Interest in affective concepts related to the use of technology and information systems has grown enormously in recent decades (Davidson, 1998; Salazar-Concha et al., 2021), especially concerning its “dark side,” that is, negative emotions (Salanova et al., 2014; Tarafdar et al., 2019). However, there are many conceptual and methodological gaps in the investigation of this topic (Agogo & Hess, 2018).

Three concepts are close to that of technology anxiety. In chronological order of appearance, they are “computer anxiety” (Masters, 1967), “technophobia” (Paschen & Gresser, 1974), and “technostress” (Brod, 1982). Although used as synonyms, probably because they have been investigated from areas outside Psychology, such as information technologies or human resources, the difference can be seen in their names: compared to phobia, anxiety is an emotional response that is not necessarily abnormal; regarding stress, anxiety is a specific emotion like anger or sadness (Agogo & Hess, 2018; Khasawneh, 2018; Tacy, 2016). In this sense, “technology anxiety” is a generalization to other technologies of the original “computer anxiety”.

Older people are especially vulnerable to technology anxiety. For example, two studies developed in Italy showed that high levels of computer anxiety in older people were associated with disuse, lack of autonomy, and the need for help in the management of technology, linked to high levels of technophobia (di Giacomo et al., 2020; di Giacomo et al., 2020).

The risks of technology anxiety can transcend playful or social aspects, causing harm in other areas. In the professional context, Hou et al. (2017) experimentally demonstrated that the combination of advanced age and technophobia generated great reading difficulties on screens. Tams (2017), concerning the increased technology anxiety of older workers, proposed an explanation that is based on differences in inhibitory control compared with younger adults.

As for health, technology anxiety linked to internet use is associated with lower well-being and quality of life (Nimrod, 2018a). It can even interfere with the acceptability of the use of surveillance devices in older people with heart disease (Tsai et al., 2020) or tumor monitoring systems (Özdemir-Güngör & Camgöz-Akdag, 2018). Technology anxiety is considered a risk factor for older people’s health, reaching critical levels during the COVID-19 pandemic (Nimrod, 2021, 2022). If age is linked to low levels of education, the problem is aggravated, to the point of requiring psychoeducational intervention programs to address it.

Psychometric measures of “technostress” (Nimrod, 2018b; Penado Abilleira et al., 2020), “technophobia” (Martínez-Córcoles et al., 2017), and “computer anxiety” (Raub, 1981) have been developed. Concerning technology anxiety, Meuter et al. (2003) developed the first measure by adapting that of Raub (1981) to any information and communication technology (ICT) in a consumer study on self-service technologies. This produced a unifactorial instrument, with nine items, reliability of = .90, predicting the use of self-service technology well above other variables such as age, gender, income level, or educational level. Niemelä-Nyrhinen (2007) tested the psychometric properties of this scale with a sample of 620 Finnish baby boomers, finding a bifactorial structure. The factor they called “fear” was composed of the first six items, which are drafted in the sense of anxiety, and they obtained a reliability of = .89. The factor they called “confidence” was composed of the last three items, drafted in the opposite direction to anxiety but they did not report the reliability, although they stated that it was significantly lower, probably due to the smaller number of items.

López-Bonilla and López-Bonilla (2012) adapted a scale composed of the items of that of Meuter et al. (2003) and another scale of “computer anxiety” (Loyd & Gressard, 1984) to Spanish, using a sample of 819 university students. To our knowledge, there is no psychometric measure of technology anxiety in Spanish validated in the older population.

Considering the above, the aims of this work were, firstly, to adapt the Technology Anxiety Scale to Spanish in the older population and, secondly, to differentially determine its fit to a unifactorial (Meuter et al., 2003) and bifactorial structure (Niemelä-Nyrhinen, 2007).

The process of translating the items was described in the Procedure section. The resulting scale in Spanish is shown in Appendix.

The descriptive data of the items are shown in Table 1. As can be seen, the items with a higher mean were “technological terminology sounds like confusing jargon to me” and “I’m sure I can learn technology-related skills”, whereas the item “I feel apprehensive about the use of technology” is the one that presented a lower mean. The total score of the scale in this sample has a mean of 25.82 with 9 being the minimum score and 45 the maximum score. Therefore, this sample presents moderately high values of technology anxiety.

Table 1

Note. M = mean; SD = standard deviation; Sk = skewness; Kt = kurtosis.

As for the factor analyses, as can be seen in Table 2, Model 1 (one-dimensional) showed acceptable fit indices, although the two-factor model clearly presented better values.

Tabla 2

Note. M1 = one factor model; M2 = two correlated factor model; χ² = chi-square; df = degrees of freedom; CFI = comparative fit index; RMSEA = root mean square error of approximation.

All the χ² are p < .001.

All factor loadings were higher than .600, as shown in Figure 1. In addition, the Confidence factor and the Fear factor had a negative and high correlation. CRI values were good both for the Fear (CR = .924) and the Confidence (CR = .773) factors, as occurred with the AVE values (Fear = .714, Confidence = .609). Both the overall Cronbach’s alpha (α = .899) and for each subscale (Fear α = .896, Confidence α = .717) were good.

Figure 1

Regarding the results of concurrent validity, the fear factor showed negative and significant correlations with Personal Relationships with Others (r = -.286; p < .001), Personal Growth (r = -.209, p < .001), Quality of Life (r = -.153, p < .001), and Health Satisfaction (r = -.188, p < .001), and no significant correlation with Self-acceptance (r = -.041, p = .438). On the other hand, the Confidence factor showed positive and significant correlations with Personal Relationships with Others (r = .293, p < .001), Personal Growth (r = .248, p < .001), Quality of Life (r = .253, p < .001), and Health Satisfaction (r = .270, p < .001) and did not show significant correlation with Self-acceptance (r = .079, p = .134).

Using the two-factor model, which showed better fit, gender invariance analysis was performed. Table 3 shows that this model fit men’s group well and women’s group excellently. After this confirmation, we verified that the criteria for the scalar invariance of the instrument were met and, therefore, the latent means between genders could be compared. The results showed that, by setting the reference group (men) to 0, there were no group differences either in the Fear factor (b = 0.080, z = 0.092, p = .359) or the Confidence factor (b = 0.057, z = 0.620, p = .535).

Tabla 3

Note. df = degrees of freedom; χ² = chi-square; Δχ² = difference in chi-square; Δdf = difference in degrees of freedom; CFI = comparative fit index; TLI = Tucker-Lewis index; SRMR = standard root mean square residual.

All χ² are significant at p < .001.

Our objective was to validate the Technology Anxiety Scale (Meuter et al., 2003) in the Spanish-speaking older population.

After the process of cross-cultural adaptation of the items, which was carried out without problems, both the unifactorial structure proposed by Meuter et al. (2003) and that of two correlated factors found by Niemelä-Nyrhinen (2007) showed adequate fit. Therefore, we can conclude, preliminarily, that this instrument is suitable for measuring technology anxiety in older Spaniards. Of course, our results are in line with those of Niemelä-Nyrhinen (2007), and therefore support the use of the scale to measure two inversely correlated factors, which correspond to the direction of the drafting of the items: the factor called “fear”, which integrates the first six items, reflecting negative emotionality towards technology, and the factor called “confidence”, which integrates the last three items and reflects trust, that is, a positive experience of the relationship with technology.

We cannot compare our fit indices with those of the two studies that serve as a reference because neither of them performed confirmatory factor analyses, but only exploratory factor analyses. As a reference, our factor loadings, ranging between .63 and .90, were even higher than those obtained by Meuter et al. (2003), between .58 and .89, and very similar to those obtained by Niemelä-Nyrhinen (2007), between .61 and .91. Nor can we take as a reference the scale validated in Spanish university students by López-Bonilla and López-Bonilla (2012), not only because they merged the technology anxiety scale of Meuter et al. (2003) with another “computer anxiety” scale (Loyd & Gressard, 1984), but also because they did not report the fit indices of the confirmatory factor analysis in their work.

Also, although the two previous studies were conducted with people of both genders, 53% and 59% of women respectively, the validity of the instrument for both genders had not actually been established. Our study confirms the factorial invariance of the scale by gender and therefore endorses the use of this scale in men and women.

Regarding concurrent validity, it was confirmed that older adults with higher technology anxiety had lower scores on well-being, quality of life, and health satisfaction, in line with the results obtained by Nimrod (2018a) and Pfaffinger et al. (2021).

The study presents some limitations. First, due to the COVID-19 pandemic situation, the questionnaire was applied precisely using technology, through Google Forms, also distributed through technology such as email, instant messaging, or social networks. This could have produced a sample bias favoring participants with lower levels of technology anxiety. In any case, the fact that it was their relatives who urged the older people to carry out the survey could mitigate this bias. Secondly, and concerning the previous limitation, although the sample was sufficient to reach the desired statistical power, it would have been desirable to have a higher number of participants, a fact that perhaps was also conditioned by the moment of application, during the COVID-19 pandemic. Finally, as argued in the introduction, to facilitate the application to older participants and even to those with potential cognitive impairment (Mora Simón et al., 2012; Vega Alonso et al., 2018), we decided to simplify the response format, going from seven to five Likert-type alternatives. In fact, the sample was finally made up of people with an average age of more than 78 years, and more than 70% had no studies or had only primary studies. However, despite being the desired effect on our part, the reduction of the response alternatives may have influenced the good factorial fit we found. In any case, these limitations imply that this validation should be considered preliminary and that studies with larger and more representative samples are required.

Evidence indicates that technology anxiety is one of the main barriers to older people’s adaptation to digital life (di Giacomo et al., 2019, 2020; Nimrod, 2018b; Tsai et al., 2020). Interventions are therefore required to counter it, which, in turn, need appropriate screening tools for this purpose. In this sense, the main practical implication of our study has been to demonstrate the preliminary validity of the Technology Anxiety Scale to evaluate technology anxiety in older Spaniards.