Hardiness, gender, and age as predictors of attitudes towards new technologies

Background and Problem. This study examines the predictive power of hardiness, gender, and age regarding attitudes toward new technologies.
Methods. A sample of 454 adult volunteers (aged 18–54 years) residing in three major Russian cities—Moscow, St. Petersburg, and Yekaterinburg—participated in the research. Data were collected using self-report questionnaires: the Hardiness Survey and the Technology Attitude Questionnaire. Statistical analyses, including descriptive statistics, the T-test, and Automatic Linear Modeling, were performed to identify significant predictors of various technological attitudes. Results. The findings indicate that age and gender are significant predictors of both technophilia and technophobia, suggesting potential generational and gender-related disparities in technology adoption and apprehension. Furthermore, the “control” and “challenge” components of hardiness significantly affect technophobia, technopessimism, and technorationalism, implying that psychological hardiness may mediate these attitudes. However, the constructed models demonstrated limited explanatory power, highlighting the need to consider additional variables, such as socioeconomic status, education level, and individual technology experience.

Introduction

The advent of a technology-saturated era necessitates continuous societal adaptation to emerging innovations. Based on an analytical review of the scientific literature, Ajzen (2001) defines attitude as an integral evaluative categorization, positioning a psychological object along a continuum of evaluative judgments, which, according to the author, allows for the prediction of behavioral intentions and subsequent behavior. Expanding on this perspective, Т. Nestic et al. (2017) conceptualize attitude towards new technologies as a multifaceted socio-psychological phenomenon incorporating cognitive, emotional-evaluative, and behavioral constituents. In the context of perceiving contemporary technologies, four dominant types of attitudes are recognized: technophobia, technophilia, techno-optimism, and techno-pessimism. Technophilia is characterized by a proactive openness and enthusiasm regarding the assimilation and application of innovative solutions. Techno-rationalism implies a conscious and purposeful utilization of technological advancements. Technophobia manifests as difficulties in mastering and anxieties associated with new technologies. Techno-pessimism, conversely, reflects beliefs concerning the potential social dangers and adverse consequences of technological progress (Soldatova et al., 2021). Research by Т. Nestic (2020) demonstrates that technophilia and technophobia are not opposing poles but rather distinct phenomena that can coexist.

Odai Y. Khasawneh (2018) emphasizes the prevalence of research focusing on computer anxiety, while overlooking the broader spectrum of contemporary technologies. The author identifies five latent factors that determine technophobia: techno-paranoia (the belief that technology is used for surveillance), techno-anxiety (generalized apprehension regarding technology), techno-fear (anxiety experienced when interacting with new technologies), cyber-rebellion (fear of artificial intelligence and robotic systems), and mobile device aversion (fear of using mobile phones). Based on these findings, technophobia is defined as an irrational fear or anxiety in response to emerging technologies that are radically transforming established ways of life (Odai Y. Khasawneh, 2018).

А.Dorokhov and А. Gusev conceptualize technophobia as a broad-spectrum construct encompassing not only adverse emotional responses to interactions with technological devices or novel technologies but also aversive behavioral tendencies and discomfort associated with their utilization. Conversely, technophilia, as interpreted by these authors, represents a constructively positive attitude toward technology intertwined with a degree of dependence. The structure of technophilia comprises three distinct components: techno-enthusiasm (a motivational facet) characterized by aspirations and intentions regarding technological applications; techno-addiction (a behavioral component) manifested through active technology utilization and a concomitant need for it; and techno-regulation (an emotional dimension) evinced by a desire for software and hardware updates and the gratification derived therefrom (Dorokhov & Gusev, 2023).

1.1 The Relationship Between Attitudes Toward New Technologies and Age

Empirical studies demonstrate a complex interrelationship between age and attitudes concerning new technologies.  G. Soldatova and colleagues have shown that adolescents and their parents commonly exhibit technophilia and technorationalism, while technophobia scores remain minimal. Among adolescents, technophilia correlates with active use of digital devices in everyday life, whereas, among parents, it is associated with general user activity. Notably, older parents exhibit heightened technophobia and diminished technorationalism and technophilia, suggesting that age constitutes a significant determinant of technology perception (Soldatova et al., 2021). This aligns with the broader concept of a “digital generation gap,” where differences in early exposure and socialization with technology create distinct cognitive and affective schemas towards innovations (Vogels, 2019). Recent research further delineates this gap by highlighting how differential exposure to digital environments across the lifespan shapes neurocognitive adaptations and affective responses to technology, with younger cohorts displaying greater neural plasticity in areas associated with technology interaction (Lutz & Tamò-Larrieux, 2020).

А. Saenko (2024) investigated the interplay between attitudes toward robots (defined as a specific technology grounded in artificial intelligence), value orientations, and the fulfillment of security needs across Generations X, Y, and Z. The study sample consisted of 102 respondents aged 18 to 55 years residing in Moscow and the Moscow region: 29 Generation X individuals (born 1968–1981), 38 Generation Y individuals (born 1982–2000), and 35 Generation Z individuals (born 2001 and later). The findings revealed statistically significant differences between Generations X and Z. Generation Z exhibited significantly higher levels of technophilia, plausibly attributable to their upbringing in a digital environment and the consequent development of a positive perception of technologies, particularly robots. Conversely, Generation X demonstrated greater technopessimism, manifesting as skepticism and apprehension regarding the potential adverse consequences of implementing new technologies across diverse domains of life. Technophobia scores were minimal across all age cohorts, suggesting a general lack of pronounced fear or rejection of technology among participants. Technopessimism was more pronounced in Generations X and Y compared to technophilia, possibly indicating a more considered and pragmatic approach to evaluating the potential of technologies among members of these generations (Saenko, 2024).

1.2 Attitudes Toward New Technologies and Gender

N. Volkova demonstrated that men aged 21-35 years with high IQ scores exhibit more pronounced technophilia. In contrast, women in the same age range with high IQ scores exhibit less technophilia but demonstrate an increase in technorationality. While technophilia increases with intelligence in both men and women aged 21-35 years, it decreases in men and women aged 36-54 years. Technorationality increases with intelligence in women aged 21-55 years and men aged 21-35 years; however, technorationality decreases in men aged 36-54 years. Technopessimism decreases with intelligence in men aged 21-55 years (Volkova, 2024).

A. Saenko examined the association between technophobia and technophilia with intrasubjective factors across diverse age groups (N = 240, aged 20 to 51 years, M = 31.31, SD = 11.54; 113 men, M = 31.87, SD = 12.28; 127 women). The findings indicated that men exhibited higher levels of technophilia compared to women, particularly in young adulthood, whereas women demonstrated greater technophobia, also primarily in young adulthood (Saenko, 2024).

A study by G. Soldatova, T. Nestik, E. Rasskazova, and E. Dorokhov revealed gender-based differences in attitudes toward technology among adolescents. Male participants demonstrated statistically significantly higher levels of technophilia and technorationalism compared to female participants. However, this trend was not observed in the adult population (Soldatova et al., 2021). In contrast, a study by Anthony, Clarke, and Anderson, conducted on a sample of 176 South African students enrolled in introductory computer science and psychology courses, found no significant gender differences in levels of technophobia (Anthony et al., 2000). Therefore, the influence of gender on the perception of technology remains a subject of debate, necessitating further investigation that accounts for diverse age groups and sociocultural contexts.

Recent meta-analyses further clarify this relationship. A comprehensive review by Koch, R. et al. (2022) confirms that men consistently report higher technology self-efficacy and more positive attitudes, whereas women report higher levels of technology anxiety, a core component of technophobia. This is often attributed to gendered socialization patterns and differing experiences in STEM domains from an early age (Koch et al., 2022).

1.3 The relationship between attitudes towards new technology and personality traits

A study by T. Nestik, which covered 1,600 Russians, showed that techno-optimism and willingness to use new technologies are associated with values of openness to change, while technophobia is associated with values of conservation (Nestik, 2020).

A study conducted by L.M. Anthony, M.C. Clarke, and S.J. Anderson (2000) among students at the University of South Africa examined the level of technophobia, a negative psychological reaction to technology that manifests itself in the form of fear and anxiety. Rosen and Weil’s tools were used to measure technophobia and the NEO-Five Factor Inventory to assess personality traits. The results showed that approximately 33% of the students demonstrated moderate or high levels of technophobia. Technophobia was positively correlated with neuroticism (anxiety, depression, vulnerability to stress) and negatively with openness (interest in new things and innovations). The level of technophobia decreased as the experience of using technology increased, but a weak correlation with age was noted (L.M. Anthony et al., 2000).

A. Soldatova and her colleagues found that technophilia was positively associated with life satisfaction in both real and virtual contexts. However, the association between technophilia and hardiness was weak (r = 0.07). A weak positive correlation was also observed between technophilia and hardiness («control» scale (beliefs about the ability to influence situations)). Conversely, technophobia exhibited negative associations with life satisfaction in both real and virtual contexts and with hardiness and its engagement and control dimensions. The strongest negative association was observed between technophobia and control (r = -0.30). Technopessimism also demonstrated negative associations, although less pronounced than those of technophobia, with life satisfaction in the real world and with hardiness. No significant associations were found with engagement, control, or challenge (Soldatova et al., 2025).

A. Diomin and A. Stepanova examined attitudes toward the speed of social processes (N = 521, mean age 31.5 years, 48.8% male, 65.6% with higher education). They found that technophilia was negatively associated with “Rejection of Social Acceleration” (r = -0.431), which indicates a relationship between higher degrees of technophilia and lower resistance to social change. Conversely, high technophobia related to higher levels of resistance to changes. A similar positive relationship was found between technopessimism and “Rejection of Social Acceleration” (r = 0.334). Technorationalism did not demonstrate a robust association with attitudes toward social change (r = -0.091). Consistent with this, technophobia and technopessimism are associated with rejection of social acceleration, technophilia is associated with the acceptance of social change, and, in this specific study, technorationalism did not reveal a significant relation. Demin, Stepanova,2023)

A study by I. Filenko and S. Moiseev involving residents of Tomsk and the Siberian region (N = 518, 18.9% male, mean age = 21.4 years) demonstrated a statistically significant relationship between technopessimism and various dimensions of attitudes toward artificial intelligence (AI) technologies. Specifically, technopessimism was negatively associated with scores on the “perceived interaction efficiency with AI” scale (r = –0.418, p < 0.001), the “emotional attitude toward AI” scale (r = –0.528, p < 0.001), and the “acceptance of AI technologies” scale (r = –0.532, p < 0.001) (Filenko, Moiseev; 2025).

A. Saenko showed technophilia is associated with a high degree of self-efficacy in establishing relationships with the surrounding environment and other individuals and openness to new experiences and changes. Technophobia relates to both a low degree of self-efficacy in establishing relationships with the surrounding environment and other individuals and a high level of distrust towards others. Technorationalism is associated with high self-confidence in evaluation and prediction skills, alongside an orientation towards values of security and independence. Finally, technopessimism relates to a both a low degree of self-efficacy in establishing relationships with the surrounding environment and other individuals and high interpersonal distrust (Saenko, 2024).

B. Shestova within the framework of systemic-dynamic model of aesthetic experience, addressed issue of attitudes toward new technologies and showed technophilia correlated directly with interest in new media art and positive aesthetic responses (N = 750 aged 16 to 38 (M = 23.86, SD = 6.53), of whom 84% were women comprised students from Stroganov Moscow State Academy of Arts and Industry (MGHPU) and Moscow Institute of Psychoanalysis and secondary school students from Moscow). Conversely, technophobia had an inverse effect, reducing engagement with and interest in new art forms. Technorationalism enhanced cognitive understanding of artworks and stimulated the development of original perspectives on creativity. Technopessimism diminished overall aesthetic receptivity and created barriers to deep immersion in the world of new art forms (Shestova, 2025).

M. Ponomareva and O. Agrinenko investigated the relationship between attitudes towards technology and the degree of role conflict experienced by women raising minor children. The study included 31 working mothers, with a mean age of 39.4 years. Forty-five percent of the mothers were in the 36–45 age range, and 100% were married. A majority of the mothers (48.4%) had two children. A direct positive correlation was found (r = 0.340, p = 0.05) between technophobia and feelings of guilt toward family: women who experienced feelings of guilt about working were more likely to exhibit technophobia. A direct positive correlation was also found (r = 0.396, p = 0.028) between technorationalism and spousal disapproval: when husbands disapproved of their wives’ employment, the women were more likely to engage in the rational use of technology (Ponomareva & Agrinenko, 2025).

The role of hardiness is further supported by recent research on digital resilience. Studies within the technostress literature indicate that personal resources like control and challenge beliefs buffer against anxiety and stress induced by rapid technological change. For instance, individuals high in hardiness are better equipped to reframe technological challenges as opportunities, aligning with the core tenets of the challenge dimension (Califf et al., 2020).

1.4 Hypotheses

In sum, the reviewed literature furnishes a multifaceted understanding of attitudes toward new technologies, highlighting the interplay of age, gender, and personality traits in shaping these perceptions. While technophilia and technorationalism appear prevalent among younger generations and are associated with openness and higher intelligence, technophobia and technopessimism are more pronounced in older cohorts and correlate with values of conservation and neuroticism (Saenko, 2024; Volkova, 2024; Nestik, 2020; Anthony et al., 2000). Gender-based differences, though inconsistent across studies, suggest that men may exhibit greater technophilia, while women may experience higher levels of technophobia, particularly in young adulthood (Saenko, 2024; Soldatova et al., 2021). This aligns with meta-analytic findings showing males generally report higher computer self-efficacy and more positive attitudes, whereas females report higher computer anxiety, a core component of technophobia (Cai, Fan, & Du, 2017; Koch et al., 2022).

The connection between attitudes and personal well-being further underscores the significance of understanding these perceptions. Studies indicate that technophilia is positively associated with life satisfaction, whereas technophobia and, to a lesser extent, technopessimism exhibit negative correlations with life satisfaction and hardiness (Soldatova et al., 2021). Moreover, attitudes toward technology are linked to the acceptance of social acceleration, with technophilia facilitating adaptation to rapid change and technophobia fostering resistance (Diomin & Stepanova, 2023). The predicted role of hardiness is supported by the broader literature on psychological resilience buffering against technostress, where personal resources like control beliefs are negatively associated with stressors arising from technology use (Tarafdar, Pullins, & Ragu-Nathan, 2015). Furthermore, recent models of technology acceptance, such as the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), highlight the moderating role of age, gender, and personal innovativeness, providing a robust framework for our hypotheses (Venkatesh et al., 2016).

These findings collectively emphasize the dynamic and context-dependent nature of attitudes toward new technologies. As technology continues to permeate all facets of modern life, further research is crucial to elucidate the evolving nuances of these attitudes, informing strategies to mitigate technophobia and promote the responsible and inclusive adoption of technological advancements across diverse demographic groups.

A nuanced understanding of these relationships is essential for fostering constructive technology integration. This study aims to investigate the influence of hardiness, gender, and age on attitudes toward new technologies. Based on the existing literature, the following hypotheses were formulated:

(H1): Hardiness, gender, and age are statistically significant predictors of attitudes toward new technologies.

(H2): Age is inversely correlated with technophilia and directly correlated with technophobia and technopessimism.

(H3): Gender influences technophilia/technophobia (the direction of the influence is exploratory, based on conflicting data from previous research). Informed by recent meta-analyses (Koch et al., 2022), we specifically predict that men will score higher on technophilia and lower on technophobia compared to women.

(H4): Hardiness relates positively to technophilia and technorationalism and negatively to technophobia and technopessimism. Specifically, we expect the ‘control’ and ‘challenge’ components to be key buffers against negative attitudes, as suggested by technostress coping literature (Califf et al., 2020).

Method

2.1. Samples

The empirical study encompassed 454 volunteers residing in Moscow, St. Petersburg, and Yekaterinburg. The age of participants ranged from 18 to 54 years. Women constituted 61.8% of the sample. A considerable proportion possessed higher education at the time of the study. Data collection transpired between 2024 and 2025.

2.2 The inclusion criteria

The inclusion criteria for the study were the absence of diagnosed mental disorders and a preserved level of functional status. Before the start of the study, participants signed consent to the study, and consent to the processing of personal data. Participants were informed about the study’s objectives and methods. The research was conducted anonymously and free of charge.

2.3. Measures

This study employed the Hardiness Survey developed by Maddi (1998) and adapted for a Russian sample by D.A. Leontiev and E.I. Rasskazova (Leontiev & Rasskazova, 2006). Hardiness, as a personality disposition, comprises three relatively independent components: commitment (a dedication to fostering connectedness to people and events and mitigating isolation), control (influence can be felt, thus reducing a sense of being helpless challenging circumstances), and challenge (the view that life’s obstacles represent opportunities for growth rather than insecurity). The development of the hardiness test was predicated on six scales consistent with these three dimensions: the alienation test of S. Maddi, D. Jackson’s personality test, and J. Rotter’s locus of control test. The Russian adaptation of the questionnaire incorporates 45 direct and reverse-scored items. According to D. Leontiev and E. Rasskazova, statistically significant differences may manifest between men’s and women’s scores contingent upon their profession. Respondents evaluated the congruence of 45 statements with their dimensions on a four-point scale (“no” – 0 points, “more no than yes” – 1 point, “more yes than no” – 2 points, “yes” – 3 points). For score calculation, direct items are assigned values from 0 to 3, and reverse-scored items are assigned values from 3 to 0.

Attitudes toward new technologies were assessed using the “Technology Attitude Questionnaire” (Soldatova et al., 2021). This questionnaire encompasses scales that evaluate diverse aspects of technology interaction: technophobia (rejection and difficulty in mastering technology), technophilia (positive and enthusiastic attitude toward technology), technorationalism (critical and conscious use of technology), and technopessimism (fears regarding the potential negative social ramifications of technological progress). Raw scale scores were transformed into wall scales. Skewness and kurtosis values were less than 1, indicating adherence to a normal distribution.

2.4. Statistical data processing

IBM SPSS Statistics 28.0 for statistical data processing and encompassed:

• Descriptive analysis to the evaluation data distribution via normal and severity (mean, standard deviation, Skewness, and Kurtosis).
• The assessment used a skewness measurement.
• Percentile normalization for raw scores.
• Automatic Linear Modeling (LINEAR) with all-possible-subsets, and employing Akaike’s Information Criterion Corrected (AICC) as a model selection metric (Yang, 2013). Model quality was assessed using the Information Criterion, with lower values indicating greater accuracy.

Results

3.1.                Descriptive analysis

The sample (N = 454) can be considered sufficiently representative. The mean age of participants was 28.87 years (SD = 11.085), reflecting a broad age range and a distribution with a slight positive skew toward younger individuals. The gender composition was primarily female, comprising 62% of the sample.

All measurement scales (hardiness and attitudes toward new technology) were measured on a 10-point scale, with mean scores centered around 5.5, indicating a central tendency in the data. Skewness and kurtosis values fell within the ±1 range, which is indicative of an approximately normal distribution. Mean scores across all hardiness subscales (Commitment, Control, and Challenge) were remarkably similar, with mean scores averaging approximately 5.53.
The construct of technophobia exhibited the highest degree of variability (SD = 2.01), reflecting significant individual differences in technology-related anxiety. Similarly, technorationalism demonstrated notable variability (SD = 2.04), suggesting varying approaches to the rational use of technologies (see Table 1).

Table 1. Descriptive statistics

3.2. Attitudes toward new technologies in men and women

The results (Table 2) confirm hypothesis H3, which posits a gender-based difference in technophilia and technophobia. These findings are consistent with those reported by Saenko (2024) and partially align with the results of Soldatova et al. (2021). Specifically, men exhibited greater enthusiasm toward technology, whereas women demonstrated higher levels of anxiety. These observations are in line with the meta-analytic trend identified by Koch et al. (2022), further supporting the robustness of gender as a predictor of attitudes toward technology.

Mean scores for all scales were centered around the midpoint (~5.5). Technophobia exhibited the highest degree of variability (SD = 2.01). Descriptive statistics (Table 1) and independent samples t-tests (Table 2) confirmed significant gender differences across several dimensions. Men reported significantly higher levels of technophilia (M = 5.87) than women (M = 5.23), t (452) = 3.47, p = .001. Conversely, women reported significantly higher technophobia scores (M = 5.59) compared to men (M = 5.11), t (452) = –2.50, p = .013. No statistically significant gender differences were observed for technorationalism or technopessimism.

Table 2. Attitudes toward new technologies in men and women (t-test)

3.3.  Automatic Linear Modeling

Multiple regression analyses were conducted to examine how demographic factors (gender, age) and personality traits (hardiness components) predict technology-related attitudes. This section presents the results for technophilia, technophobia, technorationalism, and technopessimism, focusing on model fit, significant predictors, and hypothesis testing.

Table 3. Regression models of hardiness, gender, and age predicting attitudes toward new technologies

Technophilia. The model explained 5.4% of the variance. Significant predictors included gender (β = 0.481) and age (β = –0.258). These results suggest that younger males are more prone to exhibiting technophilia. Thus, hypothesis H2 (inverse relationship with age) is partially confirmed, while hypothesis H3 is confirmed. A marginal positive relationship was observed for commitment (p = .050), indicating a potential link between social connectedness and positive technology attitudes that warrants further investigation.

Technophobia. The model accounted for 7.5% of the variance. Significant predictors were age (β = 0.265), gender (β = –0.416), control (β = –0.172), and challenge (β = –0.148). These findings indicate that older women with lower levels of control and challenge are more prone to technophobia. Consequently, hypothesis H2 (direct relationship with age), H3, and H4 (negative relationship with hardiness components) are confirmed. The inclusion of hardiness components, particularly control, aligns with technostress models, which posit that perceived control serves as a critical moderator of technology-induced anxiety (Califf et al., 2020).

Technorationalism. The model explained only 0.6% of the variance. The sole significant predictor was commitment (β = 0.235). These results suggest that the model explains almost none of the variance in technorationalism. Hypothesis H4 is therefore partially confirmed (positive relationship with commitment). The minimal variance explained indicates that technorationalism—conceptualized as a conscious, evaluative stance toward technology—may be more strongly influenced by cognitive factors, domain-specific knowledge, or situational demands not captured in the present model.

Technopessimism. The model accounted for 2.2% of the variance. Significant predictors included age (β = 0.393) and challenge (β = –0.407). These findings suggest that older individuals with lower levels of challenge are more pessimistic about technology. Hypothesis H2 (direct relationship with age) is confirmed, and H4 is partially confirmed (negative relationship with challenge). The strong negative coefficient for challenge is conceptually coherent: viewing life changes as threats rather than opportunities maps directly onto a pessimistic outlook regarding technological progress.

The low R² values across all models, while confirming the statistical significance of the predictors examined, underscore the inherent complexity of technology-related attitudes. This observation aligns with integrative frameworks such as the Unified Theory of Acceptance and Use of Technology (UTAUT2), which posits that behavioral intention is shaped by a confluence of factors including performance expectancy, effort expectancy, social influence, facilitating conditions, hedonic motivation, price value, and habit—moderated by age, gender, and experience (Venkatesh et al., 2016). The present findings suggest that hardiness may function as a personality-based “facilitating condition” or coping resource. However, future research should incorporate these additional constructs to develop more comprehensive explanatory models.

Discussion

The present study aimed to investigate the predictive roles of hardiness, gender, and age in shaping attitudes toward new technologies. The findings partially support the proposed hypotheses and contribute to a nuanced understanding of the psychological and demographic factors influencing technology adoption and apprehension.

Interpretation of Key Findings

Age emerged as a significant predictor across multiple attitudes. Consistent with H2, older age was associated with higher technophobia and technopessimism, and lower technophilia. This aligns with generational digital divide theories and prior research (Soldatova et al., 2021; Saenko, 2024), suggesting that younger cohorts, raised in digitally saturated environments, develop more positive and integrated relationships with technology. The inverse relationship with technophilia, however, was modest, suggesting that factors beyond age—such as tech exposure, self-efficacy, or occupational demands—may moderate this link. This is consistent with the ‘age’ moderator in UTAUT2, where the effect of performance expectancy on behavioral intention is stronger for younger users (Venkatesh et al., 2016).

Gender significantly predicted technophilia and technophobia, supporting H3. Men reported higher technophilia, while women reported higher technophobia. This echoes findings by Saenko (2024) and suggests that gender socialization, differing self-efficacy beliefs in STEM domains, or risk perception may play a role. Interestingly, no gender differences were found for technorationalism or technopessimism, indicating that once initial enthusiasm or anxiety is accounted for, men and women may adopt similarly pragmatic or cautious outlooks on technology’s societal impact. Our results strengthen the evidence base for including gender as a key moderator in technology adoption models, particularly for affective components like anxiety and enthusiasm.

Hardiness components showed differentiated effects. The “control” and “challenge” dimensions were negatively associated with technophobia, supporting H4. Individuals who believe they can influence outcomes and perceive challenges as opportunities are less likely to experience technology-related anxiety. This aligns with Soldatova et al. (2025), who found technophobia negatively correlated with control. The “challenge” component also negatively predicted technopessimism, while “commitment” positively predicted technorationalism. This suggests that psychological resilience fosters a more critical yet open engagement with technology, rather than outright fear or pessimism. These findings extend the literature on digital resilience, positioning hardiness as a personal resource that buffers against technostress and fosters adaptive technology engagement (Califf et al., 2020).

Theoretical and Practical Implications

Theoretically, this study underscores that attitudes toward technology are not merely a function of exposure but are deeply intertwined with personality constructs like hardiness. The partial support for H1 suggests that while demographic factors matter, intrapersonal resources significantly shape how technology is perceived and integrated into one’s life. Our study bridges personality psychology (hardiness) with technology acceptance research, suggesting that future iterations of models like UTAUT2 could benefit from including resilience traits as distal antecedents or moderators.

Practically, these findings can inform tailored interventions. For older adults or those high in technophobia, programs could focus on enhancing perceived control and reframing technology as a manageable challenge. Gender-sensitive digital literacy initiatives may help balance engagement levels. Educators and policymakers should recognize that promoting “hardy” mindsets may reduce tech-related anxiety and foster more adaptive technology use.

Limitations and Future Research

The study has several limitations. First, the cross-sectional design precludes causal inferences. Second, the models exhibited low explanatory power (R² < 8%), indicating that variables not measured—such as education, socioeconomic status, prior tech experience, cultural values, or specific personality traits like openness—may account for more variance. Future research should incorporate these factors longitudinally.  Specifically, integrating constructs from established technology acceptance models (e.g., performance/effort expectancy from UTAUT2) with hardiness could yield more powerful predictive models.

Additionally, the sample, while sizable, was drawn from three major Russian cities and was predominantly female, limiting generalizability to rural populations or other cultural contexts. Qualitative approaches could further illuminate the subjective experiences behind these quantitative trends.

Conclusions

Numerous studies (Soldatova et al., 2021; Saenko, 2024) demonstrated a correlation between age and attitudes toward emerging technologies. Younger cohorts tend to exhibit greater technophilia, while older cohorts display a greater tendency toward technopessimism and technophobia. The current study supports this trend. Age emerges as a significant predictor of technophilia, technophobia, and technopessimism. Specifically, age exerts a positive influence on technophobia, indicating that increasing age is associated with a higher probability of experiencing technology-related fear and anxiety.

Despite inconsistencies observed in prior research regarding the impact of gender on attitudes toward technology (Soldatova et al., 2021; Anthony et al., 2000), the present study identifies gender as a significant predictor of both technophilia and technophobia.

Soldatova et al. (2025) hypothesized a relationship between attitudes toward technology and hardiness, particularly the dimension of control. The current investigation demonstrates that components of hardiness—specifically, control and challenge—influence technophobia and technorationalism. Challenge, defined as the perception of difficulties as opportunities, exhibits a positive association with both technorationalism and technopessimism. The negative effect of control on technophobia aligns with Soldatova’s findings, which suggest an inverse relationship between technophobia and perceived control.

The relatively weak explanatory power of the models, as indicated by the R² values, warrants further investigation into potential mediating or moderating variables not included in the current analysis. These could include socioeconomic status, educational attainment, specific technology usage patterns, and personality traits beyond hardiness (Venkatesh et al., 2003). The prominent role of challenge in predicting technopessimism and technorationalism suggests that individuals who perceive technological advancements as opportunities for growth and intellectual stimulation are less likely to express concerns about their negative societal consequences and are more likely to adopt a critical, conscious approach to technology adoption.

The study’s limitations include its cross-sectional design, which precludes causal inferences, and the specific demographic characteristics of the sample, which limit the generalizability of the findings to other populations. Future research should employ longitudinal designs to examine the dynamic interplay between hardiness, gender, age, and attitudes toward new technologies over time. Furthermore, qualitative methods could provide richer insights into the lived experiences of individuals with diverse attitudes toward technology.

In conclusion, this study provides preliminary evidence for the influence of hardiness, gender, and age on attitudes toward new technologies. Although the observed effects are modest, they highlight the importance of considering individual differences in the design and implementation of technology-related interventions and policies. Further research is needed to elucidate the complex interplay of factors shaping attitudes toward technology and to develop more robust predictive models.

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