimin with Kaiser normalization [40]. Then the authors analyzed the exploratory factors by using a confirmatory factor analysis (CFA) with FACTOR software [41]. In addition, they used the Hull method to select the number of factors according to the EFA results, including high communalities, high factor loadings to support sample size, and minimum items per factor (MIF) [42,43,44], considering the comparison possibilities with Wang et al. [21] and Penado-Abilleira et al. [38]. It was necessary to obtain a report of the indicators: Chi-square/degree freedom ratio (χ2/df), root mean square error of approximation (RMSEA), adjusted goodness-of-fit index (AGFI), goodness-of-fit index GFI, comparative fit index (CFI), (RFI), normed fit index (NFI), non-normed fit index (NNFI), and Root Mean Square of Residuals (RMSR) [45]. See Table 1.
Finally, the internal reliability of the resulting instrument will be validated by calculating Cronbach’s Alpha by SPSS 23 software [48,49].

Sample Characterization

The Technostress Scale for University Students (TS4US) was applied in the first academic semester 2021 to a set of 212 participants (≥ 200, overcoming small sample sizes for factorial analysis) [43], university students from the Chile Central-South Zone, which concentrates more than 70 percent of the national and university population [50,51], as shown in Table 2 (Available data in Table S1: TS4US_data_22_var, Supplementary Materials).

3. Results

3.1. Exploratory Factor Analysis

Firstly, we analyzed the possible prevalence of the factors identified by Wang and Li [21] for Chinese university teachers and Penado-Abilleira et al. [38] for Spanish university students. These five original factors establish the stresses for technology use between personal capabilities and organizational demands (abilities-demands organization, ADO), personal capabilities and technology demands (abilities-demands technology, ADT), personal needs and organizational resources to perform their tasks (needs-supplies organization, NSO), personal needs and their own available technology resources (needs-supplies technology, NST), and interpersonal relationships between students (person-people factor, PPF). Univariate descriptive statistical analysis was applied, and no ordinal variable presented variance equal to zero, so all of them contributed to the common variance. But in terms of skewness and kurtosis, 3 variables present kurtosis (kurt) problems: VAR_05 (kurt = −1.109), VAR_10 (kurt = −1.281), and VAR 22 (kurt = −1.160), see Figure 1.
Table 3 and Table 4 show the unrestricted results of the exploratory factor analysis preserving the 18 variables and determining with SPSS 23 a KMO of 0.897 and Bartlett’s test with a Chi-square of 2432.170 with 171 degrees of freedom and a significance level of 0.000 for the four factors TS4US instrument. The authors achieved a 59.476% explained variance proportion. Although these results were individually valued as positive, it was also observed that factor 4 did not comply with the minimum variables recommended per factor (>3) [42,43,44], which led us to test another variable reduction alternative.
Table 5 and Table 6 show the result of the unrestricted exploratory factor analysis preserving 19 variables and determining identical results for KMO and Bartlett’s Test with the use of SPSS 23 for the three factors TS4US instrument and achieving a 54.264% explained variance proportion.

3.2. Confirmatory Factor Analysis

Additionally, the authors successfully adapted the 19 variables analyzed dataset for 19 variables to confirmatory factor analysis (CFA) with the use of the FACTOR software. The CFA obtained a KMO-Kaiser-Meyer-Olkin-equal to 0.89743 (>0.8) and Bartlett’s test of sphericity with a Chi-Square 9668.9 with 171 degrees of freedom and a significance level of 0.000010. Those results were significant and good enough to present the adequacy of the Pearson correlation matrix.
The Hull method for selecting the number of three factors was implemented with an adequacy of the Pearson correlation matrix. Then the authors reduced the TS4US questionnaire according to its latent variables in three factors (see Table 7).
Table 8 sets out the proposed model results in comparison with the resulting validity and reliability values in Wang et al. [21] and Penado-Abilleira et al. [38], for the RMSEA, AGFI, GFI, CFI and RMSR indicators by FACTOR software. In comparative terms, the proposed model reports an RMSEA with an acceptable fit equal to Wang et al. [21], AGFI and GFI with a good fit equal to Penado-Abilleira et al. [38], CFI with a good fit in contrast to the acceptable fit reported by Wang et al. [21], and RMSR with a good fit in contrast to the acceptable fit reported by Penado-Abilleira et al. [38].
Finally, Table 9 shows the instrument’s internal reliability by SPSS 23 software, with a total Cronbach’s Alpha of 92.5% for the set of 19 items, whose definitive scale is presented in Chilean Spanish in Appendix B.

4. Discussion

In this research, through an exploratory and confirmatory factor analysis, the factors identified by Wang and Li [21] for Chinese university teachers and Penado-Abilleira et al. [38] for Spanish university students were analyzed in the context of Chilean university students from public and private institutions. As a result of these analyses, within the Chilean university students participating in this study, the model explaining technostress was reduced from five to three factors, with the loss of three variables 5 (ADO05), 10 (ADT01), and 22 (PPF04), a product of the instrument’s adjustment to the specific sample composition; maintaining factors (theoretical constructs) that made it possible to measure the phenomenon of technostress. It was observed that the variables that make up the measurement scale tend to be grouped into the main factors that are part of various theories that explain stress, as detailed below.
The variables corresponding to the tensions between personal needs and organizational resources (NSO) and personal needs and technological resources (NST) are grouped in the first factor; it is known that the availability and usability of resources is a moderating factor on stress [52]. It should be noted that, in this factor, the variables that make up the personal needs or demands and technological resources (NST) that are associated with the self-perception of usefulness of available ICTs are grouped together, resulting in a factor on personal needs and available resources (needs-supplies resources, NSR). In this regard, the perceived usefulness of ICT use has been defined as a factor which inhibits technostress [53,54].
Then, there is a second factor, which groups those variables associated with interpersonal relationships (PPF) among students, maintaining this factor according to Wang and Li [21] and Penado-Abilleira et al. [38], which is associated with the social support given by peers and peer learning in the face of novel and potentially distressing processes [2,12], which constitutes a protective factor mitigating technostress [55].
Also, the third factor includes those variables associated with the relationship between personal capabilities and organizational demands (ADO), and personal capabilities and technological demands (ADT). If work demand is not in line with capabilities, this can lead to stress manifestations [56,57,58,59]. On the other hand, it includes variables on personal needs and technological resources (NST), but those associated with technological overload, which might be a cause for technostress rather than from the organizational type [9,60,61]. Because of high demand and lack of resources for working, ICT is associated with increased technostress [13,52]. In sum, the third factor relates personal capabilities and techno-organizational demands, specifically techno-educational demands (abilities-demands techno-educational, ADTE).
Finally, in the absence of a meta-analysis to ensure a broad application of a valid scale [62,63], the local validation is a contribution to the global empirical case set to clarify the theoretical constructs incorporated in a psychometric instrument with the ability to measure technostress in university students in different countries and cultures, as well as its practical implications in local terms to expand the technostress studies in university students.

5. Conclusions

This article validated a scale to measure technostress in Chilean university students in the context of hybrid education, based on activities combined and carried out in different environments (physical or virtual) and times (synchronous or asynchronous) [64,65], using as a basis a technostress questionnaire for Chinese university teachers and its adaptation for Spanish university students. The exploratory and confirmatory factor analyses eliminated three items from the original scales but generated important changes by reordering the other 19 items in only three factors, establishing an important difference with the previous versions that contemplated five factors, with a good internal consistency and having as a central axis the stress produced by the misfit between the person and his/her environment. Thus, the instrument allowed for the creations of a scale for measuring technostress in Chilean university students (TS4US) based on the following factors: Abilities-Demands Techno-Educational (ADTE), Needs-Supplies Resources (NSR), and Person-People Factor (PPF). It will also allow for a continuation of the measurements of technostress in a local context, increasing the studies on this topic already carried out in different actors of the Chilean educational system.
Although all the parameters that ensure the quality of the sample size were met in this study and the sample exceeded the small sample cut-off for a factor analysis [43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66], a limitation of this work was not having a larger sample, which we will address after the validation of this psychometric instrument. On the other hand, the article has been limited only to the validity analysis on the psychometric measurement scale studied, without analyzing other dimensions. Also, future research lines will allow for a series of local studies on technostress in the Chilean educational system [18,19,25,67] to be completed, as well as for extensive measurements to be developed on technostress in university students, and further work relating technostress with other aspects, such as techno-addiction, cyberbullying, transformation of learning processes and engagement to educational work, and stimulating ‘learning by doing’ [5,6,68,69,70].

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/ijerph192114493/s1, Table S1: TS4US_data_22_var.

Author Contributions

A.V.-M. and C.E.-M.: conceptualization, formal analysis, and project administration; A.V.-M. and N.C.-B.: methodology; A.V.-M.: software; A.V.-M., C.E.-M. and H.B.-C.: validation; A.V.-M.: data curation; A.V.-M., C.E.-M., H.B.-C., N.C.-B. and P.A.-A.: writing—original draft preparation; A.V.-M., N.C.-B., H.B.-C. and C.E.-M.: writing—review and editing; A.V.-M.: supervision; A.V.-M., P.A.-A. and N.C.-B.: funding acquisition for publishing fees. All authors have read and agreed to the published version of the manuscript.

Funding

The publication fee (article processing charge, APC) was partially funded by Universidad Central de Chile (Code: ACD219201) and was partially funded through the publication incentive fund by Universidad Andres Bello (Code: CC21500), and the Universidad Autónoma de Chile (Code: CC456001).

Institutional Review Board Statement

Not applicable, no animal and human studies are presented in this manuscript. And no potentially identifiable human images or data is presented in this study.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study, by initial question in a self-administered survey.

Data Availability Statement

Data Availability in Supplementary Materials.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A. Applied Questionnaire (In Spanish of Current Use in Chile)

Table
Table A1. Applied Technostress Questionnaire.
Table A1. Applied Technostress Questionnaire.
PreviousVariableQuestions (In Chilean Spanish)
ADO1VAR_01Me resulta difícil satisfacer las demandas de mi universidad con respecto al uso de las tecnologías de información
ADO2VAR_02Me resulta difícil implementar con eficacia las indicaciones de mi universidad sobre el uso de las tecnologías de información
ADO3VAR_03Mi capacidad actual es insuficiente para implementar las indicaciones de mi universidad sobre el uso de las tecnologías de información
ADO4VAR_04Mis habilidades actuales son insuficientes para implementar las indicaciones de mi universidad sobre el uso las tecnologías de la información
ADO5VAR_05Me resulta difícil organizar mi horario de estudio actual para cumplir con las indicaciones de mi universidad sobre sobre el uso de las tecnologías de información
NSO1VAR_06Mi universidad no me brinda suficiente inducción para usar las tecnologías de información de manera efectiva en mis actividades académicas
NSO2VAR_07Mi universidad no me brinda incentivos suficientes para utilizar las tecnologías de información de manera efectiva en mis actividades como estudiante
NSO3VAR_08La inducción desarrollada por mi universidad no es muy útil para lograr un uso efectivo de las tecnologías de información
NSO4VAR_09En mi universidad no existe una cultura que fomente el uso de herramientas innovadoras como las tecnologías de información
ADT1VAR_10Me siento presionado/a para usar las tecnologías de información de manera efectiva en mis trabajos universitarios
ADT2VAR_11Me resulta difícil utilizar las tecnologías de información de manera efectiva debido al poco tiempo y esfuerzo que le dedico
ADT3VAR_12Me resulta difícil hacer frente a las altas demandas de las tecnologías de información con mi capacidad actual
ADT4VAR_13Me resulta difícil ponerme al día con los rápidos cambios de las tecnologías de información
NST1VAR_14Las tecnologías de información en mi universidad no son efectivas para ayudarme a aumentar mi desempeño académico
NST2VAR_15Las tecnologías de información en mi universidad no son muy importantes
NST3VAR_16Estoy abrumado/a por la gran variedad de tecnologías de información que se utilizan en mi universidad
NST4VAR_17Las diversas tecnologías de información complican mi proceso de toma de decisiones académicas
NST5VAR_18Me molesta el uso excesivo de las tecnologías de información en mi universidad
PPF1VAR_19No tengo el apoyo suficiente de mis compañeros para el uso de las tecnologías de información
PPF2VAR_20Mis compañeros no son positivos con respecto al uso innovador de las tecnologías de información en mi universidad
PPF3VAR_21No tengo un equipo con el que colaborar para encontrar una forma eficaz de usar las tecnologías de información en mis actividades como estudiante universitario
PPF4VAR_22A menudo siento que estoy solo explorando el uso innovador de las tecnologías de la información

Appendix B. Definitive TS4US Scale (Presented in Chilean Spanish)

Table
Table A2. TS4US Questionnaire.
Table A2. TS4US Questionnaire.
TS4USVariableQuestions (In Chilean Spanish)
ADTE01VAR_01Me resulta difícil satisfacer las demandas de mi universidad con respecto al uso de las tecnologías de información
ADTE02VAR_02Me resulta difícil implementar con eficacia las indicaciones de mi universidad sobre el uso de las tecnologías de información
ADTE03VAR_03Mi capacidad actual es insuficiente para implementar las indicaciones de mi universidad sobre el uso de las tecnologías de información
ADTE04VAR_04Mis habilidades actuales son insuficientes para implementar las indicaciones de mi universidad sobre el uso las tecnologías de la información
ADTE05VAR_05Me resulta difícil organizar mi horario de estudio actual para cumplir con las indicaciones de mi universidad sobre sobre el uso de las tecnologías de información
ADTE06VAR_11Me resulta difícil utilizar las tecnologías de información de manera efectiva debido al poco tiempo y esfuerzo que le dedico
ADTE07VAR_12Me resulta difícil hacer frente a las altas demandas de las tecnologías de información con mi capacidad actual
ADTE08VAR_13Me resulta difícil ponerme al día con los rápidos cambios de las tecnologías de información
ADTE09VAR_16Estoy abrumado/a por la gran variedad de tecnologías de información que se utilizan en mi universidad
ADTE10VAR_17Las diversas tecnologías de información complican mi proceso de toma de decisiones académicas
ADTE11VAR_18Me molesta el uso excesivo de las tecnologías de información en mi universidad
NSR01VAR_06Mi universidad no me brinda suficiente inducción para usar las tecnologías de información de manera efectiva en mis actividades académicas
NSR02VAR_07Mi universidad no me brinda incentivos suficientes para utilizar las tecnologías de información de manera efectiva en mis actividades como estudiante
NSR03VAR_08La inducción desarrollada por mi universidad no es muy útil para lograr un uso efectivo de las tecnologías de información
NSR04VAR_09En mi universidad no existe una cultura que fomente el uso de herramientas innovadoras como las tecnologías de información
NSR05VAR_14Las tecnologías de información en mi universidad no son efectivas para ayudarme a aumentar mi desempeño académico
NSR06VAR_15Las tecnologías de información en mi universidad no son muy importantes
PPF1VAR_19No tengo el apoyo suficiente de mis compañeros para el uso de las tecnologías de información
PPF2VAR_20Mis compañeros no son positivos con respecto al uso innovador de las tecnologías de información en mi universidad
PPF3VAR_21No tengo un equipo con el que colaborar para encontrar una forma eficaz de usar las tecnologías de información en mis actividades como estudiante universitario
PPF4VAR_22A menudo siento que estoy solo explorando el uso innovador de las tecnologías de la información

References

  1. Abdullah, F.; Ward, R.; Ahmed, E. Investigating the influence of the most commonly used external variables of TAM on students’ Perceived Ease of Use (PEOU) and Perceived Usefulness (PU) of e-portfolios. Comput. Hum. Behav. 2016, 63, 75–90. [Google Scholar] [CrossRef]
  2. Zokirovna, O.D. The Effectiveness of Implementation of ICT in Learning Process. Eur. Sch. J. 2020, 1, 9–11. Available online: https://scholarzest.com/index.php/esj/article/view/76 (accessed on 10 October 2022).
  3. Büyükbaykal, C.I. Communication Technologies and Education in the Information Age. Procedia Soc. Behav. Sci. 2015, 174, 636–640. [Google Scholar] [CrossRef] [Green Version]
  4. Talebian, S.; Mohammadi, H.M.; Rezvanfar, A. Information and Communication Technology (ICT) in Higher Education: Advantages, Disadvantages, Conveniences and Limitations of Applying E-learning to Agricultural Students in Iran. Procedia Soc. Behav. Sci. 2014, 152, 300–305. [Google Scholar] [CrossRef] [Green Version]
  5. Ben Youssef, A.; Dahmani, M.; Ragni, L. ICT Use, Digital Skills and Students’ Academic Performance: Exploring the Digital Divide. Information 2022, 13, 129. [Google Scholar] [CrossRef]
  6. Laleye, A.M. Practical and Technological Skills: An Inevitable Social Engineering Tool for Sustainable Development. Eur. J. Educ. Pedagog. 2022, 3, 171–177. [Google Scholar] [CrossRef]
  7. Sianes, A.; Vega-Muñoz, A.; Tirado-Valencia, P.; Ariza-Montes, A. Impact of the Sustainable Development Goals on the academic research agenda. A scientometric analysis. PLoS ONE 2022, 17, e0265409. [Google Scholar] [CrossRef]
  8. International Labour Organization. Estrés en el Trabajo: Un Reto Colectivo. 2016. Available online: https://www.ilo.org/safework/info/publications/WCMS_466549/lang--es/index.htm (accessed on 10 October 2022).
  9. Tarafdar, M.; Tu, Q.; Ragu-Nathan, B.S.; Ragu-Nathan, T.S. The Impact of Technostress on Role Stress and Productivity. J. Manag. Inf. Syst. 2007, 24, 301–328. [Google Scholar] [CrossRef] [Green Version]
  10. Brod, C. Technostress: The Human Cost of the Computer Revolution; Addison Wesley Publishing Company: Boston, MA, USA, 1984. [Google Scholar]
  11. Bondanini, G.; Giorgi, G.; Ariza-Montes, A.; Vega-Muñoz, A.; Andreucci-Annunziata, P. Technostress Dark Side of Technology in the Workplace: A Scientometric Analysis. Int. J. Environ. Res. Public Health 2020, 17, 8013. [Google Scholar] [CrossRef]
  12. Nisafani, A.S.; Kiely, G.; Mahony, C. Workers’ technostress: A review of its causes, strains, inhibitors, and impacts. J. Decis. Syst. 2020, 29, 243–258. [Google Scholar] [CrossRef]
  13. Salanova, M.; Llorens, S.; Cifre, E. The dark side of technologies: Technostress among users of information and communication technologies. Int. J. Psychol. 2013, 48, 422–436. [Google Scholar] [CrossRef] [PubMed]
  14. Tarafdar, M.; Cooper, C.L.; Stich, J. The technostress trifecta-techno eustress, techno distress and design: Theoretical directions and an agenda for research. Inf. Syst. J. 2019, 29, 6–42. [Google Scholar] [CrossRef] [Green Version]
  15. Albirini, A. Teachers’ attitudes toward information and communication technologies: The case of Syrian EFL teachers. Comput. Educ. 2006, 47, 373–398. [Google Scholar] [CrossRef]
  16. Joo, Y.J.; Lim, K.Y.; Kim, N.H. The effects of secondary teachers’ technostress on the intention to use technology in South Korea. Comput. Educ. 2016, 95, 114–122. [Google Scholar] [CrossRef]
  17. Truzoli, R.; Pirola, V.; Conte, S. The impact of risk and protective factors on online teaching experience in high school Italian teachers during the COVID-19 pandemic. J. Comput. Assist. Learn. 2021, 37, 940–952. [Google Scholar] [CrossRef]
  18. Estrada-Muñoz, C.; Castillo, D.; Vega-Muñoz, A.; Boada-Grau, J. Teacher Technostress in the Chilean School System. Int. J. Environ. Res. Public Health 2020, 17, 5280. [Google Scholar] [CrossRef]
  19. Estrada-Muñoz, C.; Vega-Muñoz, A.; Castillo, D.; Müller-Pérez, S.; Boada-Grau, J. Technostress of Chilean Teachers in the Context of the COVID-19 Pandemic and Teleworking. Int. J. Environ. Res. Public Health 2021, 18, 5458. [Google Scholar] [CrossRef]
  20. Jena, R. Technostress in ICT enabled collaborative learning environment: An empirical study among Indian academician. Comput. Hum. Behav. 2015, 51 Pt B, 1116–1123. [Google Scholar] [CrossRef]
  21. Wang, X.; Li, B. Technostress among University Teachers in Higher Education: A Study Using Multidimensional Person-Environment Misfit Theory. Front. Psychol. 2019, 10, 1791. [Google Scholar] [CrossRef] [Green Version]
  22. Li, L.; Wang, X. Technostress inhibitors and creators and their impacts on university teachers’ work performance in higher education. Cogn. Technol. Work 2021, 23, 315–330. [Google Scholar] [CrossRef]
  23. Abilleira, M.P.; Rodicio-García, M.-L.; Deus, M.P.R.-D.; Mosquera-González, M.J. Technostress in Spanish University Teachers During the COVID-19 Pandemic. Front. Psychol. 2021, 12, 496. [Google Scholar] [CrossRef]
  24. Zainun, N.F.H.; Johari, J.; Adnan, Z. Technostress and Commitment to Change: The Moderating Role of Internal Communication. Int. J. Public Adm. 2020, 43, 1327–1339. [Google Scholar] [CrossRef]
  25. Estrada-Muñoz, C.; Vega-Muñoz, A.; Boada-Grau, J.; Castillo, D.; Müller-Pérez, S.; Contreras-Barraza, N. Impact of Techno-Creators and Techno-Inhibitors on Techno-Stress Manifestations in Chilean Kindergarten Directors in the Context of the COVID-19 Pandemic and Teleworking. Front. Psychol. 2022, 13, 865784. [Google Scholar] [CrossRef] [PubMed]
  26. Al-Qallaf, C.L. Librarians and Technology in Academic and Research Libraries in Kuwait: Perceptions and Effects. Libri 2006, 56, 168–179. [Google Scholar] [CrossRef]
  27. Wang, X.; Tan, S.C.; Li, L. Technostress in university students’ technology-enhanced learning: An investigation from multidimensional person-environment misfit. Comput. Hum. Behav. 2020, 105, 106208. [Google Scholar] [CrossRef]
  28. González-López, O.R.; Buenadicha-Mateos, M.; Sánchez-Hernández, M. Overwhelmed by Technostress? Sensitive Archetypes and Effects in Times of Forced Digitalization. Int. J. Environ. Res. Public Health 2021, 18, 4216. [Google Scholar] [CrossRef]
  29. Upadhyaya, P.; Vrinda. Impact of technostress on academic productivity of university students. Educ. Inf. Technol. 2021, 26, 1647–1664. [Google Scholar] [CrossRef]
  30. Zhao, G.; Wang, Q.; Wu, L.; Dong, Y. Exploring the Structural Relationship Between University Support, Students’ Technostress, and Burnout in Technology-enhanced Learning. Asia Pac. Educ. Res. 2021, 31, 463–473. [Google Scholar] [CrossRef]
  31. Almaiah, M.A.; Al-Khasawneh, A.; Althunibat, A. Exploring the critical challenges and factors influencing the E-learning system usage during COVID-19 pandemic. Educ. Inf. Technol. 2020, 25, 5261–5280. [Google Scholar] [CrossRef]
  32. Alvarez, J.; Labraña, J.; Brunner, J.J. La educación superior técnico profesional frente a nuevos desafíos: La Cuarta Revolución Industrial y la Pandemia por COVID-19. Rev. Educ. Política Soc. 2021, 6, 11–38. [Google Scholar] [CrossRef]
  33. Watermeyer, R.; Crick, T.; Knight, C.; Goodall, J. COVID-19 and digital disruption in UK universities: Afflictions and affordances of emergency online migration. High. Educ. 2021, 81, 623–641. [Google Scholar] [CrossRef] [PubMed]
  34. Mok, K.H.; Xiong, W.; Ke, G.; Cheung, J.O.W. Impact of COVID-19 pandemic on international higher education and student mobility: Student perspectives from mainland China and Hong Kong. Int. J. Educ. Res. 2021, 105, 101718. [Google Scholar] [CrossRef] [PubMed]
  35. Gopal, R.; Singh, V.; Aggarwal, A. Impact of online classes on the satisfaction and performance of students during the pandemic period of COVID 19. Educ. Inf. Technol. 2021, 26, 6923–6947. [Google Scholar] [CrossRef] [PubMed]
  36. Bennett, D.; Knight, E.; Rowley, J. The role of hybrid learning spaces in enhancing higher education students’ employability. Br. J. Educ. Technol. 2020, 51, 1188–1202. [Google Scholar] [CrossRef] [Green Version]
  37. Coates, H.; Xie, Z.; Hong, X. Engaging transformed fundamentals to design global hybrid higher education. Stud. High. Educ. 2021, 46, 166–176. [Google Scholar] [CrossRef]
  38. Abilleira, M.P.; Rodicio-García, M.L.; Ríos-De-Deus, M.P.; Mosquera-González, M.J. Technostress in Spanish University Students: Validation of a Measurement Scale. Front. Psychol. 2020, 11, 582317. [Google Scholar] [CrossRef]
  39. Hughes, D.J. Psychometric Validity. In The Wiley Handbook of Psychometric Testing; Irwing, P., Booth, T., Hughes, D.J., Eds.; John Wiley & Sons Ltd.: Hoboken, NJ, USA, 2018; pp. 751–779. [Google Scholar] [CrossRef]
  40. Lloret-Segura, S.; Ferreres-Traver, A.; Hernandez, A.; Tomás, I. Exploratory Item Factor Analysis: A practical guide revised and up-dated. An. Psicol. 2014, 30, 1151–1169. [Google Scholar] [CrossRef]
  41. Ferrando, P.J.; Lorenzo-Seva, U. Program FACTOR at 10: Origins, development and future directions. Psicothema 2017, 29, 236–240. [Google Scholar] [CrossRef]
  42. Lorenzo-Seva, U.; Timmerman, M.E.; Kiers, H.A.L. The Hull Method for Selecting the Number of Common Factors. Multivar. Behav. Res. 2011, 46, 340–364. [Google Scholar] [CrossRef]
  43. Kyriazos, T.A. Applied Psychometrics: Sample Size and Sample Power Considerations in Factor Analysis (EFA, CFA) and SEM in General. Psychology 2018, 09, 2207–2230. [Google Scholar] [CrossRef]
  44. Velicer, W.F.; Fava, J.L. Affects of variable and subject sampling on factor pattern recovery. Psychol. Methods 1998, 3, 231–251. [Google Scholar] [CrossRef]
  45. Sun, J. Assessing Goodness of Fit in Confirmatory Factor Analysis. Meas. Eval. Couns. Dev. 2005, 37, 240–256. [Google Scholar] [CrossRef]
  46. Schermelleh-Engel, K.; Moosbrugger, H.; Müller, H. Evaluating the Fit of Structural Equation Models: Tests of Significance and Descriptive Goodness-of-Fit Measures. Methods Psychol. Res. 2003, 8, 23–74. Available online: https://www.stats.ox.ac.uk/~snijders/mpr_Schermelleh.pdf (accessed on 9 August 2022).
  47. Kalkan, K.; Kelecioğlu, H. The Effect of Sample Size on Parametric and Nonparametric Factor Analytical Methods. Educ. Sci. Theory Pract. 2016, 16, 153–171. [Google Scholar] [CrossRef] [Green Version]
  48. Puentes-Martínez, L.; Díaz-Rábago, A.B. Reliability and construct validity of the Perceived Stress Scale in medical students. Rev. de Cienc. Médicas de Pinar del Río 2019, 23, 373–379. Available online: http://ref.scielo.org/d6gmjs (accessed on 10 October 2022).
  49. Murguia-Ramirez, A.C.; Pozos-Radillo, B.E.; Plascencia-Campos, A.R. Work stress and its relationship with socio-labor factors in teachers of a public preparatory school and of a private one. Rev. Cuba. de Salud y Trab. 2019, 20, 52–57. Available online: http://revsaludtrabajo.sld.cu/index.php/revsyt/article/view/81 (accessed on 10 October 2022).
  50. National Statistics Institute. Population and Housing Census. Available online: https://www.ine.cl/estadisticas/sociales/censos-de-poblacion-y-vivienda/censo-de-poblacion-y-vivienda (accessed on 24 October 2022).
  51. National Education Council. Regional INDICES. Available online: https://www.cned.cl/indices_New_~/regional.php (accessed on 24 October 2022).
  52. Demerouti, E.; Bakker, A.B.; Nachreiner, F.; Schaufeli, W.B. The job demands-resources model of burnout. J. Appl. Psychol. 2001, 86, 499–512. [Google Scholar] [CrossRef]
  53. Ayyagari, R.; Grover, V.; Purvis, R. Technostress: Technological Antecedents and Implications. MIS Q. 2011, 35, 831–858. [Google Scholar] [CrossRef] [Green Version]
  54. Lee, J. Does Stress from Cell Phone Use Increase Negative Emotions at Work? Soc. Behav. Personal. Int. J. 2016, 44, 705–715. [Google Scholar] [CrossRef]
  55. Johnson, J.V.; Hall, A.M. Job strain, work place social support, and cardiovascular disease: A cross-sectional study of a random sample of the Swedish working population. Am. J. Public Health 1988, 78, 1336–1342. [Google Scholar] [CrossRef] [Green Version]
  56. French, J.R.P., Jr.; Rodgers, W.; Cobb, S. Adjustment as person-environment fit. In Coping and Adaptation; Coelho, G.V., Hamburg, D.A., Adams, J.E., Eds.; Basic Books: New York, NY, USA, 1974; pp. 316–333. [Google Scholar]
  57. Harrison, R.V. Person-environment fit and job stress. In Stress at Work; Cooper, C.L., Payne, R., Eds.; John Wiley and Sons: Hoboken, NJ, USA, 1978; pp. 175–205. [Google Scholar]
  58. Caplan, R.D. Person-environment fit theory and organizations: Commensurate dimensions, time perspectives, and mechanisms. J. Vocat. Behav. 1987, 31, 248–267. [Google Scholar] [CrossRef] [Green Version]
  59. Caplan, R.D.; Van Harrison, R. Person-Environment Fit Theory: Some History, Recent Developments, and Future Directions. J. Soc. Issues 1993, 49, 253–275. [Google Scholar] [CrossRef]
  60. Tarafdar, M.; Tu, Q.; Ragu-Nathan, T.S.; Ragu-Nathan, B.S. Crossing to the dark side: Examining, creators, outcomes, and inhibitors of technostress. Commun. ACM 2011, 54, 113–120. [Google Scholar] [CrossRef]
  61. Hwang, I.; Cha, O. Examining technostress creators and role stress as potential threats to employees’ information security compliance. Comput. Hum. Behav. 2018, 81, 282–293. [Google Scholar] [CrossRef]
  62. Ones, D.S.; Viswesvaran, C.; Schmidt, F.L. Realizing the full potential of psychometric meta-analysis for a cumulative science and practice of human resource management. Hum. Resour. Manag. Rev. 2017, 27, 201–215. [Google Scholar] [CrossRef]
  63. Dahlke, J.A.; Wiernik, B.M. psychmeta: An R Package for Psychometric Meta-Analysis. Appl. Psychol. Meas. 2019, 43, 415–416. [Google Scholar] [CrossRef]
  64. Graham, C.R. Blended Learning Systems: Definition, Current Trends, and Future Directions. In Handbook of Blended Learning: Global Perspectives, Local Designs; Bonk, C.J., Graham, C.R., Eds.; Pfeiffer Publishing: San Francisco, CA, USA, 2006; pp. 3–21. Available online: https://media.wiley.com/product_data/excerpt/86/07879775/0787977586-3.pdf (accessed on 30 May 2022).
  65. Porter, W.W.; Graham, C.; Spring, K.A.; Welch, K.R. Blended learning in higher education: Institutional adoption and implementation. Comput. Educ. 2014, 75, 185–195. [Google Scholar] [CrossRef]
  66. Ferrando, P.J.; Lorenzo-Seva, U.; Hernández-Dorado, A.; José Muñiz, J. Decalogue for the Factor Analysis of Test Items. Psicothema 2022, 34, 7–17. [Google Scholar] [CrossRef]
  67. Vega-Muñoz, A.; Estrada-Muñoz, C. Evaluating Technostress to Improve Teaching Performance: Chilean Higher Education Case. In Evaluating Mental Workload for Improved Work-Place Performance; Realyvásquez-Vargas, A., Arredondo-Soto, K., Hernández-Escobedo, G., González-Reséndiz, J., Eds.; IGI Global: Hershey, PA, USA, 2020; pp. 161–183. [Google Scholar] [CrossRef]
  68. Valdez-Bonilla, H.; Ron-Murguía, C. Escala Utrecht de Engagement en el Trabajo (Utrecht Work Engagement Scale, UWES). Utrecht: Occupational Health Psychology Unit Utrecht University. 2011. Available online: https://www.wilmarschaufeli.nl/publications/Schaufeli/Test%20Manuals/Test_manual_UWES_Espanol.pdf (accessed on 10 August 2022).
  69. Elipe, P.; Mora-Merchán, J.A.; Nacimiento, L. Development and Validation of an Instrument to Assess the Impact of Cyberbullying: The Cybervictimization Emotional Impact Scale. Cyberpsychology Behav. Soc. Netw. 2017, 20, 479–485. [Google Scholar] [CrossRef]
  70. Moon, S.J.; Hwang, J.S.; Kim, J.Y.; Shin, A.L.; Bae, S.M.; Kim, J.W. Psychometric Properties of the Internet Addiction Test: A Systematic Review and Meta-Analysis. Cyberpsychology Behav. Soc. Netw. 2018, 21, 473–484. [Google Scholar] [CrossRef]
Ijerph 19 14493 g001 550
Figure 1. Excluded variable histograms: (a) VAR_05; (b) VAR_10; and (c) VAR_22.
Figure 1. Excluded variable histograms: (a) VAR_05; (b) VAR_10; and (c) VAR_22.
Ijerph 19 14493 g001
Table
Table 1. Validation and reliability reported in previous articles and parameters.
Table 1. Validation and reliability reported in previous articles and parameters.
ArticleCountrySampleMethodFactorsMIFχ2/dfRMSEAAGFIGFICFIRFINFINNFIRMSR
Wang et al. [21]China343EFA/CFA542.06 *0.06 *NRNR0.95 *NR0.91 *NRNR
Penado-Abilleira et al. [38]Spain1744EFA/CFA53NRNR0.994 **0.995 **NR0.993 **0.994 **NR0.054 *
Schermelleh-Engel et al. [46]Parameters≥200Good fit-NR≥0
≤2		≤0.05≥0.90
≤1.00		≥0.95
≤1.00		≥0.97
≤1.00		>0.90 +≥0.95
≤1.00		≥0.97
≤1.00		<0.05 ++
Acceptable fit-≥3>2
≤3		>0.05
≤0.08		≥0.85
<0.90		≥0.90
<0.95		≥0.95
<0.97		NR≥0.90
<0.95		≥0.95
<0.97		≥0.05
≤0.08 ++
NR: not reported. ** Good fit; * acceptable fit. + indicated in Penado-Abilleira et al. [38]. ++ indicated in Kalkan et al. [47].
Table
Table 2. Participant sample characterization.
Table 2. Participant sample characterization.
VariableCategory/LevelFrequencyPercentage
University TypeState University7133.5%
Private University14166.5%
Educational LevelPostgraduate104.7%
Undergraduate20295.3%
Educational JourneyDaytime (synchronous)12960.8%
Evening (synchronous)3014.2%
Weekends (synchronous)62.8%
Online (asynchronous)4722.2%
Age LevelLess than 20 years old6731.6%
21 to 30 years11654.7%
31 to 40 years old2210.4%
41 to 50 years old52.4%
51 to 60 years old10.5%
60 years and older10.5%
Average ageN/AN/A
Job Condition
(Chilean census standard)		I am looking for a job for the first time (unemployed)136.1%
I am unemployed (unemployed)219.9%
I am physically or mentally unable to work (unemployed)31.4%
I am exclusively studying (not working)11453.8%
I am busy5626.4%
I am not interested in working52.4%
GenderFemale15171.2%
Male6128.8%
Table
Table 3. Communalities.
Table 3. Communalities.
Variable123467891112131415161718192021
Initial0.6390.6700.7070.7130.6690.6100.7210.5810.5050.7440.6330.4970.4650.4800.6380.4940.4620.4680.470
Extraction0.5680.5730.7180.8330.6610.6410.7420.6040.4270.7550.6550.4490.4050.4970.6830.4770.5240.5630.525
Table
Table 4. Exploratory Factor Analysis for 4 factors.
Table 4. Exploratory Factor Analysis for 4 factors.
KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.0.897
Bartlett’s Test of SphericityApprox. Chi-Square2432.170
Degree of freedom171
Significance0.000
Pattern Matrix a
IDVariableFactor
1234
ADO1VAR_010.678
ADO2VAR_020.534
ADO3VAR_03 0.692
ADO4VAR_04 0.864
NSO1VAR_06 0.783
NSO2VAR_07 0.739
NSO3VAR_08 0.902
NSO4VAR_09 0.774
ADT3VAR_120.603
ADT4VAR_130.702
NST1VAR_14 0.623
NST2VAR_15 0.544
NST3VAR_160.729
NST4VAR_170.709
NST5VAR_180.651
PPF1VAR_19 0.695
PPF2VAR_20 0.712
PPF3VAR_21 0.628
% of Variance41.17610.0834.6553.561
Cumulative %41.17651.25955.91559.476
Factor Correlation Matrix b
Factor1234
11.000
20.4581.000
30.3690.4921.000
40.5530.3670.3141.000
a Extraction Method: Unweighted Least Squares. Rotation Method: Oblimin with Kaiser Normalization. Rotation converged in 8 iterations. b Extraction Method: Unweighted Least Squares. Rotation Method: Oblimin with Kaiser Normalization.
Table
Table 5. Communalities.
Table 5. Communalities.
Variable123467891112131415161718192021
Initial0.6390.6700.7070.7130.6690.6100.7210.5810.5050.7440.6330.4970.4650.4800.6380.4940.4620.4680.470
Extraction0.5410.5810.5500.5030.6500.6380.7460.6020.4240.7680.6470.4320.4020.4140.6470.4290.5090.5400.519
Table
Table 6. Exploratory Factor Analysis for 3 factors.
Table 6. Exploratory Factor Analysis for 3 factors.
Pattern Matrix a
IDVariableFactor
123
ADO1VAR_010.724
ADO2VAR_020.733
ADO3VAR_030.727
ADO4VAR_040.691
NSO1VAR_06 0.789
NSO2VAR_07 0.746
NSO3VAR_08 0.916
NSO4VAR_09 0.776
ADT2VAR_110.652
ADT3VAR_120.887
ADT4VAR_130.831
NST1VAR_14 0.614
NST2VAR_15 0.539
NST3VAR_160.649
NST4VAR_170.745
NST5VAR_180.620
PPF1VAR_19 0.683
PPF2VAR_20 0.720
PPF3VAR_21 0.624
% of Variance40.9529.9374.583
Cumulative %40.95250.89055.473
Factor Correlation Matrix b
Factor123
11.000
20.5361.000
30.4470.5031.000
a Extraction Method: Unweighted Least Squares. Rotation Method: Oblimin with Kaiser Normalization. Rotation converged in 5 iterations. b Extraction Method: Unweighted Least Squares. Rotation Method: Oblimin with Kaiser Normalization.
Table
Table 7. Confirmatory Factor Analysis for 3 factors.
Table 7. Confirmatory Factor Analysis for 3 factors.
Rotated Loading Matrix
PreviousTS4USVariableFactor
123
ADO1ADTE01VAR_01 0.732
ADO2ADTE02VAR_02 0.745
ADO3ADTE03VAR_03 0.734
ADO4ADTE04VAR_04 0.696
NSO1NSR01VAR_060.794
NSO2NSR02VAR_070.747
NSO3NSR03VAR_080.924
NSO4NSR04VAR_090.779
ADT2ADTE05VAR_11 0.657
ADT3ADTE06VAR_12 0.894
ADT4ADTE07VAR_13 0.841
NST1NSR05VAR_140.612
NST2NSR06VAR_150.535
NST3ADTE08VAR_16 0.657
NST4ADTE09VAR_17 0.749
NST5ADTE10VAR_18 0.629
PPF1PPF1VAR_19 0.714
PPF2PPF2VAR_20 0.760
PPF3PPF3VAR_21 0.652
% of Variance43.20412.1126.991
Cumulative %43.20455.31662.307
Factor Correlation Matrix
Factor123
11.000
20.5101.000
30.5580.5451.000
Table
Table 8. Validation and reliability reported in previous articles and parameters.
Table 8. Validation and reliability reported in previous articles and parameters.
ArticleCountrySampleMethodFactorsMIFχ2/dfRMSEAAGFIGFICFIRFINFINNFIRMSR
Wang et al. [21]China343EFA/CFA542.06 *0.06 *NRNR0.95 *NR0.91 *NRNR
Penado-Abilleira et al. [38]Spain1744EFA/CFA53NRNR0.994 **0.995 **NR0.993 **0.994 **NR0.054 *
Proposed ModelChile212EFA/CFA34NR0.072 *0.986 **0.990 **0.979 **NRNR0.970 **0.047 **
Schermelleh-Engel et al. [46]Parameters≥200Good fit-NR≥0
≤2		≤0.05≥0.90
≤1.00		≥0.95
≤1.00		≥0.97
≤1.00		>0.90 +≥0.95
≤1.00		≥0.97
≤1.00		<0.05 ++
Acceptable fit-≥3>2
≤3		>0.05
≤0.08		≥0.85
<0.90		≥0.90
<0.95		≥0.95
<0.97		NR≥0.90
<0.95		≥0.95
<0.97		≥0.05
≤0.08 ++
NR: not reported. ** Good fit; * acceptable fit. + indicated in Penado-Abilleira et al. [38]. ++ indicated in Kalkan et al. [47].
Table
Table 9. Reliability statistics.
Table 9. Reliability statistics.
FactorFactor NameCronbach’s AlphaCronbach’s Alpha Base on Standardized ItemsNumber of Items
1Needs-Supplies Resources, NSR0.8870.8866
2Person-People Factor, PPF0.7530.7543
3Abilities-Demands Techno-Educational, ADTE0.9210.92210
TotalTS4US scale0.9250.92519
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Vega-Muñoz, A.; Estrada-Muñoz, C.; Andreucci-Annunziata, P.; Contreras-Barraza, N.; Bilbao-Cotal, H. Validation of a Measurement Scale on Technostress for University Students in Chile. Int. J. Environ. Res. Public Health 2022, 19, 14493. https://doi.org/10.3390/ijerph192114493

AMA Style

Vega-Muñoz A, Estrada-Muñoz C, Andreucci-Annunziata P, Contreras-Barraza N, Bilbao-Cotal H. Validation of a Measurement Scale on Technostress for University Students in Chile. International Journal of Environmental Research and Public Health. 2022; 19(21):14493. https://doi.org/10.3390/ijerph192114493

Chicago/Turabian Style

Vega-Muñoz, Alejandro, Carla Estrada-Muñoz, Paola Andreucci-Annunziata, Nicolas Contreras-Barraza, and Heidi Bilbao-Cotal. 2022. "Validation of a Measurement Scale on Technostress for University Students in Chile" International Journal of Environmental Research and Public Health 19, no. 21: 14493. https://doi.org/10.3390/ijerph192114493

APA Style

Vega-Muñoz, A., Estrada-Muñoz, C., Andreucci-Annunziata, P., Contreras-Barraza, N., & Bilbao-Cotal, H. (2022). Validation of a Measurement Scale on Technostress for University Students in Chile. International Journal of Environmental Research and Public Health, 19(21), 14493. https://doi.org/10.3390/ijerph192114493

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop