1. Introduction
Usage of the Internet and information and communication technology (ICT) has significantly disrupted both workplaces [
1] and everyday life [
2]. It is especially important for senior citizens (65 years and older), whose Internet usage significantly increased from the beginning of the Web 2.0 technology era in 2010 [
3]. However, senior citizens have problems accessing and using modern digital devices and thus face challenges in the qualitative use of the Internet [
4]. Various authors have diverse focuses when researching the use of the Internet in the older population, investigating the availability of Internet services [
5], the level of trust and readiness when using the Internet and Internet-based applications [
6,
7], and awareness of the risks of using the Internet [
8].
As reported by Croatian Bureau of Statistics data, the average age of the Croatian population increased from 37 years in 1991 to 44 years in 2021 [
9], following the trend in most of the European countries [
10]. According to [
11], there is a noticeable increase in the proportion of people over the age of 65, and their share in the total population is expected to increase. Thus, in 2001, 15.7% of people living in the Republic of Croatia were over the age of 65; in 2013, this share was 17.7%; and the most recent census revealed that nearly 25% of the Republic of Croatia’s population belonged to that segment [
9,
11].
A brief review of the literature reveals that a few studies conducted in Croatia address the issue of elderly digital inclusion. According to the results of a study conducted by [
12], due to the relatively low level of digital literacy among Croatian senior citizens, knowledge transfer “from the young to the elderly” needs to be made to achieve adequate inclusion of senior citizens in an information-based society. The findings of the study conducted by [
13] show that the barriers related to seniors’ adoption of digital technologies and their use of digital health and social care services provided by Croatian institutions are very similar to those in many other countries. This paper is develo** a structural equation model that tests the association with self-efficacy, social impact, and social support on the Intensity of Internet usage and observed barriers to Internet usage for senior citizens. This research is conducted as part of the project “SENIOR 2030—a thematic network for active ageing policy in Croatia”, develo** a proposal for an active ageing strategy based on the Silver Economy.
The remainder of the article is organised as follows. After the introduction, a literature review and hypothesis development are presented. The methodology section describes the data collection, the development of the research instrument, and the statistical analysis process. Descriptive statistics, confirmatory factor analysis, and structural equation modelling are used to analyse and test the data and hypotheses. The results are further analysed and discussed. The limitations of the study are highlighted. Finally, the paper concludes with a brief overview of the research findings. Limitations and recommendations for future research are pointed out.
2. Literature Review and Hypothesis Development
In general, the term social influence refers to an alteration in the individual’s behaviour, thinking, and feelings resulting from his presence and interaction with other people or groups of people [
14]. Likewise, in their work, [
15] explains how the social influence process certainly shapes the individual’s further behaviour and thinking. This influence process lays the foundations for the later identity development of the individual, his later decisions, and future socialisation [
15]. Similarly, various authors accentuate how social influence strongly impacts each person’s behaviour progress [
16,
17].
Ref. [
16] developed the “attitude–social influence–efficacy model” in their work. Self-efficacy can be explained as a tendency in which people are motivated to behave in a certain way if they have sufficient self-confidence that they will be able to perform such behaviour and that such behaviour will achieve the desired goal [
18]. According to [
17], there are three types of self-efficacy which include (1) reactions of the individual that are motivated by self-satisfaction and reactions that are motivated by dissatisfaction with oneself; (2) perception of self-efficacy in achieving one’s goals; and (3) adjustment of planned objectives based on one’s advancement. Accordingly, it can be concluded that self-efficacy and social influence shape an emotional state, opinions, and motivations [
19].
In addition to social influence and self-efficacy, many authors also emphasise the concept of social support as one of the factors that influence the formation of an individual in the context of his further behaviour [
20]. Social support can be defined as the individual’s perception or experience of care, appreciation, respect, acceptance, and social inclusion, which can be provided by the society with which the individual interacts [
21]. Therefore, social support for an individual can be provided by family, friends, colleagues, acquaintances, and all other participants in the individual’s social life.
Social support, social influence, and self-efficiency interact, which is crucial in using ICT among senior citizens [
22]. According to all of the above and to examine the mutual relations between the terms mentioned above: social influence, social support, and self-efficacy, the three following hypotheses are defined and investigated in this work:
H1. Social influence is positively related to self-efficacy.
H2. Social support is positively related to self-efficacy.
H3. Social influence is positively related to social support.
When observing the use of information and communication technologies, including the Internet, among older people, the authors of papers often emphasise self-efficacy as one of the main determinants of the relationship [
22,
23,
24]. According to [
23], self-efficacy can be defined as an individual’s positive or negative opinion of successfully using the Internet by themselves. In their work, ref. [
25] describe the term Internet self-efficacy as the ability of an individual to perform certain Internet activities independently to realise the desired opportunities and services provided by the Internet. Consequently, the authors of this work defined the following hypothesis:
H4. Self-efficacy is positively related to the Internet usage intensity.
Furthermore, many authors in their works point out that social influence encourages older people to use ICT [
22,
23,
26]. However, it is important to know that social influence in such a context refers to recommendations and opinions about the use of the Internet and digital technology that come from their immediate environment; that is, resources that are given to them by their peers or family [
22,
26,
27]. According to de Veer et al. (2015), social influence as the perception of older users about the importance of using the new system, in this case, the Internet, comes from the well-known theoretical model called the Unified Theory of Acceptance and Use of Technology (UTAUT), which examines the plan of each person in which he defines his behaviour in one way or another. Generally, within the UTAUT model, the individual’s intention to use ICT is influenced by four determinants: (1) the individual’s belief that the accepted technology will help him to improve the performance of business tasks; (2) the individual’s attitude about the complexity of using the accepted technology; (3) respect for the opinion of the environment about whether or not one should use the planned technology in order to be accepted; and (4) personal attitude about the technological and organisational readiness of the system in which it operates [
28]. Therefore, the following hypothesis was examined in this paper:
H5. Social influence is positively related to the Internet usage intensity.
Moreover, various authors emphasise social support as an important factor for successfully using ICT and the Internet, such as [
22,
24,
29,
30]. According to [
31], social support is vital for older people to accept ICTs, such as using tablets for communication. Similarly, ref. [
32] points out the importance of social support in adopting and using information and communication technologies and the Internet. Moreover, in their study, ref. [
33,
34] accentuates the significance of social support as support from acquaintances in the context of proper support in learning how to manage new technology or in the context of emotional support in using information and communication technology. Accordingly, the next hypothesis is imposed and investigated in this work:
H6. Social support is positively related to the Internet usage intensity.
As previously mentioned, self-efficacy is an individual’s awareness that they can perform certain activities with the skills they possess [
17,
19,
25]. Also, a person with a higher level of self-efficacy will put more effort and willingness to overcome possible obstacles that can stand in the way of preferred behaviour [
17,
19,
25]. In this way, it is assumed that an individual who believes that he has sufficient skills to use information technology, hence, to search the Internet and use the provided Internet services, will definitely and more easily overcome any obstacles in Internet usage [
25]. Likewise, ref. [
33,
34] in his work emphasises how it is necessary to improve self-efficacy due to the challenges of using the Internet, specifically in online transactions. Accordingly, the authors of this paper want to examine the negative influence of self-efficacy on Internet use obstacles by forming the following hypothesis:
H7. Self-efficacy is negatively related to Internet usage obstacles.
Various authors are investigating how social influence can reduce potential obstacles in new information and communication technologies [
35,
36,
37]. For instance, the results of [
35] revealed how social influence positively reduces potential resistance to innovation in banking services provided by the Internet. Similarly, ref. [
38] emphasises the importance of social influence, especially the influence of family members, in overcoming any insecurities that could cause refusal of Internet usage. In their work, ref. [
37] accentuates how it is necessary to familiarise the individual’s social environment with the application of e-learning so that he receives sufficient information, knowledge, and skills from his environment to successfully overcome any challenges in learning how to use e-learning platforms. Therefore, the following hypothesis is established for the examination in this paper:
H8. Social influence is negatively related to Internet usage obstacles.
In addition to self-efficacy and social influence, authors from different areas of research note that social support also successfully contributes to eliminating potential obstacles in applying information communication technology [
31,
39]. Generally, social support can help people more easily deal with the negative side effects of stress caused by different life situations which require them to change their existing behaviour or thinking [
40,
41]. According to [
39], social support enhances the motivation of older people to overcome possible difficulties that come with learning new digital skills and generally using information and communication technologies. Likewise, ref. [
31] reveals that older people encounter certain difficulties in learning how to work with information communication technology or challenges in its active use; the help and encouragement of their family and friends will be crucial in continuing to use the new technology and its functionalities. For that reason, the final hypothesis is recognised and investigated in this work:
H9. Social support is negatively related to the Internet usage obstacles.
After defining the hypotheses following the results of the literature review of the researched area, a conceptual research model was developed (
Figure 1).
4. Results
4.1. Sample Characteristics
The sociodemographic characteristics of the sample are presented in
Table 2. A total of 701 responses were collected. A percentage of 61.1% of respondents identified themselves as female, while 38.9% identified themselves as male. A percentage of 35.4% of the respondents are between 65 and 69 years old; 28.1% are in the 70–74 age group; and 20.3% are in the 75–79 age group. Finally, 12.1% of respondents are between 80 and 84 years of age, while 4.1% are 85 or older.
In the sample, the majority of respondents, 56.9%, live in urban settlements, and 16.1% of respondents live in suburban settlements. In contrast, 26.0% of respondents live in rural settlements. A percentage of 1% of the respondents from the sample say that they live in a house isolated from a settlement. Most older people from our sample have a three- or four-year high school diploma (49.8%), followed by 32.0% of respondents with a university education. A percentage of 11.6% of the respondents have completed only elementary school, and 6.7% reported having no formal education or completing only a few years of elementary school.
4.2. Descriptive Statistics
The descriptive statistics for the three main constructs are given in
Table 3,
Table 4 and
Table 5, and the correlation matrix for the first three constructs is presented in
Table 6.
The highest mean value for the construct Internet usage (4.62) is obtained for the variable C11_8 (
Table 3). Variable C11_8 measures help from friends and family in Internet services, suggesting that the strongest incentive for older people to use the Internet comes from concrete informatics support from their closest social relationships. The smallest mean value (3.77) was obtained for variable C11_6, which measures the perceived social influence of friends as an incentive for Internet use. The smallest mean value of this Internet usage factor indicates that the social influence of friends to use the Internet is relatively low compared to other Internet usage factors in the older population.
The intensity of obstacles to Internet usage is summarised in
Table 4. As shown in
Table 4, the majority of respondents, with a percentage of 39.8%, say they encounter only one obstacle when using Internet services. This percentage is followed by a relatively high percentage of 36.8% of respondents who indicated that they do not encounter any obstacles when using the Internet. Finally, 11.6% of older people in our sample identified two obstacles, and 11.8% said they encountered three or more obstacles when using Internet services.
The intensity of Internet services usage is summarised as follows: 44% of respondents state they have three or more reasons for using the Internet, i.e., three or more types of services that they regularly use, which represents the majority of the sample. A percentage of 7.8% say they regularly use two types of Internet services, and 6.0% say they regularly use one Internet service. However, 42.1% of respondents also do not regularly use a single Internet service.
A non-parametric correlation analysis was conducted for the items of the construct Internet usage to assess the consistency of the measurement instrument. Items of the same construct are expected to correlate, indicating that they are similar enough to measure the same variable in the instrument.
Spearman correlation coefficients are presented in
Table 5, and they show predominantly high correlations (>0.5) between construct variables, with several values below but close to the 0.5 value. Since no negative or low correlations were found, and the correlation coefficients are positive and strong, the correlation analysis confirms the consistency of the measurement instrument [
47].
In addition, the coefficients are very high (>0.7) between the items of each dimension of Internet usage, i.e., between the items of self-efficacy (C11_1–C11_3), social influence (C11_4–C11_6), and social support (C11_7–C11_9). Since the coefficients for the items in a particular dimension are higher than those between the items belonging to different dimensions, the correlation analysis first gave insight into the uniqueness of the latent variables, which will be further investigated in the CFA analysis and confirms the nomological validity.
4.3. Exploratory Factor Analysis
We used an exploratory factor analysis (EFA) to examine the underlying structure of the measurement instrument, following the a priori assumption that any item may be associated with any factor [
50]. Since the main statistical analysis method used in the study was confirmatory factor analysis, EFA was used as a supplement for the preliminary exploration of instrument properties.
Three latent factors were extracted using the principal component analysis with varimax rotation, as presented in
Table 6. Common thresholds for moderate and acceptable loadings to interpret the factors range from 0.4 to 0.7, while values of 0.8 or greater indicate a strong accuracy of the items [
51]. One of our items, C11_10, demonstrated cross-loading, as it loaded on two factors (PC1 and PC2). According to [
51], the item should be discarded if loaded adequately to strong, i.e., 0.5 or above on several factors. Therefore, since item C11_10 had two loaders above the cut-off value of 0.5, it was excluded from further analysis, as it did not uniquely represent one factor. All other items (C11_1–C11_9) had loaders above 0.7 and 0.8, i.e., loaded strongly on one factor.
Since the convergent validity was examined using the EFA, a variable was deleted; as a result, we continued with the validation of factor analysis in the following step, using nine items (C11_1–C11_9) in a three-factor model.
4.4. Confirmatory Factor Analysis
We used a confirmatory factor analysis (CFA) procedure to inspect the properties of the measurement model. As a first step in evaluating our measurement model, we observed overall model fit using common goodness-of-fit indicators to check the representativeness of the model. After that, we tested convergent validity by examining factor loadings, composite reliability (CR), and average variance extracted (AVE) for each construct. Finally, discriminant validity was assessed by applying the Fornell–Larcker criterion.
CFA analysis was performed for the entire sample. A three-factor model was established, with the factors being namely self-efficacy, social influence, and social support. The graph of items with their established factors is presented in
Figure 2. Manifest variables C11_1–C11_3 were loaded on the self-efficacy factor; the variables C11_4–C11_6 were loaded on the social influence factor; and the variables C11_7–C11_9 were loaded on the social support factor. All latent factors were assumed to be correlated with each other, as the variance of the factors was set to 1.0. A covariance matrix with a maximum likelihood (ML) method was used to estimate the parameters.
Goodness-of-fit indicators are shown in
Table 7. The Chi-square of 43.438, with 24 degrees of freedom and
p = 0.009, is statistically significant at a 95% confidence level and therefore does not indicate an adequate fit of the model. However, in such a case, other tests should be performed to reject or accept the proposed measurement model, such as the Chi-square statistic ratio (χ
2/df) and other goodness-of-fit indicators [
45]. A (χ
2/df) of 1.81 is considered very good, as it is below the recommended ratio of 3 or less (Hoe, 2008) and even below the more conservative ratio of 2 or less [
46]. Further, the value for RMSEA is 0.034, well below the threshold of 0.08. The same is true for the SRMR of 0.017, below the conservative threshold of 0.05 (Hair et al., 2014). CFI is 0.997, which exceeds the recommended higher value of 0.94 for CFI [
46]. Other goodness-of-fit measures also have satisfactory values (TLI = 0.995 > 0.95; GFI = 0.987 > 0.95), indicating that the measurement model fits the data well [
45].
Since the fit of the overall model was satisfactory, we proceeded with the estimates of the factor loadings presented in
Table 8. Factor loadings are correlation coefficients between the manifest variable and the latent factor/construct, thus indicating how well the variable represents the construct. The higher the loading, the more representative the variable is of the factor. All unstandardised loading estimates are statistically significant, which is required to establish convergent validity. In addition, all standardised factor loadings are higher than the ideal or preferred value of 0.7 or greater [
45]. Therefore, the results confirm that the variables represent their latent factor well.
Additionally, we calculated the AVE and the CR to investigate convergent validity further. The coefficient of construct reliability (CR) is analogous to the coefficient alpha and is commonly used to estimate reliability in the CFA–SEM method (Hair et al., 2014). CR is above the threshold of 0.7 for all latent variables, indicating adequate internal consistency of the item scales. Finally, we estimated the average variance extracted (AVE) because it is a more stringent criterion for internal consistency. AVE estimates for each construct exceed the recommended minimum value of 0.5 [
45]. Since all estimates of factor loadings, construct reliability (CR), and average variance extracted (AVE) exceed the recommended minimums of 0.7, 0.7, and 0.5, respectively, convergent validity was confirmed.
We apply a widely used approach to assess discriminant validity recommended by [
46], also known as the Fornell–Larcker criterion. According to this criterion (
Table 9), the AVE estimate for a given factor should be higher than the squared inter-construct correlations related to that factor. Since all diagonal elements (AVE) are higher than the corresponding (underlying) off-diagonal elements (squared inter-construct correlations), we can conclude that the discriminant validity of our measurement model has also been demonstrated.
4.5. Structural Equation Modelling
While the measurement model assessment indicates how well the manifest (measured) variables represent the latent variables (constructs), the structural model assessment in SEM is used to examine the relationships between the constructs, i.e., the structure of the model. Because the measurement model for our data showed strong convergent and discriminant validity, we proceeded to evaluate the proposed structural model of the research.
First, we assessed the fit of the model using goodness-of-fit indicators. Then, we moved to path analysis to test the proposed hypothesis. This step applied the SEM procedure to the entire sample, while parameter estimates were obtained using the maximum likelihood method (ML) of covariance matrices. A one-factor loading estimate was set to 1 for each construct to determine the latent factor scale.
Table 10 provides estimates of the commonly used indices for evaluating the structural model.
As can be seen, all indicators met the requirements for the acceptable values for a good model fit. The ratio of the Chi-square statistic (χ
2/df) is 1.634, well below the benchmark ratio of 3 to 1 [
50] and even below the more conservative maximum ratio of 2 [
45]. Other absolute fit indices, RMSEA and SRMR, are also well below the maximum thresholds of 0.07 and 0.08, respectively. The incremental fit indices, namely CFI, TLI, NFI, and NNFI, are also in the acceptable range of 0.95 or more [
45]. Moreover, all incremental indices have values close to 1, indicating the model’s strong structural validity.
Since the goodness-of-fit indicators pointed out a sound specification of our model, we were able to proceed with the path analysis to test whether the hypothesised theoretical relationships among the constructs applied to our research context. The path analysis is shown in
Table 11.
As expected, significant and positive relationships were found between social influence and self-efficacy (H1); social support and self-efficacy (H2); and social influence and social support (H3).
The path coefficient is positive and significant at 1% for the relationship between the intensity of Internet usage and self-efficacy (H4), with a path coefficient of 0.912 and p < 0.01. In contrast, the significance of the path coefficients between the intensity of Internet usage on the one side and social influence and social support on the other side was not demonstrated in the model of our research (H5 and H6, respectively).
The path coefficient is negative and significant at 1% for the relationship between obstacles in Internet usage and self-efficacy, with a path coefficient of −0.279, with p < 0.01, indicating a strong negative correlation between these two variables (H7). The path between obstacles in Internet usage and social influence has an estimated value of 0.177, with p < 0.05 indicating a significant positive relationship between the two constructs at 5%. However, H8 was not confirmed because the relationship direction was different than expected (positive instead of negative). In addition, the relationship was not significant between obstacles in Internet usage and social support (H9).
5. Discussion
Table 12 summarises research results in the context of hypothesis testing. The path analysis confirmed the hypothesised theoretical relationships in the structural model for six hypotheses, namely H1, H2, H3, H4, H7, and H9, while hypotheses H5, H6, and H7 were not supported.
Concerning the determinants of the model of Internet usage among older people, the results imply that the opinions of family, relatives, and friends about using the Internet (measured by social influence) are associated with the personal attitude of each elderly user about the pleasure and simplicity of using the Internet (measured by self-efficacy) as well as the support of their family, friends, and acquaintances in using the Internet, which confirms hypotheses H1 and H3. Generally, from the definition of social influence, it can be explained how social influence is related to forming individual thoughts, opinions, feelings, and behaviour about using s-commerce (e-commerce using social media) due to the individual’s interactions with his environment [
20,
52]. According to [
53], the perception and expectations of family, friends, relatives, media, and community in general in Internet usage strongly affect forming individuals’ attitudes, interests, and opinions toward Internet usefulness and quality; this is in line with the confirmed hypothesis H1. Also, this finding aligns with [
22], whose results show how social influence impacts computer self-efficacy among seniors. According to different authors [
20,
54,
55], social influence encourages people to acquire new knowledge, solve problems to meet other people’s expectations, be socially involved, and improve self-perception, which will consequently impact the need for a higher level of social support and more intensive social interactions. Such conclusions can support the examined and confirmed hypothesis H3.
Similarly, hypothesis H2 is confirmed by showing how social support provided by the social environment in which the seniors interact also positively influences the seniors’ self-efficacy in using the Internet. The given result from the hypothesis H2 is in line with the previous study [
31], in which they emphasise how to continue realising one’s own positive opinion of information and communication technology: the encouragement and support of the environment are extremely important. Moreover, the finding obtained from hypothesis H2 can be confirmed by [
22], who also emphasises the influence of social support on computer self-efficacy among elderly users of information and communication technology.
Concerning the determinants of intensity of Internet usage, the results suggest that the consumption of Internet content and the use of electronic services for older people (measured by the intensity of Internet usage) can be explained by their abilities and perceived comfort in navigating the Internet (measured by self-efficacy), thus confirming H4. At the same time, it is not influenced by incentives or support coming from their close peers (measured by social influence and social support), thus not confirming H5 and H6. The obtained result from the H4 hypothesis is also confirmed in the existing literature. For instance, Ref. [
56] also proved the importance of self-efficacy on the intensity of Internet usage. Likewise, Ref. [
57] also confirmed how higher self-efficacy in general Internet and communication Internet usage positively influences informational Internet activities among non-expert users.
Moreover, in their study, Ref. [
23] stresses how self-efficacy is directly linked to higher intention in e-Health application usage. The result from hypothesis H5 is in line with other studies confirming that accepting and using new technology has not been influenced by the individual perception and knowledge of how others from their environment use the same technology [
58]. According to [
59], social support can be classified into three categories: (i) providing emotional support, (ii) providing support in the context of information and advice, and (iii) providing support in the context of help with daily tasks or finances. Given that the Internet has become a part of everyday life, it can be considered that providing support around the Internet is instrumental; it has been shown in research to cause a higher level of depression among the elderly population [
59,
60,
61]. Moreover, receiving received, and not just perceived, social support in some studies resulted in higher levels of emotional stress among respondents due to potential negative social interactions, conflicts, and feelings of dependence on others that can accompany support from other people [
59,
60,
61]. Therefore, such results of previously conducted studies can support the findings for hypothesis H6.
Concerning the determinants of the obstacles in Internet usage, the results indicate that the obstacles for older people, similar to the intensity of Internet usage, can be explained by the level of personal ability and comfort of using the Internet (measured by self-efficacy). The obtained result from hypothesis H7 is also proved by [
62], in which the authors argue how individuals with a higher level of self-efficacy perceive difficulties as less challenging than individuals with a lower level of self-efficacy; this consequently enables them to overcome obstacles more easily. The social influence of the immediate environment has a counterintuitive positive effect on obstacles in Internet usage, indicating that higher social support increases anxiety in Internet usage, resulting in a higher perception of obstacles in Internet usage. The association of social support with obstacles in Internet usage was not confirmed. These findings about hypothesis H8 suggest that, as [
63] points out, social influence has a strong relationship with causing anxiety when using Internet services, for instance, social media. The findings based on the result of hypothesis H9 can be related to the result of hypothesis H6 and, therefore, explained by the fact that potential social support can cause a feeling of dependence on others by older people who would potentially like to independently solve the challenges they encounter by using the Internet. Moreover, such an explanation can also be supported by the confirmation of hypothesis 4, which showed that for seniors, their personal opinions about the comfort and ease of use of the Internet are significant for deciding to use it more intensively. Also, such findings can suggest that the sampled seniors are computer-literate enough to use the Internet independently.
Figure 3 presents the summary of the testing of research model.
6. Conclusions
6.1. Summary of the Research
In recent decades, developed nations have observed two significant trends: a growing number of senior citizens and a rapid integration of ICT. This study delves into how self-efficacy, social support, and influence affect Internet usage among Croatian senior citizens. The research aimed to show the extent to which social influence, self-efficacy, and social support are associated with the intensity of Internet use and reducing barriers to Internet use among older users. Through survey research and structural equation modelling, it was found that self-efficacy positively impacts the intensity of Internet usage while also reducing related obstacles. Social influence directly lessens these obstacles and indirectly affects Internet usage intensity via self-efficacy. Meanwhile, social support indirectly influences the intensity of Internet usage. These findings underscore the importance of educational programs that boost seniors’ perceived self-efficacy in Internet usage.
6.2. Theoretical Implications
The work presented in this study has significant theoretical implications as it contributes to the current body of literature investigating characteristics that facilitate the use of information and communication technology (ICT) among older individuals. It also enhances our understanding of the impact of social influence, social support, and self-efficacy within this context. The theoretical contributions of this study offer a thorough comprehension of the various elements that impact the utilisation of the Internet by older individuals. These contributions highlight the significance of social influence, social support, and self-efficacy in sha** Internet usage patterns among this demographic.
The study’s findings establish a correlation between the viewpoints of family members, relatives, and friends towards Internet usage and the personal attitudes of senior individuals towards the enjoyment and ease of using the Internet. This finding aligns with other scholarly investigations that emphasise the role of social influence in sha** individuals’ attitudes and behaviours regarding s-commerce and Internet use.
The study additionally validates that social support derived from the surrounding context has a favourable impact on the self-efficacy of older individuals in utilising the Internet. This finding is consistent with other research that underscores the significance of contextual factors in fostering favourable attitudes towards information and communication technologies.
The prevalence of Internet usage among older adults can be attributed to their self-efficacy, while social influence and social support do not appear to have a significant impact. This discovery aligns with other studies that emphasise the significance of self-efficacy in determining the level of Internet usage and the acceptance of e-Health applications.
The challenges encountered by elderly individuals in utilising the Internet can be ascribed to their level of self-efficacy. Individuals with elevated levels of self-efficacy tend to regard difficulties as less formidable and are more adept at surmounting them. It is noteworthy that evidence suggests that increased levels of social support can potentially contribute to heightened anxiety in Internet usage, resulting in an amplified impression of barriers. This indicates that using Internet services may lead to anxiety due to social influence. At the same time, the senior population may experience feelings of dependence due to potential social assistance.
The results indicate that the seniors who participated in the research have sufficient computer literacy that enables them to utilise the Internet autonomously. The individual’s subjective viewpoints regarding the convenience and user-friendliness of Internet usage substantially influence their inclination to engage with it more extensively.
In conclusion, this study’s findings indicate a notable positive correlation between social impact, social support, and older adults’ self-efficacy. Additionally, the findings indicated a strong correlation between the social influence experienced by older Internet users and the level of support received from their family, friends, and relatives. In contrast, of the three criteria examined concerning the intensity of Internet usage among older individuals, only self-efficacy has demonstrated a statistically significant positive correlation. Moreover, based on the findings of the conducted research, it is evident that self-efficacy is the sole factor that exhibits a substantial negative impact on the difficulties encountered during Internet usage. Furthermore, it was shown that social influence exhibited a significant correlation with the obstacles encountered by older individuals in utilising the Internet.
6.3. Practical Implications
The practical consequences of this study are the potential adaptation of Internet service providers’ functionality to cater to the needs of older customers, hence facilitating their inclusion in the dynamic realm of digitalisation.
The importance of self-efficacy in influencing the extent and challenges associated with Internet usage among older adults necessitates the development of customised educational initiatives. The primary objective of these programmes should be to improve seniors’ self-assurance and proficiency in utilising the Internet, fostering a sense of competence and reducing apprehension about digital obstacles.
Given that the attitudes of older individuals regarding Internet usage are influenced by the perspectives of their family members, relatives, and friends, it is imperative to engage these social groups in any digital literacy initiative. Organising workshops or instructional sessions can facilitate the participation of senior individuals and their close companions, fostering a conducive environment for elderly individuals to acquire knowledge and adjust accordingly.
The findings of this study may serve as a significant foundation for mental health interventions, as they suggest that while social support can have positive effects, an excessive amount of assistance or reliance on others can result in increased anxiety and a heightened perception of barriers. Considering the potential anxiety often linked to Internet services, particularly social media, it is advisable for practical programmes to integrate modules that specifically target these concerns. The curriculum may encompass instructional modules about various aspects of digital safety, including but not limited to online security measures, privacy management mechanisms, and fostering constructive online engagements.
Future projects and initiatives may be directed towards advancing user-friendly digital platforms. The prioritisation of user-friendly platforms by tech companies and service providers is crucial in facilitating the uptake of the Internet among seniors, as personal perceptions regarding comfort and ease of use significantly influence their decision-making process. Simplified interfaces, increased text size, intuitive designs, and comprehensive training can potentially enhance digital accessibility for older individuals. These platforms have the potential to incorporate an automatic feedback mechanism, which would assist educators and programme organisers in comprehending the distinct requirements, obstacles, and preferences of senior participants. This measure can guarantee the continued relevance and efficacy of digital literacy programmes.
One potential strategy for disseminating information to the general public regarding the potential beneficial impact they can have on the digital journeys of elderly individuals is implementing awareness campaigns. By comprehending the significant impact of social influence, individuals can adopt a proactive approach to promoting and directing the elderly within their social networks.
Ultimately, establishing support groups and communities for seniors to share their experiences, concerns, and solutions pertaining to Internet usage can prove advantageous. These platforms have the potential to provide chances for peer-to-peer learning, thereby mitigating feelings of loneliness and fostering a sense of community among older individuals who use the Internet.
By integrating these practical implications into strategies and programmes, there is a notable potential to enhance the digital experience for older adults. This enhancement would result in increased Internet usage and instil a sense of confidence and ease while navigating online platforms.
6.4. Limitations and Future Research Directions
First, the primary limitation of this study is its geographic scope. The sample was drawn exclusively from one country, which may not represent senior citizens’ experiences and attitudes in other cultural or socio–economic contexts. Second, the sample predominantly consisted of urban and highly educated elderly individuals. This demographic bias may have influenced the results, as urban and educated seniors might have different levels of exposure, comfort, and proficiency with the Internet compared to their rural or less-educated counterparts. The third limitation stems from the cross-sectional design. The study’s design captures a snapshot in time without tracking changes or evolutions in attitudes and behaviours, which limits the ability to infer causality or observe how perceptions and usage patterns might evolve.
Directions for future research include the following: First, to enhance the generalisability of the findings, future research should consider a multi-country approach, considering significant differences between European countries according to the level of digital transformation [
64]. Comparing results across countries can provide insights into cultural, economic, or infrastructural factors influencing seniors’ Internet usage. Second, given the urban and educated bias in the current study, future research should deliberately include participants from rural areas and diverse educational backgrounds, offering a more comprehensive understanding of the challenges and motivations across different demographic groups. Third, implementing a longitudinal design can help track changes in seniors’ Internet usage and attitudes over time, which would be particularly valuable in understanding the long-term effects of interventions or the impact of rapidly evolving digital landscapes on the elderly. Fourth, as recommended, in-depth interviews and case studies can provide richer, more nuanced insights into seniors’ experiences and challenges with the Internet. Qualitative research can uncover deeper motivations, fears, and barriers that might not be evident in survey data. Finally, future research directions could cover the diverse digital platforms and support systems. Future studies could explore seniors’ interactions with various digital platforms, not just the Internet, including mobile apps, smart home devices, and other emerging technologies, to understand their adaptability and challenges in a broader digital context. Given the importance of social influence and support in the current study, future research could delve deeper into the role of different support systems, like community centres, tech workshops, or peer-led groups, in enhancing digital literacy among seniors.