Sage Journals: Discover world-class research

Abstract

Although prior research has established the effectiveness of gratitude interventions in fostering prosocial behavior, their application in digital contexts remains relatively underexplored. This study examined how experience type (gratitude vs. hassle) and expression mode (private, non-digital vs. shareable, digital) influence individual well-being and prosocial intentions. In a 2 × 2 between-subjects experimental design, 706 participants recruited from an online U.S. sample engaged in a brief reflective writing exercise based on recent experiences. Results showed that gratitude reflections significantly enhanced well-being outcomes; however, experience type did not affect prosocial intentions. In contrast, expression mode and its interaction with experience type significantly influenced prosocial behavior, suggesting that the mode of expression plays a key role. Linguistic content analysis further revealed that digital gratitude reflections were associated with more prosocial and socially oriented language. These findings highlight the potential of digital gratitude interventions as scalable, low-cost tools for enhancing psychological well-being and encouraging prosocial engagement in the post-pandemic era.

Keywords

COVID-19 pandemic mental health well-being digital intervention gratitude prosocial behavior linguistic analysis

Introduction

The mental well-being of individuals has faced significant challenges in recent years, particularly due to the prolonged effects of the COVID-19 pandemic. Widespread disruptions to daily routines, heightened stress levels, and increased social isolation have contributed to deteriorating mental health outcomes across populations (Giuntellaa et al., 2021). For instance, over 60% of parents in the United States have reported negative impacts on their children’s education, alongside growing concerns about mental health (Kirzinger et al., 2023). Additionally, about half of all adults have experienced declines in mental well-being (World Health Organization, 2022). Given these ongoing challenges, there is a pressing need for effective, scalable interventions that can support individual well-being and foster positive social engagement in the post-pandemic era.

One promising avenue for addressing these challenges is the promotion of prosocial behavior (e.g., Aknin et al., 2013; Curry et al., 2018). Prosocial behavior refers to voluntary actions intended to benefit others, ranging from simple acts like helping or sharing to more complex actions such as volunteering, donating, or advocating for social causes (Penner et al., 2005). Prior research has reported that engaging in prosocial behavior yields significant benefits, both for individuals and society. Individuals who engage in prosocial behavior experience enhanced positive emotions (Snippe et al., 2017) and greater subjective well-being (Moynihan et al., 2015). Furthermore, prosocial behavior fosters positive social attitudes and strengthens community bonds (Eisenberg et al., 2010). As such, interventions that encourage prosocial behavior have the potential to positively influence individual well-being and promote healthier, more cohesive communities.

Among the various interventions designed to encourage prosocial behavior, gratitude interventions have received particular attention. Gratitude, defined as the recognition of positive outcomes from external sources (Emmons and McCullough, 2003), can be fostered through various techniques. Two common approaches to gratitude interventions include gratitude counting and gratitude journaling. Prior research has demonstrated that these practices can enhance subjective well-being (e.g., Cregg & Cheavens, 2021; Emmons & McCullough, 2003; Seligman et al., 2005) and strengthen feelings of closeness and social connection (Wood et al., 2008), while also promoting prosocial behavior (Emmons & McCullough, 2003).

Despite the demonstrated benefits of gratitude interventions, relatively little is known about how gratitude interventions function in digital contexts in terms of fostering prosocial behavior. In today’s increasingly digitalized world, gratitude interventions conducted through digital mediums may differ in their effectiveness compared to non-digital, private practices. For example, digital environments offer distinct affordances—such as visibility, shareability, and audience feedback—that can evoke stronger emotional responses (Bettis et al., 2022). Additionally, digital contexts can be a powerful avenue in reinforcing social norms and fostering a sense of community (Grüning et al., 2024), potentially leading to greater motivation for prosocial behavior as individuals witness the impact of their expressions of gratitude on others. Therefore, understanding how digital environments might influence prosocial behavior is essential.

In digital spaces, individuals are often exposed to both positive (e.g., gratitude) and negative (e.g., hassle) narratives (Haidt et al., 2025). Their expressive behavior may be shaped by perceived audience, social norms, or concerns about privacy and authenticity (e.g., Rains & Brunner, 2018; Walther, 2007). While digital gratitude sharing can create a sense of connection and visibility, sharing negative experiences such as hassles may serve as a mechanism for seeking social support from a diverse audience (Vitak & Ellison, 2013), though such disclosures may be perceived differently depending on the medium.

Given these dynamics, this study seeks to fill the gap in the literature by examining how the type of experience (gratitude vs. hassle) and the mode of expression (non-digital vs. digital) jointly influence two key outcomes: individual well-being and prosocial behavioral intentions. We define expression mode as the medium through which participants share their reflection. Participants completed a short reflective writing task about a recent experience, expressed either privately (non-digital) or in a format simulating public sharing (digital).

By integrating emotional experience and communication modality, this research contributes to the literature by examining how digital platforms can be leveraged to foster psychological well-being and prosocial engagement. It addresses the growing demand for accessible, low-cost interventions that can be implemented in various institutional settings—including schools, workplaces, healthcare contexts, and online communities. Ultimately, this study sheds light on how gratitude, when expressed through contemporary modes of communication, may play a role in promoting healthier, more socially connected individuals and communities in the post-pandemic world.

Goals and Hypotheses

The primary goal of this study is to evaluate the effectiveness of a brief reflective writing exercise in influencing well-being and prosocial intentions. The study includes two independent variables: experience type, reflecting on experiences where participants felt either grateful or hassled, and expression mode, consisting of either private, non-digital or shareable, digital methods.

We hypothesized that engaging in a reflective writing exercise on recent gratitude experiences, compared to focusing on hassle experiences, would lead to increased individual well-being and higher prosocial intentions. Additionally, we predicted that using digital methods for the writing task, as opposed to traditional non-digital methods, would enhance individual well-being and prosocial intentions. Furthermore, we anticipated that the impact of experience type would vary depending on expression mode. Specifically, we expected that the digital gratitude intervention would be effective in enhancing well-being and prosocial intentions, when compared to the hassle conditions.

We formulated the following hypotheses:

H1: Experience type will have a significant main effect on well-being and prosocial intentions, such that reflecting on gratitude will lead to higher well-being and greater prosociality than reflecting on hassles.

H2: Expression mode will have a significant main effect, with digital expression producing higher well-being and prosocial intentions than non-digital expression.

H3: There will be a significant interaction between experience type and expression mode, such that the impact of gratitude (vs. hassle) will be greater in the digital condition than in the non-digital condition.

In addition to testing these primary hypotheses, we examined potential predictors of prosocial behavior using regression analysis, incorporating sociodemographic and psychological variables. Finally, we conducted a linguistic analysis of participants’ written responses using a natural language processing approach to gain deeper insights into their emotional and social responses associated with different writing conditions.

This integrative approach allows for a deeper understanding of how content (experience type) and context (expression mode) jointly shape emotional outcomes and behavioral intentions in increasingly digital environments.

Research Methods

Study Design and Ethical Considerations

The study employed a 2 × 2 between-subjects full factorial design, manipulating experience type (gratitude vs. hassle) and expression mode (digital vs. non-digital). Participants were randomly assigned to one of the four experimental conditions.

All participants provided informed consent before beginning the study. The consent form described the study’s purpose, the voluntary nature of participation, the procedures involved, and the right to withdraw at any point without penalty. Participants were assured of the anonymity and confidentiality of their responses, which were used solely for research purposes.

To minimize the risk of harm, the study involved low-risk activities—specifically, a reflective writing task and survey responses. All prompts were emotionally neutral or mild in valence. Participants showing signs of distress were excluded from analysis, and all participants received a debriefing at the conclusion of the study. Given the minimal risks and the potential societal benefits—such as insights into how emotional expression may support well-being and prosocial behavior—the study’s design was deemed ethically appropriate.

Sampling

Participants were recruited via Amazon’s Mechanical Turk (MTurk), an online platform for crowdsourced data collection. Eligibility criteria included being at least 18 years old, residing in the United States, and being a regular user of social media. A total of 783 participants initially completed the study. We applied four pre-established exclusion criteria: (1) completion time under three minutes (based on pilot testing), (2) low-effort responses in the writing task, (3) failure to follow task instructions, and (4) failure of at least one of two attention check questions. Seventy-seven participants were excluded, resulting in a final sample of 706 across the four conditions. This sample size exceeded those reported in comparable studies. For example, Oliveira et al. (2021) included 253 participants in Portugal and 281 in Brazil across two experimental conditions, indicating our sample size was well-powered for between-group comparisons.

Participant Characteristics

Of the 706 participants included in the final analyses, 48.4% identified as male and 50.0% as female. Participants ranged in age from 18 to 78 years (M = 39.57, SD = 11.90). In terms of ethnicity, 70.5% identified as Caucasian, 11.5% as African American, 7.5% as Latino, and 7.1% as Asian. Educational backgrounds were diverse: 9.3% held a high school diploma or equivalent, 28.3% had some college or an associate degree, 42.9% held a bachelor’s degree, and 18.1% held a graduate or professional degree. Regarding relationship status, 62.1% reported being in a relationship, while 38.0% were single and not in a relationship.

Procedure

Upon accepting the task on MTurk, participants were redirected to a Qualtrics XM survey. After confirming eligibility and providing informed consent, they were randomly assigned to one of the four experimental conditions. The study took approximately 7 to 10 min to complete and included the following components: (1) screening for eligibility questions, (2) a writing task about a gratitude or hassle experience from the past week, expressed in either a digital or non-digital format, (3) questionnaires measuring emotional states, prosocial intentions, manipulation checks, and task perceptions, and (4) sociodemographic questions. The order of items within each questionnaire was randomized to reduce response bias.

Stimuli Materials

Each experimental condition used a distinct version of the writing instructions tailored to the corresponding experience type and expression mode. The instructions were adapted from prior work by Emmons and McCullough (2003) and Oliveira et al. (2021) and modified to include scenarios involving digital platforms. Full instructions are provided in the Supplementary Information.

Measures

Our primary focus centered on prosocial intentions as the main dependent variable. However, we also included measures associated with well-being, such as gratitude state and various emotions, to gain a more comprehensive understanding of the factors that drive prosocial behavior.

Gratitude State

We used three items from the Gratitude Questionnaire-Six Item Form (GQ-6, McCullough et al., 2002). The gratitude scale included statements such as “I have so much in life to be thankful for,”“When I look at the world, I don’t see much to be grateful for,” and “I am grateful for the people, events, and situations currently in my life.” Participants provided responses on a scale ranging from 1 (strongly disagree) to 7 (strongly agree). In the present sample, this scale showed robust internal reliability with a Cronbach’s alpha value of .80.

Affect-Adjective Scale

We used nine items from the Affect-Adjective Scale (AAS; Diener & Emmons, 1985) and categorized emotions into three types. Positive emotions included items such as “happy,”“pleased,” and “peaceful”; negative emotions included items such as “angry,”“depressed,” and “bored”; and social emotions consisted of items associated with socially oriented negative emotions such as “uncomfortable,”“guilty,” and “indebted.” Participants provided responses on a scale ranging from 1 (not at all) to 7 (extremely). In the present sample, this scale exhibited robust reliability and fell within acceptable limits, with Cronbach’s alpha values of .93 for positive emotions, .71 for negative emotions, and .71 for social emotions.

Prosocial Intentions

Participants were then asked to indicate the extent to which they intended to engage in prosocial behavior. We used six items from the Prosocialness Scale for Adults (PSA; Caprara et al., 2005). Prosocial intentions included items such as “I help my friends/colleagues in their activities,”“I share the things that I have with my friends,”“I try to help others,”“I am empathic to those who are in need,”“I intensely feel what others feel,” and “I put myself in the shoes of others.” Participants rated each item on a scale ranging from 1 (never) to 7 (always). This scale showed strong reliability with a Cronbach’s alpha value of .91.

Task Perception

Participants were also asked to provide their perceptions of the writing task within the study, which involved responding to the following items: “The task was easy to complete,”“The task made me feel better emotionally,” and “I will implement this task regularly because it helps me to feel better emotionally.” Participants provided ratings for each statement on a scale ranging from 1 (strongly disagree) to 7 (strongly agree). This scale exhibited robust reliability with a Cronbach’s alpha value of .75.

Finally, participants also completed sociodemographic items and manipulation checks confirming the experience type they were instructed to focus on and their attentiveness to the study.

Data Analysis

We conducted two-way ANOVAs to examine the main and interaction effects of experience type and expression mode on prosocial intentions and well-being measures. One-way ANOVAs were used to compare the digital gratitude condition with the other conditions. To explore predictors of prosocial intentions, we performed stepwise linear regression analysis. Finally, we used Linguistic Inquiry and Word Count (LIWC; Tausczik and Pennebaker, 2010) to analyze the open-ended writing responses, assessing linguistic markers related to psychological and social states.

Results

Preliminary Analysis

We first examined the correlations among the main study variables. As shown in Table 1, measures of individual well-being were significantly associated with prosocial intentions. Additionally, gender correlated significantly with prosocial intentions, consistent with prior findings that females tend to report higher levels of prosocial behavior (Xiao et al., 2019). However, gender did not show significant associations with any of the well-being measures.

Table 1.

Correlations Between Main Variables.

	1	2	3	4	5	6
1. Prosocial intentions	−
2. Gratitude state	.36***	−
3. Positive emotions	.43***	.57***	−
4. Negative emotions	−.17***	−.50***	−.51***	−
5. Social emotions	−.09**	−.42***	−.44***	.78***	−
6. Gender	.08**	.04	−.03	−.00	−.01	−
7. Education	−.02	−.00	.07*	.00	−.00	−.05

p < .1, **p < .05, ***p < .01. Gender (1 = female, 0 = otherwise) and Education (1 = bachelor’s degree or graduate/professional degree, 0 = otherwise).

Hypotheses Testing

A series of two-way ANOVAs were conducted to assess the effects of experience type and expression mode on well-being and prosocial intentions.

Significant main effects of experience type were observed on all well-being outcomes. Participants in the gratitude conditions reported higher state gratitude, F(1,702) = 13.87, p < .001, ηp² = .019, and more positive emotions, F(1,702) = 64.80, p < .001, ηp² = .085, compared to those in the hassle conditions. Additionally, gratitude experiences led to greater reductions in both negative emotions, F(1,702) = 27.12, p < .001, ηp² = .037, and social emotions, F(1,702) = 22.63, p < .001, ηp² = .031. However, expression mode did not have a significant main effect on any well-being measure, nor were there significant interactions between experience type and expression mode for these outcomes.

In contrast, results for prosocial intentions revealed a different pattern. No significant main effects of experience type were found, F(1,702) = 2.16, p = .142, ηp² = .003, indicating that gratitude and hassle reflections produced comparable levels of prosocial intent. However, expression mode had a significant effect on prosocial intentions: participants in the non-digital conditions reported higher prosocial intentions, F(1,702) = 4.02, p = .045, ηp² = .006, compared to those in the digital conditions. Moreover, a significant interaction was found between experience type and expression mode, F(1,702) = 4.06, p = .044, ηp² = .006. Table 2 contains descriptive statistics (means and standard deviations) for all main variables.

Table 2.

Analysis of Variance: Means.

Condition	Gratitude		Positive		Negative		Social		Prosocial
	M	SE	M	SE	M	SE	M	SE	M	SE
Gratitude, Non-digital	5.96	.16	4.87	.24	1.63	.15	1.60	.17	5.07	.16
Gratitude, Digital	5.91	.16	4.63	.24	1.77	.16	1.71	.18	5.07	.17
Hassle, Non-digital	5.73	.16	3.85	.23	2.06	.15	2.00	.17	5.11	.16
Hassle, Digital	5.52	.16	3.69	.24	2.18	.16	2.17	.18	4.78	.17

Follow-up independent t-tests further clarified these effects. No significant difference in prosocial intentions emerged between the digital gratitude condition (M = 5.07, SD = 1.09) and either the non-digital gratitude condition (M = 5.07, SD = 1.09; p = 1.000) or hassle condition (M = 5.11, SD = 1.07; p = .982). However, digital gratitude produced marginally higher prosocial intentions than digital hassle (M = 4.78, SD = 1.18; p = .074).

Participants’ perceptions of the writing task were also analyzed. While there was no significant difference between the digital and non-digital gratitude conditions (M = 5.21, SD = 1.18 vs. M = 5.32, SD = 1.17; p = .817), participants in the digital gratitude condition reported significantly more favorable perceptions than those in both the non-digital hassle (M = 4.38, SD = 1.20; p < .001) and digital hassle conditions (M = 4.35, SD = 1.20; p < .001).

Predictors of Prosocial Intentions

To identify predictors of prosocial behavior, we conducted a stepwise regression using two models as shown in Table 3. Model 1 examined the impact of experimental variables (experience type and expression mode) on prosocial intentions, while controlling for demographic variables. Model 2 extended Model 1 by adding well-being measures to evaluate their additional predictive power.

Table 3.

Prosocial Intentions Model Results.

Variable	Model 1	Model 2
Intercept	4.488***(.185)	2.369***(.314)
Experience type	.291**(.120)	.020(.108)
Expression mode	.349***(.117)	.285***(.103)
Experience type × Expression mode	−.367**(.166)	−.358**(.147)
Age	.000(.004)	−.002(.003)
Gender	.174**(.084)	.211***(.074)
Race	.140(.095)	.138(.084)
Education	−.023(.086)	−.091(.076)
Relationship	.168*(.087)	.008(.077)
Gratitude state		.184***(.043)
Positive emotions		.278***(.029)
Negative emotions		−.017(.058)
Social emotions		.160***(.048)

p < .1, **p < .05, ***p < .01. Standard errors appear in parentheses. Experience type (1 = gratitude, 0 = hassle) and Expression mode (1 = non-digital, 0 = digital). Socio-demographic variables were simplified to improve clarity and interpretation: Gender (1 = female, 0 = otherwise), Race (1 = Caucasian, 0 = otherwise), Education (1 = bachelor’s degree or graduate/professional degree, 0 = otherwise), and Relationship (1 = in some kind of relationship, 0 = otherwise).

Model 2 accounted for significantly more variance in prosocial intentions (25.2%) compared to Model 1 (3.1%), F(4, 693) = 51.35, p < .001. Accordingly, we interpreted the results of Model 2 as the final model. Consistent with the two-way ANOVA results, expression mode and its interaction with experience type were significant predictors of prosocial intentions. Among demographic variables, only gender emerged as a significant factor, indicating that females were more likely to engage in prosocial behavior (Xiao et al., 2019), consistent with the correlation reported in Table 1. Additionally, all well-being variables, except for negative emotions, were significant predictors of prosocial intentions, indicating the central role of emotional and psychological states in motivating prosocial behavior.

Linguistic Analysis

To explore the emotional and social tone of participants’ written reflections, we utilized the LIWC tool (Tausczik & Pennebaker, 2010). This analysis is valuable as it allows us to assess the frequency of specific linguistic features that reflect emotional expression, authenticity, and social orientation.

We focused on two LIWC summary measures: “authentic,” which reflects perceived honesty in the language, and “emotional tone,” which represents the degree of positivity expressed. Additionally, we considered two psychographic measures: “affect,” which assesses positive and negative tones, and “social process,” which captures language related to social behaviors, such as care and help. Each LIWC score represents the proportion of words in a participant’s writing that belong to the respective category. The results of the LIWC analysis are presented in Table 4.

Table 4.

Linguistic Analysis Results.

		Gratitude		Hassle
Category	Description	Non-digital	Digital	Non-digital	Digital
Summary variables
Authentic	Perceived honesty	89.52	91.23	91.54	84.78
Emotional tone	Degree of positive tone	95.45	95.62	7.57	6.18
Affect
Positive tone	Good, well, new, love	6.83	7.01	1.74	1.55
Negative tone	Bad, wrong, hate	0.94	1.08	3.14	3.20
Social process
Social behavior	Said, love, say, care	3.38	3.69	3.68	4.29
Prosocial behavior	Care, help, thank	1.14	1.53	0.46	0.40
Politeness	Thank, please, thanks	0.16	0.22	0.06	0.18

Our findings revealed notable differences in language use across conditions. Participants in the digital gratitude condition scored slightly higher on authenticity (91.23) compared to those in the non-digital gratitude condition (89.52). In contrast, for hassle experiences, the non-digital condition was perceived as more authentic (91.54) than the digital condition (84.78), suggesting that digital formats may reduce perceived genuineness when expressing negative experiences.

Both gratitude conditions exhibited high levels of emotional tone—95.62 (digital) and 95.45 (non-digital)—indicating strong positivity regardless of expression mode. In contrast, hassle conditions showed markedly lower scores. Affect-related positive tone was also slightly higher in the digital gratitude condition (7.01) compared to non-digital (6.83). For hassle conditions, both digital (1.55) and non-digital (1.74) conditions yielded low positive tone scores, with digital also showing slightly higher negative tone (3.20 vs. 3.14), indicating that digital formats might intensify emotional expression, both positive and negative.

In terms of social process, digital conditions featured more social words than non-digital ones—3.69 vs. 3.31 (gratitude) and 4.29 vs. 4.04 (hassle). Notably, prosocial behavior expressions were significantly higher in the digital gratitude condition (1.53) compared to the non-digital gratitude condition (1.14), and both far surpassed the hassle conditions (0.46 for non-digital and 0.40 for digital). This suggests that expressing gratitude digitally may be especially effective in eliciting prosocial expression, possibly due to enhanced visibility, feedback opportunities, and the interconnectedness of digital platforms. Additionally, politeness expressions were slightly more frequent in the digital gratitude condition (0.22) than in the non-digital gratitude condition (0.16), further underscoring the role of digital contexts enhancing social and ethical language.

Discussion

Overview of Key Findings

This study examined how experience type and expression mode influence individual well-being and prosocial behavior. While experience type significantly affected emotional outcomes, expression mode was more predictive of prosocial intentions. Notably, a significant interaction between experience type and expression mode was observed, indicating that the effects of reflective writing depend on both what is expressed and how it is communicated. Linguistic analysis further revealed that digital gratitude expressions differed in authenticity and emotional tone, highlighting potential psychological mechanisms specific to digital communication contexts.

Expression Mode and Prosocial Intentions

Although previous research has emphasized the role of gratitude in promoting prosocial behavior (Emmons & McCullough, 2003), our results did not support a main effect of experience type on prosocial intentions. This suggests that the relationship between gratitude and prosociality may be contingent on contextual or individual factors. One possibility is that individual differences, such as baseline prosocial tendencies, moderated the impact of the gratitude reflection. Additionally, the brevity of the writing task may have limited participants’ emotional engagement.

By contrast, expression mode emerged as a significant factor of prosocial behavior. Participants in non-digital conditions reported higher prosocial intentions, with this effect varying by experience type. These results suggest that digital environments may activate unique psychological processes—such as heightened audience awareness, imagined social presence, or self-presentation motives—that influence social behavior (e.g., Bargh et al., 2002; Bayer et al., 2018; Gonzales & Hancock, 2011). Even without explicit sharing, digital writing may evoke a sense of social visibility, thereby reinforcing social norms and prosocial self-concepts.

Although effect sizes were modest, statistically significant findings in large-scale, scalable interventions can have meaningful implications. In real-world applications—such as educational programs, wellness initiatives, or digital platforms—even small behavioral shifts may accumulate to produce broader societal benefits. Nevertheless, these findings should be interpreted with caution when considering the practical implications of digital gratitude interventions.

Well-Being and Its Link to Prosocial Behavior

Experience type significantly influenced emotional outcomes, with gratitude reflections increasing state gratitude and positive emotions while reducing negative and socially oriented emotions. These findings are consistent with prior work showing that reflecting on gratitude can enhance well-being. Expression mode, however, did not significantly affect well-being outcomes, nor did it interact with experience type.

Importantly, several well-being indicators—especially positive emotions and state gratitude—significantly predicted prosocial intentions, as did gender. These results support a growing body of research linking psychological well-being and social behavior. Positive affective states may promote openness, empathy, and willingness to engage in actions that benefit others. The observed associations underscore potential feedback loops in which emotional flourishing fosters social connection, which in turn further enhances well-being.

Linguistic Insights and the Mechanisms of Digital Expression

The linguistic analysis using LIWC adds interpretive depth to our findings. Digital gratitude writing was associated with a higher emotional tone and slightly greater authenticity compared to its non-digital counterpart. Conversely, non-digital expressions of hassle experiences were perceived as more authentic, suggesting that digital mediums may differentially affect how positive versus negative experiences are expressed or perceived.

These linguistic patterns may reflect underlying psychological mechanisms. Authenticity—defined as spontaneous, self-revealing language (Pennebaker et al., 2003)—is often associated with deeper self-reflection and sincerity, which can enhance perceived social connectedness (Kernis & Goldman, 2006). Higher emotional tone, as described by Fredrickson’s (2001) broaden-and-build theory, may reflect positive affective states that support expansive thinking and social engagement.

Moreover, participants in the digital gratitude condition used more prosocial and polite language, suggesting that digital writing may not only shape emotional expression but also enhance social orientation. While formal mediation analyses were beyond the scope of this study, these linguistic markers provide potential insight into how expressive tasks influence downstream social behavior.

Conclusion

This study explored how experience type and expression mode influence individual well-being and prosocial intentions. While gratitude reflections enhanced emotional well-being, expression mode was a stronger driver of prosocial intentions. The interaction between experience type and expression mode highlights the importance of considering both content and medium in designing interventions. Despite the modest effect sizes, the simplicity, scalability, and low cost of digital gratitude interventions make them a promising strategy for fostering well-being and social connection in the post-pandemic era.

Limitations and Directions for Future Research

Several limitations should be acknowledged. First, the writing task was intentionally brief—limited to three to five sentences—to promote scalability and reduce fatigue. While effective for engagement, this may have limited the depth of emotional reflection or cognitive processing, potentially affecting the observed effects. Future studies could examine whether longer or more elaborative writing tasks yield stronger or sustained effects.

Second, the use of MTurk for participant recruitment may limit generalizability. MTurk users are typically more digitally literate and familiar with online communication than the broader population. Future research should replicate this work with more diverse samples, including individuals with limited digital access or lower technology fluency.

Third, while our sample was demographically diverse, we did not examine subgroup-specific responses. Populations such as adolescents, caregivers, or individuals disproportionately affected by pandemic-related stressors may respond differently to gratitude interventions. Identifying such variation could help tailor interventions for those who may benefit most.

Fourth, the study measured only short-term outcomes. It remains unclear whether the observed effects persist over time or whether repeated engagement leads to cumulative benefits. Longitudinal research is needed to evaluate the persistence and developmental impact of digital expression tasks.

Finally, future studies should explore how these interventions function in applied settings—such as schools, workplaces, healthcare institutions, or civic programs. Understanding how to adapt digital gratitude writing for specific environments will be crucial for broader implementation across a range of institutional and cultural contexts.

Practical Implications

The findings offer several practical implications. In educational settings, brief reflective writing exercises could be integrated into advisory periods or wellness curricula to promote emotional regulation and peer connection. In mental health contexts, digital journaling platforms may complement therapeutic practices by enhancing emotional awareness and fostering social bonds. In workplaces, digital gratitude prompts could serve as a component of wellness programs, supporting positive culture and collegial engagement—particularly in remote or hybrid environments.

Although the behavioral effects observed were modest, their scalability and minimal cost make digital gratitude interventions a valuable addition to broader initiatives aimed at improving individual well-being and encouraging prosocial engagement across diverse populations.

Footnotes

Acknowledgements

The author appreciates Professor Ye Hu for his support in this research.

ORCID iD

Joonho Park

Ethical Considerations

This study was approved by the University of Houston Institutional Review Board (Ref. STUDY00004394) on September 14, 2023. All participants provided informed consent prior to their participation.

Author Contributions

The author has created the entire article and is solely responsible for the main text, discussion, conclusion, data analysis, and analytical evaluation of the results obtained.

Funding

The author received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The author declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data supporting the results of this study will be made available by the author, without undue reservation.

References

Aknin

L. B.

Dunn

E. W.

Whillans

A. V.

Grant

A. M.

Norton

M. I.

(2013). Making a difference matters: Impact unlocks the emotional benefits of prosocial spending. Journal of Economic Behavior & Organization, 88, 90–95. https://doi.org/10.1016/j.jebo.2013.01.008

Bargh

J. A.

McKenna

K. Y. A.

Fitzsimons

G. M.

(2002). Can you see the real me? Activation and expression of the "true self" on the Internet. Journal of Social Issues, 58(1), 33–48. https://doi.org/10.1111/1540-4560.00247

Bayer

J. B.

Triệu

Ellison

N. B.

(2018). Social media elements, ecologies, and effects. Annual Review of Psychology, 69, 471–497. https://doi.org/10.1146/annurev-psych-010419-050944

Bettis

A. H.

Burke

T. A.

Nesi

Liu

R. T.

(2022). Digital technologies for emotion-regulation assessment and intervention: A conceptual review. Clinical Psychological Science, 10(1), 3–26. https://doi.org/10.1177/21677026211011982

Caprara

G. V.

Steca

Zelli

Capanna

(2005). A new scale for measuring adults’ prosocialness. European Journal of Psychological Assessment, 21(2), 77–89. https://doi.org/10.1027/1015-5759.21.2.77

Cregg

D. R.

Cheavens

J. S.

(2021). Gratitude interventions: Effective self-help? A meta-analysis of the impact on symptoms of depression and anxiety. Journal of Happiness Studies, 22, 413–445. https://doi.org/10.1007/s10902-020-00236-6

Curry

O. S.

Rowland

L. A.

Van Lissa

C. J.

Zlotowitz

McAlaney

Whitehouse

(2018). Happy to help? A systematic review and meta-analysis of the effects of performing acts of kindness on the well-being of the actor. Journal of Experimental Social Psychology, 76, 320–329. https://doi.org/10.1016/j.jesp.2018.02.014

Diener

Emmons

R. A.

(1985). The independence of positive and negative affect. Journal of Personality and Social Psychology, 47(5), 1105–1117. https://doi.org/10.1037/0022-3514.47.5.1105

Eisenberg

Eggum

N. D.

Di Giunta

(2010). Empathy-related responding: Associations with prosocial behavior, aggression, and intergroup relations. Social Issues and Policy Review, 4(1), 143–180. https://doi.org/10.1111/j.1751-2409.2010.01020.x

10.

Emmons

R. A.

McCullough

M. E.

(2003). Counting blessings versus burdens: An experimental investigation of gratitude and subjective well-being in daily life. Journal of Personality and Social Psychology, 84(2), 377–389. https://doi.org/10.1037/0022-3514.84.2.377

11.

Fredrickson

B. L.

(2001). The role of positive emotions in positive psychology: The broaden-and-build theory of positive emotions. American Psychologist, 56(3), 218–226. https://doi.org/10.1037/0003-066X.56.3.218

12.

Giuntellaa

Hyde

Saccardob

Sadoffc.

. (2021). Lifestyle and mental health disruptions during COVID-19. Proceedings of the National Academy of Sciences, 118(9), e2016632118. https://doi.org/10.1073/pnas.2016632118

13.

Gonzales

A. L.

Hancock

J. T.

(2011). Mirror, mirror on my Facebook wall: Effects of exposure to Facebook on self-esteem. Cyberpsychology, Behavior, and Social Networking, 14(1-2), 79–83. https://doi.org/10.1089/cyber.2009.0411

14.

Grüning

D.J.

Kamin

Panizza

Katsaros

Lorenz-Spreen

(2024). A framework for promoting online prosocial behavior via digital interventions. Communications Psychology, 2(6). https://doi.org/10.1038/s44271-023-00052-7

15.

Haidt

Rausch

Twenge

(2025). Social media and mental health: A collaborative review. Unpublished manuscript, New York University. Accessed at. tinyurl.com/SocialMediaMentalHealthReview

16.

Kernis

M. H.

Goldman

B. M.

(2006). A multicomponent conceptualization of authenticity: Theory and research. Advances in Experimental Social Psychology, 38, 283–357. https://doi.org/10.1016/S0065-2601(06)38006-9

17.

Kirzinger

Presiado

Valdes

Hamel

Brodie

(2023). The COVID-19 pandemic: Insights from three years of KFF polling. https://www.kff.org/health-information-trust/poll-finding/the-covid-19-pandemic-insights-from-three-years-of-kff-polling/

18.

McCullough

M. E.

Emmons

R. A.

Tsang

J.-A.

(2002). The grateful disposition: A conceptual and empirical topography. Journal of Personality and Social Psychology, 82(1), 112–127. https://doi.org/10.1037//0022-3514.82.1.112

19.

Moynihan

D. P.

Deleire

Enami

(2015). A life worth living: Evidence on the relationship between prosocial values and happiness. The American Review of Public Administration, 45(3), 311–326. https://doi.org/10.1177/0275074013493657

20.

Oliveira

Baldé

Madeira

Ribeiro

Arriaga

(2021). The impact of writing about gratitude on the intention to engage in prosocial behaviors during the COVID-19 outbreak. Frontiers in Psychology, 12:588691. https://doi.org/10.3389/fpsyg.2021.588691

21.

Pennebaker

J. W.

Mehl

M. R.

Niederhoffer

K. G.

(2003). Psychological aspects of natural language use: Our words, our selves. Annual Review of Psychology, 54, 547–577. https://doi.org/10.1146/annurev.psych.54.101601.145041

22.

Penner

L. A.

Dovidio

J. E.

Piliavin

J. A.

Schroeder

D. A.

(2005). Prosocial behavior: Multilevel perspectives. Annual Review of Psychology, 56, 365–392. https://doi.org/10.1146/annurev.psych.56.091103.070141

23.

Rains

S. A.

Brunner

S. R.

(2018). The outcomes of broadcasting self-disclosure using new communication technologies: Responses to disclosure vary across one’s social network. Communication Research, 45(5), 659–687. https://doi.org/10.1177/0093650215598836

24.

Seligman

M. E. P.

Steen

T. A.

Park

Peterson

(2005). Positive psychology progress: Empirical validation of interventions. American Psychologist, 60(5), 410–421. https://doi.org/10.1037/0003-066X.60.5.410

25.

Snippe

Jeronimus

B. F.

Marije

A. H. R.

Bos

E. H.

De Jonge

Wichers

(2017). The reciprocity of prosocial behavior and positive affect in daily life. Journal of Personality, 86(2), 139–146. https://doi.org/10.1111/jopy.12299

26.

Tausczik

Y. R.

Pennebaker

J. W.

(2010). The psychological meaning of words: LIWC and computerized text analysis methods. Journal of Language and Social Psychology, 29(1), 24–54. https://doi.org/10.1177/0261927X09351676

27.

Vitak

Ellison

N. B.

(2013). “There’s a network out there you might as well tap”: Exploring the benefits of and barriers to exchanging informational and support-based resources on Facebook. New Media & Society, 15(2), 243–259. https://doi.org/10.1177/1461444812451566

28.

Walther

J. B.

(2007). Selective self-presentation in computer-mediated communication: Hyperpersonal dimensions of technology, language, and cognition. Computers in Human Behavior, 23(5), 2538–2557. https://doi.org/10.1016/j.chb.2006.05.002

29.

Wood

A. M.

Maltby

Gillett

Linley

P. A.

Joseph

(2008). The role of gratitude in the development of social support, stress, and depression: Two longitudinal studies. Journal of Research in Personality, 42(4), 854–871. https://doi.org/10.1016/j.jrp.2007.11.003

30.

World Health Organization (2022). World mental health report: Transforming mental health for all. WHO. https://www.who.int/publications/i/item/9789240049338

31.

Xiao

S. X.

Hashi

E. C.

Korous

K. M.

Eisenberg

(2019). Gender differences across multiple types of prosocial behavior in adolescence: A meta-analysis of the prosocial tendency measure-revised (PTM-R). Journal of Adolescence, 77, 41–58. https://doi.org/10.1016/j.adolescence.2019.09.003

Gratitude in the Digital Age: The Effects of Experience Type and Expression Mode on Well-Being and Prosocial Intentions

Abstract

Keywords

Introduction

Goals and Hypotheses

Research Methods

Study Design and Ethical Considerations

Sampling

Participant Characteristics

Procedure

Stimuli Materials

Measures

Gratitude State

Affect-Adjective Scale

Prosocial Intentions

Task Perception

Data Analysis

Results

Preliminary Analysis

Hypotheses Testing

Predictors of Prosocial Intentions

Linguistic Analysis

Discussion

Overview of Key Findings

Expression Mode and Prosocial Intentions

Well-Being and Its Link to Prosocial Behavior

Linguistic Insights and the Mechanisms of Digital Expression

Conclusion

Limitations and Directions for Future Research

Practical Implications

Footnotes

Acknowledgements

ORCID iD

Ethical Considerations

Author Contributions

Funding

Declaration of Conflicting Interests

Data Availability Statement

References