Unpacking the Complex Interplay Between Internet Usage and Well-being Among Older Adults: Insights from a Socioemotional Selectivity Perspective
- Open Access
- 01.10.2025
- Original Paper
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Abstract
1 Introduction
The advent of the internet has revolutionized various aspects of daily life, influencing how individuals obtain information, interact with others, and entertain themselves. While much of the available research has focused on younger populations (e.g., adolescents, younger adults), particularly regarding the negative aspects of internet use (Haidt, 2024; Twenge, 2017), such as problematic internet usage (Cai et al., 2023; Chen & Fan, 2024) and internet addiction (Lozano-Blasco et al., 2022), relatively little is known about the impact of the internet on older adults. This is a significant research gap, given that older adults represent an increasing demographic worldwide and may engage with and be affected by internet use differently from younger adults. To address this gap, we examined the differential associations of internet use on well-being in younger and older adults in this study. Drawing on a lifespan perspective and socioemotional selectivity theory (Carstensen, 2006; Carstensen et al., 2000), we explored how age moderates the relationship between internet usage time and well-being and investigated the specific mechanisms underlying internet use among older adults. The study aimed to answer the following research questions: (1) Does the relationship between internet usage time and well-being differ between younger and older adults? (2) What are the key motivations behind internet use among older adults, and how do these motivations influence well-being? Addressing these questions will contribute to a more nuanced understanding of internet use across the lifespan.
1.1 Literature Review
1.1.1 Internet Usage Time and Well-being: Why Age Matters?
The psychological impact of internet use differs significantly between younger adults, who are digital natives, and older adults, who are digital immigrants (Prensky, 2001). Younger individuals, who have grown up with the internet, are accustomed to its pervasive influence on daily life. This habitual use means that younger individuals often take the benefits of the internet for granted. However, their constant exposure also increases their susceptibility to negative psychological effects, such as online social comparison (Deri et al., 2017; Parsons et al., 2021), cyberbullying (Kee et al., 2024; Kowalski et al., 2014), and internet gaming disorder (Thomas et al., 2024; Wang et al., 2023), all of which can negatively impact well-being and mental health (Haidt, 2024; Orben et al., 2022).
In contrast, older adults, as digital immigrants, typically encounter the internet later in life. This later adoption requires them to actively learn and adapt to new technologies (Cecutti et al., 2021). As a result, older adults may experience greater appreciation for the advantages of the internet. For example, engaging with digital platforms allows older adults to maintain and even expand their social networks, reducing feelings of loneliness and social isolation, which are prevalent concerns in older populations (Akhter-Khan et al., 2023; Gerlach et al., 2024). Additionally, the internet can serve as a valuable tool for older adults to access health information, manage chronic conditions, and maintain cognitive function (Aggarwal et al., 2020; Busch et al., 2021).
However, most studies investigating the association between internet usage time and well-being have not simultaneously compared younger and older adults, often examining these two groups separately. Therefore, two independent and large representative datasets were used in Study 1 (Studies 1a and 1b) to analyze the relationship between internet usage time and well-being across the adult lifespan. Although age was treated as a continuous variable in our analysis, we hypothesized that the nature of this association would differ by life stage: among younger adults, increased internet usage time may be negatively associated with well-being, whereas among older adults, it may be positively associated with well-being.
1.1.2 A Socioemotional Selectivity Perspective of the Relationship Between Older Adults’ Internet Usage Time and Well-being
To further understand the mechanisms underlying the positive association of internet use on well-being among older adults, we applied socioemotional selectivity theory. This theory posits that as people age, their time perspective shifts from an open-ended future to a more limited horizon, leading them to prioritize present-oriented socioemotional goals over future-oriented instrumental goals (Carstensen, 1993, 2006; Carstensen et al., 1999; Charles & Carstensen, 2010). Socioemotional goals refer to the motivation to enhance social connections and maintain close, supportive relationships, whereas instrumental goals (also known as knowledge-based goals) focus on the acquisition of new information and skills for future use. Older adults prioritize socioemotional goals, selectively investing time and energy in emotionally rewarding relationships. This prioritization is believed to contribute significantly to their overall well-being. In contrast, younger individuals, who perceive an extended future, are more likely to pursue instrumental goals, focusing on acquiring resources for long-term benefits (Carstensen, 2006; Carstensen et al., 1999).
From this perspective, the internet provides older adults with opportunities to meet their socioemotional goals, such as maintaining relationships through social media, video calls, and emails, which reduce feelings of loneliness and social isolation (Schlomann et al., 2020). Although older adults’ instrumental goals are less prioritized, the internet remains a useful tool for older adults to engage in lifelong learning and access essential services (Busch et al., 2021; Cecutti et al., 2021). By highlighting different motivational priorities across the lifespan, socioemotional selectivity theory provides a framework for understanding why older adults derive significant psychological benefits from internet use. Therefore, Study 2 specifically focused on the motivations behind internet use among older adults, differentiating between socioemotional and instrumental goals. We hypothesized that older adults demonstrate a stronger preference for socioemotional goals than instrumental goals. Additionally, we hypothesized that socioemotional goals would show an indirect association between internet usage time and well-being, with a stronger association strength than that observed for instrumental goals.
2 Study 1a
2.1 Methods
2.1.1 Participants
The data were sourced from the database of the Taiwan Social Change Survey (phase 7, wave 3), a cross-sectional survey conducted by the Institute of Sociology, Academia Sinica. The Taiwan Social Change Survey surveys varying topics annually, with each major theme measured in five-year cycles. The survey targeted adult residents of Taiwan aged 18 years and older. A stratified three-stage probability sampling method proportional to population size was employed, utilizing townships, villages, and individuals as the sampling units to ensure the representativeness of the sample. The data collection period of Study 1a spanned from August 6 to December 12, 2017. Data were collected through face‒to-face interviews employing tablets and the computer-assisted personal interviewing (CAPI) method, which involves a structured questionnaire. After excluding participants with missing values for the main measurements, a total of 1,725 participants were included in the analysis. Among these participants, 52% were female, with a mean age of 45.33 years (SD = 16.34), ranging from 19 to 95 years.
2.1.2 Measurement
2.1.2.1 Internet Usage Time
The question “How much time do you spend on the internet on an average day? Please specify in hours and minutes.” was used to measure internet usage time. These data were subsequently converted into total hours, with a mean internet usage time of 3.36 h (SD = 3.35).
2.1.2.2 Well-being
Two items were utilized to encompass both the psychological and physical dimensions of well-being: “Overall, how happy do you feel these days?” (rated from 1 = very unhappy to 4 = very happy), and “How would you rate your health condition over the past year?” (rated from 1 = very bad to 5 = very good). The correlation between these two items was positive, r = .32, p <.001. Since the two items used different scale points, we first standardized them (Z-score, M = 0, SD = 1) before computing the average score, with higher scores indicating greater well-being.
2.1.2.3 Socioeconomic Status
In our study, we adopted a more nuanced approach by controlling for socioeconomic status (SES) among older adults since previous research has consistently demonstrated a significant relationship between SES and well-being, particularly emphasizing the impact of subjective socioeconomic status (Tan et al., 2020). Therefore, we employed the single-item ladder measure of SES, a tool that graphically depicts the social hierarchy within an individual’s country using a 10-rung ladder. This ladder metaphorically places individuals who have the most wealth, highest educational achievements, and most prestigious occupations at the apex. In contrast, the base represents those with the least financial resources, educational attainment, and occupational prestige. The participants were asked to position themselves on this ladder on the basis of their perceived social standing within their country. The average subjective socioeconomic status in Study 1a was reported as M = 5.34 (SD = 1.63).
2.2 Data Analysis Methods
We utilized Hayes’ (2022) PROCESS macro version 4.2, specifically Model 1, to examine the moderating effect of age on the relationship between internet usage time and well-being. This analysis allowed us to assess the interaction effect between age and internet usage time on well-being. We entered age, internet usage time, and the interaction term (age × internet usage time) as predictors of well-being. Additionally, we conducted simple slope analyses at specific values of age (i.e., one standard deviation below the mean, the mean, and one standard deviation above the mean) to further investigate the nature of the interaction effect. These reference points correspond to approximately 29 years (younger adults), 46 years (middle-aged adults), and 62 years (older adults) in Study 1a, which align with the demographic distribution in our sample. This approach, though sometimes described in terms of younger, average, and older adults, retains age as a continuous moderator in the analysis. Internet usage time was also probed at ± 1 SD from the mean within each of these age points. Because the ± 1 SD values vary depending on the age reference point, the interaction plots use relative terms (“less” vs. “more” usage) rather than fixed numerical labels along the x-axis in Figs. 1 and 2. To ensure the robustness of our results, we included gender (dummy coded, 0 = female and 1 = male) and socioeconomic status as covariates in our analysis.
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2.3 Results
The moderation analysis conducted via Hayes’ (2022) PROCESS macro version 4.2 (Model 1) revealed a negative association between age and well-being, β = − .10, p = .002, whereas internet usage time was not associated with well-being, β = .02, p = .43. Furthermore, the interaction term of age × internet usage time was significant, β = .15, p < .001. The simple slope analysis (see Fig. 1) conducted via the PROCESS macro version 4.2 indicated that for younger participants (−1 SD point-estimated age was approximately 29 years old), internet usage time was negatively associated with well-being, β = − .12, p = .001. For middle-aged adults (mean point-estimated age was approximately 46 years old), there was no significant association between internet usage time and well-being, β = .02, p = .43. Notably, for older participants (+ 1 SD point-estimated age was approximately 62 years old), internet usage time was positively associated with well-being, β = .17, p < .001. These results remained robust after the inclusion of gender and socioeconomic status as covariates (age × internet usage time interaction effect, β = 0.11, p =.003; simple slope analysis among young adults: β = − .13, p = .001; middle-aged adults: β = − .02, p =.57; and older adults: β = .09, p = .04).
Fig. 1
Relationships among age, internet usage time, and well-being
In summary, the findings from Study 1a were consistent with our hypothesis that internet usage time has a negative association with well-being of younger adults, corroborating the results of previous research on negative aspects of internet use (Haidt, 2024; Twenge, 2017). Conversely, for older adults, internet use time appears positively associated with well-being. However, the data from Study 1a are more temporally distant and were collected prior to the impact of the COVID-19 pandemic. To understand the robustness of the findings, Study 1b was conducted to investigate the moderating effect of age on the association between internet use time and well-being, employing more recent data collected before and during the COVID-19 pandemic.
3 Study 1b
3.1 Methods
3.1.1 Participants
As mentioned in the section on Study 1a, the Taiwan Social Change Survey conducts investigations on the same themes in five-year cycles. Consequently, Study 1b involved analyzing data from the most recent cycle (phase 8, wave 3). The sampling method used to ensure a representative sample of the Taiwanese population in the survey was consistent with that used in Study 1a. The data collection period spanned from June 19, 2022, to February 12, 2023. After excluding participants with missing values for the main measurements, a total of 1,715 participants were included in the analysis. Of these participants, 53% were female, with a mean age of 51.31 years (SD = 16.93), ranging from 19 to 92 years.
3.1.2 Measurement
3.1.2.1 Internet Usage Time
The items measuring internet usage time in Study 1b were the same as those used in Study 1a, with a mean internet usage time of 3.83 h (SD = 3.56).
3.1.2.1.1 Well-being
The items measuring well-being were the same as Study 1a (one item for physical well-being, and the other for psychological well-being). Only a slightly modification was made in the rating range of psychological well-being, where the range was adjusted from a 4-point to a 5-point Likert scale. All other aspects remained entirely consistent with those of Study 1a. The correlation between these two items was positive, r =.38, p <.001. Consequently, the average of these two items was calculated to determine the well-being index, with higher scores indicating greater well-being. The mean well-being score in Study 1b was 3.72 points (SD = 0.74).
3.1.2.1.2 Socioeconomic Status
The item measuring socioeconomic status was the same as that used in Study 1a. The average subjective socioeconomic status in the present study was reported as M = 4.79, SD = 1.73.
3.2 Data Analysis Methods
The data analysis procedures were the same as those in Study 1a.
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3.3 Results
The moderation analysis conducted via Hayes’ (2022) PROCESS macro version 4.2 (Model 1) revealed a negative association between age and well-being (β = − .12, p < .001), whereas internet usage time was not associated with well-being (β = .06, p = .06). Furthermore, the interaction term of age × internet usage time was significant, β = .14, p < .001. The simple slope analysis (see Fig. 2) conducted via the PROCESS macro version 4.2 indicated that for younger adults (−1 SD point-estimated age was approximately 34 years old), internet usage time was negatively associated with well-being, β = − .08, p = .01. For middle-aged adults, there was no significant association between internet usage time and well-being (mean point-estimated age was approximately 50 years old), β = .06, p = .06. Notably, for older participants (1 SD point-estimated age was approximately 67 years old), internet usage time was positively associated with well-being, β = .20, p < .001. Furthermore, even when controlling for gender and socioeconomic status, the age × internet usage time interaction effect (β = − .10, p = .003) on the association with well-being did not significantly differ (young adults: β = − .10, p = .003; middle-aged adults: β = .01, p = .77; older adults: β = .12, p < .001).1
Fig. 2
Relationships among age, internet usage time, and well-being
In summary, the findings from Study 1b confirmed our hypothesis that internet usage time is negatively associated with the well-being of younger adults, whereas for older adults, internet usage time is positively associated with well-being. In addition, in Study 1b, data collection was conducted more recently and during the COVID-19 pandemic. Despite these circumstances, the results of Study 1b were similar to those of Study 1a, thereby further highlighting the robustness of the phenomenon observed. To delve deeper into the benefits of internet usage for older adults, a comprehensive investigation of the motivations behind internet use among older adults was performed in Study 2. This study aimed to uncover the mechanisms through which the goals of internet usage influence well-being.
4 Study 2
There were three research purposes in Study 2. First, we aimed to re-examine the association between internet usage time and well-being among a new, large sample of only older adults to enhance our understanding of the robustness of this relationship. Additionally, we aimed to categorize and compare motivations for internet use, specifically by analyzing socioemotional and instrumental goals, with the expectation that older adults demonstrate a stronger preference for socioemotional goals than instrumental goals. Finally, we aimed to explore the indirect associations of these two types of motivation in the relationship between internet usage time and well-being among older adults, hypothesizing that the association strength of socioemotional goals would surpass that of instrumental goals.
4.1 Methods
4.1.1 Participants
This study employed a questionnaire survey method in which questionnaires were distributed across seven major counties and cities in Taiwan (Keelung City, Taipei City, New Taipei City, Taoyuan City, Yilan County, Hualien County, Kinmen County) via the Department of Lifelong Education, Ministry of Education. The data were collected between July 1, 2021, and September 30, 2021. The participation criteria required individuals to be aged 65 years or older, capable of understanding the questionnaire content, and able to complete the questionnaire independently. All questionnaires were self-administered. The participants were primarily recruited through advertisements disseminated by senior learning centers and the senior online community. All participants used the electronic version of the questionnaire. After the participants with missing values for the main measurements were excluded, 795 participants were available for analysis; 80.5% of the participants were female, with a mean age of 69.58 years (SD = 4.32), range from 65 to 93 years.
4.1.2 Measurement
4.1.2.1 Internet Usage Time
The measurement was the same as that in Study 1, with a mean internet usage time of 3.06 h (SD = 2.05).
4.1.2.2 Well-being
Unlike in Study 1, which used only two items as indicators of well-being, in Study 2, we employed a widely used well-being measure, the Satisfaction with Life Scale, to capture well-being more comprehensively (Anglim et al., 2020; Diener et al., 1985). The Chinese version of the Satisfaction with Life Scale has also shown great reliability and validity in Taiwanese samples (Chang et al., 2015; Wu & Yao, 2006). Participants are asked to rate items on a 4-point Likert scale ranging from 1 (strongly disagree) to 4 (strongly agree), with a sample item being “In most ways, my life is close to my ideal.” Higher scores indicate greater well-being. In Study 2, the Cronbach’s α for the scale was 0.87.
4.1.2.3 Dual Motivations for Interest Use
We developed a seven-item scale to measure two types of motivation for interest use on the basis of the socioemotional selectivity perspective and measured the reasons for internet usage (Allaire et al., 2013; Busch et al., 2021; Tammisalo et al., 2024). The participants rated the items on a 4-point Likert scale ranging from 1 (never) to 4 (always) to indicate their internet behaviors. To validate this measurement, we initially conducted an exploratory factor analysis, which yielded a KMO of 0.85, and then utilized the iterative principal factor method for extraction with promax rotation. The results identified a two-factor solution. The first factor is labeled socioemotional goal and has four items, including “sharing personal life events on social media platforms (e.g., check-ins, posts, or photos)”, “text messaging, voice and video calls”, “engaging with social media (e.g., Facebook, Instagram, LINE)”, and “listening to music, radio, watching videos, and gaming.” The second factor is labeled instrumental goal and has three items, including “work-related use”, “participating in online courses for learning”, and “searching for information, online purchases, and utilizing financial services.” The items within each subscale were averaged in this study for subsequent analyses. The Cronbach’s α was 0.80 for the socioemotional motivation subscale and 0.65 for the instrumental motivation subscale.
4.1.2.4 Socioeconomic Status
The measurement was the same as that in Study 1.
4.2 Data Analysis Methods
First, we performed descriptive statistics and correlation analyses to assess the associations among key variables. Next, a paired t test was used to compare the strength of socioemotional and instrumental goals among older adults. Furthermore, to examine the indirect associations of socioemotional and instrumental goals in the relationship between internet usage time and well-being, we conducted an analysis using the PROCESS macro version 4.2 (Hayes, 2022), employing Model 4. In this analysis, internet usage time was treated as the predictor variable, socioemotional and instrumental goals were included as parallel variables reflecting potential intermediary processes, and well-being was treated as the outcome variable. Age, gender, and subjective socioeconomic status (SES) were included as covariates. The bootstrapping method with 10,000 resamples was applied to estimate confidence intervals for indirect effects. Additionally, a contrast test was conducted to compare the relative strengths of the two indirect effects.
4.3 Results
First, the correlations and descriptive statistics for the measurements are presented in Table 1. The correlational analysis demonstrated that internet usage time was positively associated with well-being, r = .14, p < .001. This corresponds with the findings from Study 1, which indicate that for older adults, increased internet usage time is positively associated with well-being. Second, with respect to the dual motivations for internet use, the results of the paired t test indicate that older adults show a stronger preference for socioemotional goals (M = 3.06, SD = 2.05) than instrumental goals (M = 2.34, SD = 0.78), t = 28.36, p < .001, Cohen’s d = 1.01. This finding aligns with the socioemotional selectivity perspective, suggesting that emotion-based motivations are more influential than knowledge-based motivations for older adults.
Table 1
Descriptive statistics and correlations of measurements in study 2 (N = 795).
1. | 2. | 3. | 4. | 5. | 6. | |
|---|---|---|---|---|---|---|
1. Age | ||||||
2. Socioeconomic status | .05 | |||||
3. Internet usage time | − .12** | .03 | ||||
4. Socio-emotional goal | − .16*** | .12** | .42*** | |||
5. Instrumental goal | − .20*** | .15*** | .40*** | .58*** | ||
6. Well-being | − .07* | .14*** | .14*** | .33*** | .26*** | |
Mean | 69.58 | 5.56 | 3.06 | 3.00 | 2.34 | 3.07 |
SD | 4.32 | 1.83 | 2.05 | 0.66 | 0.78 | 0.38 |
Fig. 3
Indirect effect of internet usage time on well-being mediated by dual motivations
Additionally, analysis using PROCESS macro version 4.2 revealed a significant difference in the indirect associations of socioemotional and instrumental goals, with the association strength through socioemotional goals being significantly greater than that through instrumental goals (contrast = .07, 95% CI = .01 to .13). In summary, although both types of motivation showed indirect associations between internet usage time and well-being, the association strength through socioemotional goals was stronger than that through instrumental goals. This finding aligns with socioemotional selectivity theory, which suggests that older adults tend to prioritize socioemotional needs.
5 General Discussion
This study aimed to explore the relationship between internet usage time and well-being across different life span, with a particular focus on examining the motivations through which internet use enhances well-being among older adults on the basis of socioemotional selectivity theory. A total of two large, nationwide representative samples from the Taiwan Social Change Survey were utilized in the first part of this study to analyze this relationship, and a total of 3,440 participants were included (Studies 1a and 1b). The results from both samples consistently demonstrated that internet usage time is negatively associated with well-being of younger adults. In contrast, for older adults, internet usage time is positively associated with well-being. Furthermore, these associations remained even after controlling for socioeconomic status and were observed both before and during the COVID-19 pandemic, thereby indicating the stability and robustness of the association. The second part of the study (Study 2) delved deeper into the motivations behind internet usage among older adults, framed through the lens of socioemotional selectivity theory. The motivations for internet usage are categorized into socioemotional goals and instrumental goals. The mean comparison indicated that older adults have a stronger preference for socioemotional goals than for instrumental goals. Moreover, analysis of indirect associations revealed that both types of motivation were statistically linked to the relationship between internet usage time and well-being among older adults. However, a contrast comparison showed that the association strength through socioemotional goals was significantly greater than that of instrumental goals. This finding underscores the pivotal role of socioemotional goals for older adults, aligns with the propositions of socioemotional selectivity theory, and highlights the importance of these goals in shaping the indirect associations between internet use and well-being.
The results fill a critical research gap by providing empirical evidence on the differential associations of internet use on well-being across life span, particularly focusing on older adults, who have been underrepresented in previous studies. Unlike previous research that primarily examines younger individuals or treats older adults as a homogeneous group, our study highlights age-related variations in the association between internet use and well-being. Finally, the reliability and robustness of the conclusions of this study were strengthened by utilizing a nationally representative dataset in Study 1 and a more detailed, psychometrically validated measurement approach in Study 2. This methodological rigor ensures that our findings contribute meaningful insights to the literature on internet use and well-being.
5.1 Implications
These findings are particularly intriguing because they sit at the intersection of technology, aging, and human well-being. These findings challenge the stereotype of older adults as technophobic (Kim et al., 2023; Nimrod, 2018) and highlights their adaptive strategies in using digital tools to meet socioemotional goals. Moreover, this study addresses a critical gap in the research literature by considering the heterogeneity among older adults regarding internet usage patterns (socioemotional vs. instrumental goals) and their corresponding positive outcomes. Therefore, understanding the important role of the socioemotional goals of older adults can provide insights into how digital literacy and internet accessibility can be tailored to enhance their lives (Hulur & Macdonald, 2020).
Older adults often face unique challenges such as social isolation, loneliness, and reduced physical mobility, which can adversely affect their well-being (Akhter-Khan et al., 2023). Therefore, with respect to practical implications, as the population ages, the demand for age-friendly technologies and digital inclusion initiatives will increase. By identifying how internet usage affects the well-being of older adults, stakeholders, including policy-makers, health care providers, and technology developers, can create more effective programs and services. These efforts can help mitigate the risks of digital exclusion and promote a greater quality of life for older adults. On the basis of our findings, the internet offers a platform to mitigate these challenges by facilitating both socioemotional and instrumental goals. Even though the effect of instrumental goals was weaker than that of socioemotional goals in our study, instrumental goals still play a unique role in the association with well-being. For example, online health resources empower older adults with information to manage their health conditions better. Digital learning and educational websites provide mental stimulation and training programs, contributing to cognitive enhancement.
5.2 Limitations and Future Research Directions
Although this research provides insight into the positive effect of internet usage time for older adults, there are still some potential risks associated with internet use that can ultimately harm well-being. For example, older adults may be more vulnerable to cybersecurity threats, such as online scams, phishing, and identity theft, due to a lack of familiarity with digital security measures (Burton et al., 2022). In addition, older adults may be more susceptible to misinformation or fake news, which can affect their perceptions and decisions (Brashier & Schacter, 2020). A recent study conducted by Baribi-Bartov et al. (2024) also demonstrated that older adults were “supersharers” of fake news. Finally, the digital divide can still be a barrier, with some older adults lacking the necessary skills or access to fully benefit from the internet. This disparity can exacerbate feelings of exclusion and frustration, potentially diminishing their overall well-being (Cui et al., 2024). Therefore, future studies could consider incorporating measures of internet use risk to gain a more comprehensive understanding of how older adults are affected. This approach will help to identify and mitigate potential negative impacts on older adults’ well-being.
With respect to the limitations of our research design, the first limitation concerns the age range of the sample; although the studies analyzed data from two large, nationally representative social surveys (Studies 1a and 1b), it is important to note that the participants were all over the age of 18 years. Research on internet usage has often focused on the adolescent stage (Cai et al., 2023; Kee et al., 2024; Parsons et al., 2021). While our studies revealed a negative association between internet usage time and well-being among young adults, future research could consider including younger populations, such as adolescents, to gain a more comprehensive understanding of the differing impacts of internet usage across different life span. Second, while Study 2 examined the indirect associations involving socioemotional and instrumental goals, it did not include a direct measure of cognitive functioning, which may also play a role in the relationship between internet usage and well-being. It is possible that older adults with greater cognitive capacity are more likely to engage with the internet, and their higher well-being could be a reflection of their cognitive abilities rather than internet use per se. Future studies should consider controlling for cognitive functioning to better isolate the effects of internet use on well-being.
Another important limitation concerns employment status. Younger adults are more likely to be employed and may spend more time on the internet for work-related purposes, whereas many older adults are retired. This distinction may partly account for the negative association observed in younger adults (Study 1a and 1b), as work-related internet use can be associated with stress, pressure, and time demands, which may negatively impact well-being. Future research should account for employment status and work-related internet use to better understand their potential impact on the relationship between internet use and well-being across different life span. Finally, another limitation of this study is the use of indirect association analysis with cross-sectional data, which prevents the establishment of causal relationships. Without temporal precedence, the observed associations may be influenced by unmeasured confounding factors or bidirectional effects. To address this, future research should employ longitudinal designs to track internet usage and well-being over time, allowing for stronger causal inferences. Additionally, experimental or intervention-based studies could further validate the proposed mechanisms by manipulating internet engagement and assessing its effects on well-being across different life span.
5.3 Conclusion
In summary, our studies demonstrated a robust positive association between internet usage time and well-being in older adults, and highlighted the indirect association through socioemotional goals in this relationship. Through the lens of socioemotional selectivity theory, our studies provide insights into the relatively underexplored area of internet usage and well-being among older adults. This research promises to contribute significantly to the broader discourse on aging, technology, and well-being, making it a critical and timely topic for further investigation. This exploration not only advances academic knowledge but also holds the potential to inform practical strategies that enhance the lives of older adults in an increasingly digital world.
Acknowledgements
Funding National Science and Technology Council, Taiwan, R.O.C, 110-2628-H-001-003-MY4. National Science and Technology Council (111-2628-H-003 -007 -MY3).
Declarations
Conflict of interest
The authors declare that they have no competing financial interests or personal relationships that could influence the results reported in this paper.
Ethical Approval
Ethical approval was granted by the board of ethics of the Academia Sinica. The study was performed in accordance with the 1964 Declaration of Helsinki and informed consent was gained prior to the study commencing.
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